Difference Analysis of Wave Disaster Characteristics Induced by Landslides of Different Water Entry Scales

다양한 크기의 산사태로 인한 물 침입으로 인한 해일 위험 특성의 차이 분석.

Difference Analysis of Wave Disaster Characteristics Induced by Landslides of Different Water Entry Scales

王雷，  解明礼，  黄会宝，  柯虎，  高强人民珠江   2024年45卷第2期DOI：10.3969/j.issn.1001-9235.2024.02.003

纸质出版日期：2024

Abstract

This paper conducts a three-dimensional numerical analysis on the surges generated by landslides of different water entry scales, and analyzes the characteristics of surge disasters induced by landslides of different water entry scales, such as surge height, surge speed, and bank climbing height. Meanwhile, the impact of surges caused by landslides of different water entry scales on the dam is explored.

The FLOW-3D numerical simulation method is employed to simulate and analyze the entire process of landslide instability, surge formation and propagation, surge climbing, and surge backflow. The results show that the maximum climbing height of the surge generated by the 3. 1 million m~3 landslide of water entry is 54. 5 m on the opposite bank, and the surge height in front of the dam is 6. 69 m.

The surge has a small area of overflow at the right bank dam shoulder. The surge generated by the 0. 8 million m~3 landslide of water entry has a maximum climbing height of 26. 00 m on the opposite bank, and the surge height in front of the dam is 5. 38 m, without influence exerted by the surge on the dam safety. The results indicate that the induced surge caused by 3. 1×10~6 m~3 landslide of water entry is more catastrophic than that brought by 0. 8×10~6 m~3 landslide of water entry.

Difference Analysis of Wave Disaster Characteristics Induced by Landslides of Different Water Entry Scales
Difference Analysis of Wave Disaster Characteristics Induced by Landslides of Different Water Entry Scales

출판물

Pearl River, 2024, Vol 45, Issue 2, p18

ISSN

1001-9235

간행물 유형

Academic Journal

DOI

10.3969/j.issn.1001-9235.2024.02.003

PDF View
Estimating maximum initial wave amplitude of subaerial landslide tsunamis: A three-dimensional modelling approach

Estimating maximum initial wave amplitude of subaerial landslide tsunamis: A three-dimensional modelling approach

해저 산사태 쓰나미의 최대 초기 파동 진폭 추정: 3차원 모델링 접근법

Ramtin Sabeti a, Mohammad Heidarzadeh ab

aDepartment of Architecture and Civil Engineering, University of Bath, Bath BA27AY, UK
bHydroCoast Consulting Engineers Ltd, Bath, UK

https://doi.org/10.1016/j.ocemod.2024.102360

Highlights

  • •Landslide travel distance is considered for the first time in a predictive equation.
  • •Predictive equation derived from databases using 3D physical and numerical modeling.
  • •The equation was successfully tested on the 2018 Anak Krakatau tsunami event.
  • •The developed equation using three-dimensional data exhibits a 91 % fitting quality.

Abstract

Landslide tsunamis, responsible for thousands of deaths and significant damage in recent years, necessitate the allocation of sufficient time and resources for studying these extreme natural hazards. This study offers a step change in the field by conducting a large number of three-dimensional numerical experiments, validated by physical tests, to develop a predictive equation for the maximum initial amplitude of tsunamis generated by subaerial landslides. We first conducted a few 3D physical experiments in a wave basin which were then applied for the validation of a 3D numerical model based on the Flow3D-HYDRO package. Consequently, we delivered 100 simulations using the validated model by varying parameters such as landslide volume, water depth, slope angle and travel distance. This large database was subsequently employed to develop a predictive equation for the maximum initial tsunami amplitude. For the first time, we considered travel distance as an independent parameter for developing the predictive equation, which can significantly improve the predication accuracy. The predictive equation was tested for the case of the 2018 Anak Krakatau subaerial landslide tsunami and produced satisfactory results.

Keywords

Tsunami, Subaerial landslide, Physical modelling, Numerical simulation, FLOW-3D HYDRO

1. Introduction and literature review

The Anak Krakatau landslide tsunami on 22nd December 2018 was a stark reminder of the dangers posed by subaerial landslide tsunamis (Ren et al., 2020; Mulia et al. 2020a; Borrero et al., 2020; Heidarzadeh et al., 2020; Grilli et al., 2021). The collapse of the volcano’s southwest side into the ocean triggered a tsunami that struck the Sunda Strait, leading to approximately 450 fatalities (Syamsidik et al., 2020; Mulia et al., 2020b) (Fig. 1). As shown in Fig. 1, landslide tsunamis (both submarine and subaerial) have been responsible for thousands of deaths and significant damage to coastal communities worldwide. These incidents underscored the critical need for advanced research into landslide-generated waves to aid in hazard prediction and mitigation. This is further emphasized by recent events such as the 28th of November 2020 landslide tsunami in the southern coast mountains of British Columbia (Canada), where an 18 million m3 rockslide generated a massive tsunami, with over 100 m wave run-up, causing significant environmental and infrastructural damage (Geertsema et al., 2022).

Fig 1

Physical modelling and numerical simulation are crucial tools in the study of landslide-induced waves due to their ability to replicate and analyse the complex dynamics of landslide events (Kim et al., 2020). In two-dimensional (2D) modelling, the discrepancy between dimensions can lead to an artificial overestimation of wave amplification (e.g., Heller and Spinneken, 2015). This limitation is overcome with 3D modelling, which enables the scaled-down representation of landslide-generated waves while avoiding the simplifications inherent in 2D approaches (Erosi et al., 2019). Another advantage of 3D modelling in studying landslide-generated waves is its ability to accurately depict the complex dynamics of wave propagation, including lateral and radial spreading from the slide impact zone, a feature unattainable with 2D models (Heller and Spinneken, 2015).

Physical experiments in tsunami research, as presented by authors such as Romano et al. (2020), McFall and Fritz (2016), and Heller and Spinneken (2015), have supported 3D modelling works through validation and calibration of the numerical models to capture the complexities of wave generation and propagation. Numerical modelling has increasingly complemented experimental approach in tsunami research due to the latter’s time and resource-intensive nature, particularly for 3D models (Li et al., 2019; Kim et al., 2021). Various numerical approaches have been employed, from Eulerian and Lagrangian frameworks to depth-averaged and Navier–Stokes models, enhancing our understanding of tsunami dynamics (Si et al., 2018; Grilli et al., 2019; Heidarzadeh et al., 2017, 2020; Iorio et al., 2021; Zhang et al., 2021; Kirby et al., 2022; Wang et al., 2021, 2022; Hu et al., 2022). The sophisticated numerical techniques, including the Particle Finite Element Method and the Immersed Boundary Method, have also shown promising results in modelling highly dynamic landslide scenarios (Mulligan et al., 2020; Chen et al., 2020). Among these methods and techniques, FLOW-3D HYDRO stands out in simulating landslide-generated tsunami waves due to its sophisticated technical features such as offering Tru Volume of Fluid (VOF) method for precise free surface tracking (e.g., Sabeti and Heidarzadeh 2022a). TruVOF distinguishes itself through a split Lagrangian approach, adeptly reducing cumulative volume errors in wave simulations by dynamically updating cell volume fractions and areas with each time step. Its intelligent adaptation of time step size ensures precise capture of evolving free surfaces, offering unparalleled accuracy in modelling complex fluid interfaces and behaviour (Flow Science, 2023).

Predictive equations play a crucial role in assessing the potential hazards associated with landslide-generated tsunami waves due to their ability to provide risk assessment and warnings. These equations can offer swift and reasonable evaluations of potential tsunami impacts in the absence of detailed numerical simulations, which can be time-consuming and expensive to produce. Among multiple factors and parameters within a landslide tsunami generation, the initial maximum wave amplitude (Fig. 1) stands out due to its critical role. While it is most likely that the initial wave generated by a landslide will have the highest amplitude, it is crucial to clarify that the term “initial maximum wave amplitude” refers to the highest amplitude within the first set of impulse waves. This parameter is essential in determining the tsunami’s impact severity, with higher amplitudes signalling a greater destructive potential (Sabeti and Heidarzadeh 2022a). Additionally, it plays a significant role in tsunami modelling, aiding in the prediction of wave propagation and the assessment of potential impacts.

In this study, we initially validate the FLOW-3D HYDRO model through a series of physical experiments conducted in a 3D wave tank at University of Bath (UK). Upon confirmation of the model’s accuracy, we use it to systematically vary parameters namely landslide volume, water depth, slope angle, and travel distance, creating an extensive database. Alongside this, we perform a sensitivity analysis on these variables to discern their impacts on the initial maximum wave amplitude. The generated database was consequently applied to derive a non-dimensional predictive equation aimed at estimating the initial maximum wave amplitude in real-world landslide tsunami events.

Two innovations of this study are: (i) The predictive equation of this study is based on a large number of 3D experiments whereas most of the previous equations were based on 2D results, and (ii) For the first time, the travel distance is included in the predictive equation as an independent parameter. To evaluate the performance of our predictive equation, we applied it to a previous real-world subaerial landslide tsunami, i.e., the Anak Krakatau 2018 event. Furthermore, we compare the performance of our predictive equation with other existing equations.

2. Data and methods

The methodology applied in this research is a combination of physical and numerical modelling. Limited physical modelling was performed in a 3D wave basin at the University of Bath (UK) to provide data for calibration and validation of the numerical model. After calibration and validation, the numerical model was employed to model a large number of landslide tsunami scenarios which allowed us to develop a database for deriving a predictive equation.

2.1. Physical experiments

To validate our numerical model, we conducted a series of physical experiments including two sets in a 3D wave basin at University of Bath, measuring 2.50 m in length (WL), 2.60 m in width (WW), and 0.60 m in height (WH) (Fig. 2a). Conducting two distinct sets of experiments (Table 1), each with different setups (travel distance, location, and water depth), provided a robust framework for validation of the numerical model. For wave measurement, we employed a twin wire wave gauge from HR Wallingford (https://equipit.hrwallingford.com). In these experiments, we used a concrete prism solid block, the dimensions of which are outlined in Table 2. In our experiments, we employed a concrete prism solid block with a density of 2600 kg/m3, chosen for its similarity to the natural density of landslides, akin to those observed with the 2018 Anak Krakatau tsunami, where the landslide composition is predominantly solid rather than granular. The block’s form has also been endorsed in prior studies (Watts, 1998; Najafi-Jilani and Ataie-Ashtiani, 2008) as a suitable surrogate for modelling landslide-induced waves. A key aspect of our methodology was addressing scale effects, following the guidelines proposed by Heller et al. (2008) as it is described in Table 1. To enhance the reliability and accuracy of our experimental data, we conducted each physical experiment three times which revealed all three experimental waveforms were identical. This repetition was aimed at minimizing potential errors and inconsistencies in laboratory measurements.

Fig 2

Table 1. The locations and other information of the laboratory setups for making landslide-generated waves in the physical wave basin. This table details the specific parameters for each setup, including slope range (α), slide volume (V), kinematic viscosity (ν), water depth (h), travel distance (D), surface tension coefficient of water (σ), Reynolds number (R), Weber number (W), and the precise coordinates of the wave gauges (WG).

Labα(°)V (m³)h (m)D (m)WG’s Location(ν) (m²/s)(σ) (N/m)Acceptable range for avoiding scale effects*Observed values of W and R ⁎⁎
Lab 1452.60 × 10−30.2470.070X1=1.090 m1.01 × 10−60.073R > 3.0 × 105R1 = 3.80 × 105
Y1=1.210 m
W1 = 8.19 × 105
Z1=0.050mW >5.0 × 103
Lab 2452.60 × 10−30.2460.045X2=1.030 m1.01 × 10−60.073R2 = 3.78 × 105
Y2=1.210 mW2 = 8.13 × 105
Z2=0.050 m

⁎

The acceptable ranges for avoiding scale effects are based on the study by Heller et al. (2008).⁎⁎

The Reynolds number (R) is given by g0.5h1.5/ν, with ν denoting the kinematic viscosity. The Weber number (W) is W = ρgh2/σ, where σ represents surface tension coefficient and ρ = 1000kg/m3 is the density of water. In our experiments, conducted at a water temperature of approximately 20 °C, the kinematic viscosity (ν) and the surface tension coefficient of water (σ) are 1.01 × 10−6 m²/s and 0.073 N/m, respectively (Kestin et al., 1978).

Table 2. Specifications of the solid block used in physical experiments for generating subaerial landslides in the laboratory.

Solid-block attributesProperty metricsGeometric shape
Slide width (bs)0.26 mImage, table 2
Slide length (ls)0.20 m
Slide thickness (s)0.10 m
Slide volume (V)2.60 × 10−3 m3
Specific gravity, (γs)2.60
Slide weight (ms)6.86 kg

2.2. Numerical simulations applying FLOW-3D hydro

The detailed theoretical framework encompassing the governing equations, the computational methodologies employed, and the specific techniques used for tracking the water surface in these simulations are thoroughly detailed in the study by Sabeti et al. (2024). Here, we briefly explain some of the numerical details. We defined a uniform mesh for our flow domain, carefully crafted with a fine spatial resolution of 0.005 m (i.e., grid size). The dimensions of the numerical model directly matched those of our wave basin used in the physical experiment, being 2.60 m wide, 0.60 m deep, and 2.50 m long (Fig. 2). This design ensures comprehensive coverage of the study area. The output intervals of the numerical model are set at 0.02 s. This timing is consistent with the sampling rates of wave gauges used in laboratory settings. The friction coefficient in the FLOW-3D HYDRO is designated as 0.45. This value corresponds to the Coulombic friction measurements obtained in the laboratory, ensuring that the simulation accurately reflects real-world physical interactions.

In order to simulate the landslide motion, we applied coupled motion objects in FLOW-3D-HYDRO where the dynamics are predominantly driven by gravity and surface friction. This methodology stands in contrast to other models that necessitate explicit inputs of force and torque. This approach ensures that the simulation more accurately reflects the natural movement of landslides, which is heavily reliant on gravitational force and the interaction between sliding surfaces. The stability of the numerical simulations is governed by the Courant Number criterion (Courant et al., 1928), which dictates the maximum time step (Δt) for a given mesh size (Δx) and flow speed (U). According to Courant et al. (1928), this number is required to stay below one to ensure stability of numerical simulations. In our simulations, the Courant number is always maintained below one.

In alignment with the parameters of physical experiments, we set the fluid within the mesh to water, characterized by a density of 1000 kg/m³ at a temperature of 20 °C. Furthermore, we defined the top, front, and back surfaces of the mesh as symmetry planes. The remaining surfaces are designated as wall types, incorporating no-slip conditions to accurately simulate the interaction between the fluid and the boundaries. In terms of selection of an appropriate turbulence model, we selected the k–ω model that showed a better performance than other turbulence methods (e.g., Renormalization-Group) in a previous study (Sabeti et al., 2024). The simulations are conducted using a PC Intel® Core™ i7-10510U CPU with a frequency of 1.80 GHz, and a 16 GB RAM. On this PC, completion of a 3-s simulation required approximately 12.5 h.

2.3. Validation

The FLOW-3D HYDRO numerical model was validated using the two physical experiments (Fig. 3) outlined in Table 1. The level of agreement between observations (Oi) and simulations (Si) is examined using the following equation:(1)�=|��−����|×100where ε represents the mismatch error, Oi denotes the observed laboratory values, and Si represents the simulated values from the FLOW-3D HYDRO model. The results of this validation process revealed that our model could replicate the waves generated in the physical experiments with a reasonable degree of mismatch (ε): 14 % for Lab 1 and 8 % for Lab 2 experiments, respectively (Fig. 3). These values indicate that while the model is not perfect, it provides a sufficiently close approximation of the real-world phenomena.

Fig 3

In terms of mesh efficiency, we varied the mesh size to study sensitivity of the numerical results to mesh size. First, by halving the mesh size and then by doubling it, we repeated the modelling by keeping other parameters unchanged. This analysis guided that a mesh size of ∆x = 0.005 m is the most effective for the setup of this study. The total number of computational cells applying mesh size of 0.005 m is 9.269 × 106.

2.4. The dataset

The validated numerical model was employed to conduct 100 simulations, incorporating variations in four key landslide parameters namely water depth, slope angle, slide volume, and travel distance. This methodical approach was essential for a thorough sensitivity analysis of these variables, and for the creation of a detailed database to develop a predictive equation for maximum initial tsunami amplitude. Within the model, 15 distinct slide volumes were established, ranging from 0.10 × 10−3 m3 to 6.25 × 10−3 m3 (Table 3). The slope angle varied between 35° and 55°, and water depth ranged from 0.24 m to 0.27 m. The travel distance of the landslides was varied, spanning from 0.04 m to 0.07 m. Detailed configurations of each simulation, along with the maximum initial wave amplitudes and dominant wave periods are provided in Table 4.

Table 3. Geometrical information of the 15 solid blocks used in numerical modelling for generating landslide tsunamis. Parameters are: ls, slide length; bs, slide width; s, slide thickness; γs, specific gravity; and V, slide volume.

Solid blockls (m)bs (m)s (m)V (m3)γs
Block-10.3100.2600.1556.25 × 10−32.60
Block-20.3000.2600.1505.85 × 10−32.60
Block-30.2800.2600.1405.10 × 10−32.60
Block-40.2600.2600.1304.39 × 10−32.60
Block-50.2400.2600.1203.74 × 10−32.60
Block-60.2200.2600.1103.15 × 10−32.60
Block-70.2000.2600.1002.60 × 10−32.60
Block-80.1800.2600.0902.11 × 10−32.60
Block-90.1600.2600.0801.66 × 10−32.60
Block-100.1400.2600.0701.27 × 10−32.60
Block-110.1200.2600.0600.93 × 10−32.60
Block-120.1000.2600.0500.65 × 10−32.60
Block-130.0800.2600.0400.41 × 10−32.60
Block-140.0600.2600.0300.23 × 10−32.60
Block-150.0400.2600.0200.10 × 10−32.60

Table 4. The numerical simulation for the 100 tests performed in this study for subaerial solid-block landslide-generated waves. Parameters are aM, maximum wave amplitude; α, slope angle; h, water depth; D, travel distance; and T, dominant wave period. The location of the wave gauge is X=1.030 m, Y=1.210 m, and Z=0.050 m. The properties of various solid blocks are presented in Table 3.

Test-Block Noα (°)h (m)D (m)T(s)aM (m)
1Block-7450.2460.0290.5100.0153
2Block-7450.2460.0300.5050.0154
3Block-7450.2460.0310.5050.0156
4Block-7450.2460.0320.5050.0158
5Block-7450.2460.0330.5050.0159
6Block-7450.2460.0340.5050.0160
7Block-7450.2460.0350.5050.0162
8Block-7450.2460.0360.5050.0166
9Block-7450.2460.0370.5050.0167
10Block-7450.2460.0380.5050.0172
11Block-7450.2460.0390.5050.0178
12Block-7450.2460.0400.5050.0179
13Block-7450.2460.0410.5050.0181
14Block-7450.2460.0420.5050.0183
15Block-7450.2460.0430.5050.0190
16Block-7450.2460.0440.5050.0197
17Block-7450.2460.0450.5050.0199
18Block-7450.2460.0460.5050.0201
19Block-7450.2460.0470.5050.0191
20Block-7450.2460.0480.5050.0217
21Block-7450.2460.0490.5050.0220
22Block-7450.2460.0500.5050.0226
23Block-7450.2460.0510.5050.0236
24Block-7450.2460.0520.5050.0239
25Block-7450.2460.0530.5100.0240
26Block-7450.2460.0540.5050.0241
27Block-7450.2460.0550.5050.0246
28Block-7450.2460.0560.5050.0247
29Block-7450.2460.0570.5050.0248
30Block-7450.2460.0580.5050.0249
31Block-7450.2460.0590.5050.0251
32Block-7450.2460.0600.5050.0257
33Block-1450.2460.0450.5050.0319
34Block-2450.2460.0450.5050.0294
35Block-3450.2460.0450.5050.0282
36Block-4450.2460.0450.5050.0262
37Block-5450.2460.0450.5050.0243
38Block-6450.2460.0450.5050.0223
39Block-7450.2460.0450.5050.0196
40Block-8450.2460.0450.5050.0197
41Block-9450.2460.0450.5050.0198
42Block-10450.2460.0450.5050.0184
43Block-11450.2460.0450.5050.0173
44Block-12450.2460.0450.5050.0165
45Block-13450.2460.0450.4040.0153
46Block-14450.2460.0450.4040.0124
47Block-15450.2460.0450.5050.0066
48Block-7450.2020.0450.4040.0220
49Block-7450.2040.0450.4040.0219
50Block-7450.2060.0450.4040.0218
51Block-7450.2080.0450.4040.0217
52Block-7450.2100.0450.4040.0216
53Block-7450.2120.0450.4040.0215
54Block-7450.2140.0450.5050.0214
55Block-7450.2160.0450.5050.0214
56Block-7450.2180.0450.5050.0213
57Block-7450.2200.0450.5050.0212
58Block-7450.2220.0450.5050.0211
59Block-7450.2240.0450.5050.0208
60Block-7450.2260.0450.5050.0203
61Block-7450.2280.0450.5050.0202
62Block-7450.2300.0450.5050.0201
63Block-7450.2320.0450.5050.0201
64Block-7450.2340.0450.5050.0200
65Block-7450.2360.0450.5050.0199
66Block-7450.2380.0450.4040.0196
67Block-7450.2400.0450.4040.0194
68Block-7450.2420.0450.4040.0193
69Block-7450.2440.0450.4040.0192
70Block-7450.2460.0450.5050.0190
71Block-7450.2480.0450.5050.0189
72Block-7450.2500.0450.5050.0187
73Block-7450.2520.0450.5050.0187
74Block-7450.2540.0450.5050.0186
75Block-7450.2560.0450.5050.0184
76Block-7450.2580.0450.5050.0182
77Block-7450.2590.0450.5050.0183
78Block-7450.2600.0450.5050.0191
79Block-7450.2610.0450.5050.0192
80Block-7450.2620.0450.5050.0194
81Block-7450.2630.0450.5050.0195
82Block-7450.2640.0450.5050.0195
83Block-7450.2650.0450.5050.0197
84Block-7450.2660.0450.5050.0197
85Block-7450.2670.0450.5050.0198
86Block-7450.2700.0450.5050.0199
87Block-7300.2460.0450.5050.0101
88Block-7350.2460.0450.5050.0107
89Block-7360.2460.0450.5050.0111
90Block-7370.2460.0450.5050.0116
91Block-7380.2460.0450.5050.0117
92Block-7390.2460.0450.5050.0119
93Block-7400.2460.0450.5050.0121
94Block-7410.2460.0450.5050.0127
95Block-7420.2460.0450.4040.0154
96Block-7430.2460.0450.4040.0157
97Block-7440.2460.0450.4040.0162
98Block-7450.2460.0450.5050.0197
99Block-7500.2460.0450.5050.0221
100Block-7550.2460.0450.5050.0233

In all these 100 simulations, the wave gauge was consistently positioned at coordinates X=1.09 m, Y=1.21 m, and Z=0.05 m. The dominant wave period for each simulation was determined using the Fast Fourier Transform (FFT) function in MATLAB (MathWorks, 2023). Furthermore, the classification of wave types was carried out using a wave categorization graph according to Sorensen (2010), as shown in Fig. 4a. The results indicate that the majority of the simulated waves are on the border between intermediate and deep-water waves, and they are categorized as Stokes waves (Fig. 4a). Four sample waveforms from our 100 numerical experiments are provided in Fig. 4b.

Fig 4

The dataset in Table 4 was used to derive a new predictive equation that incorporates travel distance for the first time to estimate the initial maximum tsunami amplitude. In developing this equation, a genetic algorithm optimization technique was implemented using MATLAB (MathWorks 2023). This advanced approach entailed the use of genetic algorithms (GAs), an evolutionary algorithm type inspired by natural selection processes (MathWorks, 2023). This technique is iterative, involving selection, crossover, and mutation processes to evolve solutions over several generations. The goal was to identify the optimal coefficients and powers for each landslide parameter in the predictive equation, ensuring a robust and reliable model for estimating maximum wave amplitudes. Genetic Algorithms excel at optimizing complex models by navigating through extensive combinations of coefficients and exponents. GAs effectively identify highly suitable solutions for the non-linear and complex relationships between inputs (e.g., slide volume, slope angle, travel distance, water depth) and the output (i.e., maximum initial wave amplitude, aM). MATLAB’s computational environment enhances this process, providing robust tools for GA to adapt and evolve solutions iteratively, ensuring the precision of the predictive model (Onnen et al., 1997). This approach leverages MATLAB’s capabilities to fine-tune parameters dynamically, achieving an optimal equation that accurately estimates aM. It is important to highlight that the nondimensionalized version of this dataset is employed to develop a predictive equation which enables the equation to reproduce the maximum initial wave amplitude (aM) for various subaerial landslide cases, independent of their dimensional differences (e.g., Heler and Hager 2014; Heller and Spinneken 2015; Sabeti and Heidarzadeh 2022b). For this nondimensionalization, we employed the water depth (h) to nondimensionalize the slide volume (V/h3) and travel distance (D/h). The slide thickness (s) was applied to nondimensionalize the water depth (h/s).

2.5. Landslide velocity

In discussing the critical role of landslide velocity for simulating landslide-generated waves, we focus on the mechanisms of landslide motion and the techniques used to record landslide velocity in our simulations (Fig. 5). Also, we examine how these methods were applied in two distinct scenarios: Lab 1 and Lab 2 (see Table 1 for their details). Regarding the process of landslide movement, a slide starts from a stationary state, gaining momentum under the influence of gravity and this acceleration continues until the landslide collides with water, leading to a significant reduction in its speed before eventually coming to a stop (Fig. 5) (e.g., Panizzo et al. 2005).

Fig 5

To measure the landslide’s velocity in our simulations, we attached a probe at the centre of the slide, which supplied a time series of the velocity data. The slide’s velocity (vs) peaks at the moment it enters the water (Fig. 5), a point referred to as the impact time (tImp). Following this initial impact, the slides continue their underwater movement, eventually coming to a complete halt (tStop). Given the results in Fig. 5, it can be seen that Lab 1, with its longer travel distance (0.070 m), exhibits a higher peak velocity of 1.89 m/s. This increase in velocity is attributed to the extended travel distance allowing more time for the slide to accelerate under gravity. Whereas Lab 2, featuring a shorter travel distance (0.045 m), records a lower peak velocity of 1.78 m/s. This difference underscores how travel distance significantly influences the dynamics of landslide motion. After reaching the peak, both profiles show a sharp decrease in velocity, marking the transition to submarine motion until the slides come to a complete stop (tStop). There are noticeable differences observable in Fig. 5 between the Lab-1 and Lab-2 simulations, including the peaks at 0.3 s . These variations might stem from the placement of the wave gauge, which differs slightly in each scenario, as well as the water depth’s minor discrepancies and, the travel distance.

2.6. Effect of air entrainment

In this section we examine whether it is required to consider air entrainment for our modelling or not as the FLOW-3D HYDRO package is capable of modelling air entrainment. The process of air entrainment in water during a landslide tsunami and its subsequent transport involve two key components: the quantification of air entrainment at the water surface, and the simulation of the air’s transport within the fluid (Hirt, 2003). FLOW-3D HYDRO employs the air entrainment model to compute the volume of air entrained at the water’s surface utilizing three approaches: a constant density model, a variable density model accounting for bulking, and a buoyancy model that adds the Drift-FLUX mechanism to variable density conditions (Flow Science, 2023). The calculation of the entrainment rate is based on the following equation:(2)�������=������[2(��−�����−2�/���)]1/2where parameters are: Vair, volume of air; Cair, entrainment rate coefficient; As, surface area of fluid; ρ, fluid density; k, turbulent kinetic energy; gn, gravity normal to surface; Lt, turbulent length scale; and σ, surface tension coefficient. The value of k is directly computed from the Reynolds-averaged Navier-Stokes (RANS) (k–w) calculations in our model.

In this study, we selected the variable density + Drift-FLUX model, which effectively captures the dynamics of phase separation and automatically activates the constant density and variable density models. This method simplifies the air-water mixture, treating it as a single, homogeneous fluid within each computational cell. For the phase volume fractions f1and f2​, the velocities are expressed in terms of the mixture and relative velocities, denoted as u and ur, respectively, as follows:(3)��1��+�.(�1�)=��1��+�.(�1�)−�.(�1�2��)=0(4)��2��+�.(�2�)=��2��+�.(�2�)−�.(�1�2��)=0

The outcomes from this simulation are displayed in Fig. 6, which indicates that the influence of air entrainment on the generated wave amplitude is approximately 2 %. A value of 0.02 for the entrained air volume fraction means that, in the simulated fluid, approximately 2 % of the volume is composed of entrained air. In other words, for every unit volume of the fluid-air mixture at that location, 2 % is air and the remaining 98 % is water. The configuration of Test-17 (Table 4) was employed for this simulation. While the effect of air entrainment is anticipated to be more significant in models of granular landslide-generated waves (Fritz, 2002), in our simulations we opted not to incorporate this module due to its negligible impact on the results.

Fig 6

3. Results

In this section, we begin by presenting a sequence of our 3D simulations capturing different time steps to illustrate the generation process of landslide-generated waves. Subsequently, we derive a new predictive equation to estimate the maximum initial wave amplitude of landslide-generated waves and assess its performance.

3.1. Wave generation and propagation

To demonstrate the wave generation process in our simulation, we reference Test-17 from Table 4, where we employed Block-7 (Tables 3, 4). In this configuration, the slope angle was set to 45°, with a water depth of 0.246 m and a travel distance at 0.045 m (Fig. 7). At 0.220 s, the initial impact of the moving slide on the water is depicted, marking the onset of the wave generation process (Fig. 7a). Disturbances are localized to the immediate area of impact, with the rest of the water surface remaining undisturbed. At this time, a maximum water particle velocity of 1.0 m/s – 1.2 m/s is seen around the impact zone (Fig. 7d). Moving to 0.320 s, the development of the wave becomes apparent as energy transfer from the landslide to the water creates outwardly radiating waves with maximum water particle velocity of up to around 1.6 m/s – 1.8 m/s (Fig. 7b, e). By the time 0.670 s, the wave has fully developed and is propagating away from the impact point exhibiting maximum water particle velocity of up to 2.0 m/s – 2.1 m/s. Concentric wave fronts are visible, moving outwards in all directions, with a colour gradient signifying the highest wave amplitude near the point of landslide entry, diminishing with distance (Fig. 7c, f).

Fig 7

3.2. Influence of landslide parameters on tsunami amplitude

In this section, we investigate the effects of various landslide parameters namely slide volume (V), water depth (h), slipe angle (α) and travel distance (D) on the maximum initial wave amplitude (aM). Fig. 8 presents the outcome of these analyses. According to Fig. 8, the slide volume, slope angle, and travel distance exhibit a direct relationship with the wave amplitude, meaning that as these parameters increase, so does the amplitude. Conversely, water depth is inversely related to the maximum initial wave amplitude, suggesting that the deeper the water depth, the smaller the maximum wave amplitude will be (Fig. 8b).

Fig 8

Fig. 8a highlights the pronounced impact of slide volume on the aM, demonstrating a direct correlation between the two variables. For instance, in the range of slide volumes we modelled (Fig. 8a), The smallest slide volume tested, measuring 0.10 × 10−3 m3, generated a low initial wave amplitude (aM= 0.0066 m) (Table 4). In contrast, the largest volume tested, 6.25 × 10−3 m3, resulted in a significantly higher initial wave amplitude (aM= 0.0319 m) (Table 4). The extremities of these results emphasize the slide volume’s paramount impact on wave amplitude, further elucidated by their positions as the smallest and largest aM values across all conducted tests (Table 4). This is corroborated by findings from the literature (e.g., Murty, 2003), which align with the observed trend in our simulations.

The slope angle’s influence on aM was smooth. A steady increase of wave amplitude was observed as the slope angle increased (Fig. 8c). In examining travel distance, an anomaly was identified. At a travel distance of 0.047 m, there was an unexpected dip in aM, which deviates from the general increasing trend associated with longer travel distances. This singular instance could potentially be attributed to a numerical error. Beyond this point, the expected pattern of increasing aM with longer travel distances resumes, suggesting that the anomaly at 0.047 m is an outlier in an otherwise consistent trend, and thus this single data point was overlooked while deriving the predictive equation. Regarding the inverse relationship between water depth and wave amplitude, our result (Fig. 8b) is consistent with previous reports by Fritz et al. (2003), (2004), and Watts et al. (2005).

The insights from Fig. 8 informed the architecture of the predictive equation in the next Section, with slide volume, travel distance, and slope angle being multiplicatively linked to wave amplitude underscoring their direct correlations with wave amplitude. Conversely, water depth is incorporated as a divisor, representing its inverse relationship with wave amplitude. This structure encapsulates the dynamics between the landslide parameters and their influence on the maximum initial wave amplitude as discussed in more detail in the next Section.

3.3. Predictive equation

Building on our sensitivity analysis of landslide parameters, as detailed in Section 3.2, and utilizing our nondimensional dataset, we have derived a new predictive equation as follows:(5)��/ℎ=0.015(tan�)0.10(�ℎ3)0.90(�ℎ)0.10(ℎ�)−0.11where, V is sliding volume, h is water depth, α is slope angle, and s is landslide thickness. It is important to note that this equation is valid only for subaerial solid-block landslide tsunamis as all our experiments were for this type of waves. The performance of this equation in predicting simulation data is demonstrated by the satisfactory alignment of data points around a 45° line, indicating its accuracy and reliability with regard to the experimental dataset (Fig. 9). The quality of fit between the dataset and Eq. (5) is 91 % indicating that Eq. (5) represents the dataset very well. Table 5 presents Eq. (5) alongside four other similar equations previously published. Two significant distinctions between our Eq. (5) and these others are: (i) Eq. (5) is derived from 3D experiments, whereas the other four equations are based on 2D experiments. (ii) Unlike the other equations, our Eq. (5) incorporates travel distance as an independent parameter.

Fig 9

Table 5. Performance comparison among our newly-developed equation and existing equations for estimating the maximum initial amplitude (aM) of the 2018 Anak Krakatau subaerial landslide tsunami. Parameters: aM, initial maximum wave amplitude; h, water depth; vs, landslide velocity; V, slide volume; bs, slide width; ls, slide length; s, slide thickness; α, slope angle; and ����, volume of the final immersed landslide. We considered ����= V as the slide volume.

EventPredictive equationsAuthor (year)Observed aM (m) ⁎⁎Calculated aM (m)Error, ε (%) ⁎⁎⁎⁎
2018 Anak Krakatau tsunami (Subaerial landslide) *��/ℎ=1.32���ℎNoda (1970)1341340
��/ℎ=0.667(0.5(���ℎ)2)0.334(���)0.754(���)0.506(�ℎ)1.631Bolin et al. (2014) ⁎⁎⁎13459424334
��/ℎ=0.25(������ℎ2)0.8Robbe-Saule et al. (2021)1343177
��/ℎ=0.4545(tan�)0.062(�ℎ3)0.296(ℎ�)−0.235Sabeti and Heidarzadeh (2022b)1341266
��/ℎ=0.015(tan�)0.10(�ℎ3)0.911(�ℎ)0.10(ℎ�)−0.11This study1341302.9

⁎

Geometrical and kinematic parameters of the 2018 Anak Krakatau subaerial landslide based on Heidarzadeh et al. (2020), Grilli et al. (2019) and Grilli et al. (2021): V=2.11 × 107 m3, h= 50 m; s= 114 m; α= 45°; ls=1250 m; bs= 2700 m; vs=44.9 m/s; D= 2500 m; aM= 100 m −150 m.⁎⁎

aM= An average value of aM = 134 m is considered in this study.⁎⁎⁎

The equation of Bolin et al. (2014) is based on the reformatted one reported by Lindstrøm (2016).⁎⁎⁎⁎

Error is calculated using Eq. (1), where the calculated aM is assumed as the simulated value.

Additionally, we evaluated the performance of this equation using the real-world data from the 2018 Anak Krakatau subaerial landslide tsunami. Based on previous studies (Heidarzadeh et al., 2020; Grilli et al., 2019, 2021), we were able to provide a list of parameters for the subaerial landslide and associated tsunami for the 2018 Anak Krakatau event (see footnote of Table 5). We note that the data of the 2018 Anak Krakatau event was not used while deriving Eq. (5). The results indicate that Eq. (5) predicts the initial amplitude of the 2018 Anak Krakatau tsunami as being 130 m indicating an error of 2.9 % compared to the reported average amplitude of 134 m for this event. This performance indicates an improvement compared to the previous equation reported by Sabeti and Heidarzadeh (2022a) (Table 5). In contrast, the equations from Robbe-Saule et al. (2021) and Bolin et al. (2014) demonstrate higher discrepancies of 4200 % and 77 %, respectively (Table 5). Although Noda’s (1970) equation reproduces the tsunami amplitude of 134 m accurately (Table 5), it is crucial to consider its limitations, notably not accounting for parameters such as slope angle and travel distance.

It is essential to recognize that both travel distance and slope angle significantly affect wave amplitude. In our model, captured in Eq. (5), we integrate the slope angle (α) through the tangent function, i.e., tan α. This choice diverges from traditional physical interpretations that often employ the cosine or sine function (e.g., Heller and Hager, 2014; Watts et al., 2003). We opted for the tangent function because it more effectively reflects the direct impact of slope steepness on wave generation, yielding superior estimations compared to conventional methods.

The significance of this study lies in its application of both physical and numerical 3D experiments and the derivation of a predictive equation based on 3D results. Prior research, e.g. Heller et al. (2016), has reported notable discrepancies between 2D and 3D wave amplitudes, highlighting the important role of 3D experiments. It is worth noting that the suitability of applying an equation derived from either 2D or 3D data depends on the specific geometry and characteristics inherent in the problem being addressed. For instance, in the case of a long, narrow dam reservoir, an equation derived from 2D data would likely be more suitable. In such contexts, the primary dynamics of interest such as flow patterns and potential wave propagation are predominantly two-dimensional, occurring along the length and depth of the reservoir. This simplification to 2D for narrow dam reservoirs allows for more accurate modelling of these dynamics.

This study specifically investigates waves initiated by landslides, focusing on those characterized as solid blocks instead of granular flows, with slope angles confined to a range of 25° to 60°. We acknowledge the additional complexities encountered in real-world scenarios, such as dynamic density and velocity of landslides, which could affect the estimations. The developed equation in this study is specifically designed to predict the maximum initial amplitude of tsunamis for the aforementioned specified ranges and types of landslides.

4. Conclusions

Both physical and numerical experiments were undertaken in a 3D wave basin to study solid-block landslide-generated waves and to formulate a predictive equation for their maximum initial wave amplitude. At the beginning, two physical experiments were performed to validate and calibrate a 3D numerical model, which was subsequently utilized to generate 100 experiments by varying different landslide parameters. The generated database was then used to derive a predictive equation for the maximum initial wave amplitude of landslide tsunamis. The main features and outcomes are:

  • •The predictive equation of this study is exclusively derived from 3D data and exhibits a fitting quality of 91 % when applied to the database.
  • •For the first time, landslide travel distance was considered in the predictive equation. This inclusion provides more accuracy and flexibility for applying the equation.
  • •To further evaluate the performance of the predictive equation, it was applied to a real-world subaerial landslide tsunami (i.e., the 2018 Anak Krakatau event) and delivered satisfactory performance.

CRediT authorship contribution statement

Ramtin Sabeti: Conceptualization, Methodology, Validation, Software, Visualization, Writing – review & editing. Mohammad Heidarzadeh: Methodology, Data curation, Software, Writing – review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Funding

RS is supported by the Leverhulme Trust Grant No. RPG-2022-306. MH is funded by open funding of State Key Lab of Hydraulics and Mountain River Engineering, Sichuan University, grant number SKHL2101. We acknowledge University of Bath Institutional Open Access Fund. MH is also funded by the Great Britain Sasakawa Foundation grant no. 6217 (awarded in 2023).

Acknowledgements

Authors are sincerely grateful to the laboratory technician team, particularly Mr William Bazeley, at the Faculty of Engineering, University of Bath for their support during the laboratory physical modelling of this research. We appreciate the valuable insights provided by Mr. Brian Fox (Senior CFD Engineer at Flow Science, Inc.) regarding air entrainment modelling in FLOW-3D HYDRO. We acknowledge University of Bath Institutional Open Access Fund.

Data availability

  • All data used in this study are given in the body of the article.

References

PDF 원문 보기
Fig. 3. Free surface and substrate profiles in all Sp and Ls cases at t = 1 s, t = 3 s, and t = 5 s, arranged left to right (note: the colour contours correspond to the horizontal component of the flow velocity (u), expressed in m/s).

Numerical investigation of dam break flow over erodible beds with diverse substrate level variations

다양한 기질 수준 변화를 갖는 침식성 층 위의 댐 파손 흐름에 대한 수치 조사

Alireza Khoshkonesh1, Blaise Nsom2, Saeid Okhravi3*, Fariba Ahmadi Dehrashid4, Payam Heidarian5,
Silvia DiFrancesco6
1 Department of Geography, School of Social Sciences, History, and Philosophy, Birkbeck University of London, London, UK.
2 Université de Bretagne Occidentale. IRDL/UBO UMR CNRS 6027. Rue de Kergoat, 29285 Brest, France.
3 Institute of Hydrology, Slovak Academy of Sciences, Dúbravská cesta 9, 84104, Bratislava, Slovak Republic.
4Department of Water Science and Engineering, Faculty of Agriculture, Bu-Ali Sina University, 65178-38695, Hamedan, Iran.
5 Department of Civil, Environmental, Architectural Engineering and Mathematics, University of Brescia, 25123 Brescia, Italy.
6Niccol`o Cusano University, via Don C. Gnocchi 3, 00166 Rome, Italy. * Corresponding author. Tel.: +421-944624921. E-mail: saeid.okhravi@savba.sk

Abstract

This study aimed to comprehensively investigate the influence of substrate level difference and material composition on dam break wave evolution over two different erodible beds. Utilizing the Volume of Fluid (VOF) method, we tracked free surface advection and reproduced wave evolution using experimental data from the literature. For model validation, a comprehensive sensitivity analysis encompassed mesh resolution, turbulence simulation methods, and bed load transport equations. The implementation of Large Eddy Simulation (LES), non-equilibrium sediment flux, and van Rijn’s (1984) bed load formula yielded higher accuracy compared to alternative approaches. The findings emphasize the significant effect of substrate level difference and material composition on dam break morphodynamic characteristics. Decreasing substrate level disparity led to reduced flow velocity, wavefront progression, free surface height, substrate erosion, and other pertinent parameters. Initial air entrapment proved substantial at the wavefront, illustrating pronounced air-water interaction along the bottom interface. The Shields parameter experienced a one-third reduction as substrate level difference quadrupled, with the highest near-bed concentration observed at the wavefront. This research provides fresh insights into the complex interplay of factors governing dam break wave propagation and morphological changes, advancing our comprehension of this intricate phenomenon.

이 연구는 두 개의 서로 다른 침식층에 대한 댐 파괴파 진화에 대한 기질 수준 차이와 재료 구성의 영향을 종합적으로 조사하는 것을 목표로 했습니다. VOF(유체량) 방법을 활용하여 자유 표면 이류를 추적하고 문헌의 실험 데이터를 사용하여 파동 진화를 재현했습니다.

모델 검증을 위해 메쉬 해상도, 난류 시뮬레이션 방법 및 침대 하중 전달 방정식을 포함하는 포괄적인 민감도 분석을 수행했습니다. LES(Large Eddy Simulation), 비평형 퇴적물 플럭스 및 van Rijn(1984)의 하상 부하 공식의 구현은 대체 접근 방식에 비해 더 높은 정확도를 산출했습니다.

연구 결과는 댐 붕괴 형태역학적 특성에 대한 기질 수준 차이와 재료 구성의 중요한 영향을 강조합니다. 기판 수준 차이가 감소하면 유속, 파면 진행, 자유 표면 높이, 기판 침식 및 기타 관련 매개변수가 감소했습니다.

초기 공기 포집은 파면에서 상당한 것으로 입증되었으며, 이는 바닥 경계면을 따라 뚜렷한 공기-물 상호 작용을 보여줍니다. 기판 레벨 차이가 4배로 증가함에 따라 Shields 매개변수는 1/3로 감소했으며, 파면에서 가장 높은 베드 근처 농도가 관찰되었습니다.

이 연구는 댐 파괴파 전파와 형태학적 변화를 지배하는 요인들의 복잡한 상호 작용에 대한 새로운 통찰력을 제공하여 이 복잡한 현상에 대한 이해를 향상시킵니다.

Keywords

Dam break; Substrate level difference; Erodible bed; Sediment transport; Computational fluid dynamics CFD.

Fig. 3. Free surface and substrate profiles in all Sp and Ls cases at t = 1 s, t = 3 s, and t = 5 s, arranged left to right (note: the colour contours correspond to the horizontal component of the flow velocity (u), expressed in m/s).
Fig. 3. Free surface and substrate profiles in all Sp and Ls cases at t = 1 s, t = 3 s, and t = 5 s, arranged left to right (note: the colour contours correspond to the horizontal component of the flow velocity (u), expressed in m/s).
PDF Download

REFERENCES

Aleixo, R., Soares-Frazão, S., Zech, Y., 2010. Velocity profiles in
dam-break flows: water and sediment layers. In: Proc. Int. Conf.
on Fluvial Hydraulics “River Flow 2010”, pp. 533–540.
An, S., Ku, H., Julien, P.Y., 2015. Numerical modelling of local
scour caused by submerged jets. Maejo Int. J. Sci. Technol., 9, 3,
328–343.
Bahmanpouri, F., Daliri, M., Khoshkonesh, A., Namin, M.M.,
Buccino, M., 2021. Bed compaction effect on dam break flow over
erodible bed; experimental and numerical modeling. J. Hydrol.,
594, 125645. https://doi.org/10.1016/j.jhydrol.2020.125645
Baklanov, A., 2007. Environmental risk and assessment modelling
– scientific needs and expected advancements. In: Ebel, A.,
Davitashvili, T. (Eds.): Air, Water and Soil Quality Modelling
for Risk and Impact Assessment Springer, Dordrecht, pp. 29–44.
Biscarini, C., Di Francesco, S., Nardi, F., Manciola, P., 2013.
Detailed simulation of complex hydraulic problems with
macroscopic and mesoscopic mathematical methods. Math.
Probl. Eng., 928309. https://doi.org/10.1155/2013/928309
Cao, Z., Pender, G., Wallis, S., Carling, P., 2004. Computational
dam-break hydraulics over erodible sediment bed. J. Hydraul.
Eng., 130, 7, 689–703.
Catucci, D., Briganti, R., Heller, V., 2021. Numerical validation of novel
scaling laws for air entrainment in water. Proc. R. Soc. A, 477, 2255,20210339. https://doi.org/10.1098/rspa.2021.0339
Dehrashid, F.A., Heidari, M., Rahimi, H., Khoshkonesh, A., Yuan,
S., Tang, X., Lu, C., Wang, X., 2023. CFD modeling the flow
dynamics in an open channel with double-layered vegetation.
Model. Earth Syst. Environ., 9, 1, 543–555.
Desombre, J., Morichon, D., Mory, M., 2013. RANS v2-f simulation
of a swash event: Detailed flow structure. Coastal Eng., 71, 1–12.
Dodangeh, E., Afzalimehr, H., 2022. Incipient motion of sediment
particles in the presence of bed forms under decelerating and
accelerating flows. J. Hydrol. Hydromech., 70, 1, 89–102.
Dong, Z., Wang, J., Vetsch, D.F., Boes, R.M., Tan, G., 2019.
Numerical simulation of air entrainment on stepped
spillways. In: E-proceedings of the 38th IAHR World Congress
(pp. 1494). September 1–6, 2019, Panama City, Panama. DOI:
10.3850/38WC092019-0755
Flow3D [computer software]. 2023. Santa Fe, NM: Flow Science,
Inc.
Fraccarollo, L., Capart, H., 2002. Riemann wave description of
erosional dam-break flows. J. Fluid Mech., 461, 183–228.
Gu, Z., Wang, T., Meng, W., Yu, C.H., An, R., 2023. Numerical
investigation of silted-up dam-break flow with different silted-up
sediment heights. Water Supply, 23, 2, 599–614.
Gualtieri, P., De Felice, S., Pasquino, V., Doria, G.P., 2018. Use of
conventional flow resistance equations and a model for the
Nikuradse roughness in vegetated flows at high submergence. J.
Hydrol. Hydromech., 66, 1, 107–120.
Heller, V., 2011. Scale effects in physical hydraulic engineering
models. J. Hydraul. Res., 49, 3, 293–306.
Hirt, C.W., 2003. Modeling turbulent entrainment of air at a free
surface. Flow Science, Inc.
Hirt, C.W., Nichols, B.D., 1981. Volume of fluid (VOF) method for
the dynamics of free boundaries. J. Comput. Phys., 39, 1, 201–
225.
Issakhov, A., Zhandaulet, Y., Nogaeva, A., 2018. Numerical
simulation of dam break flow for various forms of the obstacle
by VOF method. Int. J. Multiphase Flow, 109, 191–206.
Khayyer, A., Gotoh, H., 2010. On particle-based simulation of a dam
break over a wet bed. J. Hydraul. Res., 48, 2, 238–249.
Khoshkonesh, A., Daliri, M., Riaz, K., Dehrashid, F.A.,
Bahmanpouri, F., Di Francesco, S., 2022. Dam-break flow
dynamics over a stepped channel with vegetation. J. Hydrol., 613,128395. https://doi.org/10.1016/j.jhydrol.2022.128395
Khoshkonesh, A., Nsom, B., Gohari, S., Banejad, H., 2019.
A comprehensive study on dam-break flow over dry and wet
beds. Ocean Eng., 188, 106279.
https://doi.org/10.1016/j.oceaneng.2019.106279
Khoshkonesh, A., Sadeghi, S.H., Gohari, S., Karimpour, S., Oodi,
S., Di Francesco, S., 2023. Study of dam-break flow over a
vegetated channel with and without a drop. Water Resour.
Manage., 37, 5, 2107–2123.
Khosravi, K., Chegini, A.H.N., Cooper, J., Mao, L., Habibnejad, M.,
Shahedi, K., Binns, A., 2021. A laboratory investigation of bedload transport of gravel sediments under dam break flow. Int. J.
Sediment Res., 36, 2, 229–234.
Kim, Y., Zhou, Z., Hsu, T.J., Puleo, J.A., 2017. Large eddy
simulation of dam‐break‐driven swash on a rough‐planar beach.
J. Geophys. Res.: Oceans, 122, 2, 1274–1296.
Kocaman, S., Ozmen-Cagatay, H., 2012. The effect of lateral
channel contraction on dam break flows: Laboratory experiment.
J. Hydrol., 432, 145–153.
Leal, J.G., Ferreira, R.M., Cardoso, A.H., 2006. Dam-break wavefront celerity. J. Hydraul. Eng., 132, 1, 69–76.
Leal, J.G.A.B., Ferreira, R.M., Cardoso, A.H., 2003. Dam-break
wave propagation over a cohesionless erodible bed. In: Proc.
30rd IAHR Congress, 100, 261–268.
Li, Y. L., Ma, Y., Deng, R., Jiang, D.P., Hu, Z., 2019. Research on
dam-break induced tsunami bore acting on the triangular
breakwater based on high order 3D CLSVOF-THINC/WLICIBM approaching. Ocean Eng., 182, 645–659.
Li, Y.L., Yu, C.H., 2019. Research on dam-break flow induced front
wave impacting a vertical wall based on the CLSVOF and level
set methods. Ocean Eng., 178, 442–462.
Mei, S., Chen, S., Zhong, Q., Shan, Y., 2022. Detailed numerical
modeling for breach hydrograph and morphology evolution
during landslide dam breaching. Landslides, 19, 12, 2925–2949.
Meng, W., Yu, C.H., Li, J., An, R., 2022. Three-dimensional simulation
of silted-up dam-break flow striking a rigid structure. Ocean Eng.,
261, 112042. https://doi.org/10.1016/j.oceaneng.2022.112042
Meyer-Peter, E., Müller, R., 1948. Formulas for bed-load transport.
In: IAHSR 2nd meeting, Stockholm, appendix 2. IAHR.
Nielsen, P., 1984. Field measurements of time-averaged suspended
sediment concentrations under waves. Coastal Eng., 8, 1, 51–72.
Nielsen, P., 2018. Bed shear stress, surface shape and velocity field
near the tips of dam-breaks, tsunami and wave runup. Coastal
Eng., 138, 126–131.
Nsom, B., Latrache, N., Ramifidisoa, L., Khoshkonesh, A., 2019.
Analytical solution to the stability of gravity-driven stratified
flow of two liquids over an inclined plane. In: 24th French
Mechanics Congress in Brest. Brest, p. 244178.
Nsom, B., Ravelo, B., Ndong, W., 2008. Flow regimes in horizontal
viscous dam-break flow of Cayous mud. Appl. Rheol., 18, 4,
43577-1. https://doi.org/10.1515/arh-2008-0012
Oguzhan, S., Aksoy, A.O., 2020. Experimental investigation of the
effect of vegetation on dam break flood waves. J. Hydrol.
Hydromech., 68, 3, 231–241.
Okhravi, S., Gohari, S., Alemi, M., Maia, R., 2022. Effects of bedmaterial gradation on clear water scour at single and group of
piles. J. Hydrol. Hydromech., 70, 1, 114–127.
Okhravi, S., Gohari, S., Alemi, M., Maia, R., 2023. Numerical
modeling of local scour of non-uniform graded sediment for two
arrangements of pile groups. Int. J. Sediment Res., 38, 4, 597–614.
Parambath, A., 2010. Impact of tsunamis on near shore wind power
units. Master’s Thesis. Texas A&M University. Available
electronically from https://hdl.handle.net/1969.1/ETD-TAMU2010-12-8919
Pintado-Patiño, J.C., Puleo, J.A., Krafft, D., Torres-Freyermuth, A.,

  • Hydrodynamics and sediment transport under a dambreak-driven swash: An experimental study. Coastal Eng., 170,
  • https://doi.org/10.1016/j.coastaleng.2021.103986
    Riaz, K., Aslam, H.M.S., Yaseen, M.W., Ahmad, H.H.,
    Khoshkonesh, A., Noshin, S., 2022. Flood frequency analysis
    and hydraulic design of bridge at Mashan on river Kunhar. Arch.
    Hydroengineering Environ. Mech., 69, 1, 1–12.
    Ritter, A., 1892. Die Fortpflanzung der Wasserwellen. Zeitschrift
    des Vereines Deutscher Ingenieure, 36, 33, 947–954. (In
    German.)
    Smagorinsky, J., 1963. General circulation experiments with the
    primitive equations: I. The basic experiment. Mon. Weather
    Rev., 91, 3, 99–164.
    Soulsby, R.L., 1997. Dynamics of marine sands: a manual for
    practical applications. Oceanogr. Lit. Rev., 9, 44, 947.
    Spinewine, B., Capart, H., 2013. Intense bed-load due to a sudden
    dam-break. J. Fluid Mech., 731, 579–614.
    Van Rijn, L.C., 1984. Sediment transport, part I: bed load transport.
    J. Hydraul. Eng., 110, 10, 1431–1456.
    Vosoughi, F., Rakhshandehroo, G., Nikoo, M.R., Sadegh, M.,
  • Experimental study and numerical verification of
    silted-up dam break. J. Hydrol., 590, 125267.
    https://doi.org/10.1016/j.jhydrol.2020.125267
    Wu, W., Wang, S.S., 2008. One-dimensional explicit finite-volume
    model for sediment transport. J. Hydraul. Res., 46, 1, 87–98.
    Xu, T., Huai, W., Liu, H., 2023. MPS-based simulation of
    dam-break wave propagation over wet beds with a
    sediment layer. Ocean Eng., 281, 115035.
    https://doi.org/10.1016/j.oceaneng.2023.115035
    Yang, S., Yang, W., Qin, S., Li, Q., Yang, B., 2018. Numerical study
    on characteristics of dam-break wave. Ocean Eng., 159, 358–371.
    Yao, G.F., 2004. Development of new pressure-velocity solvers in
    FLOW-3D. Flow Science, Inc., USA.
PDF Download
The distribution of the computed maximum current speed during the entire duration of the NAMI DANCE and FLOW-3D simulations. The resolution of computational domain is 10 m

Performance Comparison of NAMI DANCE and FLOW-3D® Models in Tsunami Propagation, Inundation and Currents using NTHMP Benchmark Problems

NTHMP 벤치마크 문제를 사용하여 쓰나미 전파, 침수 및 해류에서 NAMI DANCE 및 FLOW-3D® 모델의 성능 비교

Pure and Applied Geophysics volume 176, pages3115–3153 (2019)Cite this article

Abstract

Field observations provide valuable data regarding nearshore tsunami impact, yet only in inundation areas where tsunami waves have already flooded. Therefore, tsunami modeling is essential to understand tsunami behavior and prepare for tsunami inundation. It is necessary that all numerical models used in tsunami emergency planning be subject to benchmark tests for validation and verification. This study focuses on two numerical codes, NAMI DANCE and FLOW-3D®, for validation and performance comparison. NAMI DANCE is an in-house tsunami numerical model developed by the Ocean Engineering Research Center of Middle East Technical University, Turkey and Laboratory of Special Research Bureau for Automation of Marine Research, Russia. FLOW-3D® is a general purpose computational fluid dynamics software, which was developed by scientists who pioneered in the design of the Volume-of-Fluid technique. The codes are validated and their performances are compared via analytical, experimental and field benchmark problems, which are documented in the ‘‘Proceedings and Results of the 2011 National Tsunami Hazard Mitigation Program (NTHMP) Model Benchmarking Workshop’’ and the ‘‘Proceedings and Results of the NTHMP 2015 Tsunami Current Modeling Workshop”. The variations between the numerical solutions of these two models are evaluated through statistical error analysis.

현장 관찰은 연안 쓰나미 영향에 관한 귀중한 데이터를 제공하지만 쓰나미 파도가 이미 범람한 침수 지역에서만 가능합니다. 따라서 쓰나미 모델링은 쓰나미 행동을 이해하고 쓰나미 범람에 대비하는 데 필수적입니다.

쓰나미 비상 계획에 사용되는 모든 수치 모델은 검증 및 검증을 위한 벤치마크 테스트를 받아야 합니다. 이 연구는 검증 및 성능 비교를 위해 NAMI DANCE 및 FLOW-3D®의 두 가지 숫자 코드에 중점을 둡니다.

NAMI DANCE는 터키 중동 기술 대학의 해양 공학 연구 센터와 러시아 해양 연구 자동화를 위한 특별 조사국 연구소에서 개발한 사내 쓰나미 수치 모델입니다. FLOW-3D®는 Volume-of-Fluid 기술의 설계를 개척한 과학자들이 개발한 범용 전산 유체 역학 소프트웨어입니다.

코드의 유효성이 검증되고 분석, 실험 및 현장 벤치마크 문제를 통해 코드의 성능이 비교되며, 이는 ‘2011년 NTHMP(National Tsunami Hazard Mitigation Program) 모델 벤치마킹 워크숍의 절차 및 결과’와 ”절차 및 NTHMP 2015 쓰나미 현재 모델링 워크숍 결과”. 이 두 모델의 수치 해 사이의 변동은 통계적 오류 분석을 통해 평가됩니다.

The distribution of the computed maximum current speed during the entire duration of the NAMI DANCE and FLOW-3D simulations. The resolution of computational domain is 10 m
The distribution of the computed maximum current speed during the entire duration of the NAMI DANCE and FLOW-3D simulations. The resolution of computational domain is 10 m

References

  • Allan, J. C., Komar, P. D., Ruggiero, P., & Witter, R. (2012). The March 2011 Tohoku tsunami and its impacts along the U.S. West Coast. Journal of Coastal Research, 28(5), 1142–1153. https://doi.org/10.2112/jcoastres-d-11-00115.1.Article Google Scholar 
  • Apotsos, A., Buckley, M., Gelfenbaum, G., Jafe, B., & Vatvani, D. (2011). Nearshore tsunami inundation and sediment transport modeling: towards model validation and application. Pure and Applied Geophysics, 168(11), 2097–2119. https://doi.org/10.1007/s00024-011-0291-5.Article Google Scholar 
  • Barberopoulou, A., Legg, M. R., & Gica, E. (2015). Time evolution of man-made harbor modifications in San Diego: effects on Tsunamis. Journal of Marine Science and Engineering, 3, 1382–1403.Article Google Scholar 
  • Basu, D., Green, S., Das, K., Janetzke, R. and Stamatakos, J. (2009). Numerical Simulation of Surface Waves Generated by a Subaerial Landslide at Lituya Bay, Alaska. Proceedings of 28th International Conference on Ocean, Offshore and Arctic Engineering. Honolulu, Hawaii, USA.
  • Briggs, M. J., Synolakis, C. E., Harkins, G. S., & Green, D. R. (1995). Laboratory experiments of tsunami run-up on a circular island. Pure and Applied Geophysics, 144(3/4), 569–593.Article Google Scholar 
  • Cheung, K. F., Bai, Y., & Yamazaki, Y. (2013). Surges around the Hawaiian Islands from the 2011 Tohoku Tsunami. Journal of Geophysical Research: Oceans, 118, 5703–5719. https://doi.org/10.1002/jgrc.20413.Google Scholar 
  • Choi, B. H., Dong, C. K., Pelinovsky, E., & Woo, S. B. (2007). Three-dimensional Simulation of Tsunami Run-up Around Conical Island. Coastal Engineering, 54, 618–629.Article Google Scholar 
  • Cox, D., T. Tomita, P. Lynett, R.A., Holman. (2008). Tsunami Inundation with Macroroughness in the Constructed Environment. Proceedings of 31st International Conference on Coastal Engineering, ASCE, pp. 1421–1432.
  • Flow Science. (2002). FLOW-3D User’s Manual.
  • Hirt, C. W., & Nichols, B. D. (1981). Volume of fluid (VOF) method for the dynamics of free boundaries. Journal of Computational Physics, 39, 201–225.Article Google Scholar 
  • Horrillo, J., Grilli, S. T., Nicolsky, D., Roeber, V., & Zang, J. (2015). Performance benchmarking Tsunami models for NTHMP’s inundation mapping activities. Pure and Applied Geophysics, 172, 869–884.Article Google Scholar 
  • http://nws.weather.gov/nthmp/documents/nthmpWorkshopProcMerged.pdf.
  • http://nws.weather.gov/nthmp/documents/NTHMP_Currents_Workshop_Report.pdf.
  • Kim, K. O., Kim, D. C., Choi, B.-H., Jung, T. K., Yuk, J. H., & Pelinovsky, E. (2015). The role of diffraction effects in extreme run-up inundation at Okushiri Island due to 1993 Tsunami. Natural Hazards and Earth Systems Sciences, 15, 747–755.Article Google Scholar 
  • Liu, P. L.-F. (1994). Model equations for wave propagations from deep to shallow water. (P.-F. Liu, Ed.) Advances in Coastal and Ocean Engineering, 1, 125–158.
  • Liu, P. L.-F., Yeh, H., & Synolakis, C. E. (2008). Advanced numerical models for simulating Tsunami waves and run-up. Advances in Coastal and Ocean Engineering, 10, 344.Google Scholar 
  • Lynett, P. J., Borrero, J., Son, S., Wilson, R., & Miller, K. (2014). Assessment of the tsunami-induced current hazard. Geophysical Research Letters, 41, 2048–2055. https://doi.org/10.1002/2013GL058680.Article Google Scholar 
  • Lynett, P. J., Gately, K., Wilson, R., Montoya, L., Arcas, D., Aytore, B., et al. (2017). Inter-model analysis of Tsunami-induced coastal currents. Ocean Modelling, 114, 14–32.Article Google Scholar 
  • Lynett, P. J., Wu, T.-R., & Liu, P. L.-F. (2002). Modeling wave run-up with depth-integrated equations. Coastal Engineering, 46(2), 89–107.Article Google Scholar 
  • Macias, J., Castro, M. J., Ortega, S., Escalante, C., & Gonzalez-Vida, J. M. (2017). Performance benchmarking of Tsunami-HySEA model for nthmp’s inundation mapping activities. Pure and Applied Geophysics, 174, 3147–3183.Article Google Scholar 
  • Matsuyama, M., & Tanaka, H. (2001). An experimental study of the highest run-up height in the 1993 Hokkaidō Nansei-Oki Earthquake Tsunami. Proceedings of ITS, 2001, 879–889.Google Scholar 
  • National Tsunami Hazard Mitigation Program. 2012. Proceedings and Results of the 2011 NTHMP Model Benchmarking Workshop. Boulder: U.S. Department of Commerce/NOAA/NTHMP; (NOAA Special Report). p. 436.
  • National Tsunami Hazard Mitigation Program. (2017). Proceedings and Results of the National Tsunami Hazard Mitigation Program 2015 Tsunami Current Modeling Workshop, February 9-10, 2015, Portland, Oregon: compiled by Patrick Lynett and Rick Wilson, p 194.
  • Necmioglu, O., & Ozel, N. M. (2014). An earthquake source sensitivity analysis for Tsunami propagation in the Eastern Mediterranean. Oceanography, 27(2), 76–85.Article Google Scholar 
  • Nichols, B.D. and Hirt, C.W. (1975). Methods for Calculating Multi-Dimensional, Transient Free Surface Flows Past Bodies. Proceedings of 1st International Conference Num. Ship Hydrodynamics. Gaithersburg.
  • Nicolsky, D. J., Suleimani, E. N., & Hansen, R. A. (2011). Validation and verification of a numerical model for Tsunami propagation and run-up. Pure and Applied Geophysics, 168(6), 1199–1222.Article Google Scholar 
  • NOAA Center for Tsunami Research: Tsunami Run-up onto a Complex Three-dimensional Beach; Monai Valley. (n.d). Retrieved from: https://nctr.pmel.noaa.gov/benchmark/Laboratory/Laboratory_MonaiValley/.
  • Park, H., Cox, D. T., Lynett, P. J., Wiebe, D. M., & Shin, S. (2013). Tsunami inundation modeling in constructed environments: a physical and numerical comparison of free-surface elevation, velocity, and momentum flux. Coastal Engineering, 79, 9–21.Article Google Scholar 
  • Patel, V. M., Dholakia, M. B., & Singh, A. P. (2016). Emergency preparedness in the case of Makran Tsunami: a case study on Tsunami risk visualization for the Western Parts of Gujarat, India. Geomatics Natural Hazard and Risk, 7(2), 826–842.Article Google Scholar 
  • Pelinovsky, E., Kim, D.-C., Kim, K.-O., & Choi, B.-H. (2013). Three-dimensional simulation of extreme run-up heights during the 2004 Indonesian and 2011 Japanese Tsunamis. Vienna: EGU General Assembly.Google Scholar 
  • Rueben, M., Holman, R., Cox, D., Shin, S., Killian, J., & Stanley, J. (2011). Optical measurements of Tsunami inundation through an urban waterfront modeled in a large-scale laboratory basin. Coastal Engineering, 58, 229–238.Article Google Scholar 
  • Shuto, N. (1991). Numerical simulation of Tsunamis—its present and near future. Natural Hazards, 4, 171–191.Article Google Scholar 
  • Synolakis, C. E. (1986). The run-up of long waves. Ph.D. Thesis. California Institute of Technology, Pasadena, California.
  • Synolakis, C. E., Bernard, E. N., Titov, V. V., Kanoglu, U. & Gonzalez, F. (2007). Standards, criteria, and procedures for NOAA evaluation of Tsunami Numerical Models. 55 p. Seattle, Washington: NOAA OAR Special Report, Contribution No 3053, NOAA/OAR/PMEL.
  • Synolakis, C. E., Bernard, E. N., Titov, V. V., Kanoglu, U., & Gonzalez, F. I. (2008). Validation and verification of Tsunami numerical models. Pure and Applied Geophysics, 165, 2197–2228.Article Google Scholar 
  • Tolkova, E. (2014). Land-water boundary treatment for a tsunami model with dimensional splitting. Pure and Applied Geophysics, 171(9), 2289–2314.Article Google Scholar 
  • Velioglu, D. (2017). Advanced two- and three-dimensional Tsunami models: benchmarking and validation. Ph.D. Thesis. Middle East Technical University, Ankara.
  • Velioglu, D., Kian, R., Yalciner, A.C. and Zaytsev, A. (2016). Performance assessment of NAMI DANCE in Tsunami evolution and currents using a benchmark problem. (R. Signell, Ed.) J. Mar. Sci. Eng., 4(3), 49.
  • Wu, T. (2001). A unified theory for modeling water waves. Advances in Applied Mechanics, 37, 1–88.Article Google Scholar 
  • Wu, N.-J., Hsiao, S.-C., Chen, H.-H., & Yang, R.-Y. (2016). The study on solitary waves generated by a piston-type wave maker. Ocean Engineering, 117, 114–129.Article Google Scholar 
  • Yalciner, A. C., Dogan, P. and Sukru. E. (2005). December 26 2004, Indian Ocean Tsunami Field Survey, North of Sumatra Island. UNESCO.
  • Yalciner, A. C., Gülkan, P., Dilmen, I., Aytore, B., Ayca, A., Insel, I., et al. (2014). Evaluation of Tsunami scenarios For Western Peloponnese, Greece. Bollettino di Geofisica Teorica ed Applicata, 55, 485–500.Google Scholar 
  • Yen, B. C. (1991). Hydraulic resistance in open channels. In B. C. Yen (Ed.), Channel flow resistance: centennial of manning’s formula (pp. 1–135). Highlands Ranch: Water Resource Publications.Google Scholar 
  • Zaitsev, A. I., Kovalev, D. P., Kurkin, A. A., Levin, B. V., Pelinovskii, E. N., Chernov, A. G., et al. (2009). The Tsunami on Sakhalin on August 2, 2007: mareograph evidence and numerical simulation. Tikhookeanskaya Geologiya, 28, 30–35.Google Scholar 

Download references

Acknowledgements

The authors wish to thank Dr. Andrey Zaytsev due to his undeniable contributions to the development of in-house numerical model, NAMI DANCE. The Turkish branch of Flow Science, Inc. is also acknowledged. Finally, the National Tsunami Hazard Mitigation Program (NTHMP), who provided most of the benchmark data, is appreciated. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Author notes

  1. Deniz Velioglu SogutPresent address: 1212 Computer Science, Department of Civil Engineering, Stony Brook University, Stony Brook, NY, 11794, USA

Authors and Affiliations

  1. Middle East Technical University, 06800, Ankara, TurkeyDeniz Velioglu Sogut & Ahmet Cevdet Yalciner

Corresponding author

Correspondence to Deniz Velioglu Sogut.

Ethics declarations

Conflicts of Interest

The authors declare no conflict of interest.

Reprints and Permissions

About this article

Cite this article

Velioglu Sogut, D., Yalciner, A.C. Performance Comparison of NAMI DANCE and FLOW-3D® Models in Tsunami Propagation, Inundation and Currents using NTHMP Benchmark Problems. Pure Appl. Geophys. 176, 3115–3153 (2019). https://doi.org/10.1007/s00024-018-1907-9

Download citation

  • Received22 December 2017
  • Revised16 May 2018
  • Accepted24 May 2018
  • Published07 June 2018
  • Issue Date01 July 2019
  • DOIhttps://doi.org/10.1007/s00024-018-1907-9

Keywords

  • Tsunami
  • depth-averaged shallow water
  • Reynolds-averaged Navier–Stokes
  • benchmarking
  • NAMI DANCE
  • FLOW-3D®
원문 보기
Figure 2 Modeling the plant with cylindrical tubes at the bottom of the canal.

Optimized Vegetation Density to Dissipate Energy of Flood Flow in Open Canals

열린 운하에서 홍수 흐름의 에너지를 분산시키기 위해 최적화된 식생 밀도

Mahdi Feizbahr,1Navid Tonekaboni,2Guang-Jun Jiang,3,4and Hong-Xia Chen3,4
Academic Editor: Mohammad Yazdi

Abstract

강을 따라 식생은 조도를 증가시키고 평균 유속을 감소시키며, 유동 에너지를 감소시키고 강 횡단면의 유속 프로파일을 변경합니다. 자연의 많은 운하와 강은 홍수 동안 초목으로 덮여 있습니다. 운하의 조도는 식물의 영향을 많이 받기 때문에 홍수시 유동저항에 큰 영향을 미친다. 식물로 인한 흐름에 대한 거칠기 저항은 흐름 조건과 식물에 따라 달라지므로 모델은 유속, 유속 깊이 및 수로를 따라 식생 유형의 영향을 고려하여 유속을 시뮬레이션해야 합니다. 총 48개의 모델을 시뮬레이션하여 근관의 거칠기 효과를 조사했습니다. 결과는 속도를 높임으로써 베드 속도를 감소시키는 식생의 영향이 무시할만하다는 것을 나타냅니다.

Abstract

Vegetation along the river increases the roughness and reduces the average flow velocity, reduces flow energy, and changes the flow velocity profile in the cross section of the river. Many canals and rivers in nature are covered with vegetation during the floods. Canal’s roughness is strongly affected by plants and therefore it has a great effect on flow resistance during flood. Roughness resistance against the flow due to the plants depends on the flow conditions and plant, so the model should simulate the current velocity by considering the effects of velocity, depth of flow, and type of vegetation along the canal. Total of 48 models have been simulated to investigate the effect of roughness in the canal. The results indicated that, by enhancing the velocity, the effect of vegetation in decreasing the bed velocity is negligible, while when the current has lower speed, the effect of vegetation on decreasing the bed velocity is obviously considerable.

1. Introduction

Considering the impact of each variable is a very popular field within the analytical and statistical methods and intelligent systems [1–14]. This can help research for better modeling considering the relation of variables or interaction of them toward reaching a better condition for the objective function in control and engineering [15–27]. Consequently, it is necessary to study the effects of the passive factors on the active domain [28–36]. Because of the effect of vegetation on reducing the discharge capacity of rivers [37], pruning plants was necessary to improve the condition of rivers. One of the important effects of vegetation in river protection is the action of roots, which cause soil consolidation and soil structure improvement and, by enhancing the shear strength of soil, increase the resistance of canal walls against the erosive force of water. The outer limbs of the plant increase the roughness of the canal walls and reduce the flow velocity and deplete the flow energy in vicinity of the walls. Vegetation by reducing the shear stress of the canal bed reduces flood discharge and sedimentation in the intervals between vegetation and increases the stability of the walls [38–41].

One of the main factors influencing the speed, depth, and extent of flood in this method is Manning’s roughness coefficient. On the other hand, soil cover [42], especially vegetation, is one of the most determining factors in Manning’s roughness coefficient. Therefore, it is expected that those seasonal changes in the vegetation of the region will play an important role in the calculated value of Manning’s roughness coefficient and ultimately in predicting the flood wave behavior [43–45]. The roughness caused by plants’ resistance to flood current depends on the flow and plant conditions. Flow conditions include depth and velocity of the plant, and plant conditions include plant type, hardness or flexibility, dimensions, density, and shape of the plant [46]. In general, the issue discussed in this research is the optimization of flood-induced flow in canals by considering the effect of vegetation-induced roughness. Therefore, the effect of plants on the roughness coefficient and canal transmission coefficient and in consequence the flow depth should be evaluated [47, 48].

Current resistance is generally known by its roughness coefficient. The equation that is mainly used in this field is Manning equation. The ratio of shear velocity to average current velocity  is another form of current resistance. The reason for using the  ratio is that it is dimensionless and has a strong theoretical basis. The reason for using Manning roughness coefficient is its pervasiveness. According to Freeman et al. [49], the Manning roughness coefficient for plants was calculated according to the Kouwen and Unny [50] method for incremental resistance. This method involves increasing the roughness for various surface and plant irregularities. Manning’s roughness coefficient has all the factors affecting the resistance of the canal. Therefore, the appropriate way to more accurately estimate this coefficient is to know the factors affecting this coefficient [51].

To calculate the flow rate, velocity, and depth of flow in canals as well as flood and sediment estimation, it is important to evaluate the flow resistance. To determine the flow resistance in open ducts, Manning, Chézy, and Darcy–Weisbach relations are used [52]. In these relations, there are parameters such as Manning’s roughness coefficient (n), Chézy roughness coefficient (C), and Darcy–Weisbach coefficient (f). All three of these coefficients are a kind of flow resistance coefficient that is widely used in the equations governing flow in rivers [53].

The three relations that express the relationship between the average flow velocity (V) and the resistance and geometric and hydraulic coefficients of the canal are as follows:where n, f, and c are Manning, Darcy–Weisbach, and Chézy coefficients, respectively. V = average flow velocity, R = hydraulic radius, Sf = slope of energy line, which in uniform flow is equal to the slope of the canal bed,  = gravitational acceleration, and Kn is a coefficient whose value is equal to 1 in the SI system and 1.486 in the English system. The coefficients of resistance in equations (1) to (3) are related as follows:

Based on the boundary layer theory, the flow resistance for rough substrates is determined from the following general relation:where f = Darcy–Weisbach coefficient of friction, y = flow depth, Ks = bed roughness size, and A = constant coefficient.

On the other hand, the relationship between the Darcy–Weisbach coefficient of friction and the shear velocity of the flow is as follows:

By using equation (6), equation (5) is converted as follows:

Investigation on the effect of vegetation arrangement on shear velocity of flow in laboratory conditions showed that, with increasing the shear Reynolds number (), the numerical value of the  ratio also increases; in other words the amount of roughness coefficient increases with a slight difference in the cases without vegetation, checkered arrangement, and cross arrangement, respectively [54].

Roughness in river vegetation is simulated in mathematical models with a variable floor slope flume by different densities and discharges. The vegetation considered submerged in the bed of the flume. Results showed that, with increasing vegetation density, canal roughness and flow shear speed increase and with increasing flow rate and depth, Manning’s roughness coefficient decreases. Factors affecting the roughness caused by vegetation include the effect of plant density and arrangement on flow resistance, the effect of flow velocity on flow resistance, and the effect of depth [45, 55].

One of the works that has been done on the effect of vegetation on the roughness coefficient is Darby [56] study, which investigates a flood wave model that considers all the effects of vegetation on the roughness coefficient. There are currently two methods for estimating vegetation roughness. One method is to add the thrust force effect to Manning’s equation [47, 57, 58] and the other method is to increase the canal bed roughness (Manning-Strickler coefficient) [45, 59–61]. These two methods provide acceptable results in models designed to simulate floodplain flow. Wang et al. [62] simulate the floodplain with submerged vegetation using these two methods and to increase the accuracy of the results, they suggested using the effective height of the plant under running water instead of using the actual height of the plant. Freeman et al. [49] provided equations for determining the coefficient of vegetation roughness under different conditions. Lee et al. [63] proposed a method for calculating the Manning coefficient using the flow velocity ratio at different depths. Much research has been done on the Manning roughness coefficient in rivers, and researchers [49, 63–66] sought to obtain a specific number for n to use in river engineering. However, since the depth and geometric conditions of rivers are completely variable in different places, the values of Manning roughness coefficient have changed subsequently, and it has not been possible to choose a fixed number. In river engineering software, the Manning roughness coefficient is determined only for specific and constant conditions or normal flow. Lee et al. [63] stated that seasonal conditions, density, and type of vegetation should also be considered. Hydraulic roughness and Manning roughness coefficient n of the plant were obtained by estimating the total Manning roughness coefficient from the matching of the measured water surface curve and water surface height. The following equation is used for the flow surface curve:where  is the depth of water change, S0 is the slope of the canal floor, Sf is the slope of the energy line, and Fr is the Froude number which is obtained from the following equation:where D is the characteristic length of the canal. Flood flow velocity is one of the important parameters of flood waves, which is very important in calculating the water level profile and energy consumption. In the cases where there are many limitations for researchers due to the wide range of experimental dimensions and the variety of design parameters, the use of numerical methods that are able to estimate the rest of the unknown results with acceptable accuracy is economically justified.

FLOW-3D software uses Finite Difference Method (FDM) for numerical solution of two-dimensional and three-dimensional flow. This software is dedicated to computational fluid dynamics (CFD) and is provided by Flow Science [67]. The flow is divided into networks with tubular cells. For each cell there are values of dependent variables and all variables are calculated in the center of the cell, except for the velocity, which is calculated at the center of the cell. In this software, two numerical techniques have been used for geometric simulation, FAVOR™ (Fractional-Area-Volume-Obstacle-Representation) and the VOF (Volume-of-Fluid) method. The equations used at this model for this research include the principle of mass survival and the magnitude of motion as follows. The fluid motion equations in three dimensions, including the Navier–Stokes equations with some additional terms, are as follows:where  are mass accelerations in the directions x, y, z and  are viscosity accelerations in the directions x, y, z and are obtained from the following equations:

Shear stresses  in equation (11) are obtained from the following equations:

The standard model is used for high Reynolds currents, but in this model, RNG theory allows the analytical differential formula to be used for the effective viscosity that occurs at low Reynolds numbers. Therefore, the RNG model can be used for low and high Reynolds currents.

Weather changes are high and this affects many factors continuously. The presence of vegetation in any area reduces the velocity of surface flows and prevents soil erosion, so vegetation will have a significant impact on reducing destructive floods. One of the methods of erosion protection in floodplain watersheds is the use of biological methods. The presence of vegetation in watersheds reduces the flow rate during floods and prevents soil erosion. The external organs of plants increase the roughness and decrease the velocity of water flow and thus reduce its shear stress energy. One of the important factors with which the hydraulic resistance of plants is expressed is the roughness coefficient. Measuring the roughness coefficient of plants and investigating their effect on reducing velocity and shear stress of flow is of special importance.

Roughness coefficients in canals are affected by two main factors, namely, flow conditions and vegetation characteristics [68]. So far, much research has been done on the effect of the roughness factor created by vegetation, but the issue of plant density has received less attention. For this purpose, this study was conducted to investigate the effect of vegetation density on flow velocity changes.

In a study conducted using a software model on three density modes in the submerged state effect on flow velocity changes in 48 different modes was investigated (Table 1).

Table 1 

The studied models.

The number of cells used in this simulation is equal to 1955888 cells. The boundary conditions were introduced to the model as a constant speed and depth (Figure 1). At the output boundary, due to the presence of supercritical current, no parameter for the current is considered. Absolute roughness for floors and walls was introduced to the model (Figure 1). In this case, the flow was assumed to be nonviscous and air entry into the flow was not considered. After  seconds, this model reached a convergence accuracy of .

Figure 1 

The simulated model and its boundary conditions.

Due to the fact that it is not possible to model the vegetation in FLOW-3D software, in this research, the vegetation of small soft plants was studied so that Manning’s coefficients can be entered into the canal bed in the form of roughness coefficients obtained from the studies of Chow [69] in similar conditions. In practice, in such modeling, the effect of plant height is eliminated due to the small height of herbaceous plants, and modeling can provide relatively acceptable results in these conditions.

48 models with input velocities proportional to the height of the regular semihexagonal canal were considered to create supercritical conditions. Manning coefficients were applied based on Chow [69] studies in order to control the canal bed. Speed profiles were drawn and discussed.

Any control and simulation system has some inputs that we should determine to test any technology [70–77]. Determination and true implementation of such parameters is one of the key steps of any simulation [23, 78–81] and computing procedure [82–86]. The input current is created by applying the flow rate through the VFR (Volume Flow Rate) option and the output flow is considered Output and for other borders the Symmetry option is considered.

Simulation of the models and checking their action and responses and observing how a process behaves is one of the accepted methods in engineering and science [87, 88]. For verification of FLOW-3D software, the results of computer simulations are compared with laboratory measurements and according to the values of computational error, convergence error, and the time required for convergence, the most appropriate option for real-time simulation is selected (Figures 2 and 3 ).

Figure 2 

Modeling the plant with cylindrical tubes at the bottom of the canal.

Figure 3 

Velocity profiles in positions 2 and 5.

The canal is 7 meters long, 0.5 meters wide, and 0.8 meters deep. This test was used to validate the application of the software to predict the flow rate parameters. In this experiment, instead of using the plant, cylindrical pipes were used in the bottom of the canal.

The conditions of this modeling are similar to the laboratory conditions and the boundary conditions used in the laboratory were used for numerical modeling. The critical flow enters the simulation model from the upstream boundary, so in the upstream boundary conditions, critical velocity and depth are considered. The flow at the downstream boundary is supercritical, so no parameters are applied to the downstream boundary.

The software well predicts the process of changing the speed profile in the open canal along with the considered obstacles. The error in the calculated speed values can be due to the complexity of the flow and the interaction of the turbulence caused by the roughness of the floor with the turbulence caused by the three-dimensional cycles in the hydraulic jump. As a result, the software is able to predict the speed distribution in open canals.

2. Modeling Results

After analyzing the models, the results were shown in graphs (Figures 4–14 ). The total number of experiments in this study was 48 due to the limitations of modeling.


(d)


(a)


(b)


(c)


(d)


(a)


(b)


(c)


(d)

  • (a)
    (a)
  • (b)
    (b)
  • (c)
    (c)
  • (d)
    (d)

Figure 4 

Flow velocity profiles for canals with a depth of 1 m and flow velocities of 3–3.3 m/s. Canal with a depth of 1 meter and a flow velocity of (a) 3 meters per second, (b) 3.1 meters per second, (c) 3.2 meters per second, and (d) 3.3 meters per second.

Figure 5 

Canal diagram with a depth of 1 meter and a flow rate of 3 meters per second.

Figure 6 

Canal diagram with a depth of 1 meter and a flow rate of 3.1 meters per second.

Figure 7 

Canal diagram with a depth of 1 meter and a flow rate of 3.2 meters per second.

Figure 8 

Canal diagram with a depth of 1 meter and a flow rate of 3.3 meters per second.


(d)


(a)


(b)


(c)


(d)


(a)


(b)


(c)


(d)

  • (a)
    (a)
  • (b)
    (b)
  • (c)
    (c)
  • (d)
    (d)

Figure 9 

Flow velocity profiles for canals with a depth of 2 m and flow velocities of 4–4.3 m/s. Canal with a depth of 2 meters and a flow rate of (a) 4 meters per second, (b) 4.1 meters per second, (c) 4.2 meters per second, and (d) 4.3 meters per second.

Figure 10 

Canal diagram with a depth of 2 meters and a flow rate of 4 meters per second.

Figure 11 

Canal diagram with a depth of 2 meters and a flow rate of 4.1 meters per second.

Figure 12 

Canal diagram with a depth of 2 meters and a flow rate of 4.2 meters per second.

Figure 13 

Canal diagram with a depth of 2 meters and a flow rate of 4.3 meters per second.


(d)


(a)


(b)


(c)


(d)


(a)


(b)


(c)


(d)

  • (a)
    (a)
  • (b)
    (b)
  • (c)
    (c)
  • (d)
    (d)

Figure 14 

Flow velocity profiles for canals with a depth of 3 m and flow velocities of 5–5.3 m/s. Canal with a depth of 2 meters and a flow rate of (a) 4 meters per second, (b) 4.1 meters per second, (c) 4.2 meters per second, and (d) 4.3 meters per second.

To investigate the effects of roughness with flow velocity, the trend of flow velocity changes at different depths and with supercritical flow to a Froude number proportional to the depth of the section has been obtained.

According to the velocity profiles of Figure 5, it can be seen that, with the increasing of Manning’s coefficient, the canal bed speed decreases.

According to Figures 5 to 8, it can be found that, with increasing the Manning’s coefficient, the canal bed speed decreases. But this deceleration is more noticeable than the deceleration of the models 1 to 12, which can be justified by increasing the speed and of course increasing the Froude number.

According to Figure 10, we see that, with increasing Manning’s coefficient, the canal bed speed decreases.

According to Figure 11, we see that, with increasing Manning’s coefficient, the canal bed speed decreases. But this deceleration is more noticeable than the deceleration of Figures 5–10, which can be justified by increasing the speed and, of course, increasing the Froude number.

With increasing Manning’s coefficient, the canal bed speed decreases (Figure 12). But this deceleration is more noticeable than the deceleration of the higher models (Figures 5–8 and 10, 11), which can be justified by increasing the speed and, of course, increasing the Froude number.

According to Figure 13, with increasing Manning’s coefficient, the canal bed speed decreases. But this deceleration is more noticeable than the deceleration of Figures 5 to 12, which can be justified by increasing the speed and, of course, increasing the Froude number.

According to Figure 15, with increasing Manning’s coefficient, the canal bed speed decreases.

Figure 15 

Canal diagram with a depth of 3 meters and a flow rate of 5 meters per second.

According to Figure 16, with increasing Manning’s coefficient, the canal bed speed decreases. But this deceleration is more noticeable than the deceleration of the higher model, which can be justified by increasing the speed and, of course, increasing the Froude number.

Figure 16 

Canal diagram with a depth of 3 meters and a flow rate of 5.1 meters per second.

According to Figure 17, it is clear that, with increasing Manning’s coefficient, the canal bed speed decreases. But this deceleration is more noticeable than the deceleration of the higher models, which can be justified by increasing the speed and, of course, increasing the Froude number.

Figure 17 

Canal diagram with a depth of 3 meters and a flow rate of 5.2 meters per second.

According to Figure 18, with increasing Manning’s coefficient, the canal bed speed decreases. But this deceleration is more noticeable than the deceleration of the higher models, which can be justified by increasing the speed and, of course, increasing the Froude number.

Figure 18 

Canal diagram with a depth of 3 meters and a flow rate of 5.3 meters per second.

According to Figure 19, it can be seen that the vegetation placed in front of the flow input velocity has negligible effect on the reduction of velocity, which of course can be justified due to the flexibility of the vegetation. The only unusual thing is the unexpected decrease in floor speed of 3 m/s compared to higher speeds.


(c)


(a)


(b)


(c)


(a)


(b)


(c)

  • (a)
    (a)
  • (b)
    (b)
  • (c)
    (c)

Figure 19 

Comparison of velocity profiles with the same plant densities (depth 1 m). Comparison of velocity profiles with (a) plant densities of 25%, depth 1 m; (b) plant densities of 50%, depth 1 m; and (c) plant densities of 75%, depth 1 m.

According to Figure 20, by increasing the speed of vegetation, the effect of vegetation on reducing the flow rate becomes more noticeable. And the role of input current does not have much effect in reducing speed.


(c)


(a)


(b)


(c)


(a)


(b)


(c)

  • (a)
    (a)
  • (b)
    (b)
  • (c)
    (c)

Figure 20 

Comparison of velocity profiles with the same plant densities (depth 2 m). Comparison of velocity profiles with (a) plant densities of 25%, depth 2 m; (b) plant densities of 50%, depth 2 m; and (c) plant densities of 75%, depth 2 m.

According to Figure 21, it can be seen that, with increasing speed, the effect of vegetation on reducing the bed flow rate becomes more noticeable and the role of the input current does not have much effect. In general, it can be seen that, by increasing the speed of the input current, the slope of the profiles increases from the bed to the water surface and due to the fact that, in software, the roughness coefficient applies to the channel floor only in the boundary conditions, this can be perfectly justified. Of course, it can be noted that, due to the flexible conditions of the vegetation of the bed, this modeling can show acceptable results for such grasses in the canal floor. In the next directions, we may try application of swarm-based optimization methods for modeling and finding the most effective factors in this research [2, 7, 8, 15, 18, 89–94]. In future, we can also apply the simulation logic and software of this research for other domains such as power engineering [95–99].


(c)


(a)


(b)


(c)


(a)


(b)


(c)

  • (a)
    (a)
  • (b)
    (b)
  • (c)
    (c)

Figure 21 

Comparison of velocity profiles with the same plant densities (depth 3 m). Comparison of velocity profiles with (a) plant densities of 25%, depth 3 m; (b) plant densities of 50%, depth 3 m; and (c) plant densities of 75%, depth 3 m.

3. Conclusion

The effects of vegetation on the flood canal were investigated by numerical modeling with FLOW-3D software. After analyzing the results, the following conclusions were reached:(i)Increasing the density of vegetation reduces the velocity of the canal floor but has no effect on the velocity of the canal surface.(ii)Increasing the Froude number is directly related to increasing the speed of the canal floor.(iii)In the canal with a depth of one meter, a sudden increase in speed can be observed from the lowest speed and higher speed, which is justified by the sudden increase in Froude number.(iv)As the inlet flow rate increases, the slope of the profiles from the bed to the water surface increases.(v)By reducing the Froude number, the effect of vegetation on reducing the flow bed rate becomes more noticeable. And the input velocity in reducing the velocity of the canal floor does not have much effect.(vi)At a flow rate between 3 and 3.3 meters per second due to the shallow depth of the canal and the higher landing number a more critical area is observed in which the flow bed velocity in this area is between 2.86 and 3.1 m/s.(vii)Due to the critical flow velocity and the slight effect of the roughness of the horseshoe vortex floor, it is not visible and is only partially observed in models 1-2-3 and 21.(viii)As the flow rate increases, the effect of vegetation on the rate of bed reduction decreases.(ix)In conditions where less current intensity is passing, vegetation has a greater effect on reducing current intensity and energy consumption increases.(x)In the case of using the flow rate of 0.8 cubic meters per second, the velocity distribution and flow regime show about 20% more energy consumption than in the case of using the flow rate of 1.3 cubic meters per second.

Nomenclature

n:Manning’s roughness coefficient
C:Chézy roughness coefficient
f:Darcy–Weisbach coefficient
V:Flow velocity
R:Hydraulic radius
g:Gravitational acceleration
y:Flow depth
Ks:Bed roughness
A:Constant coefficient
:Reynolds number
∂y/∂x:Depth of water change
S0:Slope of the canal floor
Sf:Slope of energy line
Fr:Froude number
D:Characteristic length of the canal
G:Mass acceleration
:Shear stresses.

Data Availability

All data are included within the paper.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This work was partially supported by the National Natural Science Foundation of China under Contract no. 71761030 and Natural Science Foundation of Inner Mongolia under Contract no. 2019LH07003.

References

  1. H. Yu, L. Jie, W. Gui et al., “Dynamic Gaussian bare-bones fruit fly optimizers with abandonment mechanism: method and analysis,” Engineering with Computers, vol. 20, pp. 1–29, 2020.View at: Publisher Site | Google Scholar
  2. X. Zhao, D. Li, B. Yang, C. Ma, Y. Zhu, and H. Chen, “Feature selection based on improved ant colony optimization for online detection of foreign fiber in cotton,” Applied Soft Computing, vol. 24, pp. 585–596, 2014.View at: Publisher Site | Google Scholar
  3. J. Hu, H. Chen, A. A. Heidari et al., “Orthogonal learning covariance matrix for defects of grey wolf optimizer: insights, balance, diversity, and feature selection,” Knowledge-Based Systems, vol. 213, Article ID 106684, 2021.View at: Publisher Site | Google Scholar
  4. C. Yu, M. Chen, K. Chen et al., “SGOA: annealing-behaved grasshopper optimizer for global tasks,” Engineering with Computers, vol. 4, pp. 1–28, 2021.View at: Publisher Site | Google Scholar
  5. W. Shan, Z. Qiao, A. A. Heidari, H. Chen, H. Turabieh, and Y. Teng, “Double adaptive weights for stabilization of moth flame optimizer: balance analysis, engineering cases, and medical diagnosis,” Knowledge-Based Systems, vol. 8, Article ID 106728, 2020.View at: Google Scholar
  6. J. Tu, H. Chen, J. Liu et al., “Evolutionary biogeography-based whale optimization methods with communication structure: towards measuring the balance,” Knowledge-Based Systems, vol. 212, Article ID 106642, 2021.View at: Publisher Site | Google Scholar
  7. Y. Zhang, R. Liu, X. Wang et al., “Towards augmented kernel extreme learning models for bankruptcy prediction: algorithmic behavior and comprehensive analysis,” Neurocomputing, vol. 430, 2020.View at: Google Scholar
  8. H.-L. Chen, G. Wang, C. Ma, Z.-N. Cai, W.-B. Liu, and S.-J. Wang, “An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson׳s disease,” Neurocomputing, vol. 184, pp. 131–144, 2016.View at: Publisher Site | Google Scholar
  9. J. Xia, H. Chen, Q. Li et al., “Ultrasound-based differentiation of malignant and benign thyroid Nodules: an extreme learning machine approach,” Computer Methods and Programs in Biomedicine, vol. 147, pp. 37–49, 2017.View at: Publisher Site | Google Scholar
  10. C. Li, L. Hou, B. Y. Sharma et al., “Developing a new intelligent system for the diagnosis of tuberculous pleural effusion,” Computer Methods and Programs in Biomedicine, vol. 153, pp. 211–225, 2018.View at: Publisher Site | Google Scholar
  11. X. Xu and H.-L. Chen, “Adaptive computational chemotaxis based on field in bacterial foraging optimization,” Soft Computing, vol. 18, no. 4, pp. 797–807, 2014.View at: Publisher Site | Google Scholar
  12. M. Wang, H. Chen, B. Yang et al., “Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses,” Neurocomputing, vol. 267, pp. 69–84, 2017.View at: Publisher Site | Google Scholar
  13. L. Chao, K. Zhang, Z. Li, Y. Zhu, J. Wang, and Z. Yu, “Geographically weighted regression based methods for merging satellite and gauge precipitation,” Journal of Hydrology, vol. 558, pp. 275–289, 2018.View at: Publisher Site | Google Scholar
  14. F. J. Golrokh, G. Azeem, and A. Hasan, “Eco-efficiency evaluation in cement industries: DEA malmquist productivity index using optimization models,” ENG Transactions, vol. 1, 2020.View at: Google Scholar
  15. D. Zhao, L. Lei, F. Yu et al., “Chaotic random spare ant colony optimization for multi-threshold image segmentation of 2D Kapur entropy,” Knowledge-Based Systems, vol. 8, Article ID 106510, 2020.View at: Google Scholar
  16. Y. Zhang, R. Liu, X. Wang, H. Chen, and C. Li, “Boosted binary Harris hawks optimizer and feature selection,” Engineering with Computers, vol. 517, pp. 1–30, 2020.View at: Publisher Site | Google Scholar
  17. L. Hu, G. Hong, J. Ma, X. Wang, and H. Chen, “An efficient machine learning approach for diagnosis of paraquat-poisoned patients,” Computers in Biology and Medicine, vol. 59, pp. 116–124, 2015.View at: Publisher Site | Google Scholar
  18. L. Shen, H. Chen, Z. Yu et al., “Evolving support vector machines using fruit fly optimization for medical data classification,” Knowledge-Based Systems, vol. 96, pp. 61–75, 2016.View at: Publisher Site | Google Scholar
  19. X. Zhao, X. Zhang, Z. Cai et al., “Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients,” Computational Biology and Chemistry, vol. 78, pp. 481–490, 2019.View at: Publisher Site | Google Scholar
  20. Y. Xu, H. Chen, J. Luo, Q. Zhang, S. Jiao, and X. Zhang, “Enhanced Moth-flame optimizer with mutation strategy for global optimization,” Information Sciences, vol. 492, pp. 181–203, 2019.View at: Publisher Site | Google Scholar
  21. M. Wang and H. Chen, “Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis,” Applied Soft Computing Journal, vol. 88, Article ID 105946, 2020.View at: Publisher Site | Google Scholar
  22. Y. Chen, J. Li, H. Lu, and P. Yan, “Coupling system dynamics analysis and risk aversion programming for optimizing the mixed noise-driven shale gas-water supply chains,” Journal of Cleaner Production, vol. 278, Article ID 123209, 2020.View at: Google Scholar
  23. H. Tang, Y. Xu, A. Lin et al., “Predicting green consumption behaviors of students using efficient firefly grey wolf-assisted K-nearest neighbor classifiers,” IEEE Access, vol. 8, pp. 35546–35562, 2020.View at: Publisher Site | Google Scholar
  24. H.-J. Ma and G.-H. Yang, “Adaptive fault tolerant control of cooperative heterogeneous systems with actuator faults and unreliable interconnections,” IEEE Transactions on Automatic Control, vol. 61, no. 11, pp. 3240–3255, 2015.View at: Google Scholar
  25. H.-J. Ma and L.-X. Xu, “Decentralized adaptive fault-tolerant control for a class of strong interconnected nonlinear systems via graph theory,” IEEE Transactions on Automatic Control, vol. 66, 2020.View at: Google Scholar
  26. H. J. Ma, L. X. Xu, and G. H. Yang, “Multiple environment integral reinforcement learning-based fault-tolerant control for affine nonlinear systems,” IEEE Transactions on Cybernetics, vol. 51, pp. 1–16, 2019.View at: Publisher Site | Google Scholar
  27. J. Hu, M. Wang, C. Zhao, Q. Pan, and C. Du, “Formation control and collision avoidance for multi-UAV systems based on Voronoi partition,” Science China Technological Sciences, vol. 63, no. 1, pp. 65–72, 2020.View at: Publisher Site | Google Scholar
  28. C. Zhang, H. Li, Y. Qian, C. Chen, and X. Zhou, “Locality-constrained discriminative matrix regression for robust face identification,” IEEE Transactions on Neural Networks and Learning Systems, vol. 99, pp. 1–15, 2020.View at: Publisher Site | Google Scholar
  29. X. Zhang, D. Wang, Z. Zhou, and Y. Ma, “Robust low-rank tensor recovery with rectification and alignment,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 1, pp. 238–255, 2019.View at: Google Scholar
  30. X. Zhang, J. Wang, T. Wang, R. Jiang, J. Xu, and L. Zhao, “Robust feature learning for adversarial defense via hierarchical feature alignment,” Information Sciences, vol. 560, 2020.View at: Google Scholar
  31. X. Zhang, R. Jiang, T. Wang, and J. Wang, “Recursive neural network for video deblurring,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 03, p. 1, 2020.View at: Publisher Site | Google Scholar
  32. X. Zhang, T. Wang, J. Wang, G. Tang, and L. Zhao, “Pyramid channel-based feature attention network for image dehazing,” Computer Vision and Image Understanding, vol. 197-198, Article ID 103003, 2020.View at: Publisher Site | Google Scholar
  33. X. Zhang, T. Wang, W. Luo, and P. Huang, “Multi-level fusion and attention-guided CNN for image dehazing,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 3, p. 1, 2020.View at: Publisher Site | Google Scholar
  34. L. He, J. Shen, and Y. Zhang, “Ecological vulnerability assessment for ecological conservation and environmental management,” Journal of Environmental Management, vol. 206, pp. 1115–1125, 2018.View at: Publisher Site | Google Scholar
  35. Y. Chen, W. Zheng, W. Li, and Y. Huang, “Large group Activity security risk assessment and risk early warning based on random forest algorithm,” Pattern Recognition Letters, vol. 144, pp. 1–5, 2021.View at: Publisher Site | Google Scholar
  36. J. Hu, H. Zhang, Z. Li, C. Zhao, Z. Xu, and Q. Pan, “Object traversing by monocular UAV in outdoor environment,” Asian Journal of Control, vol. 25, 2020.View at: Google Scholar
  37. P. Tian, H. Lu, W. Feng, Y. Guan, and Y. Xue, “Large decrease in streamflow and sediment load of Qinghai-Tibetan Plateau driven by future climate change: a case study in Lhasa River Basin,” Catena, vol. 187, Article ID 104340, 2020.View at: Publisher Site | Google Scholar
  38. A. Stokes, C. Atger, A. G. Bengough, T. Fourcaud, and R. C. Sidle, “Desirable plant root traits for protecting natural and engineered slopes against landslides,” Plant and Soil, vol. 324, no. 1, pp. 1–30, 2009.View at: Publisher Site | Google Scholar
  39. T. B. Devi, A. Sharma, and B. Kumar, “Studies on emergent flow over vegetative channel bed with downward seepage,” Hydrological Sciences Journal, vol. 62, no. 3, pp. 408–420, 2017.View at: Google Scholar
  40. G. Ireland, M. Volpi, and G. Petropoulos, “Examining the capability of supervised machine learning classifiers in extracting flooded areas from Landsat TM imagery: a case study from a Mediterranean flood,” Remote Sensing, vol. 7, no. 3, pp. 3372–3399, 2015.View at: Publisher Site | Google Scholar
  41. L. Goodarzi and S. Javadi, “Assessment of aquifer vulnerability using the DRASTIC model; a case study of the Dezful-Andimeshk Aquifer,” Computational Research Progress in Applied Science & Engineering, vol. 2, no. 1, pp. 17–22, 2016.View at: Google Scholar
  42. K. Zhang, Q. Wang, L. Chao et al., “Ground observation-based analysis of soil moisture spatiotemporal variability across a humid to semi-humid transitional zone in China,” Journal of Hydrology, vol. 574, pp. 903–914, 2019.View at: Publisher Site | Google Scholar
  43. L. De Doncker, P. Troch, R. Verhoeven, K. Bal, P. Meire, and J. Quintelier, “Determination of the Manning roughness coefficient influenced by vegetation in the river Aa and Biebrza river,” Environmental Fluid Mechanics, vol. 9, no. 5, pp. 549–567, 2009.View at: Publisher Site | Google Scholar
  44. M. Fathi-Moghadam and K. Drikvandi, “Manning roughness coefficient for rivers and flood plains with non-submerged vegetation,” International Journal of Hydraulic Engineering, vol. 1, no. 1, pp. 1–4, 2012.View at: Google Scholar
  45. F.-C. Wu, H. W. Shen, and Y.-J. Chou, “Variation of roughness coefficients for unsubmerged and submerged vegetation,” Journal of Hydraulic Engineering, vol. 125, no. 9, pp. 934–942, 1999.View at: Publisher Site | Google Scholar
  46. M. K. Wood, “Rangeland vegetation-hydrologic interactions,” in Vegetation Science Applications for Rangeland Analysis and Management, vol. 3, pp. 469–491, Springer, 1988.View at: Publisher Site | Google Scholar
  47. C. Wilson, O. Yagci, H.-P. Rauch, and N. Olsen, “3D numerical modelling of a willow vegetated river/floodplain system,” Journal of Hydrology, vol. 327, no. 1-2, pp. 13–21, 2006.View at: Publisher Site | Google Scholar
  48. R. Yazarloo, M. Khamehchian, and M. R. Nikoodel, “Observational-computational 3d engineering geological model and geotechnical characteristics of young sediments of golestan province,” Computational Research Progress in Applied Science & Engineering (CRPASE), vol. 03, 2017.View at: Google Scholar
  49. G. E. Freeman, W. H. Rahmeyer, and R. R. Copeland, “Determination of resistance due to shrubs and woody vegetation,” International Journal of River Basin Management, vol. 19, 2000.View at: Google Scholar
  50. N. Kouwen and T. E. Unny, “Flexible roughness in open channels,” Journal of the Hydraulics Division, vol. 99, no. 5, pp. 713–728, 1973.View at: Publisher Site | Google Scholar
  51. S. Hosseini and J. Abrishami, Open Channel Hydraulics, Elsevier, Amsterdam, Netherlands, 2007.
  52. C. S. James, A. L. Birkhead, A. A. Jordanova, and J. J. O’Sullivan, “Flow resistance of emergent vegetation,” Journal of Hydraulic Research, vol. 42, no. 4, pp. 390–398, 2004.View at: Publisher Site | Google Scholar
  53. F. Huthoff and D. Augustijn, “Channel roughness in 1D steady uniform flow: Manning or Chézy?,,” NCR-days, vol. 102, 2004.View at: Google Scholar
  54. M. S. Sabegh, M. Saneie, M. Habibi, A. A. Abbasi, and M. Ghadimkhani, “Experimental investigation on the effect of river bank tree planting array, on shear velocity,” Journal of Watershed Engineering and Management, vol. 2, no. 4, 2011.View at: Google Scholar
  55. A. Errico, V. Pasquino, M. Maxwald, G. B. Chirico, L. Solari, and F. Preti, “The effect of flexible vegetation on flow in drainage channels: estimation of roughness coefficients at the real scale,” Ecological Engineering, vol. 120, pp. 411–421, 2018.View at: Publisher Site | Google Scholar
  56. S. E. Darby, “Effect of riparian vegetation on flow resistance and flood potential,” Journal of Hydraulic Engineering, vol. 125, no. 5, pp. 443–454, 1999.View at: Publisher Site | Google Scholar
  57. V. Kutija and H. Thi Minh Hong, “A numerical model for assessing the additional resistance to flow introduced by flexible vegetation,” Journal of Hydraulic Research, vol. 34, no. 1, pp. 99–114, 1996.View at: Publisher Site | Google Scholar
  58. T. Fischer-Antze, T. Stoesser, P. Bates, and N. R. B. Olsen, “3D numerical modelling of open-channel flow with submerged vegetation,” Journal of Hydraulic Research, vol. 39, no. 3, pp. 303–310, 2001.View at: Publisher Site | Google Scholar
  59. U. Stephan and D. Gutknecht, “Hydraulic resistance of submerged flexible vegetation,” Journal of Hydrology, vol. 269, no. 1-2, pp. 27–43, 2002.View at: Publisher Site | Google Scholar
  60. F. G. Carollo, V. Ferro, and D. Termini, “Flow resistance law in channels with flexible submerged vegetation,” Journal of Hydraulic Engineering, vol. 131, no. 7, pp. 554–564, 2005.View at: Publisher Site | Google Scholar
  61. W. Fu-sheng, “Flow resistance of flexible vegetation in open channel,” Journal of Hydraulic Engineering, vol. S1, 2007.View at: Google Scholar
  62. P.-f. Wang, C. Wang, and D. Z. Zhu, “Hydraulic resistance of submerged vegetation related to effective height,” Journal of Hydrodynamics, vol. 22, no. 2, pp. 265–273, 2010.View at: Publisher Site | Google Scholar
  63. J. K. Lee, L. C. Roig, H. L. Jenter, and H. M. Visser, “Drag coefficients for modeling flow through emergent vegetation in the Florida Everglades,” Ecological Engineering, vol. 22, no. 4-5, pp. 237–248, 2004.View at: Publisher Site | Google Scholar
  64. G. J. Arcement and V. R. Schneider, Guide for Selecting Manning’s Roughness Coefficients for Natural Channels and Flood Plains, US Government Printing Office, Washington, DC, USA, 1989.
  65. Y. Ding and S. S. Y. Wang, “Identification of Manning’s roughness coefficients in channel network using adjoint analysis,” International Journal of Computational Fluid Dynamics, vol. 19, no. 1, pp. 3–13, 2005.View at: Publisher Site | Google Scholar
  66. E. T. Engman, “Roughness coefficients for routing surface runoff,” Journal of Irrigation and Drainage Engineering, vol. 112, no. 1, pp. 39–53, 1986.View at: Publisher Site | Google Scholar
  67. M. Feizbahr, C. Kok Keong, F. Rostami, and M. Shahrokhi, “Wave energy dissipation using perforated and non perforated piles,” International Journal of Engineering, vol. 31, no. 2, pp. 212–219, 2018.View at: Publisher Site | Google Scholar
  68. M. Farzadkhoo, A. Keshavarzi, H. Hamidifar, and M. Javan, “Sudden pollutant discharge in vegetated compound meandering rivers,” Catena, vol. 182, Article ID 104155, 2019.View at: Publisher Site | Google Scholar
  69. V. T. Chow, Open-channel Hydraulics, Mcgraw-Hill Civil Engineering Series, Chennai, TN, India, 1959.
  70. X. Zhang, R. Jing, Z. Li, Z. Li, X. Chen, and C.-Y. Su, “Adaptive pseudo inverse control for a class of nonlinear asymmetric and saturated nonlinear hysteretic systems,” IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 4, pp. 916–928, 2020.View at: Google Scholar
  71. C. Zuo, Q. Chen, L. Tian, L. Waller, and A. Asundi, “Transport of intensity phase retrieval and computational imaging for partially coherent fields: the phase space perspective,” Optics and Lasers in Engineering, vol. 71, pp. 20–32, 2015.View at: Publisher Site | Google Scholar
  72. C. Zuo, J. Sun, J. Li, J. Zhang, A. Asundi, and Q. Chen, “High-resolution transport-of-intensity quantitative phase microscopy with annular illumination,” Scientific Reports, vol. 7, no. 1, pp. 7654–7722, 2017.View at: Publisher Site | Google Scholar
  73. B.-H. Li, Y. Liu, A.-M. Zhang, W.-H. Wang, and S. Wan, “A survey on blocking technology of entity resolution,” Journal of Computer Science and Technology, vol. 35, no. 4, pp. 769–793, 2020.View at: Publisher Site | Google Scholar
  74. Y. Liu, B. Zhang, Y. Feng et al., “Development of 340-GHz transceiver front end based on GaAs monolithic integration technology for THz active imaging array,” Applied Sciences, vol. 10, no. 21, p. 7924, 2020.View at: Publisher Site | Google Scholar
  75. J. Hu, H. Zhang, L. Liu, X. Zhu, C. Zhao, and Q. Pan, “Convergent multiagent formation control with collision avoidance,” IEEE Transactions on Robotics, vol. 36, no. 6, pp. 1805–1818, 2020.View at: Publisher Site | Google Scholar
  76. M. B. Movahhed, J. Ayoubinejad, F. N. Asl, and M. Feizbahr, “The effect of rain on pedestrians crossing speed,” Computational Research Progress in Applied Science & Engineering (CRPASE), vol. 6, no. 3, 2020.View at: Google Scholar
  77. A. Li, D. Spano, J. Krivochiza et al., “A tutorial on interference exploitation via symbol-level precoding: overview, state-of-the-art and future directions,” IEEE Communications Surveys & Tutorials, vol. 22, no. 2, pp. 796–839, 2020.View at: Publisher Site | Google Scholar
  78. W. Zhu, C. Ma, X. Zhao et al., “Evaluation of sino foreign cooperative education project using orthogonal sine cosine optimized kernel extreme learning machine,” IEEE Access, vol. 8, pp. 61107–61123, 2020.View at: Publisher Site | Google Scholar
  79. G. Liu, W. Jia, M. Wang et al., “Predicting cervical hyperextension injury: a covariance guided sine cosine support vector machine,” IEEE Access, vol. 8, pp. 46895–46908, 2020.View at: Publisher Site | Google Scholar
  80. Y. Wei, H. Lv, M. Chen et al., “Predicting entrepreneurial intention of students: an extreme learning machine with Gaussian barebone harris hawks optimizer,” IEEE Access, vol. 8, pp. 76841–76855, 2020.View at: Publisher Site | Google Scholar
  81. A. Lin, Q. Wu, A. A. Heidari et al., “Predicting intentions of students for master programs using a chaos-induced sine cosine-based fuzzy K-Nearest neighbor classifier,” Ieee Access, vol. 7, pp. 67235–67248, 2019.View at: Publisher Site | Google Scholar
  82. Y. Fan, P. Wang, A. A. Heidari et al., “Rationalized fruit fly optimization with sine cosine algorithm: a comprehensive analysis,” Expert Systems with Applications, vol. 157, Article ID 113486, 2020.View at: Publisher Site | Google Scholar
  83. E. Rodríguez-Esparza, L. A. Zanella-Calzada, D. Oliva et al., “An efficient Harris hawks-inspired image segmentation method,” Expert Systems with Applications, vol. 155, Article ID 113428, 2020.View at: Publisher Site | Google Scholar
  84. S. Jiao, G. Chong, C. Huang et al., “Orthogonally adapted Harris hawks optimization for parameter estimation of photovoltaic models,” Energy, vol. 203, Article ID 117804, 2020.View at: Publisher Site | Google Scholar
  85. Z. Xu, Z. Hu, A. A. Heidari et al., “Orthogonally-designed adapted grasshopper optimization: a comprehensive analysis,” Expert Systems with Applications, vol. 150, Article ID 113282, 2020.View at: Publisher Site | Google Scholar
  86. A. Abbassi, R. Abbassi, A. A. Heidari et al., “Parameters identification of photovoltaic cell models using enhanced exploratory salp chains-based approach,” Energy, vol. 198, Article ID 117333, 2020.View at: Publisher Site | Google Scholar
  87. M. Mahmoodi and K. K. Aminjan, “Numerical simulation of flow through sukhoi 24 air inlet,” Computational Research Progress in Applied Science & Engineering (CRPASE), vol. 03, 2017.View at: Google Scholar
  88. F. J. Golrokh and A. Hasan, “A comparison of machine learning clustering algorithms based on the DEA optimization approach for pharmaceutical companies in developing countries,” ENG Transactions, vol. 1, 2020.View at: Google Scholar
  89. H. Chen, A. A. Heidari, H. Chen, M. Wang, Z. Pan, and A. H. Gandomi, “Multi-population differential evolution-assisted Harris hawks optimization: framework and case studies,” Future Generation Computer Systems, vol. 111, pp. 175–198, 2020.View at: Publisher Site | Google Scholar
  90. J. Guo, H. Zheng, B. Li, and G.-Z. Fu, “Bayesian hierarchical model-based information fusion for degradation analysis considering non-competing relationship,” IEEE Access, vol. 7, pp. 175222–175227, 2019.View at: Publisher Site | Google Scholar
  91. J. Guo, H. Zheng, B. Li, and G.-Z. Fu, “A Bayesian approach for degradation analysis with individual differences,” IEEE Access, vol. 7, pp. 175033–175040, 2019.View at: Publisher Site | Google Scholar
  92. M. M. A. Malakoutian, Y. Malakoutian, P. Mostafapour, and S. Z. D. Abed, “Prediction for monthly rainfall of six meteorological regions and TRNC (case study: north Cyprus),” ENG Transactions, vol. 2, no. 2, 2021.View at: Google Scholar
  93. H. Arslan, M. Ranjbar, and Z. Mutlum, “Maximum sound transmission loss in multi-chamber reactive silencers: are two chambers enough?,,” ENG Transactions, vol. 2, no. 1, 2021.View at: Google Scholar
  94. N. Tonekaboni, M. Feizbahr, N. Tonekaboni, G.-J. Jiang, and H.-X. Chen, “Optimization of solar CCHP systems with collector enhanced by porous media and nanofluid,” Mathematical Problems in Engineering, vol. 2021, Article ID 9984840, 12 pages, 2021.View at: Publisher Site | Google Scholar
  95. Z. Niu, B. Zhang, J. Wang et al., “The research on 220GHz multicarrier high-speed communication system,” China Communications, vol. 17, no. 3, pp. 131–139, 2020.View at: Publisher Site | Google Scholar
  96. B. Zhang, Z. Niu, J. Wang et al., “Four‐hundred gigahertz broadband multi‐branch waveguide coupler,” IET Microwaves, Antennas & Propagation, vol. 14, no. 11, pp. 1175–1179, 2020.View at: Publisher Site | Google Scholar
  97. Z.-Q. Niu, L. Yang, B. Zhang et al., “A mechanical reliability study of 3dB waveguide hybrid couplers in the submillimeter and terahertz band,” Journal of Zhejiang University Science, vol. 1, no. 1, 1998.View at: Google Scholar
  98. B. Zhang, D. Ji, D. Fang, S. Liang, Y. Fan, and X. Chen, “A novel 220-GHz GaN diode on-chip tripler with high driven power,” IEEE Electron Device Letters, vol. 40, no. 5, pp. 780–783, 2019.View at: Publisher Site | Google Scholar
  99. M. Taleghani and A. Taleghani, “Identification and ranking of factors affecting the implementation of knowledge management engineering based on TOPSIS technique,” ENG Transactions, vol. 1, no. 1, 2020.View at: Google Scholar
PDF 원문보기
Sketch of approach channel and spillway of the Kamal-Saleh dam

CFD modeling of flow pattern in spillway’s approach channel

Sustainable Water Resources Management volume 1, pages245–251 (2015)Cite this article

Abstract

Analysis of behavior and hydraulic characteristics of flow over the dam spillway is a complicated task that takes lots of money and time in water engineering projects planning. To model those hydraulic characteristics, several methods such as physical and numerical methods can be used. Nowadays, by utilizing new methods in computational fluid dynamics (CFD) and by the development of fast computers, the numerical methods have become accessible for use in the analysis of such sophisticated flows. The CFD softwares have the capability to analyze two- and three-dimensional flow fields. In this paper, the flow pattern at the guide wall of the Kamal-Saleh dam was modeled by Flow 3D. The results show that the current geometry of the left wall causes instability in the flow pattern and making secondary and vortex flow at beginning approach channel. This shape of guide wall reduced the performance of weir to remove the peak flood discharge.

댐 여수로 흐름의 거동 및 수리학적 특성 분석은 물 공학 프로젝트 계획에 많은 비용과 시간이 소요되는 복잡한 작업입니다. 이러한 수력학적 특성을 모델링하기 위해 물리적, 수치적 방법과 같은 여러 가지 방법을 사용할 수 있습니다. 요즘에는 전산유체역학(CFD)의 새로운 방법을 활용하고 빠른 컴퓨터의 개발로 이러한 정교한 흐름의 해석에 수치 방법을 사용할 수 있게 되었습니다. CFD 소프트웨어에는 2차원 및 3차원 유동장을 분석하는 기능이 있습니다. 본 논문에서는 Kamal-Saleh 댐 유도벽의 흐름 패턴을 Flow 3D로 모델링하였다. 결과는 왼쪽 벽의 현재 형상이 흐름 패턴의 불안정성을 유발하고 시작 접근 채널에서 2차 및 와류 흐름을 만드는 것을 보여줍니다. 이러한 형태의 안내벽은 첨두방류량을 제거하기 위해 둑의 성능을 저하시켰다.

Introduction

Spillways are one of the main structures used in the dam projects. Design of the spillway in all types of dams, specifically earthen dams is important because the inability of the spillway to remove probable maximum flood (PMF) discharge may cause overflow of water which ultimately leads to destruction of the dam (Das and Saikia et al. 2009; E 2013 and Novak et al. 2007). So study on the hydraulic characteristics of this structure is important. Hydraulic properties of spillway including flow pattern at the entrance of the guide walls and along the chute. Moreover, estimating the values of velocity and pressure parameters of flow along the chute is very important (Chanson 2004; Chatila and Tabbara 2004). The purpose of the study on the flow pattern is the effect of wall geometry on the creation transverse waves, flow instability, rotating and reciprocating flow through the inlet of spillway and its chute (Parsaie and Haghiabi 2015a, b; Parsaie et al. 2015; Wang and Jiang 2010). The purpose of study on the values of velocity and pressure is to calculate the potential of the structure to occurrence of phenomena such as cavitation (Fattor and Bacchiega 2009; Ma et al. 2010). Sometimes, it can be seen that the spillway design parameters of pressure and velocity are very suitable, but geometry is considered not suitable for conducting walls causing unstable flow pattern over the spillway, rotating flows at the beginning of the spillway and its design reduced the flood discharge capacity (Fattor and Bacchiega 2009). Study on spillway is usually conducted using physical models (Su et al. 2009; Suprapto 2013; Wang and Chen 2009; Wang and Jiang 2010). But recently, with advances in the field of computational fluid dynamics (CFD), study on hydraulic characterist–ics of this structure has been done with these techniques (Chatila and Tabbara 2004; Zhenwei et al. 2012). Using the CFD as a powerful technique for modeling the hydraulic structures can reduce the time and cost of experiments (Tabbara et al. 2005). In CFD field, the Navier–Stokes equation is solved by powerful numerical methods such as finite element method and finite volumes (Kim and Park 2005; Zhenwei et al. 2012). In order to obtain closed-form Navier–Stokes equations turbulence models, such k − ε and Re-Normalisation Group (RNG) models have been presented. To use the technique of computational fluid dynamics, software packages such as Fluent and Flow 3D, etc., are provided. Recently, these two software packages have been widely used in hydraulic engineering because the performance and their accuracy are very suitable (Gessler 2005; Kim 2007; Kim et al. 2012; Milési and Causse 2014; Montagna et al. 2011). In this paper, to assess the flow pattern at Kamal-Saleh guide wall, numerical method has been used. All the stages of numerical modeling were conducted in the Flow 3D software.

Materials and methods

Firstly, a three-dimensional model was constructed according to two-dimensional map that was prepared for designing the spillway. Then a small model was prepared with scale of 1:80 and entered into the Flow 3D software; all stages of the model construction was conducted in AutoCAD 3D. Flow 3D software numerically solved the Navier–Stokes equation by finite volume method. Below is a brief reference on the equations that used in the software. Figure 1 shows the 3D sketch of Kamal-Saleh spillway and Fig. 2 shows the uploading file of the Kamal-Saleh spillway in Flow 3D software.

figure 1
Fig. 1
figure 2
Fig. 2

Review of the governing equations in software Flow 3D

Continuity equation at three-dimensional Cartesian coordinates is given as Eq (1).

vf∂ρ∂t+∂∂x(uAx)+∂∂x(vAy)+∂∂x(wAz)=PSORρ,vf∂ρ∂t+∂∂x(uAx)+∂∂x(vAy)+∂∂x(wAz)=PSORρ,

(1)

where u, v, z are velocity component in the x, y, z direction; A x, A y, A z cross-sectional area of the flow; ρ fluid density; PSOR the source term; v f is the volume fraction of the fluid and three-dimensional momentum equations given in Eq (2).

∂u∂t+1vf(uAx∂u∂x+vAy∂u∂y+wAz∂u∂z)=−1ρ∂P∂x+Gx+fx∂v∂t+1vf(uAx∂v∂x+vAy∂v∂y+wAz∂v∂z)=−1ρ∂P∂y+Gy+fy∂w∂t+1vf(uAx∂w∂x+vAy∂w∂y+wAz∂w∂z)=−1ρ∂P∂y+Gz+fz,∂u∂t+1vf(uAx∂u∂x+vAy∂u∂y+wAz∂u∂z)=−1ρ∂P∂x+Gx+fx∂v∂t+1vf(uAx∂v∂x+vAy∂v∂y+wAz∂v∂z)=−1ρ∂P∂y+Gy+fy∂w∂t+1vf(uAx∂w∂x+vAy∂w∂y+wAz∂w∂z)=−1ρ∂P∂y+Gz+fz,

(2)

where P is the fluid pressure; G x, G y, G z the acceleration created by body fluids; f x, f y, f z viscosity acceleration in three dimensions and v f is related to the volume of fluid, defined by Eq. (3). For modeling of free surface profile the VOF technique based on the volume fraction of the computational cells has been used. Since the volume fraction F represents the amount of fluid in each cell, it takes value between 0 and 1.

∂F∂t+1vf[∂∂x(FAxu)+∂∂y(FAyv)+∂∂y(FAzw)]=0∂F∂t+1vf[∂∂x(FAxu)+∂∂y(FAyv)+∂∂y(FAzw)]=0

(3)

Turbulence models

Flow 3D offers five types of turbulence models: Prantl mixing length, k − ε equation, RNG models, Large eddy simulation model. Turbulence models that have been proposed recently are based on Reynolds-averaged Navier–Stokes equations. This approach involves statistical methods to extract an averaged equation related to the turbulence quantities.

Steps of solving a problem in Flow 3D software

(1) Preparing the 3D model of spillway by AutoCAD software. (2) Uploading the file of 3D model in Flow 3D software and defining the problem in the software and checking the final mesh. (3) Choosing the basic equations that should be solved. (4) Defining the characteristics of fluid. (5) Defining the boundary conditions; it is notable that this software has a wide range of boundary conditions. (6) Initializing the flow field. (7) Adjusting the output. (8) Adjusting the control parameters, choice of the calculation method and solution formula. (9) Start of calculation. Figure 1 shows the 3D model of the Kamal-Saleh spillway; in this figure, geometry of the left and right guide wall is shown.

Figure 2 shows the uploading of the 3D spillway dam in Flow 3D software. Moreover, in this figure the considered boundary condition in software is shown. At the entrance and end of spillway, the flow rate or fluid elevation and outflow was considered as BC. The bottom of spillway was considered as wall and left and right as symmetry.

Model calibration

Calibration of the Flow 3D for modeling the effect of geometry of guide wall on the flow pattern is included for comparing the results of Flow 3D with measured water surface profile. Calibration the Flow 3D software could be conducted in two ways: first, changing the value of upstream boundary conditions is continued until the results of water surface profile of the Flow 3D along the spillway successfully covered the measurement water surface profile; second is the assessment the mesh sensitivity. Analyzing the size of mesh is a trial-and-error process where the size of mesh is evaluated form the largest to the smallest. With fining the size of mesh the accuracy of model is increased; whereas, the cost of computation is increased. In this research, the value of upstream boundary condition was adjusted with measured data during the experimental studies on the scaled model and the mesh size was equal to 1 × 1 × 1 cm3.

Results and discussion

The behavior of water in spillway is strongly affected by the flow pattern at the entrance of the spillway, the flow pattern formation at the entrance is affected by the guide wall, and choice of an optimized form for the guide wall has a great effect on rising the ability of spillway for easy passing the PMF, so any nonuniformity in flow in the approach channel can cause reduction of spillway capacity, reduction in discharge coefficient of spillway, and even probability of cavitation. Optimizing the flow guiding walls (in terms of length, angle and radius) can cause the loss of turbulence and flow disturbances on spillway. For this purpose, initially geometry proposed for model for the discharge of spillway dam, Kamal-Saleh, 80, 100, and 120 (L/s) were surveyed. These discharges of flow were considered with regard to the flood return period, 5, 100 and 1000 years. Geometric properties of the conducting guidance wall are given in Table 1.Table 1 Characteristics and dimensions of the guidance walls tested

Full size table

Results of the CFD simulation for passing the flow rate 80 (L/s) are shown in Fig. 3. Figure 3 shows the secondary flow and vortex at the left guide wall.

figure 3
Fig. 3

For giving more information about flow pattern at the left and right guide wall, Fig. 4 shows the flow pattern at the right side guide wall and Fig. 5 shows the flow pattern at the left side guide wall.

figure 4
Fig. 4
figure 5
Fig. 5

With regard to Figs. 4 and 5 and observing the streamlines, at discharge equal to 80 (L/s), the right wall has suitable performance but the left wall has no suitable performance and the left wall of the geometric design creates a secondary and circular flow, and vortex motion in the beginning of the entrance of spillway that creates cross waves at the beginning of spillway. By increasing the flow rate (Q = 100 L/s), at the inlet spillway secondary flows and vortex were removed, but the streamline is severely distorted. Results of the guide wall performances at the Q = 100 (L/s) are shown in Fig. 6.

figure 6
Fig. 6

Also more information about the performance of each guide wall can be derived from Figs. 7 and 8. These figures uphold that the secondary and vortex flows were removed, but the streamlines were fully diverted specifically near the left side guide wall.

figure 7
Fig. 7
figure 8
Fig. 8

As mentioned in the past, these secondary and vortex flows and diversion in streamline cause nonuniformity and create cross wave through the spillway. Figure 9 shows the cross waves at the crest of the spillway.

figure 9
Fig. 9

The performance of guide walls at the Q = 120 (L/s) also was assessed. The result of simulation is shown in Fig. 10. Figures 11 and 12 show a more clear view of the streamlines near to right and left side guide wall, respectively. As seen in Fig. 12, the left side wall still causes vortex flow and creation of and diversion in streamline.

figure 10
Fig. 10
figure 11
Fig. 11
figure 12
Fig. 12

The results of the affected left side guide wall shape on the cross wave creation are shown in Fig. 13. As seen from Fig. 3, the left side guide wall also causes cross wave at the spillway crest.

figure 13
Fig. 13

As can be seen clearly in Figs. 9 and 13, by moving from the left side to the right side of the spillway, the cross waves and the nonuniformity in flow is removed. By reviewing Figs. 9 and 13, it is found that the right side guide wall removes the cross waves and nonuniformity. With this point as aim, a geometry similar to the right side guide wall was considered instead of the left side guide wall. The result of simulation for Q = 120 (L/s) is shown in Fig. 14. As seen from this figure, the proposed geometry for the left side wall has suitable performance smoothly passing the flow through the approach channel and spillway.

figure 14
Fig. 14

More information about the proposed shape for the left guide wall is shown in Fig. 15. As seen from this figure, this shape has suitable performance for removing the cross waves and vortex flows.

figure 15
Fig. 15

Figure 16 shows the cross section of flow at the crest of spillway. As seen in this figure, the proposed shape for the left side guide wall is suitable for removing the cross waves and secondary flows.

figure 16
Fig. 16

Conclusion

Analysis of behavior and hydraulic properties of flow over the spillway dam is a complicated task which is cost and time intensive. Several techniques suitable to the purposes of study have been undertaken in this research. Physical modeling, usage of expert experience, usage of mathematical models on simulation flow in one-dimensional, two-dimensional and three-dimensional techniques, are some of the techniques utilized to study this phenomenon. The results of the modeling show that the CFD technique is a suitable tool for simulating the flow pattern in the guide wall. Using this tools helps the designer for developing the optimal shape for hydraulic structure which the flow pattern through them are important.

References

  • Chanson H (2004) 19—Design of weirs and spillways. In: Chanson H (ed) Hydraulics of open channel flow, 2nd edn. Butterworth-Heinemann, Oxford, pp 391–430Chapter Google Scholar 
  • Chatila J, Tabbara M (2004) Computational modeling of flow over an ogee spillway. Comput Struct 82:1805–1812Article Google Scholar 
  • Das MM, Saikia MD (2009) Irrigation and water power engineering. PHI Learning, New DelhiGoogle Scholar 
  • E, Department Of Army: U.S. Army Corps (2013) Hydraulic Design of Spillways. BiblioBazaar, CharlestonGoogle Scholar 
  • Fattor C, Bacchiega J (2009) Design conditions for morning-glory spillways: application to potrerillos dam spillway. Adv Water Res Hydraul Eng Springer, Berlin, pp 2123–2128Google Scholar 
  • Gessler D (2005) CFD modeling of spillway performance. Impacts Glob Clim Change. doi:10.1061/40792(173)398
  • Kim D-G (2007) Numerical analysis of free flow past a sluice gate. KSCE J Civ Eng 11:127–132Article Google Scholar 
  • Kim D, Park J (2005) Analysis of flow structure over ogee-spillway in consideration of scale and roughness effects by using CFD model. KSCE J Civ Eng 9:161–169Article Google Scholar 
  • Kim S, Yu K, Yoon B, Lim Y (2012) A numerical study on hydraulic characteristics in the ice Harbor-type fishway. KSCE J Civ Eng 16:265–272Article Google Scholar 
  • Ma X-D, Dai G-Q, Yang Q, Li G-J, Zhao L (2010) Analysis of influence factors of cavity length in the spillway tunnel downstream of middle gate chamber outlet with sudden lateral enlargement and vertical drop aerator. J Hydrodyn Ser B 22:680–686Article Google Scholar 
  • Milési G, Causse S (2014) 3D numerical modeling of a side-channel spillway. In: Gourbesville P, Cunge J, Caignaert G (eds) Advances in hydroinformatics. Springer, Singapore, pp 487–498Chapter Google Scholar 
  • Montagna F, Bellotti G, Di Risio M (2011) 3D numerical modeling of landslide-generated tsunamis around a conical island. Nat Hazards 58:591–608Article Google Scholar 
  • Novak P, Moffat AIB, Nalluri C, Narayanan R (2007) Hydraulic structures. Taylor & Francis, LondonGoogle Scholar 
  • Parsaie A, Haghiabi A (2015a) Computational modeling of pollution transmission in rivers. Appl Water Sci. doi:10.1007/s13201-015-0319-6
  • Parsaie A, Haghiabi A (2015b) The effect of predicting discharge coefficient by neural network on increasing the numerical modeling accuracy of flow over side weir. Water Res Manag 29:973–985Article Google Scholar 
  • Parsaie A, Yonesi H, Najafian S (2015) Predictive modeling of discharge in compound open channel by support vector machine technique. Model Earth Syst Environ 1:1–6Article Google Scholar 
  • Su P-L, Liao H-S, Qiu Y, Li CJ (2009) Experimental study on a new type of aerator in spillway with low Froude number and mild slope flow. J Hydrodyn Ser B 21:415–422Article Google Scholar 
  • Suprapto M (2013) Increase spillway capacity using Labyrinth Weir. Procedia Eng 54:440–446Article Google Scholar 
  • Tabbara M, Chatila J, Awwad R (2005) Computational simulation of flow over stepped spillways. Comput Struct 83:2215–2224Article Google Scholar 
  • Wang J, Chen H (2009) Experimental study of elimination of vortices along guide wall of bank spillway. Adv Water Res Hydraul Eng Springer, Berlin, pp 2059–2063Google Scholar 
  • Wang Y, Jiang C (2010) Investigation of the surface vortex in a spillway tunnel intake. Tsinghua Sci Technol 15:561–565Article Google Scholar 
  • Zhenwei MU, Zhiyan Z, Tao Z (2012) Numerical simulation of 3-D flow field of spillway based on VOF method. Procedia Eng 28:808–812Article Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Water Engineering, Lorestan University, Khorram Abad, IranAbbas Parsaie, Amir Hamzeh Haghiabi & Amir Moradinejad

Corresponding author

Correspondence to Abbas Parsaie.

Reprints and Permissions

About this article

Cite this article

Parsaie, A., Haghiabi, A.H. & Moradinejad, A. CFD modeling of flow pattern in spillway’s approach channel. Sustain. Water Resour. Manag. 1, 245–251 (2015). https://doi.org/10.1007/s40899-015-0020-9

Download citation

  • Received28 April 2015
  • Accepted28 August 2015
  • Published15 September 2015
  • Issue DateSeptember 2015
  • DOIhttps://doi.org/10.1007/s40899-015-0020-9

Share this article

Anyone you share the following link with will be able to read this content:Get shareable link

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

  • Approach channel
  • Kamal-Saleh dam
  • Guide wall
  • Flow pattern
  • Numerical modeling
  • Flow 3D software
    원문 보기
    Extratropical cyclone damage to the seawall in Dawlish, UK: eyewitness accounts, sea level analysis and numerical modelling

    영국 Dawlish의 방파제에 대한 온대 저기압 피해: 목격자 설명, 해수면 분석 및 수치 모델링

    Extratropical cyclone damage to the seawall in Dawlish, UK: eyewitness accounts, sea level analysis and numerical modelling

    Natural Hazards (2022)Cite this article

    Abstract

    2014년 2월 영국 해협(영국)과 특히 Dawlish에 영향을 미친 온대 저기압 폭풍 사슬은 남서부 지역과 영국의 나머지 지역을 연결하는 주요 철도에 심각한 피해를 입혔습니다.

    이 사건으로 라인이 두 달 동안 폐쇄되어 5천만 파운드의 피해와 12억 파운드의 경제적 손실이 발생했습니다. 이 연구에서는 폭풍의 파괴력을 해독하기 위해 목격자 계정을 수집하고 해수면 데이터를 분석하며 수치 모델링을 수행합니다.

    우리의 분석에 따르면 이벤트의 재난 관리는 성공적이고 효율적이었으며 폭풍 전과 도중에 인명과 재산을 구하기 위해 즉각적인 조치를 취했습니다. 파도 부이 분석에 따르면 주기가 4–8, 8–12 및 20–25초인 복잡한 삼중 봉우리 바다 상태가 존재하는 반면, 조위계 기록에 따르면 최대 0.8m의 상당한 파도와 최대 1.5m의 파도 성분이 나타났습니다.

    이벤트에서 가능한 기여 요인으로 결합된 진폭. 최대 286 KN의 상당한 임펄스 파동이 손상의 시작 원인일 가능성이 가장 높았습니다. 수직 벽의 반사는 파동 진폭의 보강 간섭을 일으켜 파고가 증가하고 최대 16.1m3/s/m(벽의 미터 너비당)의 상당한 오버탑핑을 초래했습니다.

    이 정보와 우리의 공학적 판단을 통해 우리는 이 사고 동안 다중 위험 계단식 실패의 가장 가능성 있는 순서는 다음과 같다고 결론을 내립니다. 조적 파괴로 이어지는 파도 충격력, 충전물 손실 및 연속적인 조수에 따른 구조물 파괴.

    The February 2014 extratropical cyclonic storm chain, which impacted the English Channel (UK) and Dawlish in particular, caused significant damage to the main railway connecting the south-west region to the rest of the UK. The incident caused the line to be closed for two months, £50 million of damage and an estimated £1.2bn of economic loss. In this study, we collate eyewitness accounts, analyse sea level data and conduct numerical modelling in order to decipher the destructive forces of the storm. Our analysis reveals that the disaster management of the event was successful and efficient with immediate actions taken to save lives and property before and during the storm. Wave buoy analysis showed that a complex triple peak sea state with periods at 4–8, 8–12 and 20–25 s was present, while tide gauge records indicated that significant surge of up to 0.8 m and wave components of up to 1.5 m amplitude combined as likely contributing factors in the event. Significant impulsive wave force of up to 286 KN was the most likely initiating cause of the damage. Reflections off the vertical wall caused constructive interference of the wave amplitudes that led to increased wave height and significant overtopping of up to 16.1 m3/s/m (per metre width of wall). With this information and our engineering judgement, we conclude that the most probable sequence of multi-hazard cascading failure during this incident was: wave impact force leading to masonry failure, loss of infill and failure of the structure following successive tides.

    Introduction

    The progress of climate change and increasing sea levels has started to have wide ranging effects on critical engineering infrastructure (Shakou et al. 2019). The meteorological effects of increased atmospheric instability linked to warming seas mean we may be experiencing more frequent extreme storm events and more frequent series or chains of events, as well as an increase in the force of these events, a phenomenon called storminess (Mölter et al. 2016; Feser et al. 2014). Features of more extreme weather events in extratropical latitudes (30°–60°, north and south of the equator) include increased gusting winds, more frequent storm squalls, increased prolonged precipitation and rapid changes in atmospheric pressure and more frequent and significant storm surges (Dacre and Pinto 2020). A recent example of these events impacting the UK with simultaneous significant damage to coastal infrastructure was the extratropical cyclonic storm chain of winter 2013/2014 (Masselink et al. 2016; Adams and Heidarzadeh 2021). The cluster of storms had a profound effect on both coastal and inland infrastructure, bringing widespread flooding events and large insurance claims (RMS 2014).

    The extreme storms of February 2014, which had a catastrophic effect on the seawall of the south Devon stretch of the UK’s south-west mainline, caused a two-month closure of the line and significant disruption to the local and regional economy (Fig. 1b) (Network Rail 2014; Dawson et al. 2016; Adams and Heidarzadeh 2021). Restoration costs were £35 m, and economic effects to the south-west region of England were estimated up to £1.2bn (Peninsula Rail Taskforce 2016). Adams and Heidarzadeh (2021) investigated the disparate cascading failure mechanisms which played a part in the failure of the railway through Dawlish and attempted to put these in the context of the historical records of infrastructure damage on the line. Subsequent severe storms in 2016 in the region have continued to cause damage and disruption to the line in the years since 2014 (Met Office 2016). Following the events of 2014, Network Rail Footnote1 who owns the network has undertaken a resilience study. As a result, it has proposed a £400 m refurbishment of the civil engineering assets that support the railway (Fig. 1) (Network Rail 2014). The new seawall structure (Fig. 1a,c), which is constructed of pre-cast concrete sections, encases the existing Brunel seawall (named after the project lead engineer, Isambard Kingdom Brunel) and has been improved with piled reinforced concrete foundations. It is now over 2 m taller to increase the available crest freeboard and incorporates wave return features to minimise wave overtopping. The project aims to increase both the resilience of the assets to extreme weather events as well as maintain or improve amenity value of the coastline for residents and visitors.

    figure 1
    Fig. 1

    In this work, we return to the Brunel seawall and the damage it sustained during the 2014 storms which affected the assets on the evening of the 4th and daytime of the 5th of February and eventually resulted in a prolonged closure of the line. The motivation for this research is to analyse and model the damage made to the seawall and explain the damage mechanisms in order to improve the resilience of many similar coastal structures in the UK and worldwide. The innovation of this work is the multidisciplinary approach that we take comprising a combination of analysis of eyewitness accounts (social science), sea level and wave data analysis (physical science) as well as numerical modelling and engineering judgement (engineering sciences). We investigate the contemporary wave climate and sea levels by interrogating the real-time tide gauge and wave buoys installed along the south-west coast of the English Channel. We then model a typical masonry seawall (Fig. 2), applying the computational fluid dynamics package FLOW3D-Hydro,Footnote2 to quantify the magnitude of impact forces that the seawall would have experienced leading to its failure. We triangulate this information to determine the probable sequence of failures that led to the disaster in 2014.

    figure 2
    Fig. 2

    Data and methods

    Our data comprise eyewitness accounts, sea level records from coastal tide gauges and offshore wave buoys as well as structural details of the seawall. As for methodology, we analyse eyewitness data, process and investigate sea level records through Fourier transform and conduct numerical simulations using the Flow3D-Hydro package (Flow Science 2022). Details of the data and methodology are provided in the following.

    Eyewitness data

    The scale of damage to the seawall and its effects led the local community to document the first-hand accounts of those most closely affected by the storms including residents, local businesses, emergency responders, politicians and engineering contractors involved in the post-storm restoration work. These records now form a permanent exhibition in the local museum in DawlishFootnote3, and some of these accounts have been transcribed into a DVD account of the disaster (Dawlish Museum 2015). We have gathered data from the Dawlish Museum, national and international news reports, social media tweets and videos. Table 1 provides a summary of the eyewitness accounts. Overall, 26 entries have been collected around the time of the incident. Our analysis of the eyewitness data is provided in the third column of Table 1 and is expanded in Sect. 3.Table 1 Eyewitness accounts of damage to the Dawlish railway due to the February 2014 storm and our interpretations

    Full size table

    Sea level data and wave environment

    Our sea level data are a collection of three tide gauge stations (Newlyn, Devonport and Swanage Pier—Fig. 5a) owned and operated by the UK National Tide and Sea Level FacilityFootnote4 for the Environment Agency and four offshore wave buoys (Dawlish, West Bay, Torbay and Chesil Beach—Fig. 6a). The tide gauge sites are all fitted with POL-EKO (www.pol-eko.com.pl) data loggers. Newlyn has a Munro float gauge with one full tide and one mid-tide pneumatic bubbler system. Devonport has a three-channel data pneumatic bubbler system, and Swanage Pier consists of a pneumatic gauge. Each has a sampling interval of 15 min, except for Swanage Pier which has a sampling interval of 10 min. The tide gauges are located within the port areas, whereas the offshore wave buoys are situated approximately 2—3.3 km from the coast at water depths of 10–15 m. The wave buoys are all Datawell Wavemaker Mk III unitsFootnote5 and come with sampling interval of 0.78 s. The buoys have a maximum saturation amplitude of 20.5 m for recording the incident waves which implies that every wave larger than this threshold will be recorded at 20.5 m. The data are provided by the British Oceanographic Data CentreFootnote6 for tide gauges and the Channel Coastal ObservatoryFootnote7 for wave buoys.

    Sea level analysis

    The sea level data underwent quality control to remove outliers and spikes as well as gaps in data (e.g. Heidarzadeh et al. 2022; Heidarzadeh and Satake 2015). We processed the time series of the sea level data using the Matlab signal processing tool (MathWorks 2018). For calculations of the tidal signals, we applied the tidal package TIDALFIT (Grinsted 2008), which is based on fitting tidal harmonics to the observed sea level data. To calculate the surge signals, we applied a 30-min moving average filter to the de-tided data in order to remove all wind, swell and infra-gravity waves from the time series. Based on the surge analysis and the variations of the surge component before the time period of the incident, an error margin of approximately ± 10 cm is identified for our surge analysis. Spectral analysis of the wave buoy data is performed using the fast Fourier transform (FFT) of Matlab package (Mathworks 2018).

    Numerical modelling

    Numerical modelling of wave-structure interaction is conducted using the computational fluid dynamics package Flow3D-Hydro version 1.1 (Flow Science 2022). Flow3D-Hydro solves the transient Navier–Stokes equations of conservation of mass and momentum using a finite difference method and on Eulerian and Lagrangian frameworks (Flow Science 2022). The aforementioned governing equations are:

    ∇.u=0∇.u=0

    (1)

    ∂u∂t+u.∇u=−∇Pρ+υ∇2u+g∂u∂t+u.∇u=−∇Pρ+υ∇2u+g

    (2)

    where uu is the velocity vector, PP is the pressure, ρρ is the water density, υυ is the kinematic viscosity and gg is the gravitational acceleration. A Fractional Area/Volume Obstacle Representation (FAVOR) is adapted in Flow3D-Hydro, which applies solid boundaries within the Eulerian grid and calculates the fraction of areas and volume in partially blocked volume in order to compute flows on corresponding boundaries (Hirt and Nichols 1981). We validated the numerical modelling through comparing the results with Sainflou’s analytical equation for the design of vertical seawalls (Sainflou 1928; Ackhurst 2020), which is as follows:

    pd=ρgHcoshk(d+z)coshkdcosσtpd=ρgHcoshk(d+z)coshkdcosσt

    (3)

    where pdpd is the hydrodynamic pressure, ρρ is the water density, gg is the gravitational acceleration, HH is the wave height, dd is the water depth, kk is the wavenumber, zz is the difference in still water level and mean water level, σσ is the angular frequency and tt is the time. The Sainflou’s equation (Eq. 3) is used to calculate the dynamic pressure from wave action, which is combined with static pressure on the seawall.

    Using Flow3D-Hydro, a model of the Dawlish seawall was made with a computational domain which is 250.0 m in length, 15.0 m in height and 0.375 m in width (Fig. 3a). The computational domain was discretised using a single uniform grid with a mesh size of 0.125 m. The model has a wave boundary at the left side of the domain (x-min), an outflow boundary on the right side (x-max), a symmetry boundary at the bottom (z-min) and a wall boundary at the top (z-max). A wall boundary implies that water or waves are unable to pass through the boundary, whereas a symmetry boundary means that the two edges of the boundary are identical and therefore there is no flow through it. The water is considered incompressible in our model. For volume of fluid advection for the wave boundary (i.e. the left-side boundary) in our simulations, we utilised the “Split Lagrangian Method”, which guarantees the best accuracy (Flow Science, 2022).

    figure 3
    Fig. 3

    The stability of the numerical scheme is controlled and maintained through checking the Courant number (CC) as given in the following:

    C=VΔtΔxC=VΔtΔx

    (4)

    where VV is the velocity of the flow, ΔtΔt is the time step and ΔxΔx is the spatial step (i.e. grid size). For stability and convergence of the numerical simulations, the Courant number must be sufficiently below one (Courant et al. 1928). This is maintained by a careful adjustment of the ΔxΔx and ΔtΔt selections. Flow3D-Hydro applies a dynamic Courant number, meaning the program adjusts the value of time step (ΔtΔt) during the simulations to achieve a balance between accuracy of results and speed of simulation. In our simulation, the time step was in the range ΔtΔt = 0.0051—0.051 s.

    In order to achieve the most efficient mesh resolution, we varied cell size for five values of ΔxΔx = 0.1 m, 0.125 m, 0.15 m, 0.175 m and 0.20 m. Simulations were performed for all mesh sizes, and the results were compared in terms of convergence, stability and speed of simulation (Fig. 3). A linear wave with an amplitude of 1.5 m and a period of 6 s was used for these optimisation simulations. We considered wave time histories at two gauges A and B and recorded the waves from simulations using different mesh sizes (Fig. 3). Although the results are close (Fig. 3), some limited deviations are observed for larger mesh sizes of 0.20 m and 0.175 m. We therefore selected mesh size of 0.125 m as the optimum, giving an extra safety margin as a conservative solution.

    The pressure from the incident waves on the vertical wall is validated in our model by comparing them with the analytical equation of Sainflou (1928), Eq. (3), which is one of the most common set of equations for design of coastal structures (Fig. 4). The model was tested by running a linear wave of period 6 s and wave amplitude of 1.5 m against the wall, with a still water level of 4.5 m. It can be seen that the model results are very close to those from analytical equations of Sainflou (1928), indicating that our numerical model is accurately modelling the wave-structure interaction (Fig. 4).

    figure 4
    Fig. 4

    Eyewitness account analysis

    Contemporary reporting of the 4th and 5th February 2014 storms by the main national news outlets in the UK highlights the extreme nature of the events and the significant damage and disruption they were likely to have on the communities of the south-west of England. In interviews, this was reinforced by Network Rail engineers who, even at this early stage, were forecasting remedial engineering works to last for at least 6 weeks. One week later, following subsequent storms the cascading nature of the events was obvious. Multiple breaches of the seawall had taken place with up to 35 separate landslide events and significant damage to parapet walls along the coastal route also were reported. Residents of the area reported extreme effects of the storm, one likening it to an earthquake and reporting water ingress through doors windows and even through vertical chimneys (Table 1). This suggests extreme wave overtopping volumes and large wave impact forces. One resident described the structural effects as: “the house was jumping up and down on its footings”.

    Disaster management plans were quickly and effectively put into action by the local council, police service and National Rail. A major incident was declared, and decisions regarding evacuation of the residents under threat were taken around 2100 h on the night of 4th February when reports of initial damage to the seawall were received (Table 1). Local hotels were asked to provide short-term refuge to residents while local leisure facilities were prepared to accept residents later that evening. Initial repair work to the railway line was hampered by successive high spring tides and storms in the following days although significant progress was still made when weather conditions permitted (Table 1).

    Sea level observations and spectral analysis

    The results of surge and wave analyses are presented in Figs. 5 and 6. A surge height of up to 0.8 m was recorded in the examined tide gauge stations (Fig. 5b-d). Two main episodes of high surge heights are identified: the first surge started on 3rd February 2014 at 03:00 (UTC) and lasted until 4th of February 2014 at 00:00; the second event occurred in the period 4th February 2014 15:00 to 5th February 2014 at 17:00 (Fig. 5b-d). These data imply surge durations of 21 h and 26 h for the first and the second events, respectively. Based on the surge data in Fig. 5, we note that the storm event of early February 2014 and the associated surges was a relatively powerful one, which impacted at least 230 km of the south coast of England, from Land’s End to Weymouth, with large surge heights.

    figure 5
    Fig. 5
    figure 6
    Fig. 6

    Based on wave buoy records, the maximum recorded amplitudes are at least 20.5 m in Dawlish and West Bay, 1.9 m in Tor Bay and 4.9 m in Chesil (Fig. 6a-b). The buoys at Tor Bay and Chesil recorded dual peak period bands of 4–8 and 8–12 s, whereas at Dawlish and West Bay registered triple peak period bands at 4–8, 8–12 and 20–25 s (Fig. 6c, d). It is important to note that the long-period waves at 20–25 s occur with short durations (approximately 2 min) while the waves at the other two bands of 4–8 and 8–12 s appear to be present at all times during the storm event.

    The wave component at the period band of 4–8 s can be most likely attributed to normal coastal waves while the one at 8–12 s, which is longer, is most likely the swell component of the storm. Regarding the third component of the waves with long period of 20 -25 s, which occurs with short durations of 2 min, there are two hypotheses; it is either the result of a local (port and harbour) and regional (the Lyme Bay) oscillations (eg. Rabinovich 1997; Heidarzadeh and Satake 2014; Wang et al. 1992), or due to an abnormally long swell. To test the first hypothesis, we consider various water bodies such as Lyme Bay (approximate dimensions of 70 km × 20 km with an average water depth of 30 m; Fig. 6), several local bays (approximate dimensions of 3.6 km × 0.6 km with an average water depth of 6 m) and harbours (approximate dimensions of 0.5 km × 0.5 km with an average water depth of 4 m). Their water depths are based on the online Marine navigation website.Footnote8 According to Rabinovich (2010), the oscillation modes of a semi-enclosed rectangle basin are given by the following equation:

    Tmn=2gd−−√[(m2L)2+(nW)2]−1/2Tmn=2gd[(m2L)2+(nW)2]−1/2

    (5)

    where TmnTmn is the oscillation period, gg is the gravitational acceleration, dd is the water depth, LL is the length of the basin, WW is the width of the basin, m=1,2,3,…m=1,2,3,… and n=0,1,2,3,…n=0,1,2,3,…; mm and nn are the counters of the different modes. Applying Eq. (5) to the aforementioned water bodies results in oscillation modes of at least 5 min, which is far longer than the observed period of 20–25 s. Therefore, we rule out the first hypothesis and infer that the long period of 20–25 s is most likely a long swell wave coming from distant sources. As discussed by Rabinovich (1997) and Wang et al. (2022), comparison between sea level spectra before and after the incident is a useful method to distinguish the spectrum of the weather event. A visual inspection of Fig. 6 reveals that the forcing at the period band of 20–25 s is non-existent before the incident.

    Numerical simulations of wave loading and overtopping

    Based on the results of sea level data analyses in the previous section (Fig. 6), we use a dual peak wave spectrum with peak periods of 10.0 s and 25.0 s for numerical simulations because such a wave would be comprised of the most energetic signals of the storm. For variations of water depth (2.0–4.0 m), coastal wave amplitude (0.5–1.5 m) (Fig. 7) and storm surge height (0.5–0.8 m) (Fig. 5), we developed 20 scenarios (Scn) which we used in numerical simulations (Table 2). Data during the incident indicated that water depth was up to the crest level of the seawall (approximately 4 m water depth); therefore, we varied water depth from 2 to 4 m in our simulation scenarios. Regarding wave amplitudes, we referred to the variations at a nearby tide gauge station (West Bay) which showed wave amplitude up to 1.2 m (Fig. 7). Therefore, wave amplitude was varied from 0.5 m to 1.5 m by considering a factor a safety of 25% for the maximum wave amplitude. As for the storm surge component, time series of storm surges calculated at three coastal stations adjacent to Dawlish showed that it was in the range of 0.5 m to 0.8 m (Fig. 5). These 20 scenarios would help to study uncertainties associated with wave amplitudes and pressures. Figure 8 shows snapshots of wave propagation and impacts on the seawall at different times.

    figure 7
    Fig. 7

    Table 2 The 20 scenarios considered for numerical simulations in this study

    Full size table

    figure 8
    Fig. 8

    Results of wave amplitude simulations

    Large wave amplitudes can induce significant wave forcing on the structure and cause overtopping of the seawall, which could eventually cascade to other hazards such as erosion of the backfill and scour (Adams and Heidarzadeh, 2021). The first 10 scenarios of our modelling efforts are for the same incident wave amplitudes of 0.5 m, which occur at different water depths (2.0–4.0 m) and storm surge heights (0.5–0.8 m) (Table 2 and Fig. 9). This is because we aim at studying the impacts of effective water depth (deff—the sum of mean sea level and surge height) on the time histories of wave amplitudes as the storm evolves. As seen in Fig. 9a, by decreasing effective water depth, wave amplitude increases. For example, for Scn-1 with effective depth of 4.5 m, the maximum amplitude of the first wave is 1.6 m, whereas it is 2.9 m for Scn-2 with effective depth of 3.5 m. However, due to intensive reflections and interferences of the waves in front of the vertical seawall, such a relationship is barely seen for the second and the third wave peaks. It is important to note that the later peaks (second or third) produce the largest waves rather than the first wave. Extraordinary wave amplifications are seen for the Scn-2 (deff = 3.5 m) and Scn-7 (deff = 3.3 m), where the corresponding wave amplitudes are 4.5 m and 3.7 m, respectively. This may indicate that the effective water depth of deff = 3.3–3.5 m is possibly a critical water depth for this structure resulting in maximum wave amplitudes under similar storms. In the second wave impact, the combined wave height (i.e. the wave amplitude plus the effective water depth), which is ultimately an indicator of wave overtopping, shows that the largest wave heights are generated by Scn-2, 7 and 8 (Fig. 9a) with effective water depths of 3.5 m, 3.3 m and 3.8 m and combined heights of 8.0 m, 7.0 m and 6.9 m (Fig. 9b). Since the height of seawall is 5.4 m, the combined wave heights for Scn-2, 7 and 8 are greater than the crest height of the seawall by 2.6 m, 1.6 m and 1.5 m, respectively, which indicates wave overtopping.

    figure 9
    Fig. 9

    For scenarios 11–20 (Fig. 10), with incident wave amplitudes of 1.5 m (Table 2), the largest wave amplitudes are produced by Scn-17 (deff = 3.3 m), Scn-13 (deff = 2.5 m) and Scn-12 (deff = 3.5 m), which are 5.6 m, 5.1 m and 4.5 m. The maximum combined wave heights belong to Scn-11 (deff = 4.5 m) and Scn-17 (deff = 3.3 m), with combined wave heights of 9.0 m and 8.9 m (Fig. 10b), which are greater than the crest height of the seawall by 4.6 m and 3.5 m, respectively.

    figure 10
    Fig. 10

    Our simulations for all 20 scenarios reveal that the first wave is not always the largest and wave interactions, reflections and interferences play major roles in amplifying the waves in front of the seawall. This is primarily because the wall is fully vertical and therefore has a reflection coefficient of close to one (i.e. full reflection). Simulations show that the combined wave height is up to 4.6 m higher than the crest height of the wall, implying that severe overtopping would be expected.

    Results of wave loading calculations

    The pressure calculations for scenarios 1–10 are given in Fig. 11 and those of scenarios 11–20 in Fig. 12. The total pressure distribution in Figs. 11, 12 mostly follows a triangular shape with maximum pressure at the seafloor as expected from the Sainflou (1928) design equations. These pressure plots comprise both static (due to mean sea level in front of the wall) and dynamic (combined effects of surge and wave) pressures. For incident wave amplitudes of 0.5 m (Fig. 11), the maximum wave pressure varies in the range of 35–63 kPa. At the sea surface, it is in the range of 4–20 kPa (Fig. 11). For some scenarios (Scn-2 and 7), the pressure distribution deviates from a triangular shape and shows larger pressures at the top, which is attributed to the wave impacts and partial breaking at the sea surface. This adds an additional triangle-shaped pressure distribution at the sea surface elevation consistent with the design procedure developed by Goda (2000) for braking waves. The maximum force on the seawall due to scenarios 1–10, which is calculated by integrating the maximum pressure distribution over the wave-facing surface of the seawall, is in the range of 92–190 KN (Table 2).

    figure 11
    Fig. 11
    figure 12
    Fig. 12

    For scenarios 11–20, with incident wave amplitude of 1.5 m, wave pressures of 45–78 kPa and 7–120 kPa, for  the bottom and top of the wall, respectively, were observed (Fig. 12). Most of the plots show a triangular pressure distribution, except for Scn-11 and 15. A significant increase in wave impact pressure is seen for Scn-15 at the top of the structure, where a maximum pressure of approximately 120 kPa is produced while other scenarios give a pressure of 7–32 kPa for the sea surface. In other words, the pressure from Scn-15 is approximately four times larger than the other scenarios. Such a significant increase of the pressure at the top is most likely attributed to the breaking wave impact loads as detailed by Goda (2000) and Cuomo et al. (2010). The wave simulation snapshots in Fig. 8 show that the wave breaks before reaching the wall. The maximum force due to scenarios 11–20 is 120–286 KN.

    The breaking wave impacts peaking at 286 KN in our simulations suggest destabilisation of the upper masonry blocks, probably by grout malfunction. This significant impact force initiated the failure of the seawall which in turn caused extensive ballast erosion. Wave impact damage was proposed by Adams and Heidarzadeh (2021) as one of the primary mechanisms in the 2014 Dawlish disaster. In the multi-hazard risk model proposed by these authors, damage mechanism III (failure pathway 5 in Adams and Heidarzadeh, 2021) was characterised by wave impact force causing damage to the masonry elements, leading to failure of the upper sections of the seawall and loss of infill material. As blocks were removed, access to the track bed was increased for inbound waves allowing infill material from behind the seawall to be fluidised and subsequently removed by backwash. The loss of infill material critically compromised the stability of the seawall and directly led to structural failure. In parallel, significant wave overtopping (discussed in the next section) led to ballast washout and cascaded, in combination with masonry damage, to catastrophic failure of the wall and suspension of the rails in mid-air (Fig. 1b), leaving the railway inoperable for two months.

    Wave Overtopping

    The two most important factors contributing to the 2014 Dawlish railway catastrophe were wave impact forces and overtopping. Figure 13 gives the instantaneous overtopping rates for different scenarios, which experienced overtopping. It can be seen that the overtopping rates range from 0.5 m3/s/m to 16.1 m3/s/m (Fig. 13). Time histories of the wave overtopping rates show that the phenomenon occurs intermittently, and each time lasts 1.0–7.0 s. It is clear that the longer the overtopping time, the larger the volume of the water poured on the structure. The largest wave overtopping rates of 16.1 m3/s/m and 14.4 m3/s/m belong to Scn-20 and 11, respectively. These are the two scenarios that also give the largest combined wave heights (Fig. 10b).

    figure 13
    Fig. 13

    The cumulative overtopping curves (Figs. 14, 15) show the total water volume overtopped the structure during the entire simulation time. This is an important hazard factor as it determines the level of soil saturation, water pore pressure in the soil and soil erosion (Van der Meer et al. 2018). The maximum volume belongs to Scn-20, which is 65.0 m3/m (m-cubed of water per metre length of the wall). The overtopping volumes are 42.7 m3/m for Scn-11 and 28.8 m3/m for Scn-19. The overtopping volume is in the range of 0.7–65.0 m3/m for all scenarios.

    figure 14
    Fig. 14
    figure 15
    Fig. 15

    For comparison, we compare our modelling results with those estimated using empirical equations. For the case of the Dawlish seawall, we apply the equation proposed by Van Der Meer et al. (2018) to estimate wave overtopping rates, based on a set of decision criteria which are the influence of foreshore, vertical wall, possible breaking waves and low freeboard:

    qgH3m−−−−√=0.0155(Hmhs)12e(−2.2RcHm)qgHm3=0.0155(Hmhs)12e(−2.2RcHm)

    (6)

    where qq is the mean overtopping rate per metre length of the seawall (m3/s/m), gg is the acceleration due to gravity, HmHm is the incident wave height at the toe of the structure, RcRc is the wall crest height above mean sea level, hshs is the deep-water significant wave height and e(x)e(x) is the exponential function. It is noted that Eq. (6) is valid for 0.1<RcHm<1.350.1<RcHm<1.35. For the case of the Dawlish seawall and considering the scenarios with larger incident wave amplitude of 1.5 m (hshs= 1.5 m), the incident wave height at the toe of the structure is HmHm = 2.2—5.6 m, and the wall crest height above mean sea level is RcRc = 0.6–2.9 m. As a result, Eq. (6) gives mean overtopping rates up to approximately 2.9 m3/s/m. A visual inspection of simulated overtopping rates in Fig. 13 for Scn 11–20 shows that the mean value of the simulated overtopping rates (Fig. 13) is close to estimates using Eq. (6).

    Discussion and conclusions

    We applied a combination of eyewitness account analysis, sea level data analysis and numerical modelling in combination with our engineering judgement to explain the damage to the Dawlish railway seawall in February 2014. Main findings are:

    • Eyewitness data analysis showed that the extreme nature of the event was well forecasted in the hours prior to the storm impact; however, the magnitude of the risks to the structures was not well understood. Multiple hazards were activated simultaneously, and the effects cascaded to amplify the damage. Disaster management was effective, exemplified by the establishment of an emergency rendezvous point and temporary evacuation centre during the storm, indicating a high level of hazard awareness and preparedness.
    • Based on sea level data analysis, we identified triple peak period bands at 4–8, 8–12 and 20–25 s in the sea level data. Storm surge heights and wave oscillations were up to 0.8 m and 1.5 m, respectively.
    • Based on the numerical simulations of 20 scenarios with different water depths, incident wave amplitudes, surge heights and peak periods, we found that the wave oscillations at the foot of the seawall result in multiple wave interactions and interferences. Consequently, large wave amplitudes, up to 4.6 m higher than the height of the seawall, were generated and overtopped the wall. Extreme impulsive wave impact forces of up to 286 KN were generated by the waves interacting with the seawall.
    • We measured maximum wave overtopping rates of 0.5–16.1 m3/s/m for our scenarios. The cumulative overtopping water volumes per metre length of the wall were 0.7–65.0 m3/m.
    • Analysis of all the evidence combined with our engineering judgement suggests that the most likely initiating cause of the failure was impulsive wave impact forces destabilising one or more grouted joints between adjacent masonry blocks in the wall. Maximum observed pressures of 286 KN in our simulations are four times greater in magnitude than background pressures leading to block removal and initiating failure. Therefore, the sequence of cascading events was :1) impulsive wave impact force causing damage to masonry, 2) failure of the upper sections of the seawall, 3) loss of infill resulting in a reduction of structural strength in the landward direction, 4) ballast washout as wave overtopping and inbound wave activity increased and 5) progressive structural failure following successive tides.

    From a risk mitigation point of view, the stability of the seawall in the face of future energetic cyclonic storm events and sea level rise will become a critical factor in protecting the rail network. Mitigation efforts will involve significant infrastructure investment to strengthen the civil engineering assets combined with improved hazard warning systems consisting of meteorological forecasting and real-time wave observations and instrumentation. These efforts must take into account the amenity value of coastal railway infrastructure to local communities and the significant number of tourists who visit every year. In this regard, public awareness and active engagement in the planning and execution of the project will be crucial in order to secure local stakeholder support for the significant infrastructure project that will be required for future resilience.

    Notes

    1. https://www.networkrail.co.uk/..
    2. https://www.flow3d.com/products/flow-3d-hydro/.
    3. https://www.devonmuseums.net/Dawlish-Museum/Devon-Museums/.
    4. https://ntslf.org/.
    5. https://www.datawell.nl/Products/Buoys/DirectionalWaveriderMkIII.aspx.
    6. https://www.bodc.ac.uk/.
    7. https://coastalmonitoring.org/cco/.
    8. https://webapp.navionics.com/#boating@8&key=iactHlwfP.

    References

    Download references

    Acknowledgements

    We are grateful to Brunel University London for administering the scholarship awarded to KA. The Flow3D-Hydro used in this research for numerical modelling is licenced to Brunel University London through an academic programme contract. We sincerely thank Prof Harsh Gupta (Editor-in-Chief) and two anonymous reviewers for their constructive review comments.

    Funding

    This project was funded by the UK Engineering and Physical Sciences Research Council (EPSRC) through a PhD scholarship to Keith Adams.

    Author information

    Authors and Affiliations

    1. Department of Civil and Environmental Engineering, Brunel University London, Uxbridge, UB8 3PH, UKKeith Adams
    2. Department of Architecture and Civil Engineering, University of Bath, Bath, BA2 7AY, UKMohammad Heidarzadeh

    Corresponding author

    Correspondence to Keith Adams.

    Ethics declarations

    Conflict of interest

    The authors have no relevant financial or non-financial interests to disclose.

    Availability of data

    All data used in this study are provided in the body of the article.

    Additional information

    Publisher’s Note

    Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

    Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

    Reprints and Permissions

    About this article

    Cite this article

    Adams, K., Heidarzadeh, M. Extratropical cyclone damage to the seawall in Dawlish, UK: eyewitness accounts, sea level analysis and numerical modelling. Nat Hazards (2022). https://doi.org/10.1007/s11069-022-05692-2

    Download citation

    • Received17 May 2022
    • Accepted17 October 2022
    • Published14 November 2022
    • DOIhttps://doi.org/10.1007/s11069-022-05692-2

    Share this article

    Anyone you share the following link with will be able to read this content:Get shareable link

    Provided by the Springer Nature SharedIt content-sharing initiative

    Keywords

    • Storm surge
    • Cyclone
    • Railway
    • Climate change
    • Infrastructure
    • Resilience
    원문보기
    Propagation of Landslide Surge in Curved River Channel and Its Interaction with Dam

    굽은 강둑 산사태의 팽창 전파 및 댐과의 상호 작용, 곡선하천의 산사태 해일 전파 및 댐과의 상호작용

    굽은 강둑 산사태의 팽창 전파 및 댐과의 상호 작용

    펑후이, 황야지에    

    1. 수자원 보존 및 환경 학교, Three Gorges University, Yichang, Hubei 443000
    • 收稿日期:2021-08-19 修回日期:2021-09-30 发布日期:2022-10-13
    • 通讯作者: Huang Yajie (1993-), Shangqiu, Henan, 석사 학위, 그의 연구 방향은 수리 구조입니다. 이메일: master_hyj@163.com
    • 作者简介:Peng Hui(1976-)는 후베이성 ​​이창에서 태어나 교수, 의사, 박사 지도교수로 주로 수력 구조의 교육 및 연구에 종사했습니다. 이메일:hpeng1976@163.com
    • 基金资助:국가핵심연구개발사업(2018YFC1508801-4)

    곡선하천의 산사태 해일 전파 및 댐과의 상호작용

    PENG Hui, HUANG Ya-jie    

    1. 중국 삼협대학 수자원환경대학 이창 443000 중국
    • Received:2021-08-19 Revised:2021-09-30 Published:2022-10-13

    Abstract

    추상적인:저수지 제방 산사태는 일반적인 지질학적 위험으로, 제때에 미리 경고하지 않으면 하천에 해일파가 발생하여 하천 교통이나 인근 수자원 보호 시설의 안전을 위험에 빠뜨릴 수 있습니다. 저수지 제방 산사태로 인한 해일파 전파 전파 Flow-3D를 이용하여 하류 댐과의 상호작용을 시뮬레이션 하였다. 수리학적 물리적 모델 시험의 타당성과 정확성을 검증하기 위하여 3차원 산사태 해지 모델을 구축하였다. 수면 높이 변화와 서지의 전파 과정에 대한 수리학적 물리적 모델 테스트. 그 동안,가장 위험한 수심과 입사각 조건은 다양한 조건에서 댐과 산사태 해일 사이의 상호 작용을 분석하여 얻었습니다. 엔지니어링 사례는 최대 동적 수두가 해일 높이의 수두보다 작고 물을 따라 감소한다는 것을 보여주었습니다. 이 경우, 서지의 정적 최대 수두에 따라 계산된 댐의 응력은 안전합니다.

    As a common geological hazard,reservoir bank landslide would most probably induce surge waves in river if not prewarned in time,endangering river traffic or the safety of nearby water conservancy facilities.The propagation of surge wave induced by the landslide of curved river bank in reservoir and its interaction with downstream dam were simulated by using Flow-3D.A three-dimensional landslide surge model was constructed to verify the validity and accuracy of hydraulic physical model test.The result of the three-dimensional numerical simulation was in good agreement with that of hydraulic physical model test in terms of the water surface height change and the propagation process of the surge.In the mean time,the most dangerous water depth and incident angle conditions were obtained by analyzing the interaction between the dam and the landslide surge under different conditions.Engineering examples demonstrated that the maximum dynamic water head was smaller than the water head of surge height,and reduced along the water depth direction.In such cases,the stress of the dam calculated according to the static maximum water head of the surge is safe.

    Key words

    슬라이드 서지, 곡선 수로형 저수지, 수치 시뮬레이션, 동적 수압, 중력 댐, slide surges, curved channel type reservoirs, numerical simulation, dynamic water pressure, gravity dam

    원문보기1
    원문보기2
    The failure propagation of weakly stable sediment: A reason for the formation of high-velocity turbidity currents in submarine canyons

    약한 안정 퇴적물의 실패 전파: 해저 협곡에서 고속 탁도 흐름이 형성되는 이유

    Abstract

    Abstract해저 협곡에서 탁도의 장거리 이동은 많은 양의 퇴적물을 심해 평원으로 운반할 수 있습니다. 이전 연구에서는 5.9~28.0m/s 범위의 다중 케이블 손상 이벤트에서 파생된 탁도 전류 속도와 0.15~7.2m/s 사이의 현장 관찰 결과에서 명백한 차이가 있음을 보여줍니다. 따라서 해저 환경의 탁한 유체가 해저 협곡을 고속으로 장거리로 흐를 수 있는지에 대한 질문이 남아 있습니다. 연구실 시험의 결합을 통해 해저협곡의 탁류의 고속 및 장거리 운동을 설명하기 위해 약안정 퇴적물 기반의 새로운 모델(약안정 퇴적물에 대한 파손 전파 모델 제안, 줄여서 WSS-PFP 모델)을 제안합니다. 및 수치 아날로그. 이 모델은 두 가지 메커니즘을 기반으로 합니다. 1) 원래 탁도류는 약하게 안정한 퇴적층의 불안정화를 촉발하고 연질 퇴적물의 불안정화 및 하류 방향으로의 이동을 촉진하고 2) 원래 탁도류가 협곡으로 이동할 때 형성되는 여기파가 불안정화로 이어진다. 하류 방향으로 약하게 안정한 퇴적물의 수송. 제안된 모델은 심해 퇴적, 오염 물질 이동 및 광 케이블 손상 연구를 위한 동적 프로세스 해석을 제공할 것입니다.

    The long-distance movement of turbidity currents in submarine canyons can transport large amounts of sediment to deep-sea plains. Previous studies show obvious differences in the turbidity current velocities derived from the multiple cables damage events ranging from 5.9 to 28.0 m/s and those of field observations between 0.15 and 7.2 m/s. Therefore, questions remain regarding whether a turbid fluid in an undersea environment can flow through a submarine canyon for a long distance at a high speed. A new model based on weakly stable sediment is proposed (proposed failure propagation model for weakly stable sediments, WSS-PFP model for short) to explain the high-speed and long-range motion of turbidity currents in submarine canyons through the combination of laboratory tests and numerical analogs. The model is based on two mechanisms: 1) the original turbidity current triggers the destabilization of the weakly stable sediment bed and promotes the destabilization and transport of the soft sediment in the downstream direction and 2) the excitation wave that forms when the original turbidity current moves into the canyon leads to the destabilization and transport of the weakly stable sediment in the downstream direction. The proposed model will provide dynamic process interpretation for the study of deep-sea deposition, pollutant transport, and optical cable damage.

    Keyword

    • turbidity current
    • excitation wave
    • dense basal layer
    • velocity
    • WSS-PFP model

    References

    Download references

    Acknowledgment

    We thank Hanru WU from Ocean University of China for his help in thesis writing, and Hao TIAN and Chenxi WANG from Ocean University of China for their helps in the preparation of the experimental materials. Guohui XU is responsible for the development of the initial concept, processing of test data, and management of coauthor contributions to the paper; Yupeng REN for the experiment setup and drafting of the paper; Yi ZHANG and Xingbei XU for the simulation part of the experiment; Houjie WANG for writing guidance; Zhiyuan CHEN for the experiment setup.

    Author information

    Authors and Affiliations

    1. Shandong Provincial Key Laboratory of Marine Environment and Geological Engineering, Qingdao, 266100, ChinaYupeng Ren, Yi Zhang, Guohui Xu, Xingbei Xu & Zhiyuan Chen
    2. Shandong Provincial Key Laboratory of Marine Environment and Geological Engineering, Ocean University of China, Qingdao, 266100, ChinaYupeng Ren & Houjie Wang
    3. Key Laboratory of Marine Environment and Ecology, Ocean University of China, Ministry of Education, Qingdao, 266100, ChinaYi Zhang, Guohui Xu, Xingbei Xu & Zhiyuan Chen

    Corresponding author

    Correspondence to Guohui Xu.

    Additional information

    Supported by the National Natural Science Foundation of China (Nos. 41976049, 41720104001) and the Taishan Scholar Project of Shandong Province (No. TS20190913), and the Fundamental Research Funds for the Central Universities (No. 202061028)

    Data Availability Statement

    The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.

    Reprints and Permissions

    About this article

    Cite this article

    Ren, Y., Zhang, Y., Xu, G. et al. The failure propagation of weakly stable sediment: A reason for the formation of high-velocity turbidity currents in submarine canyons. J. Ocean. Limnol. (2022). https://doi.org/10.1007/s00343-022-1285-0

    원문보기
    Figure 3. Comparison of water surface profiles over porous media with 12 mm particle diameter in laboratory measurements (symbols) and numerical results (lines).

    다공층에 대한 돌발 댐 붕괴의 3차원 유동 수치해석 시뮬레이션

    A. Safarzadeh1*, P. Mohsenzadeh2, S. Abbasi3
    1 Professor of Civil Eng., Water Engineering and Mineral Waters Research Center, Univ. of Mohaghegh Ardabili,Ardabil, Iran
    2 M.Sc., Graduated of Civil-Hydraulic Structures Eng., Faculty of Eng., Univ. of Mohaghegh Ardabili, Ardabil, Iran
    3 M.Sc., Graduated of Civil -Hydraulic Structures Eng., Faculty of Eng., Univ. of Mohaghegh Ardabili, Ardabil, Iran Safarzadeh@uma.ac.ir

    Highlights

    유체 이동에 의해 생성된 RBF는 Ls-Dyna에서 Fluent, ICFD ALE 및 SPH 방법으로 시뮬레이션되었습니다.
    RBF의 과예측은 유체가 메인 도메인에서 고속으로 분리될 때 발생합니다.
    이 과잉 예측은 요소 크기, 시간 단계 크기 및 유체 모델에 따라 다릅니다.
    유체 성능을 검증하려면 최대 RBF보다 임펄스가 권장됩니다.

    Abstract

    Dam break is a very important problem due to its effects on economy, security, human casualties and environmental consequences. In this study, 3D flow due to dam break over the porous substrate is numerically simulated and the effect of porosity, permeability and thickness of the porous bed and the water depth in the porous substrate are investigated. Classic models of dam break over a rigid bed and water infiltration through porous media were studied and results of the numerical simulations are compared with existing laboratory data. Validation of the results is performed by comparing the water surface profiles and wave front position with dam break on rigid and porous bed. Results showed that, due to the effect of dynamic wave in the initial stage of dam break, a local peak occurs in the flood hydrograph. The presence of porous bed reduces the acceleration of the flood wave relative to the flow over the solid bed and it decreases with the increase of the permeability of the bed. By increasing the permeability of the bed, the slope of the ascending limb of the flood hydrograph and the peak discharge drops. Furthermore, if the depth and permeability of the bed is such that the intrusive flow reaches the rigid substrate under the porous bed, saturation of the porous bed, results in a sharp increase in the slope of the flood hydrograph. The maximum values of the peak discharge at the end of the channel with porous bed occurred in saturated porous bed conditions.

    댐 붕괴는 경제, 보안, 인명 피해 및 환경적 영향으로 인해 매우 중요한 문제입니다. 본 연구에서는 다공성 기재에 대한 댐 파괴로 인한 3차원 유동을 수치적으로 시뮬레이션하고 다공성 기재의 다공성, 투과도 및 다공성 층의 두께 및 수심의 영향을 조사합니다. 단단한 바닥에 대한 댐 파괴 및 다공성 매체를 통한 물 침투의 고전 모델을 연구하고 수치 시뮬레이션 결과를 기존 실험실 데이터와 비교합니다. 결과 검증은 강체 및 다공성 베드에서 댐 파단과 수면 프로파일 및 파면 위치를 비교하여 수행됩니다. 그 결과 댐파괴 초기의 동적파동의 영향으로 홍수수문곡선에서 국부첨두가 발생하는 것으로 나타났다. 다공성 베드의 존재는 고체 베드 위의 유동에 대한 홍수파의 가속을 감소시키고 베드의 투과성이 증가함에 따라 감소합니다. 베드의 투수성을 증가시켜 홍수 수문곡선의 오름차순 경사와 첨두방류량이 감소한다. 더욱이, 만약 층의 깊이와 투과성이 관입 유동이 다공성 층 아래의 단단한 기질에 도달하는 정도라면, 다공성 층의 포화는 홍수 수문곡선의 기울기의 급격한 증가를 초래합니다. 다공층이 있는 채널의 끝단에서 최대 방전 피크값은 포화 다공층 조건에서 발생하였다.

    Keywords

    Keywords: Dams Break, 3D modeling, Porous Bed, Permeability, Flood wave

    Reference

    [1] D.L. Fread, In: Maidment, D.R. (Ed.), Flow Routing in Handbook of Hydrology, McGraw-Hill Inc., New York, USA, pp. 10(1) (1993) 1-36.
    [2] M. Morris, CADAM: Concerted Action on Dambreak Modeling – Final Report, Rep. SR 571. HR Wallingford, 2000.
    [3] H. Chanson, The Hydraulics of Open Channel Flows: an Introduction, ButterworthHeinemann, Oxford, 2004.
    [4] A. Ritter, Die Fortpflanzung der Wasserwellen (The Propagation of Water Waves), Zeitschrift Verein Deutscher Ingenieure, 36 (33) (1892) 947–954 [in German].
    [5] B. Ghimire, Hydraulic Analysis of Free-Surface Flows into Highly Permeable Porous Media and its Applications, Phd. Thesis, Kyoto University, 2009.
    [6] R. Dressler, Hydraulic Resistance Effect Upon the Dam-Break Function, Journal of Research of the National Bureau of Standards, 49 (3) 1952.
    [7] G. Lauber, and W.H. Hager, Experiments to Dambreak Wave: horizontal channel, Journal of Hydraulic Research. 36 (3) (1998) 291–307.
    [8] L.W. Tan, and V.H. Chu, Lagrangian Block Hydrodynamics of Macro Resistance in a River-Flow Model,
    [9] L. Tan, V.H. Lauber and Hager’s Dam-Break Wave Data for Numerical Model Validation, Journal of Hydraulic Research, 47 (4) (2009) 524-528.
    [10] S. Mambretti, E.D. Larcan, and D. Wrachien, 1D Modelling of Dam-Break Surges with Floating Debris, J. of Biosystems engineering, 100 (2) (2008) 297-308.
    [11] M. Pilotti, M. Tomirotti, G. Valerio, and B. Bacchi, Simplified Method for the Characterization of the Hydrograph Following a Sudden Partial Dam Break, Journal of Hydraulic Engineering, 136 (10) (2010) 693-704.
    [12] T.J. Chang, H.M. Kao, K.H. Chang, and Mi.H. Hsu, Numerical Simulation of ShallowWater Dam Break Flows in Open Channels Using Smoothed Particle Hydrodynamics, J. Hydraul. Eng., 408 (78–90) 2011.
    [13] T. Tawatchai, and W. Rattanapitikon, 2-D Modelling of Dambreak Wave Propagation on Initially Dry Bed, Thammasat Int. J. Sc. 4 (3) 1999.
    [14] Y.F. Le, Experimental Study of landslide Dam-Break Flood over Erodible Bed in open Channels. Journal of Hydrodynamics, Ser. B, 21 (5) 2006.
    [15] O. Castro-Orgaz, & H. Chanson, Ritter’s Dry-Bed Dam-Break Flows: Positive and Negative Wave Dynamics, J. of Environmental Fluid Mechanics, 17 (4) (2017) 665-694.
    [16] A. Jozdani, A.R. Kabiri-Samani, Application of Image Processing Method to Analysis of Flood Behavior Due to Dam Break, 9th Iranian Hydraulic Conference. Univ. of Tarbiat Moddares, 2011.(in persian)
    [17] A. Safarzadeh, Three Dimensional Hydrodynamics of Sudden Dam Break in Curved Channels, Journal of Modares Civil Engineering, 17(3) (2017) 77-86. (in persian)
    [18] P. C. Carman, Fluid Flow Through Granular Beds, Transactions, Institution of Chem. Eng. Res. Des. 75 (Dec): S32–S48, London, 15, (1937) 150-166.
    [19] P. Forchheimer, Wasserbewegung Durch Boden. Z. Ver. Deutsch. Ing. 45 (1901) 1782– 1788.
    [20] S. Ergun, Fluid Flow through Packed Columns. Chemical Engineering Progress, 48(2) (1952) 89-93.
    [21] A. Parsaei, S. Dehdar-Behbahani, Numerical Modeling of Cavitation on Spillway’s Flip Bucket, Frontiers of Structural and Civil Engineering, 10 (4) (2016) 438-444.
    [22] S. Dehdar-Behbahani, A. Parsaei, Numerical Modeling of Flow Pattern in Dam Spillway’s Guide Wall. Case study: Balaroud dam, Iran, Alexandria Engineering Journal, 55(1) (2016) 467-473.
    [23] A. Parsaei, AH. Haghiabi, A. Moradnejad, CFD Modeling of Flow Pattern in Spillway’s ACCEPTED MANUSCRIPT 19 Approach Channel, Sustainable Water Resources Management, 1(3) (2015) 245-251.
    [24] SH. Najafian, H. Yonesi, A. Parsaei, PH. Torabi, Physical and Numerical Modeling of Flow in Heterogeneous Roughness Non-Prismatic Compound Open Channel, Irrigation and Drainage Structures Engineering Research, 17(66) (2016) 87-104.
    [25] SH. Najafian, H. Yonesi, A. Parsaei, PH. Torabi, Physical and Numerical Modeling of Flow Properties in Prismatic Compound Open Channel with Heterogeneous Roughness, Irrigation and Drainage Structures Engineering Research, 18(68) (2017) 1-16.
    [26] A. Safarzadeh, S.H. Mohajeri, Hydrodynamics of Rectangular Broad-Crested Porous Weirs, Journal of Irrig. & Drain. Eng., 144(10) (2018) 1-12.
    [27] M. Fathi-moghaddam, M.T. Sadrabadi, M, Rahamnshahi, Numerical Simulation of the Hydraulic Performance of Triangular and Trapezoidal Gabion Weirs in Free Flow Condition, Journal of Flow Measurement & Instrumentation, 62 (2018) 93-104.
    [28] A. Parsaei, A. Moradnejad, Numerical Modeling of Flow Pattern in Spillway Approach Channel, Jordan Journal of Civil Engineering, 12(1) (2018) 1-9.

    PDF VIEW
    Fig. 6. Experiment of waves passing through a single block of porous medium.

    Generalization of a three-layer model for wave attenuation in n-block submerged porous breakwater

    NadhiraKarimaaIkhaMagdalenaabIndrianaMarcelaaMohammadFaridbaFaculty of Mathematics and Natural Sciences, Bandung Institute of Technology, 40132, IndonesiabCenter for Coastal and Marine Development, Bandung Institute of Technology, Indonesia

    Highlights

    •A new three-layer model for n-block submerged porous breakwaters is developed.

    •New analytical approach in finding the wave transmission coefficient is presented.

    •A finite volume method successfully simulates the wave attenuation process.

    •Porous media blocks characteristics and configuration can optimize wave reduction.

    Abstract

    높은 파도 진폭은 해안선에 위험한 영향을 미치고 해안 복원력을 약화시킬 수 있습니다. 그러나 다중 다공성 매체는 해양 생태계의 환경 친화적인 해안 보호 역할을 할 수 있습니다.

    이 논문에서 우리는 n개의 잠긴 다공성 미디어 블록이 있는 영역에서 파동 진폭 감소를 계산하기 위해 3층 깊이 통합 방정식을 사용합니다. 수학적 모델은 파동 전달 계수를 얻기 위해 여러 행렬 방정식을 포함하는 변수 분리 방법을 사용하여 해석적으로 해결됩니다.

    이 계수는 진폭 감소의 크기에 대한 정보를 제공합니다. 또한 모델을 수치적으로 풀기 위해 지그재그 유한 체적 방법이 적용됩니다.

    수치 시뮬레이션을 통해 다공성 매질 블록의 구성과 특성이 투과파 진폭을 줄이는 데 중요하다는 결론을 내렸습니다.

    High wave amplitudes may cause dangerous effects on the shoreline and weaken coastal resilience. However, multiple porous media can act as environmental friendly coastal protectors of the marine ecosystem. In this paper, we use three-layer depth-integrated equations to calculate wave amplitude reduction in a domain with n submerged porous media blocks. The mathematical model is solved analytically using the separation of variables method involving several matrix equations to obtain the wave transmission coefficient. This coefficient provides information about the magnitude of amplitude reduction. Additionally, a staggered finite volume method is applied to solve the model numerically. By conducting numerical simulations, we conclude that porous media blocks’ configuration and characteristics are crucial in reducing transmitted wave amplitude.

    Keywords

    Three-layer equations, Submerged porous media, Wave transmission coefficient, Finite volume method

    Fig. 1. Sketch of the problem configuration.
    Fig. 1. Sketch of the problem configuration.
    Fig. 6. Experiment of waves passing through a single block of porous medium.
    Fig. 6. Experiment of waves passing through a single block of porous medium.

    References

    [1]M. Beck, G. Lange, Managing Coasts with Natural Solutions: Guidelines for Measuring and Valuing the Coastal Protection Services of Mangroves and Coral Reefs.

    Google Scholar[2]

    Y. Zhao, Y. Liu, H. Li, A. Chang

    Oblique wave motion over multiple submerged porous bars near a vertical wall

    J. Ocean Univ. China, 16 (2017), pp. 568-574, 10.1007/s11802-017-3333-5 View PDF

    View Record in ScopusGoogle Scholar[3]C. K. Sollitt, R. H. Cross, Wave transmission through permeable breakwaters, Coast. Eng..

    Google Scholar[4]J.-F. Lee, L.-F. Tu, C.-C. Liu, Nonlinear wave evolution above rectangular submerged structures, J. Mar. Sci. Technol. 22. doi:10.6119/JMST-013-0503-3.

    Google Scholar[5]

    Y.T. Wu, C.L. Yeh, S.-C. Hsiao

    Three-dimensional numerical simulation on the interaction of solitary waves and porous breakwaters

    Coast. Eng., 85 (2014), pp. 12-29

    ArticleDownload PDFView Record in ScopusGoogle Scholar[6]

    Y. feng Xu, X. he Xia, J. hua Wang, J. jian Chen

    Numerical analysis on cnoidal wave induced response of porous seabed with definite thickness

    J. Shanghai Jiao Tong Univ. (Sci.), 18 (2013), pp. 650-654, 10.1007/s12204-013-1446-6 View PDF

    Google Scholar[7]

    D.M. Pérez-Romero, M. Ortega-Sánchez, A. Moñino, M.A. Losada

    Characteristic friction coefficient and scale effects in oscillatory porous flow

    Coast. Eng., 56 (9) (2009), pp. 931-939, 10.1016/j.coastaleng.2009.05.002

    ArticleDownload PDFView Record in ScopusGoogle Scholar[8]

    A. Torres-Freyermuth, M. Brocchini, S. Corvaro, J.C. Pintado-Patiño

    Wave attenuation over porous seabeds: a numerical study

    Ocean Model., 117 (2017), pp. 28-40, 10.1016/j.ocemod.2017.07.004

    ArticleDownload PDFView Record in ScopusGoogle Scholar[9]F. Hajivalie, S. M. Mahmoudof, Experimental study of energy dissipation at rectangular submerged breakwater, Proceedings of the 8th International Conference on Fluid Mechanics.

    Google Scholar[10]G. T. Klonaris, A. S. Metallinos, C. D. Memos, K. A. Galani, Experimental and numerical investigation of bed morphology in the lee of porous submerged breakwaters, Coast. Eng. 155.

    Google Scholar[11]

    A. Kubowicz-Grajewska

    Experimental investigation into wave interaction with a rubble-mound submerged breakwater (case study)

    J. Mar. Sci. Technol., 22 (2) (2017), pp. 313-326 View PDF

    CrossRefView Record in ScopusGoogle Scholar[12]

    S.M. Mahmoudof, F. Hajivalie

    Experimental study of hydraulic response of smooth submerged breakwaters to irregular waves

    Oceanologia, 63 (4) (2021), pp. 448-462

    ArticleDownload PDFView Record in ScopusGoogle Scholar[13]

    C. Tsai, H. Chen, F. Lee

    Wave transformation over submerged permeable breakwater on porous bottom

    Ocean Eng., 33 (2006), pp. 1623-1643, 10.1016/j.oceaneng.2005.09.006

    ArticleDownload PDFView Record in ScopusGoogle Scholar[14]

    S. Rojanakamthorn, M. Isobe, A. Watanabe

    A mathematical model of wave transformation over a submerged breakwater

    Coastal Engineering in Japan, 31 (1989), pp. 209-234, 10.1080/05785634.1989.11924515 View PDF

    View Record in ScopusGoogle Scholar[15]

    Q. Lin, Q.r. Meng, D.q. Lu

    Waves propagating over a two-layer porous barrier on a seabed

    J. Hydrodyn., 30 (3) (2018), pp. 453-462 View PDF

    CrossRefView Record in ScopusGoogle Scholar[16]X. Yu, A. T. Chwang, Wave motion through porous structures, J. Eng. Mech. 120. doi:10.1061/(ASCE)0733-9399(1994)120:5(989).

    Google Scholar[17]

    K.G. Vijay, V. Venkateswarlu, D. Karmakar

    Scattering of gravity waves by multiple submerged rubble-mound breakwaters

    Arabian J. Sci. Eng., 45 (10) (2020), pp. 8529-8550 View PDF

    CrossRefView Record in ScopusGoogle Scholar[18]

    I. Magdalena, G. Jonathan

    Water waves resonance and its interaction with submerged breakwater

    Results in Engineering, 13 (2022), Article 100343, 10.1016/j.rineng.2022.100343

    ArticleDownload PDFView Record in ScopusGoogle Scholar[19]

    I. Magdalena, K. Firdaus, D. Jayadi

    Analytical and numerical studies for wave generated by submarine landslide

    Alex. Eng. J., 61 (9) (2022), pp. 7303-7313, 10.1016/j.aej.2021.12.069

    ArticleDownload PDFView Record in ScopusGoogle Scholar[20]

    L. Arpaia, M. Ricchiuto, A.G. Filippini, R. Pedreros

    An efficient covariant frame for the spherical shallow water equations: well balanced dg approximation and application to tsunami and storm surge

    Ocean Model., 169 (2022), Article 101915, 10.1016/j.ocemod.2021.101915

    ArticleDownload PDFView Record in ScopusGoogle Scholar[21]

    M. Briani, G. Puppo, M. Ribot

    Angle dependence in coupling conditions for shallow water equations at channel junctions

    Comput. Math. Appl., 108 (2022), pp. 49-65, 10.1016/j.camwa.2021.12.021

    ArticleDownload PDFView Record in ScopusGoogle Scholar[22]

    I. Magdalena, G.R. Andadari, D.E. Reeve

    An integrated study of wave attenuation by vegetation

    Wave Motion, 110 (2022), Article 102878, 10.1016/j.wavemoti.2021.102878

    ArticleDownload PDFView Record in ScopusGoogle Scholar[23]

    I. Magdalena, R. La’lang, R. Mendoza

    Quantification of wave attenuation in mangroves in manila bay using nonlinear shallow water equations

    Results in Applied Mathematics, 12 (2021), Article 100191, 10.1016/j.rinam.2021.100191

    ArticleDownload PDFView Record in ScopusGoogle Scholar[24]

    K.T. Mandli

    A numerical method for the two layer shallow water equations with dry states

    Ocean Model., 72 (2013), pp. 80-91, 10.1016/j.ocemod.2013.08.001

    ArticleDownload PDFView Record in ScopusGoogle Scholar[25]

    M. Farhan, Z. Omar, F. Mebarek-Oudina, J. Raza, Z. Shah, R.V. Choudhari, O.D. Makinde

    Implementation of the one-step one-hybrid block method on the nonlinear equation of a circular sector oscillator

    Comput. Math. Model., 31 (2020), pp. 116-132, 10.1007/s10598-020-09480-0 View PDF

    View Record in ScopusGoogle Scholar[26]

    R. Djebali, F. Mebarek-Oudina, C. Rajashekhar

    Similarity solution analysis of dynamic and thermal boundary layers: further formulation along a vertical flat plate

    Phys. Scripta, 96 (8) (2021), Article 085206, 10.1088/1402-4896/abfe31 View PDF

    View Record in ScopusGoogle Scholar[27]

    M. Alkasassbeh, O. Zurni, F. Mebarek-Oudina, J. Raza

    Heat transfer study of convective fin with temperature,Äêdependent internal heat generation by hybrid block method

    Heat Tran. Asian Res., 48 (2019), pp. 1225-1244, 10.1002/htj.21428 View PDF

    View Record in ScopusGoogle Scholar[28]

    I. Magdalena, M.F. Eka Pebriansyah

    Numerical treatment of finite difference method for solving dam break model on a wet-dry bed with an obstacle

    Results in Engineering, 14 (2022), Article 100382, 10.1016/j.rineng.2022.100382

    ArticleDownload PDFView Record in ScopusGoogle Scholar[29]

    M. Uddin, S. Rasel, J.K. Adewole, K.S. Al Kalbani

    Finite element simulation on the convective double diffusive water-based copper oxide nanofluid flow in a square cavity having vertical wavy surfaces in presence of hydro-magnetic field

    Results in Engineering, 13 (2022), Article 100364, 10.1016/j.rineng.2022.100364

    ArticleDownload PDFView Record in ScopusGoogle Scholar[30]

    E.H.H. Al-Qadami, A.S. Abdurrasheed, Z. Mustaffa, K.W. Yusof, M. Malek, A.A. Ghani

    Numerical modelling of flow characteristics over sharp crested triangular hump

    Results in Engineering, 4 (2019), Article 100052, 10.1016/j.rineng.2019.100052

    ArticleDownload PDFView Record in ScopusGoogle Scholar[31]

    I. Magdalena, V. Kusnowo, M.I. Azis

    Widowati, 1d-2d numerical model for wave attenuation by mangroves as a porous structure

    Computation, 9 (6) (2021), pp. 1-21

    Google Scholar[32]

    I. Magdalena, M.F. Atras, L. Sembiring, M.A. Nugroho, R.S.B. Labay, M.P. Roque

    Wave transmission by rectangular submerged breakwaters

    Computation, 8 (2) (2020), pp. 1-18 View PDF

    View Record in ScopusGoogle Scholar[33]

    I. Magdalena, S.R. Pudjaprasetya

    Numerical modeling for gravity waves over submerged porous media

    Australian Journal of Basic and Applied Sciences, 9 (28) (2015), pp. 124-130

    View Record in ScopusGoogle Scholar[34]

    I. Magdalena, A. Hariz, M. Farid, M.S.B. Kusuma

    Numerical studies using staggered finite volume for dam break flow with an obstacle through different geometries

    Results in Applied Mathematics, 12 (2021), Article 100193, 10.1016/j.rinam.2021.100193

    ArticleDownload PDFView Record in ScopusGoogle Scholar[35]

    R. Walters, E. Hanert, J. Pietrzak, D. Le Roux

    Comparison of unstructured, staggered grid methods for the shallow water equations

    Ocean Model., 28 (1) (2009), pp. 106-117, 10.1016/j.ocemod.2008.12.004

    the Sixth International Workshop on Unstructured Mesh Numerical Modelling of Coastal, Shelf and Ocean Flows

    ArticleDownload PDFView Record in ScopusGoogle Scholar[36]

    F. Mebarek-Oudina

    Numerical modeling of the hydrodynamic stability in vertical annulus with heat source of different lengths, Engineering Science and Technology

    Int. J., 20 (4) (2017), pp. 1324-1333, 10.1016/j.jestch.2017.08.003

    ArticleDownload PDFView Record in ScopusGoogle Scholar[37]

    S. Pudjaprasetya, I. Magdalena

    Numerical modeling for gravity waves over submerged porous media

    Australian Journal of Basic and Applied Sciences, 9 (2015), pp. 124-130

    Google Scholar

    원문보기
    PDF 원문보기
    Figure 2.1: Types of Landslides[2]

    Landslide flow path modelling
    A Case Study on Aranayaka
    Landslide

    산사태 유로 모델링 : Aranayaka 산사태 사례 연구

    Authors:

    Malithi De Silva : University of Kelaniya

    N.M.T De Silva
    University of Colombo School of Computing
    2018

    Abstract

    산사태가 발생하기 쉬운 구릉 지역 근처에서 발생하는 최근 인구 증가 및 개발은 취약성을 증가시킵니다. 기후 변화의 영향은 산사태 위험의 가능성을 더욱 높입니다. 따라서 인명 및 재산 피해를 방지하기 위해서는 불안정한 경사면 거동에 대한 적절한 관찰과 분석이 중요합니다.

    산사태 흐름 경로 예측은 산사태 흐름 경로를 결정하는 데 중요하며 위험 매핑의 필수 요소입니다. 그러나 현상의 복잡한 특성과 관련 매개변수의 불확실성으로 인해 흐름 경로 예측은 어려운 작업입니다. 이 작업에서는 Kegalle 지역의 Aranayaka 지역의 주요 산사태 사고를 흐름 경로를 모델링하기 위한 사례 연구로 사용합니다.

    위치에서 디지털 고도 모델을 기반으로 잠재적 소스 영역이 식별되었습니다. 확산 영역 평가는 D8 및 다중 방향 흐름 알고리즘이라는 두 가지 흐름 방향 알고리즘을 기반으로 했습니다. 이 프로토타입 모델을 사용하여 사용자는 슬라이드의 최대 너비, 런아웃 거리 및 슬립 표면적과 같은 산사태 관련 통계를 대화식으로 얻을 수 있습니다.

    모델에서 얻은 결과는 실제 Aranayaka 산사태 데이터 세트와 해당 지역의 산사태 위험 지도와 비교되었습니다. D8 알고리즘을 사용하여 구현된 도구에서 생성된 산사태 흐름 경로는 65% 이상의 일치를 나타내고 다중 방향 흐름 알고리즘은 실제 흐름 경로 및 기타 관련 통계와 69% 이상의 일치를 나타냅니다.

    또한, 생성된 유동 경로 방향과 예상되는 산사태 시작 지점이 실제 산사태 경계 내부에 잘 일치합니다.

    Recent population growth and developments taking place close to landslides prone
    hilly areas increase their vulnerability. Climate change impacts further raise the
    potential of landslide hazard. Therefore, to prevent loss of lives and damage to
    property, proper observation and analysis of unstable slope behavior is crucial.
    Landslide flow path forecasting is important for determining a landslide flow route and
    it is an essential element in hazard mapping. However, due to the complex nature of
    the phenomenon and the uncertainties of associated parameters flow path prediction is
    a challenging task.
    In this work, the major landslide incident at Aranayaka area in Kegalle district is taken
    as the case study to model the flow path. At the location, potential source areas were
    identified on the basis of the Digital Elevation Model. Spreading area assessment was
    based on two flow directional algorithms namely D8 and Multiple Direction Flow
    Algorithm. Using this prototype model, a user can interactively get landslide specific
    statistics such as the maximum width of the slide, runout distance, and slip surface area.
    Results obtained by the model were compared with the actual Aranayaka landslide data
    set the landslide hazard map of the area.
    Landslide flow paths generated from the implemented tool using D8 algorithm shows
    more than 65% agreement and Multiple Direction Flow Algorithm shows more than
    69% agreement with the actual flow paths and other related statistics. Also, the
    generated flow path directions and predicted possible landslide initiation points fit
    inside the actual landslide boundary with good agreement.

    Figure 2.1: Types of Landslides[2]
    Figure 2.1: Types of Landslides[2]
    Figure 2.2: Landslide Glossary [2]
    Figure 2.2: Landslide Glossary [2]

    References

    [1] na, “Landslides 101,” [Online]. Available: https://landslides.usgs.gov/learn/ls101.php.
    [Accessed 20 01 2017].
    [2] U.S. Department of the Interior, “Landslide Types and Processes,” July 2004. [Online].
    Available: https://pubs.usgs.gov/fs/2004/3072/fs-2004-3072.html. [Accessed 20 July 2017].
    [3] “Department of conservation,” 2016. [Online]. Available:
    http://www.conservation.ca.gov/cgs/geologic_hazards/landslides/Pages/LandslideTypes.aspx.
    [Accessed 20 July 2017].
    [4] 22nd May 2016, Aerial Survey report on inundation damages and sediment disasters, 2016.
    [5] Peter V. Gorsevski, Paul Gessler and Randy B. Foltz, “Spatial Prediction of Landslide
    Hazard Using Discriminant Analysis and GIS,” in GIS in the Rockies 2000 Conference and
    Workshop Applications for the 21st Century, Denver, Colorado. , 2000.
    [6] M. Casadei, W. E. Dietrich and N. L. Miller, “Testing A Model For Predicting The Timing
    And Location Of Shallow Landslide Initiation In Soil-Mantled Landscapes,” Earth Surface
    Processes and Landforms, vol. 28, p. 925–950, 2003.
    [7] Roberto Arnaldo Trancoso Gomes, Renato Fontes, Osmar Abílio de Carvalho Júnior, Nelson
    Ferreira and Eurípedes Vargas do Amaral, “Combining Spatial Models for Shallow
    Landslides and Debris-Flows Prediction,” Remote Sensing, no. 5, pp. 2219-2237, 2013.
    [8] P. Bertolo and G. F. Wieczorek, “Calibration of numerical models for small debris flow in
    Yosemite Valley, California, USA,” Documentation and monitoring of landslides and debris
    flows for mathematical modelling and design of mitigation measures, 13 December 2005.
    [9] T. A. Gebreslassie, “Dynamic simulations of landslide runout in cohesive Soils,” Oslo, 2015.
    [10] P. Tarolli and D. G. Tarboton, “A new method for determination of most likely landslide
    initiation points and the evaluation of digital terrain model scale in terrain stability mapping,”
    Hydrology and Earth System Sciences, no. 10, p. 663–677, 2006.
    [11] G.-B. Kim, “Numerical Simulation Of Three-Dimensional Tsunami Generation By Subaerial
    Landslides,” 2013.

    [12] Giuseppe Formetta, Giovanna Capparelli, and Pasqua, “Evaluating performance of simplified
    physically based models for shallow landslide susceptibility,” Hydrol. Earth System, no. 20,
    p. 4585–4603, 2016.
    [13] F. Dai and C. Lee, “Landslide characteristics and slope instability modeling using GIS,
    Lantau Island, Hong Kong,” 2001.
    [14] S. McDougall, “Landslide runout analysis — current practice and challenges,” Canadian
    Geotechnical Colloquium, vol. 57, pp. 605-620, 2017.
    [15] P. Quinn, K. Beven, P. Chevallier And O. Planchon, “The Prediction Of Hillslope Flow Paths
    For Distributed Hydrological Modelling Using Digital Terrain Models,” Hydrological
    Processes, Vol. Vol. 5, Pp. 59-79, 1991.
    [16] V. B., “Comparison of Single and Multiple Flow Direction Algorithm for Computing
    Topographic Parameters in Topmodel,” 2000.
    [17] J.-f. G. P. T. K. T. Guang-ju ZHAO, “Comparison of two different methods for determining
    flow direction in catchment hydrological modeling,” Water Science and Engineering, vol. 2,
    no. 4, pp. 1-15, 2009.
    [18] D. G. Tarboton, “A New Method For The Determination Of Flow Directions And Upslope
    Areas In Grid Digital Elevation Models,” Water Resources Research, Vol. 33, No. 2, P. 33,
    309-319.
    [19] V. Baumann, “Debris flow susceptibility mapping at a regional scale along the National Road
    N7, Argentina,” in CGS Geotechnical Conference, Argentina, 2011.
    [20] Q. Z. Petter Pilesjö, “Theoretical Estimation Of Flow Accumulation From A Grid-Based
    Digital Elevation Model,” In Proceedings Of Gis Am/Fm Asia’97 And Geoinformatics’97
    Conference, Taipei, 1997.
    [21] G. A. D. Y. a. C. S. L. John P. WILSON, “Water in the Landscape: A Review of
    Contemporary Flow Routing Algorithms,” pp. 213-236.
    [22] “MathWorks,” The MathWorks, Inc., [Online]. Available: https://in.mathworks.com/.
    [Accessed 1 10 2017].
    [23] “What is GIS?,” Esri, [Online]. Available: http://www.esri.com/what-is-gis. [Accessed 12 11
    2017].
    [24] P. Barrett, “Paul Barrett,” Wikimedia Foundation, Inc, [Online]. Available:
    http://www.pbarrett.net/techpapers/euclid.pdf. [Accessed 05 01 2018].

    [25] S. S. Gruber, Land-surface parameters and objects in hydrology., Elsevier, 2009.
    [26] “Landscape Evolution Modeling with CHILD,” Community Surface Dynamics Modeling
    System, [Online]. Available:
    http://csdms.colorado.edu/wiki/Labs_Landscape_Evolution_Modeling_With_Child_Part_2.
    [Accessed 13 01 2018].
    [27] M. Cooper, “Depth Recovery through Linear Algebra,” in Line Drawing Interpretation,
    Springer Science & Business Media., p. 118.
    [28] A. P. Nicholas, “Cellular modelling in fluvial geomorphology,” in Earth Surface Processes
    and Landforms, 2005, p. 645–649.
    [29] “Making Successful Maps,” DroneDeploy, [Online]. Available:
    https://support.dronedeploy.com/v1.0/docs/making-successful-maps. [Accessed 15 10 2017].
    [30] “M_Map:A mapping package for Matlab,” rich@eos.ubc.ca, [Online]. Available:
    https://www.eoas.ubc.ca/~rich/map.html. [Accessed 15 01 2018].
    [31] P. Dulanjalee, “Landslide Flow Path Assessment for Susceptibility Mapping at a Regional
    Scale,” in ‘Investing in Disaster Risk Reduction for Resilience’’- NBRO holds 8th Annual
    Symposium, Colombo, 2017.
    [32] L. K. a. D. M. E. Boyagoda, ” Subsurface Geotechnical Characterization,” in International
    Symposium, NBRO, Colombo, Sri Lanka, 2016.
    [33] E. E. Duncan and A. A. Rahman, “An Amalgamation Of 3d Spatial Data Model For Surface
    And Subsurface Spatial Objects.,” in Knowing To Manage The Territory, Protect The
    Environment, Evaluate The Cultural Heritage, Rome, Italy, 2012.

    PDF 원문보기
    Figure 16: Velocity Vectors of Flow at Ghulmet

    댐 붕괴 홍수파 및 범람 매핑 시뮬레이션: A
    아타바드 호수 사례 연구

    Simulation of Dam-Break Flood Wave and Inundation Mapping: A
    Case study of Attabad Lake

    Wasim Karam1, Fayaz A. Khan2, Muhammad Alam3, Sajjad Ali4
    1Lab. Engineer, Department of Civil Engineering, University of Engineering and Technology Mardan, Pakistan,
    wasim10karam@gmail.com
    2Assistant Professor, National Institute of Urban Infrastructure Planning, University of Engineering and Technology Peshawar,
    Pakistan, fayazuet@yahoo.com
    3,4Assistant Professor, Department of Civil Engineering, University of Engineering and Technology Mardan, Pakistan,
    emalam82@gmail.com, sajjadali@uetmardan.edu.pk

    ABSTRACT

    산사태 또는 제방 댐의 파손 연구는 구성이 불확실하고 자연적이며 재해에 대해 적절하게 설계되지 않았기 때문에 다른 자연적 사건에 대한 대응 지식이 부족하기 때문에 더 중요합니다. 이 논문은 댐 ​​파괴의 수력학적 모델링의 다양한 방법을 개선하는 것을 목표로 합니다.

    현재 이 연구에서 Attabad 호수의 댐 붕괴는 전산 유체 역학 기술을 사용하여 시뮬레이션됩니다. 수치 모델(FLOW-3D)은 Reynolds 평균 Navier-Stoke 방정식을 완전히 3D로 풀어서 다양한 단면에서의 피크 유량 깊이, 피크 속도, 피크 방전, 피크 깊이까지의 시간 및 피크 방전까지의 시간을 예측하기 위해 개발되었습니다.

    표준 RNG 난류 모델을 사용하여 난류를 시뮬레이션한 다음 마을의 흐름에 대한 홍수 범람 지도와 속도 벡터를 그립니다. 결과는 Hunza 강의 수로를 통해 모델링된 홍수파의 대부분이 Hunza 강의 범람원에 포함되지만 Hunza 강의 범람원 내부에 위치한 Miaun 및 chalat와 같은 일부 마을의 경우 더 높은 위험에 있음을 보여줍니다.

    그러나 이들 마을의 예상 홍수 도달 시간은 각각 31분과 44분으로 인구를 안전한 지역으로 대피시키기에 충분한 시간인 반면, 알리 아바드에 인접한 하산 아바드와 같은 일부 마을의 경우 침수 위험이 더 높은 반면 마을의 예상 홍수 도착 시간은 12분으로 인구 대피에 충분하지 않으므로 홍수 억제를 위한 추가 홍수 보호 구조가 필요합니다.

    최고속도의 추정치는 하천평야의 더 높은 전단응력, 심한 침식의 위험, 농경지 피해, 주거지 및 형태학적 변화가 예상됨을 의미한다. 댐 파손 분석(예: 최고 깊이, 최고 속도, 홍수 도달 시간 및 홍수 범람 지도)은 향후 위험 분석 및 홍수 관리의 지침으로만 사용해야 합니다.

    Figure 2: Case Study Location on Map of Pakistan
    Figure 2: Case Study Location on Map of Pakistan
    Figure 3: Lake Condition 3 months after Landslide
    Figure 3: Lake Condition 3 months after Landslide
    Figure 5: 3D Model from the Merged DEM
    Figure 5: 3D Model from the Merged DEM
    Figure 7: Free Surface Elevation relative to local origin
    Figure 7: Free Surface Elevation relative to local origin
    Figure 8: Model of lake referenced over Google Earth Image
    Figure 8: Model of lake referenced over Google Earth Image
    Figure 9: Meshing in the 3D Terrain Model
    Figure 9: Meshing in the 3D Terrain Model
    Figure 10: Flow Depth Hydrographs of the downstream villages (A) Karim Abad (B) Ghulmet (C) Thol (D) Chalat (E) Nomal
    Figure 10: Flow Depth Hydrographs of the downstream villages (A) Karim Abad (B) Ghulmet (C) Thol (D) Chalat (E) Nomal
    Figure 11: Flow Hydrograph at Karim Abad and Nomal Bridge
    Figure 11: Flow Hydrograph at Karim Abad and Nomal Bridge
    Figure 12: Flood Inundation Map of Karim Abad
    Figure 12: Flood Inundation Map of Karim Abad
    Figure 13: Flood Inundation Map of Ghulmet
    Figure 13: Flood Inundation Map of Ghulmet
    Figure 14: Flood Inundation Map of Chalat
    Figure 14: Flood Inundation Map of Chalat
    Figure 15: Velocity Vectors of flow at Karim Abad
    Figure 15: Velocity Vectors of flow at Karim Abad
    Figure 16: Velocity Vectors of Flow at Ghulmet
    Figure 16: Velocity Vectors of Flow at Ghulmet
    Figure 17: Velocity Vectors of Flow at Chalat
    Figure 17: Velocity Vectors of Flow at Chalat

    REFERENCES

    [1]. Zhang, L. & Peng, M. & Chang, D.S. & Xu, Y. (2015).
    Dam Failure Mechanisms and Risk Assessment, First
    Ed. John Wiley and Sons, Singapore 473 pp.
    10.1002/9781118558522.
    [2]. T. L. Wahl, “Dam Breach Modeling – an Overview of
    Analysis Methods,” 2nd Jt. Fed. Interagency Conf. Las
    Vegas, NV, pp. 1–12, 2010.
    [3]. Khosravi K. “Dam Break Analysis and Flood
    Inundation Mapping : The Case Study of Sefid-Rud
    Dam,” no. August 2019. DOI:
    10.1016/B978-0-12-815998-9.00031-2
    [4]. Robb, D. M., & Vasquez, J. A. (2015). Numerical
    simulation of dam-break flows using depth-averaged
    hydrodynamic and three-dimensional CFD models.
    22nd Canadian Hydrotechnical Conference, (June).
    [5]. Mohammad Rostami, M. S. (2015). Human Life Saving
    by Simulation of Dam Break using Flow-3D. Trend in
    Life Sciences, 4(3), 308–316
    [6]. Gharbi, M., Soualmia, A., Dartus, D., & Masbernat, L.
    (2016). Comparison of 1D and 2D hydraulic models
    for floods simulation on the Medjerda River in
    Tunisia. Journal of Materials and Environmental
    Science, 7(8), 3017–3026. https://doi.org/10.1080/153
    [7]. Andrei, A., Robert, B., & Erika, B. (2017). Numerical
    Limitations of 1D Hydraulic Models Using MIKE11
    or HEC-RAS software – A case study of Baraolt
    River, Romania. IOP Conference Series: Materials
    Science and Engineering, 245(7).
    https://doi.org/10.1088/1757-899X/245/7/072010
    [8]. Henderson, F.M. (1966). Open Channel Flow. MacMillan
    Company, New York, USA, P. No 304-313
    [9]. Betsholtz, A., & Nordlöf, B. (2017). Potentials and
    limitations of 1D, 2D and coupled 1D-2D flood
    modeling in HEC-RAS. Lund University, 128.
    https://doi.org/10.1016/S0300-9440(03)00139-5
    [10].Ozmen-Cagatay, H., & Kocaman, S. (2011). Dam-break
    flow in the presence of obstacle: Experiment and CFD
    simulation. Engineering Applications of Computational
    Fluid Mechanics, 5(4), 541–552.
    https://doi.org/10.1080/19942060.2011.11015393
    [11].Toombes, L., & Chanson, H. (2011). Numerical
    Limitations of Hydraulic Models. 10th Hydraulics
    Conference, (July), 2322–2329.
    https://doi.org/10.1016/j.jalz.2016.06.1613
    [12].Zarein, M. (2015). Modeling Dam-Break Flows Using
    a 3d Mike 3 Flow Model, (January).
    [13].George, A. C., & Nair, B. T. (2015). Dam Break
    Analysis Using BOSS DAMBRK. Aquatic Procedia,
    4(Icwrcoe), 853–860.
    https://doi.org/10.1016/j.aqpro.2015.02.10
    [14].S. Roga and K. M. Pandey, “Computational Analysis of
    Supersonic Flow Regime Using Ramp Injector with
    Standard K- ω Turbulence Model” .World Academy of
    research in Science and Engineering, vol. 2, no. 1, pp.
    31–40, 2013.http:// doi.org/10.1.1.348.5862.

    원문 PDF 보기
    Fig. 5. The predicted shapes of initial breach (a) Rectangular (b) V-notch. Fig. 6. Dam breaching stages.

    Investigating the peak outflow through a spatial embankment dam breach

    공간적 제방댐 붕괴를 통한 최대 유출량 조사

    Mahmoud T.GhonimMagdy H.MowafyMohamed N.SalemAshrafJatwaryFaculty of Engineering, Zagazig University, Zagazig 44519, Egypt

    Abstract

    Investigating the breach outflow hydrograph is an essential task to conduct mitigation plans and flood warnings. In the present study, the spatial dam breach is simulated by using a three-dimensional computational fluid dynamics model, FLOW-3D. The model parameters were adjusted by making a comparison with a previous experimental model. The different parameters (initial breach shape, dimensions, location, and dam slopes) are studied to investigate their effects on dam breaching. The results indicate that these parameters have a significant impact. The maximum erosion rate and peak outflow for the rectangular shape are higher than those for the V-notch by 8.85% and 5%, respectively. Increasing breach width or decreasing depth by 5% leads to increasing maximum erosion rate by 11% and 15%, respectively. Increasing the downstream slope angle by 4° leads to an increase in both peak outflow and maximum erosion rate by 2.0% and 6.0%, respectively.

    유출 유출 수문곡선을 조사하는 것은 완화 계획 및 홍수 경보를 수행하는 데 필수적인 작업입니다. 본 연구에서는 3차원 전산유체역학 모델인 FLOW-3D를 사용하여 공간 댐 붕괴를 시뮬레이션합니다. 이전 실험 모델과 비교하여 모델 매개변수를 조정했습니다.

    다양한 매개변수(초기 붕괴 형태, 치수, 위치 및 댐 경사)가 댐 붕괴에 미치는 영향을 조사하기 위해 연구됩니다. 결과는 이러한 매개변수가 상당한 영향을 미친다는 것을 나타냅니다. 직사각형 형태의 최대 침식율과 최대 유출량은 V-notch보다 각각 8.85%, 5% 높게 나타났습니다.

    위반 폭을 늘리거나 깊이를 5% 줄이면 최대 침식률이 각각 11% 및 15% 증가합니다. 하류 경사각을 4° 증가시키면 최대 유출량과 최대 침식률이 각각 2.0% 및 6.0% 증가합니다.

    Keywords

    Spatial dam breach; FLOW-3D; Overtopping erosion; Computational fluid dynamics (CFD)

    1. Introduction

    There are many purposes for dam construction, such as protection from flood disasters, water storage, and power generation. Embankment failures may have a catastrophic impact on lives and infrastructure in the downstream regions. One of the most common causes of embankment dam failure is overtopping. Once the overtopping of the dam begins, the breach formation will start in the dam body then end with the dam failure. This failure occurs within a very short time, which threatens to be very dangerous. Therefore, understanding and modeling the embankment breaching processes is essential for conducting mitigation plans, flood warnings, and forecasting flood damage.

    The analysis of the dam breaching process is implemented by different techniques: comparative methods, empirical models with dimensional and dimensionless solutions, physical-based models, and parametric models. These models were described in detail [1]. Parametric modeling is commonly used to simulate breach growth as a time-dependent linear process and calculate outflow discharge from the breach using hydraulics principles [2]. Alhasan et al. [3] presented a simple one-dimensional mathematical model and a computer code to simulate the dam breaching process. These models were validated by small dams breaching during the floods in 2002 in the Czech Republic. Fread [4] developed an erosion model (BREACH) based on hydraulics principles, sediment transport, and soil mechanics to estimate breach size, time of formation, and outflow discharge. Říha et al. [5] investigated the dam break process for a cascade of small dams using a simple parametric model for piping and overtopping erosion, as well as a 2D shallow-water flow model for the flood in downstream areas. Goodarzi et al. [6] implemented mathematical and statistical methods to assess the effect of inflows and wind speeds on the dam’s overtopping failure.

    Dam breaching studies can be divided into two main modes of erosion. The first mode is called “planar dam breach” where the flow overtops the whole dam width. While the second mode is called “spatial dam breach” where the flow overtops through the initial pilot channel (i.e., a channel created in the dam body). Therefore, the erosion will be in both vertical and horizontal directions [7].

    The erosion process through the embankment dams occurs due to the shear stress applied by water flows. The dam breaching evolution can be divided into three stages [8], [9], but Y. Yang et al. [10] divided the breach development into five stages: Stage I, the seepage erosion; Stage II, the initial breach formation; Stage III, the head erosion; Stage IV, the breach expansion; and Stage V, the re-equilibrium of the river channel through the breach. Many experimental tests have been carried out on non-cohesive embankment dams with an initial breach to examine the effect of upstream inflow discharges on the longitudinal profile evolution and the time to inflection point [11].

    Zhang et al. [12] studied the effect of changing downstream slope angle, sediment grain size, and dam crest length on erosion rates. They noticed that increasing dam crest length and decreasing downstream slope angle lead to decreasing sediment transport rate. While the increase in sediment grain size leads to an increased sediment transport rate at the initial stages. Höeg et al. [13] presented a series of field tests to investigate the stability of embankment dams made of various materials. Overtopping and piping were among the failure tests carried out for the dams composed of homogeneous rock-fill, clay, or gravel with a height of up to 6.0 m. Hakimzadeh et al. [14] constructed 40 homogeneous cohesive and non-cohesive embankment dams to study the effect of changing sediment diameter and dam height on the breaching process. They also used genetic programming (GP) to estimate the breach outflow. Refaiy et al. [15] studied different scenarios for the downstream drain geometry, such as length, height, and angle, to minimize the effect of piping phenomena and therefore increase dam safety.

    Zhu et al. [16] examined the effect of headcut erosion on dam breach growth, especially in the case of cohesive dams. They found that the breach growth in non-cohesive embankments is slower than cohesive embankments due to the little effect of headcut. Schmocker and Hager [7] proposed a relationship for estimating peak outflow from the dam breach process.(1)QpQin-1=1.7exp-20hc23d5013H0

    where: Qp = peak outflow discharge.

    Qin = inflow discharge.

    hc = critical flow depth.

    d50 = mean sediment diameter.

    Ho = initial dam height.

    Yu et al. [17] carried out an experimental study for homogeneous non-cohesive embankment dams in a 180° bending rectangular flume to determine the effect of overtopping flows on breaching formation. They found that the main factors influencing breach formation are water level, river discharge, and embankment material diameter.

    Wu et al. [18] carried out a series of experiments to investigate the effect of breaching geometry on both non-cohesive and cohesive embankment dams in a U-bend flume due to overtopping flows. In the case of non-cohesive embankments, the non-symmetrical lateral expansion was noticed during the breach formation. This expansion was described by a coefficient ranging from 2.7 to 3.3.

    The numerical models of the dam breach can be categorized according to different parameters, such as flow dimensions (1D, 2D, or 3D), flow governing equations, and solution methods. The 1D models are mainly used to predict the outflow hydrograph from the dam breach. Saberi et al. [19] applied the 1D Saint-Venant equation, which is solved by the finite difference method to investigate the outflow hydrograph during dam overtopping failure. Because of the ability to study dam profile evolution and breach formation, 2D models are more applicable than 1D models. Guan et al. [20] and Wu et al. [21] employed both 2D shallow water equations (SWEs) and sediment erosion equations, which are solved by the finite volume method to study the effect of the dam’s geometry parameters on outflow hydrograph and dam profile evolution. Wang et al. [22] also proposed a second-order hybrid-type of total variation diminishing (TVD) finite-difference to estimate the breach outflow by solving the 2D (SWEs). The accuracy of (SWEs) for both vertical flow contraction and surface roughness has been assessed [23]. They noted that the accuracy of (SWEs) is acceptable for milder slopes, but in the case of steeper slopes, modelers should be more careful. Generally, the accuracy of 2D models is still low, especially with velocity distribution over the flow depth, lateral momentum exchange, density-driven flows, and bottom friction [24]. Therefore, 3D models are preferred. Larocque et al. [25] and Yang et al. [26] started to use three-dimensional (3D) models that depend on the Reynolds-averaged Navier-Stokes (RANS) equations.

    Previous experimental studies concluded that there is no clear relationship between the peak outflow from the dam breach and the initial breach characteristics. Some of these studies depend on the sharp-crested weir fixed at the end of the flume to determine the peak outflow from the breach, which leads to a decrease in the accuracy of outflow calculations at the microscale. The main goals of this study are to carry out a numerical simulation for a spatial dam breach due to overtopping flows by using (FLOW-3D) software to find an empirical equation for the peak outflow discharge from the breach and determine the worst-case that leads to accelerating the dam breaching process.

    2. Numerical simulation

    The current study for spatial dam breach is simulated by using (FLOW-3D) software [27], which is a powerful computational fluid dynamics (CFD) program.

    2.1. Geometric presentations

    A stereolithographic (STL) file is prepared for each change in the initial breach geometry and dimensions. The CAD program is useful for creating solid objects and converting them to STL format, as shown in Fig. 1.

    2.2. Governing equations

    The governing equations for water flow are three-dimensional Reynolds Averaged Navier-Stokes equations (RANS).

    The continuity equation:(2)∂ui∂xi=0

    The momentum equation:(3)∂ui∂t+1VFuj∂ui∂xj=1ρ∂∂xj-pδij+ν∂ui∂xj+∂uj∂xi-ρu`iu`j¯

    where u is time-averaged velocity,ν is kinematic viscosity, VF is fractional volume open to flow, p is averaged pressure and -u`iu`j¯ are components of Reynold’s stress. The Volume of Fluid (VOF) technique is used to simulate the free surface profile. Hirt et al. [28] presented the VOF algorithm, which employs the function (F) to express the occupancy of each grid cell with fluid. The value of (F) varies from zero to unity. Zero value refers to no fluid in the grid cell, while the unity value refers to the grid cell being fully occupied with fluid. The free surface is formed in the grid cells having (F) values between zero and unity.(4)∂F∂t+1VF∂∂xFAxu+∂∂yFAyv+∂∂zFAzw=0

    where (u, v, w) are the velocity components in (x, y, z) coordinates, respectively, and (Ax, Ay, Az) are the area fractions.

    2.3. Boundary and initial conditions

    To improve the accuracy of the results, the boundary conditions should be carefully determined. In this study, two mesh blocks are used to minimize the time consumed in the simulation. The boundary conditions for mesh block 1 are as follows: The inlet and sides boundaries are defined as a wall boundary condition (wall boundary condition is usually used for bound fluid by solid regions. In the case of viscous flows, no-slip means that the tangential velocity is equal to the wall velocity and the normal velocity is zero), the outlet is defined as a symmetry boundary condition (symmetry boundary condition is usually used to reduce computational effort during CFD simulation. This condition allows the flow to be transferred from one mesh block to another. No inputs are required for this boundary condition except that its location should be defined accurately), the bottom boundary is defined as a uniform flow rate boundary condition, and the top boundary is defined as a specific pressure boundary condition with assigned atmospheric pressure. The boundary conditions for mesh block 2 are as follows: The inlet is defined as a symmetry boundary condition, the outlet is defined as a free flow boundary condition, the bottom and sides boundaries are defined as a wall boundary condition, and the top boundary is defined as a specific pressure boundary condition with assigned atmospheric pressure as shown in Fig. 2. The initial conditions required to be set for the fluid (i.e., water) inside of the domain include configuration, temperature, velocities, and pressure distribution. The configuration of water depends on the dimensions and shape of the dam reservoir. While the other conditions have been assigned as follows: temperature is normal water temperature (25 °c) and pressure distribution is hydrostatic with no initial velocity.

    2.4. Numerical method

    FLOW-3D uses the finite volume method (FVM) to solve the governing equation (Reynolds-averaged Navier-Stokes) over the computational domain. A finite-volume method is an Eulerian approach for representing and evaluating partial differential equations in algebraic equations form [29]. At discrete points on the mesh geometry, values are determined. Finite volume expresses a small volume surrounding each node point on a mesh. In this method, the divergence theorem is used to convert volume integrals with a divergence term to surface integrals. After that, these terms are evaluated as fluxes at each finite volume’s surfaces.

    2.5. Turbulent models

    Turbulence is the chaotic, unstable motion of fluids that occurs when there are insufficient stabilizing viscous forces. In FLOW-3D, there are six turbulence models available: the Prandtl mixing length model, the one-equation turbulent energy model, the two-equation (k – ε) model, the Renormalization-Group (RNG) model, the two-equation (k – ω) models, and a large eddy simulation (LES) model. For simulating flow motion, the RNG model is adopted to simulate the motion behavior better than the k – ε and k – ω.

    models [30]. The RNG model consists of two main equations for the turbulent kinetic energy KT and its dissipation.εT(5)∂kT∂t+1VFuAx∂kT∂x+vAy∂kT∂y+wAz∂kT∂z=PT+GT+DiffKT-εT(6)∂εT∂t+1VFuAx∂εT∂x+vAy∂εT∂y+wAz∂εT∂z=C1.εTKTPT+c3.GT+Diffε-c2εT2kT

    where KT is the turbulent kinetic energy, PT is the turbulent kinetic energy production, GT is the buoyancy turbulence energy, εT is the turbulent energy dissipation rate, DiffKT and Diffε are terms of diffusion, c1, c2 and c3 are dimensionless parameters, in which c1 and c3 have a constant value of 1.42 and 0.2, respectively, c2 is computed from the turbulent kinetic energy (KT) and turbulent production (PT) terms.

    2.6. Sediment scour model

    The sediment scour model available in FLOW-3D can calculate all the sediment transport processes including Entrainment transport, Bedload transport, Suspended transport, and Deposition. The erosion process starts once the water flows remove the grains from the packed bed and carry them into suspension. It happens when the applied shear stress by water flows exceeds critical shear stress. This process is represented by entrainment transport in the numerical model. After entrained, the grains carried by water flow are represented by suspended load transport. After that, some suspended grains resort to settling because of the combined effect of gravity, buoyancy, and friction. This process is described through a deposition. Finally, the grains sliding motions are represented by bedload transport in the model. For the entrainment process, the shear stress applied by the fluid motion on the packed bed surface is calculated using the standard wall function as shown in Eq.7.(7)ks,i=Cs,i∗d50

    where ks,i is the Nikuradse roughness and Cs,i is a user-defined coefficient. The critical bed shear stress is defined by a dimensionless parameter called the critical shields number as expressed in Eq.8.(8)θcr,i=τcr,i‖g‖diρi-ρf

    where θcr,i is the critical shields number, τcr,i is the critical bed shear stress, g is the absolute value of gravity acceleration, di is the diameter of the sediment grain, ρi is the density of the sediment species (i) and ρf is the density of the fluid. The value of the critical shields number is determined according to the Soulsby-Whitehouse equation.(9)θcr,i=0.31+1.2d∗,i+0.0551-exp-0.02d∗,i

    where d∗,i is the dimensionless diameter of the sediment, given by Eq.10.(10)d∗,i=diρfρi-ρf‖g‖μf213

    where μf is the fluid dynamic viscosity. For the sloping bed interface, the value of the critical shields number is modified according to Eq.11.(11)θ`cr,i=θcr,icosψsinβ+cos2βtan2φi-sin2ψsin2βtanφi

    where θ`cr,i is the modified critical shields number, φi is the angle of repose for the sediment, β is the angle of bed slope and ψ is the angle between the flow and the upslope direction. The effects of the rolling, hopping, and sliding motions of grains along the packed bed surface are taken by the bedload transport process. The volumetric bedload transport rate (qb,i) per width of the bed is expressed in Eq.12.(12)qb,i=Φi‖g‖ρi-ρfρfdi312

    where Φi is the dimensionless bedload transport rate is calculated by using Meyer Peter and Müller equation.(13)Φi=βMPM,iθi-θ`cr,i1.5cb,i

    where βMPM,i is the Meyer Peter and Müller user-defined coefficient and cb,i is the volume fraction of species i in the bed material. The suspended load transport is calculated as shown in Eq.14.(14)∂Cs,i∂t+∇∙Cs,ius,i=∇∙∇DCs,i

    where Cs,i is the suspended sediment mass concentration, D is the diffusivity, and us,i is the grain velocity of species i. Entrainment and deposition are two opposing processes that take place at the same time. The lifting and settling velocities for both entrainment and deposition processes are calculated according to Eq.15 and Eq.16, respectively.(15)ulifting,i=αid∗,i0.3θi-θ`cr,igdiρiρf-1(16)usettling,i=υfdi10.362+1.049d∗,i3-10.36

    where αi is the entrainment coefficient of species i and υf is the kinematic viscosity of the fluid.

    2.7. Grid type

    Using simple rectangular orthogonal elements in planes and hexahedral in volumes in the (FLOW-3D) program makes the mesh generation process easier, decreases the required memory, and improves numerical accuracy. Two mesh blocks were used in a joined form with a size ratio of 2:1. The first mesh block is coarser, which contains the reservoir water, and the second mesh block is finer, which contains the dam. For achieving accuracy and efficiency in results, the mesh size is determined by using a grid convergence test. The optimum uniform cell size for the first mesh block is 0.012 m and for the second mesh block is 0.006 m.

    2.8. Time step

    The maximum time step size is determined by using a Courant number, which controls the distance that the flow will travel during the simulation time step. In this study, the Courant number was taken equal to 0.25 to prevent the flow from traveling through more than one cell in the time step. Based on the Courant number, a maximum time step value of 0.00075 s was determined.

    2.9. Numerical model validation

    The numerical model accuracy was achieved by comparing the numerical model results with previous experimental results. The experimental study of Schmocker and Hager [7] was based on 31 tests with changes in six parameters (d50, Ho, Bo, Lk, XD, and Qin). All experimental tests were conducted in a straight open glass-sided flume. The horizontal flume has a rectangular cross-section with a width of 0.4 m and a height of 0.7 m. The flume was provided with a flow straightener and an intake with a length of 0.66 m. All tested dams were inserted at various distances (XD) from the intake. Test No.1 from this experimental program was chosen to validate the numerical model. The different parameters used in test No.1 are as follows:

    (1) uniform sediment with a mean diameter (d50 = 0.31 mm), (2) Ho = 0.2 m, (3) Bo = 0.2 m, (4) Lk = 0.1 m,

    (5) XD = 1.0 m, (6) Qin = 6.0 lit/s, (7) Su and Sd = 2:1, (8) mass density (ρs = 2650 kg/m3) (9) Homogenous and non-cohesive embankment dam. As shown in Fig. 2, the simulation is contained within a rectangular grid with dimensions: 3.56 m in the x-direction (where 0.66 m is used as inlet, 0.9 m as dam base width, and 1.0 m as outlet), in y-direction 0.2 m (dam length), and in the z-direction 0.3 m, which represents the dam height (0.2 m) with a free distance (0.1 m) above the dam. There are two main reasons that this experimental program is preferred for the validation process. The first reason is that this program deals with homogenous, non-cohesive soil, which is available in FLOW-3D. The second reason is that this program deals with small-scale models which saves time for numerical simulation. Finally, some important assumptions were considered during the validation process. The flow is assumed to be incompressible, viscous, turbulent, and three-dimensional.

    By comparing dam profiles at different time instants for the experimental test with the current numerical model, it appears that the numerical model gives good agreement as shown in Fig. 3 and Fig. 4, with an average error percentage of 9% between the experimental results and the numerical model.

    3. Analysis and discussions

    The current model is used to study the effects of different parameters such as (initial breach shapes, dimensions, locations, upstream and downstream dam slopes) on the peak outflow discharge, QP, time of peak outflow, tP, and rate of erosion, E.

    This study consists of a group of scenarios. The first scenario is changing the shapes of the initial breach according to Singh [1], the most predicted shapes are rectangular and V-notch as shown in Fig. 5. The second scenario is changing the initial breach dimensions (i.e., width and depth). While the third scenario is changing the location of the initial breach. Eventually, the last scenario is changing the upstream and downstream dam slopes.

    All scenarios of this study were carried out under the same conditions such as inflow discharge value (Qin=1.0lit/s), dimensions of the tested dam, where dam height (Ho=0.20m), crest width.

    (Lk=0.1m), dam length (Bo=0.20m), and homogenous & non-cohesive soil with a mean diameter (d50=0.31mm).

    3.1. Dam breaching process evolution

    The dam breaching process is a very complex process due to the quick changes in hydrodynamic conditions during dam failure. The dam breaching process starts once water flows reach the downstream face of the dam. During the initial stage of dam breaching, the erosion process is relatively quiet due to low velocities of flow. As water flows continuously, erosion rates increase, especially in two main zones: the crest and the downstream face. As soon as the dam crest is totally eroded, the water levels in the dam reservoir decrease rapidly, accompanied by excessive erosion in the dam body. The erosion process continues until the water levels in the dam reservoir equal the remaining height of the dam.

    According to Zhou et al. [11], the breaching process consists of three main stages. The first stage starts with beginning overtopping flow, then ends when the erosion point directed upstream and reached the inflection point at the inflection time (ti). The second stage starts from the end of the stage1 until the occurrence of peak outflow discharge at the peak outflow time (tP). The third stage starts from the end of the stage2 until the value of outflow discharge becomes the same as the value of inflow discharge at the final time (tf). The outflow discharge from the dam breach increases rapidly during stage1 and stage2 because of the large dam storage capacity (i.e., the dam reservoir is totally full of water) and excessive erosion. While at stage3, the outflow values start to decrease slowly because most of the dam’s storage capacity was run out. The end of stage3 indicates that the dam storage capacity was totally run out, so the outflow equalized with the inflow discharge as shown in Fig. 6 and Fig. 7.

    3.2. The effect of initial breach shape

    To identify the effect of the initial breach shape on the evolution of the dam breaching process. Three tests were carried out with different cross-section areas for each shape. The initial breach is created at the center of the dam crest. Each test had an ID to make the process of arranging data easier. The rectangular shape had an ID (Rec5h & 5b), which means that its depth and width are equal to 5% of the dam height, and the V-notch shape had an ID (V-noch5h & 1:1) which means that its depth is equal to 5% of the dam height and its side slope is equal to 1:1. The comparison between rectangular and V-notch shapes is done by calculating the ratio between maximum dam height at different times (ZMax) to the initial dam height (Ho), rate of erosion, and hydrograph of outflow discharge for each test. The rectangular shape achieves maximum erosion rate and minimum inflection time, in addition to a rapid decrease in the dam reservoir levels. Therefore, the dam breaching is faster in the case of a rectangular shape than in a V-notch shape, which has the same cross-section area as shown in Fig. 8.

    Also, by comparing the hydrograph for each test, the peak outflow discharge value in the case of a rectangular shape is higher than the V-notch shape by 5% and the time of peak outflow for the rectangular shape is shorter than the V-notch shape by 9% as shown in Fig. 9.

    3.3. The effect of initial breach dimensions

    The results of the comparison between the different initial breach shapes indicate that the worst initial breach shape is rectangular, so the second scenario from this study concentrated on studying the effect of a change in the initial rectangular breach dimensions. Groups of tests were carried out with different depths and widths for the rectangular initial breach. The first group had a depth of 5% from the dam height and with three different widths of 5,10, and 15% from the dam height, the second group had a depth of 10% with three different widths of 5,10, and 15%, the third group had a depth of 15% with three different widths of 5,10, and 15% and the final group had a width of 15% with three different heights of 5, 10, and 15% for a rectangular breach shape. The comparison was made as in the previous section to determine the worst case that leads to the quick dam failure as shown in Fig. 10.

    The results show that the (Rec 5 h&15b) test achieves a maximum erosion rate for a shorter period of time and a minimum ratio for (Zmax / Ho) as shown in Fig. 10, which leads to accelerating the dam failure process. The dam breaching process is faster with the minimum initial breach depth and maximum initial breach width. In the case of a minimum initial breach depth, the retained head of water in the dam reservoir is high and the crest width at the bottom of the initial breach (L`K) is small, so the erosion point reaches the inflection point rapidly. While in the case of the maximum initial breach width, the erosion perimeter is large.

    3.4. The effect of initial breach location

    The results of the comparison between the different initial rectangular breach dimensions indicate that the worst initial breach dimension is (Rec 5 h&15b), so the third scenario from this study concentrated on studying the effect of a change in the initial breach location. Three locations were checked to determine the worst case for the dam failure process. The first location is at the center of the dam crest, which was named “Center”, the second location is at mid-distance between the dam center and dam edge, which was named “Mid”, and the third location is at the dam edge, which was named “Edge” as shown in Fig. 11. According to this scenario, the results indicate that the time of peak outflow discharge (tP) is the same in the three cases, but the maximum value of the peak outflow discharge occurs at the center location. The difference in the peak outflow values between the three cases is relatively small as shown in Fig. 12.

    The rates of erosion were also studied for the three cases. The results show that the maximum erosion rate occurs at the center location as shown in Fig. 13. By making a comparison between the three cases for the dam storage volume. The results show that the center location had the minimum values for the dam storage volume, which means that a large amount of water has passed to the downstream area as shown in Fig. 14. According to these results, the center location leads to increased erosion rate and accelerated dam failure process compared with the two other cases. Because the erosion occurs on both sides, but in the case of edge location, the erosion occurs on one side.

    3.5. The effect of upstream and downstream dam slopes

    The results of the comparison between the different initial rectangular breach locations indicate that the worst initial breach location is the center location, so the fourth scenario from this study concentrated on studying the effect of a change in the upstream (Su) and downstream (Sd) dam slopes. Three slopes were checked individually for both upstream and downstream slopes to determine the worst case for the dam failure process. The first slope value is (2H:1V), the second slope value is (2.5H:1V), and the third slope value is (3H:1V). According to this scenario, the results show that the decreasing downstream slope angle leads to increasing time of peak outflow discharge (tP) and decreasing value of peak outflow discharge. The difference in the peak outflow values between the three cases for the downstream slope is 2%, as shown in Fig. 15, but changing the upstream slope has a negligible impact on the peak outflow discharge and its time as shown in Fig. 16.

    The rates of erosion were also studied in the three cases for both upstream and downstream slopes. The results show that the maximum erosion rate increases by 6.0% with an increasing downstream slope angle by 4°, as shown in Fig. 17. The results also indicate that the erosion rates aren’t affected by increasing or decreasing the upstream slope angle, as shown in Fig. 18. According to these results, increasing the downstream slope angle leads to increased erosion rate and accelerated dam failure process compared with the upstream slope angle. Because of increasing shear stress applied by water flows in case of increasing downstream slope.

    According to all previous scenarios, the dimensionless peak outflow discharge QPQin is presented for a fixed dam height (Ho) and inflow discharge (Qin). Fig. 19 illustrates the relationship between QP∗=QPQin and.

    Lr=ho2/3∗bo2/3Ho. The deduced relationship achieves R2=0.96.(17)QP∗=2.2807exp-2.804∗Lr

    4. Conclusions

    A spatial dam breaching process was simulated by using FLOW-3D Software. The validation process was performed by making a comparison between the simulated results of dam profiles and the dam profiles obtained by Schmocker and Hager [7] in their experimental study. And also, the peak outflow value recorded an error percentage of 12% between the numerical model and the experimental study. This model was used to study the effect of initial breach shape, dimensions, location, and dam slopes on peak outflow discharge, time of peak outflow, and the erosion process. By using the parameters obtained from the validation process, the results of this study can be summarized in eight points as follows.1.

    The rectangular initial breach shape leads to an accelerating dam failure process compared with the V-notch.2.

    The value of peak outflow discharge in the case of a rectangular initial breach is higher than the V-notch shape by 5%.3.

    The time of peak outflow discharge for a rectangular initial breach is shorter than the V-notch shape by 9%.4.

    The minimum depth and maximum width for the initial breach achieve maximum erosion rates (increasing breach width, b0, or decreasing breach depth, h0, by 5% from the dam height leads to an increase in the maximum rate of erosion by 11% and 15%, respectively), so the dam failure is rapid.5.

    The center location of the initial breach leads to an accelerating dam failure compared with the edge location.6.

    The initial breach location has a negligible effect on the peak outflow discharge value and its time.7.

    Increasing the downstream slope angle by 4° leads to an increase in both peak outflow discharge and maximum rate of erosion by 2.0% and 6.0%, respectively.8.

    The upstream slope has a negligible effect on the dam breaching process.

    References

    [1]V. SinghDam breach modeling technologySpringer Science & Business Media (1996)Google Scholar[2]Wahl TL. Prediction of embankment dam breach parameters: a literature review and needs assessment. 1998.Google Scholar[3]Z. Alhasan, J. Jandora, J. ŘíhaStudy of dam-break due to overtopping of four small dams in the Czech RepublicActa Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 63 (3) (2015), pp. 717-729 View PDFCrossRefView Record in ScopusGoogle Scholar[4]D. FreadBREACH, an erosion model for earthen dam failures: Hydrologic Research LaboratoryNOAA, National Weather Service (1988)Google Scholar[5]J. Říha, S. Kotaška, L. PetrulaDam Break Modeling in a Cascade of Small Earthen Dams: Case Study of the Čižina River in the Czech RepublicWater, 12 (8) (2020), p. 2309, 10.3390/w12082309 View PDFView Record in ScopusGoogle Scholar[6]E. Goodarzi, L. Teang Shui, M. ZiaeiDam overtopping risk using probabilistic concepts–Case study: The Meijaran DamIran Ain Shams Eng J, 4 (2) (2013), pp. 185-197ArticleDownload PDFView Record in ScopusGoogle Scholar[7]L. Schmocker, W.H. HagerPlane dike-breach due to overtopping: effects of sediment, dike height and dischargeJ Hydraul Res, 50 (6) (2012), pp. 576-586 View PDFCrossRefView Record in ScopusGoogle Scholar[8]J.S. Walder, R.M. Iverson, J.W. Godt, M. Logan, S.A. SolovitzControls on the breach geometry and flood hydrograph during overtopping of noncohesive earthen damsWater Resour Res, 51 (8) (2015), pp. 6701-6724View Record in ScopusGoogle Scholar[9]H. Wei, M. Yu, D. Wang, Y. LiOvertopping breaching of river levees constructed with cohesive sedimentsNat Hazards Earth Syst Sci, 16 (7) (2016), pp. 1541-1551 View PDFCrossRefView Record in ScopusGoogle Scholar[10]Y. Yang, S.-Y. Cao, K.-J. Yang, W.-P. LiYang K-j, Li W-p. Experimental study of breach process of landslide dams by overtopping and its initiation mechanismsJ Hydrodynamics, 27 (6) (2015), pp. 872-883ArticleDownload PDFCrossRefView Record in ScopusGoogle Scholar[11]G.G.D. Zhou, M. Zhou, M.S. Shrestha, D. Song, C.E. Choi, K.F.E. Cui, et al.Experimental investigation on the longitudinal evolution of landslide dam breaching and outburst floodsGeomorphology, 334 (2019), pp. 29-43ArticleDownload PDFView Record in ScopusGoogle Scholar[12]J. Zhang, Z.-x. Guo, S.-y. CaoYang F-g. Experimental study on scour and erosion of blocked damWater Sci Eng, 5 (2012), pp. 219-229ArticleDownload PDFView Record in ScopusGoogle Scholar[13]K. Höeg, A. Løvoll, K. VaskinnStability and breaching of embankment dams: Field tests on 6 m high damsInt J Hydropower Dams, 11 (2004), pp. 88-92View Record in ScopusGoogle Scholar[14]H. Hakimzadeh, V. Nourani, A.B. AminiGenetic programming simulation of dam breach hydrograph and peak outflow dischargeJ Hydrol Eng, 19 (4) (2014), pp. 757-768View Record in ScopusGoogle Scholar[15]A.R. Refaiy, N.M. AboulAtta, N.Y. Saad, D.A. El-MollaModeling the effect of downstream drain geometry on seepage through earth damsAin Shams Eng J, 12 (3) (2021), pp. 2511-2531ArticleDownload PDFView Record in ScopusGoogle Scholar[16]Y. Zhu, P.J. Visser, J.K. Vrijling, G. WangExperimental investigation on breaching of embankmentsScience China Technological Sci, 54 (1) (2011), pp. 148-155 View PDFCrossRefView Record in ScopusGoogle Scholar[17]M.-H. Yu, H.-Y. Wei, Y.-J. Liang, Y. ZhaoInvestigation of non-cohesive levee breach by overtopping flowJ Hydrodyn, 25 (4) (2013), pp. 572-579ArticleDownload PDFCrossRefView Record in ScopusGoogle Scholar[18]S. Wu, M. Yu, H. Wei, Y. Liang, J. ZengNon-symmetrical levee breaching processes in a channel bend due to overtoppingInt J Sedim Res, 33 (2) (2018), pp. 208-215ArticleDownload PDFView Record in ScopusGoogle Scholar[19]O. Saberi, G. ZenzNumerical investigation on 1D and 2D embankment dams failure due to overtopping flowInt J Hydraulic Engineering, 5 (2016), pp. 9-18View Record in ScopusGoogle Scholar[20]M. Guan, N.G. Wright, P.A. Sleigh2D Process-Based Morphodynamic Model for Flooding by Noncohesive Dyke BreachJ Hydraul Eng, 140 (7) (2014), p. 04014022, 10.1061/(ASCE)HY.1943-7900.0000861 View PDFView Record in ScopusGoogle Scholar[21]W. Wu, R. Marsooli, Z. HeDepth-Averaged Two-Dimensional Model of Unsteady Flow and Sediment Transport due to Noncohesive Embankment Break/BreachingJ Hydraul Eng, 138 (6) (2012), pp. 503-516View Record in ScopusGoogle Scholar[22]Z. Wang, D.S. BowlesThree-dimensional non-cohesive earthen dam breach model. Part 1: Theory and methodologyAdv Water Resour, 29 (10) (2006), pp. 1528-1545ArticleDownload PDFView Record in ScopusGoogle Scholar[23]Říha J, Duchan D, Zachoval Z, Erpicum S, Archambeau P, Pirotton M, et al. Performance of a shallow-water model for simulating flow over trapezoidal broad-crested weirs. J Hydrology Hydromechanics. 2019;67:322-8.Google Scholar[24]C.B. VreugdenhilNumerical methods for shallow-water flowSpringer Science & Business Media (1994)Google Scholar[25]L.A. Larocque, J. Imran, M.H. Chaudhry3D numerical simulation of partial breach dam-break flow using the LES and k–∊ turbulence modelsJ Hydraul Res, 51 (2) (2013), pp. 145-157 View PDFCrossRefView Record in ScopusGoogle Scholar[26]C. Yang, B. Lin, C. Jiang, Y. LiuPredicting near-field dam-break flow and impact force using a 3D modelJ Hydraul Res, 48 (6) (2010), pp. 784-792 View PDFCrossRefView Record in ScopusGoogle Scholar[27]FLOW-3D. Version 11.1.1 Flow Science, Inc., Santa Fe, NM. https://wwwflow3dcom.Google Scholar[28]C.W. Hirt, B.D. NicholsVolume of fluid (VOF) method for the dynamics of free boundariesJ Comput Phys, 39 (1) (1981), pp. 201-225ArticleDownload PDFGoogle Scholar[29]S.V. PatankarNumerical heat transfer and fluid flow, Hemisphere PublCorp, New York, 58 (1980), p. 288View Record in ScopusGoogle Scholar[30]M. Alemi, R. MaiaNumerical simulation of the flow and local scour process around single and complex bridge piersInt J Civil Eng, 16 (5) (2018), pp. 475-487 View PDFCrossRefView Record in ScopusGoogle Scholar

    PDF 전체 원문 보기
    Figure 1 Location map of barrier lakes, Sichuan-Tibet region, China

    Barrier Lake의 홍수 침수 진행 및 평가지역 생태 시공간 반응 사례 연구 (쓰촨-티베트 지역)

    Flood Inundation Evolution of Barrier Lake and Evaluation of Regional Ecological Spatiotemporal Response — A Case Study of Sichuan-Tibet Region

    Abstract

    중국 쓰촨-티베트 지역은 댐 호수의 발생과 붕괴를 동반한 지진 재해가 빈번한 지역이었습니다. 댐 호수의 붕괴는 하류 직원의 생명과 재산 안전을 심각하게 위협합니다.

    동시에 국내외 학자들은 주변의 댐 호수에 대해 우려하고 있으며 호수에 대한 생태 연구는 거의 없으며 댐 호수가 생태에 미치는 영향은 우리 호수 건설 프로젝트에서 매우 중요한 계몽 의의를 가지고 있습니다.

    이 기사의 목적은 방벽호의 댐 붕괴 위험을 과학적으로 예측하고 생태 환경에 대한 영향을 조사하며 통제 조치를 제시하는 것입니다. 본 논문은 쓰촨-티베트 지역의 Diexihaizi, Tangjiashan 댐호, Hongshihe 댐의 4대 댐 호수 사건을 기반으로 원격 감지 이미지에서 수역을 추출하고 HEC-RAS 모델을 사용하여 위험이 있는지 여부를 결정합니다.

    댐 파손 여부 및 댐의 경로 예측; InVEST 모델을 이용하여 1990년부터 2020년까지 가장 작은 행정 구역(군/구)이 위치한 서식지를 평가 및 분석하고, 홍수 침수 결과를 기반으로 평가합니다. 결과는 공학적 처리 후 안정적인 댐 호수(Diexi Haizi)가 서식지 품질 지수에 안정화 효과가 있음을 보여줍니다.

    댐 호수의 형성은 인근 토지 이용 유형과 지역 경관 생태 패턴을 변화 시켰습니다. 서식지 품질 지수는 사이 호수 주변 1km 지역에서 약간 감소하지만 3km 지역과 5km 지역에서 서식지 품질이 향상됩니다. 인공 홍수 방류 및 장벽 호수의 공학적 보강이 필요합니다.

    이 논문에서 인간의 통제가 강한 지역은 다른 지역의 서식지 질 지수보다 더 잘 회복될 것입니다.

    The Sichuan-Tibet region of China has always been an area with frequent earthquake disasters, accompanied by the occurrence and collapse of dammed lakes. The collapse of dammed lakes seriously threatens the lives and property safety of downstream personnel.

    At the same time, domestic and foreign scholars are concerned about the surrounding dammed lake there are few ecological studies on the lake, and the impact of the dammed lake on the ecology has very important enlightenment significance for our lake construction project. It is the purpose of this article to scientifically predict the risk of dam break in a barrier lake, explore its impact on the ecological environment and put forward control measures.

    Based on the four major dammed lake events of Diexihaizi, Tangjiashan dammed lake, and Hongshihe dammed lake in the Sichuan-Tibet area, this paper extracts water bodies from remote sensing images and uses the HEC-RAS model to determine whether there is a risk of the dam break and whether Forecast the route of the dam; and use the InVEST model to evaluate and analyze the habitat of the smallest administrative district (county/district) where it is located from 1990 to 2020 and make an evaluation based on the results of flood inundation.

    The results show that the stable dammed lake (Diexi Haizi) after engineering treatment has a stabilizing effect on the habitat quality index. The formation of the dammed lake has changed the nearby land-use types and the regional landscape ecological pattern.

    The habitat quality index will decrease slightly in the 1 km area around Sai Lake, but the habitat quality will increase in the 3 km area and the 5 km area. Artificial flood discharge and engineering reinforcement of barrier lakes are necessary. In this paper, the areas with strong human control will recover better than other regions’ habitat quality index.

    Fengshan Jiang (  florachaing@mail.ynu.edu.cn )
    Yunnan University https://orcid.org/0000-0001-6231-6180
    Xiaoai Dai
    Chengdu University of Technology https://orcid.org/0000-0003-1342-6417
    Zhiqiang Xie
    Yunnan University
    Tong Xu
    Yunnan University
    Siqiao Yin
    Yunnan University
    Ge Qu
    Chengdu University of Technology
    Shouquan Yang
    Yunnan University
    Yangbin Zhang
    Yunnan University
    Zhibing Yang
    Yunnan University
    Jiarui Xu
    Yunnan University
    Zhiqun Hou
    Kunming institute of surveying and mapping

    Keywords

    dammed lake, regional ecology, flood simulation, habitat quality

    Figure 1 Location map of barrier lakes, Sichuan-Tibet region, China
    Figure 1 Location map of barrier lakes, Sichuan-Tibet region, China
    Figure 8 Habitat quality changes in Maoxian County
    Figure 8 Habitat quality changes in Maoxian County
    Figure 9 Habitat quality changes in Beichuan County
    Figure 9 Habitat quality changes in Beichuan County
    Figure 10 Habitat quality change map of Qingchuan County
    Figure 10 Habitat quality change map of Qingchuan County

    References

    1. Chaoying Hu H S, Tianming Zhang. 2017. Environmental impact assessment of barrier lake treatment project based on
      ecological footprint[J]. People’s Yangtze River, 48: 30-32
    2. Dai F C, Lee C F, Deng J H, et al. 2004. The 1786 earthquake-triggered landslide dam and subsequent dam-break flood on
      the Dadu River, southwestern China[J]. Geomorphology, 65.
    3. Dongjing Chen Z X 2002. Research on Ecological Security Evaluation of Inland River Basin in Northwest China——A Case
      Study of Zhangye Region in the Middle Reaches of Heihe River Basin[J]. Arid zone geography: 219-224
    4. Dongsheng Chang L Z, Yao Xu, Runqiu Huang. 2009. Risk Assessment of Overtopping Dam Burst in Hongshi River Barrier
      Lake[J]. Journal of Engineering Geology, 17: 50-55
    5. Fan X, Yunus Ali P, Jansen John D, et al. 2019. Comment on ‘Gigantic rockslides induced by fluvial incision in the Diexi
      area along the eastern margin of the Tibetan Plateau’ by Zhao et al. (2019) Geomorphology 338, 27–42[J].
      Geomorphology.
    6. Feng Yu X L, Hong Wang, Hongjing Yu. 2006. Land Use Change and Ecological Security Evaluation in Huangfuchuan
      Watershed[J]. Acta Geographica Sinica: 645-653.
    7. Hafiyyan Q, Adityawan M B, Harlan D, et al. 2021. Comparison of Taylor Galerkin and FTCS models for dam-break
      simulation[J]. IOP Conference Series: Earth and Environmental Science, 737.
    8. Haiwen Li X B 2020. Comprehensive Evaluation of the Restoration Status of Damaged Ecological Space along the
      Plateau Fragile Area of the Sichuan-Tibet Railway[J]. Journal of Railway Science and Engineering, 17: 2412-2422.
    9. Haohao Li X R, Huabin Yang. 2008. Rescue construction and thinking of Hongshihe dammed lake in Qingchuan
      County[J]. Water Conservancy and Hydropower Technology (Chinese and English): 50-51+62
    10. Hejun Chai, Runqiu Huang, Hanchao Liu I O E G, Chengdu University of Technology 1997. Analysis and Evaluation of the
      Dangerous Degree of Landslide Blocking the River[J]. Chinese Journal of Geological Hazard and Control: 2-8+16
    11. Hong Wang Y L, Lili Song, Yun Chen. 2020. Comparison of characteristics of thunderstorm and gale activity and
      environmental factors in Sichuan-Tibet area[J]. Journal of Applied Meteorology, 31: 435-446.
    1. Hongyan X, Xu H, Jiang H, et al. 2020. Potential pollen evidence for the 1933 M 7.5 Diexi earthquake and implications for
      post-seismic landscape recovery[J]. Environmental Research Letters, 15.
    2. Hui Xu J C, Zhijiu Cui, Pei Guo. 2019. Analysis of Grain Size Characteristics of Sediment in Dammed Lake——Taking Diexi
      Ancient Dammed Lake in the Upper Minjiang River as an Example[J]. Acta Sedimentologica Sinica, 37: 51-61
    3. Jian Yang B P, Min Zhao. 2014. Research on Ecological Restoration Technology in Wenchuan Earthquake-Stricken Area
      ——Taking Tangjiashan Barrier Lake Area as an Example[J]. Sichuan Building Science Research, 40: 164-167.
    4. Jian Yang B P 2017. Evaluation of Ecological Quality of Tangjiashan Dammed Lake Region in Beichuan County[J].
      People’s Yangtze River, 48: 27-32
    5. Jianfeng Chen Y W, Yang Li. 2006. Application of HEC-RAS model in flood simulation[J]. Northeast Water Resources and
      Hydropower: 12-13+42+71.
    6. Jiankang Liu Z C, Tao Yu. 2016. Dam failure risk and its impact of Hongshiyan dammed lake in Ludian, Yunnan[J].
      Journal of Mountain Science, 34: 208-215
    7. Jianrong Fan B T, Genwei Cheng, Heping Tao, Jianqiang Zhang,Dong Yan, Fenghuan Su. 2008. Information extraction of
      dammed bodies induced by the May 12 Wenchuan earthquake based on multi-source remote sensing data[J]. Journal of
      Mountain Science: 257-262.
    8. Jinghuan Tian K Z, Meng Chen, Fuxin Chai. 2012. Research on the application of HEC-RAS model in flood risk analysis
      and assessment[J]. Hydropower Energy Science, 30: 23-25
    9. Juan He X W 2015. Dam-break flood analysis based on HEC-RAS and HEC-GeoRAS[J]. Journal of Water Resources and
      Water Transport Engineering: 112-116
    10. Junwei Gan L Y, Jinjun Li. 2017. Research on the Influencing Factors of Sichuan-Tibet Tourism Industry Competitiveness
      Based on DEMATEL[J]. Arid Land Resources and Environment, 31: 197-202
    11. Lansheng Wang L Y, Xiaoqun Wang, Liping Duan 2005. Discovery of the ancient dammed lake in Diexi, Minjiang River[J].
      Journal of Chengdu University of Technology (Natural Science Edition): 1-11
    12. Ma S, Zhu J, Ya. H. Year. Construction of Risk Assessment System of Dam-break in Barrier Lake Based on Collaborative
      Workflow: 9.
    13. Ming Zeng Y C, Bingyu Zou. 2019. Discussion on the Method of Forecasting the Flood Evolution of Barrier Lake Burst——
      Taking “11·3” Jinsha River Baige Barrier Lake as an Example[J]. Water Resources and Hydropower Express, 40: 11-14
    14. Ouyang C, An H, Zhou S, et al. 2019. Insights from the failure and dynamic characteristics of two sequential landslides at
      Baige village along the Jinsha River, China Landslides[J]. 16.
    15. Peng M, Zhang L M 2012. Analysis of human risks due to dam-break floods—part 1: a new model based on Bayesian
      networks[J]. Natural Hazards, 64.
    16. Qianfeng Li Y L, Gang Liu, Zhiyun Ouyang, Hua Zheng. 2013. The Impact of Land Use Change on Ecosystem Service
      Function——Taking Miyun Reservoir Watershed as an Example[J]. Acta Ecologica Sinica, 33: 726-736.
    17. Qiang Xu G Z, Weile Li, Zhaoyang He, Xiujun Dong, Chen Guo, Wenkai Feng. 2018. Analysis and study of two landslides
      and dams blocking the river in Baige on the Jinsha River in October and November 2018[J]. Journal of Engineering
      Geology, 26: 1534-1551
    18. Qin Ji J Y, Hongju Chen, Man Li. 2019. Analysis of Economic Differences Along the Sichuan-Tibet Railway from the
      Perspective of Spatial and Industrial Decomposition[J]. Glacier permafrost: 1-14
    19. Qingchun Li Y H, Yubing Shi. 2020. Study on the stability of the residual dam in Tangjiashan dammed lake[J]. Journal of
      Underground Space and Engineering, 16: 993-998
    20. Qiwen Xiang J P, Guangze Zhang, Zhengxuan Xu, Dingkai Zhang, Wenli Tu. 2020. Monitoring and Analysis of Surface
      Deformation in Zheduo Mountain Area of Sichuan-Tibet Railway Based on SBAS Technology[J]. Surveying Engineering,
      29: 48-54+59
    1. Shangfu Kuang X W, Jinchi Huang, Yinqi Wei 2008. Analysis and Evaluation of Dam-Break Risk of Barred Lake and Its
      Influence[J]. China Water Resources: 17-21.
    2. Sheng-Hsueh Y, Yii-Wen P, Jia-Jyun D, et al. 2013. A systematic approach for the assessment of flooding hazard and risk
      associated with a landslide dam[J]. Natural Hazards, 65.
    3. Sun L 2021. Research on Fast Perception and Simulation Calculation Method of Landslide Dam in Alpine and Gorge
      Area: Taking Baige Dammed Lake as an Example[J]. Water Conservancy and Hydropower Technology (Chinese and
      English), 52: 44-52
    4. Tamiru H, O. D M 2021. Application of ANN and HEC-RAS model for flood inundation mapping in lower Baro Akobo River
      Basin, Ethiopia[J]. Journal of Hydrology: Regional Studies, 36.
    5. Tao Pan S W, Erfu Dai, Yujie Liu. 2013. Spatio-temporal changes of water supply services in the ecosystem of the Three
      Rivers Source Region based on InVEST model[J]. Journal of Applied Ecology, 24: 183-189
    6. Vera K, Sergey C, Inna K, et al. 2017. Modeling potential scenarios of the Tangjiashan Lake outburst and risk assessment
      in the downstream valley[J]. Frontiers of Earth Science, 11.
    7. Wang Z 1985. Preliminary Discussion on the Evaluation of Ecological Environment Quality in Minjiang River Basin[J].
      Journal of ecology: 29-32
    8. Wei Chen Z S, Hui Guo,Hao Wang, Ting Wei, Nan Li, Kaiyi Zhang Shuxiang Yang, Kaijia Dai. 2007. Analysis of Bird
      Resources and Habitats in Wuhan Urban Lakes and Urban Wetlands in Winter[J]. Forestry Investigation and Planning: 46-
      50
    9. Wei G, Gaohong X, Jun S, et al. 2020. Simulation of Flood Process Based on the Model of Improved Barrier Lake’s
      Gradual Dam Break Model %J Journal of Coastal Research[J]. 104.
    10. Wei X, Jiang H, Xu H, et al. 2021. Response of sedimentary and pollen records to the 1933 Diexi earthquake on the
      eastern Tibetan Plateau[J]. Ecological Indicators, 129.
    11. Wei Xu M L, Jie Yang, Chunzhi Li, Xiaojuan Shang. 2011. Risk Analysis of Flood Overflow in Huainan Section of Huaihe
      River Based on HEC-RAS[J]. Journal of Yangtze River Scientific Research Institute, 28: 13-18
    12. Weiwei Zhan R H, Xiangjun Pei, Weile Li. 2017. Research on empirical prediction model of channel type landslide-debris
      flow movement distance[J]. Journal of Engineering Geology, 25: 154-163
    13. Xianju Zheng H L, Wenhai Huang. 2015. Numerical Simulation of Reconstruction of Natural Dams Induced by Heavy Rain
      ——An Example of Tangjiashan Dammed Lake[J]. Business story: 62-63
    14. Xiao-Qun W, Xin H, Man S, et al. 2020. Possible relatedness between the outburst of the Diexi ancient dammed lake and
      ancient Chengdu’s cultural change[J]. Journal of Mountain Science, 17: 2497-2511.
    15. Xingbo Zhou X D, Yu Yao. 2019. Analysis of the dam-break flood of the Baige dammed lake on the Jinsha River[J].
      Hydroelectric Power, 45: 8-12+32
    16. Xinhua Zhang R X, Ming Wang, Zhiqiu Yu, Bingdong Li, Bo Wang. 2020. Investigation and analysis of flood disaster
      caused by dam break of Baige landslide on Jinsha River[J]. Engineering Science and Technology, 52: 89-100
    17. Xinxiao Yu B Z, Xizhi Lv, Zhige Yang. 2012. Evaluation of Forest Water Conservation Function of Beijing Mountainous
      Area Based on InVEST Model[J]. Forestry Science, 48: 1-5
    18. Xu J, Guo J, Zhang J, et al. 2021. Route choice model based on cellular automata and cumulative prospect theory: Case
      analysis of transportation network in Sichuan-Tibet region[J]. Journal of Intelligent & Fuzzy Systems, 40.
    19. Xuan Liang Z Z 2021. Research on the Influence of Numerical Simulation of Tailings Pond Based on FLOW-3D on
      Downstream[J]. Jiangxi Water Conservancy Science and Technology, 47: 11-20
    20. Yu Zheng P Z, Feng Tang, Li Zhao, Xu Zhao. 2018. Research on the Impact of Land Use Change on Habitat Quality in
      Changli County Based on InVEST Model[J]. China’s Agricultural Resources and Regionalization, 39: 121-128
    21. Yuanyuan Yang E D, Hua Fu. 2012. Research Framework of Value Evaluation of Ecosystem Service Function Based on
      InVEST Model[J]. Journal of Capital Normal University (Natural Science Edition), 33: 41-47
    1. Yunfei Ma T L, Jinbiao Xiong. 2021. Numerical simulation of dam-break flow based on VOF method and DFBI model[J].
      Applied Technology, 48: 23-28
    2. Zhe Wu X C, Beibei Liu, Jinfeng Chu, Lixu Peng. 2013. Research progress of InVEST model and its application[J]. Tropical
      Agriculture Science, 33: 58-62
    3. Zhengpeng Li Y H, Yilun Li, Yuehong Ying, Zehua Huangfu. 2021. Numerical simulation of dam-break flood in Qianping
      Reservoir based on BIM+GIS technology[J]. People’s Yellow River, 43: 160-164
    4. Zhenming Shi X X, Ming Peng, Minglang Lin. 2015. Analysis of Seepage Stability of Barrier Dam with High Permeability
      Area——Taking Hongshihe Barrier Dam as an Example[J]. Journal of Hydraulic Engineering, 46: 1162-1171.
    5. Zhu J, Qi H, Hu Y, et al. 2012. A DVGE service system for risk assessment of dam-break in barrier lake[J]. International
      Conference on Automatic Control and Artificial Intelligence (ACAI 2012).
    6. Zhu Y, Peng M, Cai S, et al. 2021. Risk-Based Warning Decision Making of Cascade Breaching of the Tangjiashan
      Landslide Dam and Two Smaller Downstream Landslide Dams[J]. Frontiers in Earth Science.
    7. Zuyu Chen G H, Qiang Zhang, Shuaifeng Wu. 2020. Disaster Mitigation Analysis of Cascade Hydropower Stations on the
      Jinsha River in “11.03” Baige Barrier Lake Emergency Treatment[J]. Hydropower, 46: 59-63
    8. Zuyu Chen S C, Lin Wang, Qiming Zhong, Qiang Zhang, Songli Jin. 2020. Inversion analysis of the “11.03” Baige barrier
      lake burst flood in the upper reaches of the Jinsha River[J]. Science in China: Technological Science, 50: 763-774.
    PDF 원문보기
    Figure 15. Localized deformations on revetment due to run-down and sliding of armor from body laboratory model (left) and numerical modeling (right).

    지속 가능한 해안 보호 구조로서 굴절식 콘크리트 블록 매트리스의 손상 메커니즘의 수치적 모델링

    Numerical Modeling of Failure Mechanisms in Articulated Concrete Block Mattress as a Sustainable Coastal Protection Structure

    Author

    Ramin Safari Ghaleh(Department of Civil Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran)

    Omid Aminoroayaie Yamini(Department of Civil Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran)

    S. Hooman Mousavi(Department of Civil Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran)

    Mohammad Reza Kavianpour(Department of Civil Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran)

    Abstract

    해안선 보호는 전 세계적인 우선 순위로 남아 있습니다. 일반적으로 해안 지역은 석회암과 같은 단단하고 비자연적이며 지속 불가능한 재료로 보호됩니다. 시공 속도와 환경 친화성을 높이고 개별 콘크리트 블록 및 보강재의 중량을 줄이기 위해 콘크리트 블록을 ACB 매트(Articulated Concrete Block Mattress)로 설계 및 구현할 수 있습니다. 이 구조물은 필수적인 부분으로 작용하며 방파제 또는 해안선 보호의 둑으로 사용할 수 있습니다. 물리적 모델은 해안 구조물의 현상을 추정하고 조사하는 핵심 도구 중 하나입니다. 그러나 한계와 장애물이 있습니다. 결과적으로, 본 연구에서는 이러한 구조물에 대한 파도의 수치 모델링을 활용하여 방파제에서의 파도 전파를 시뮬레이션하고, VOF가 있는 Flow-3D 소프트웨어를 통해 ACB Mat의 불안정성에 영향을 미치는 요인으로는 파괴파동, 옹벽의 흔들림, 파손으로 인한 인양력으로 인한 장갑의 변위 등이 있다. 본 연구의 가장 중요한 목적은 수치 Flow-3D 모델이 연안 호안의 유체역학적 매개변수를 모사하는 능력을 조사하는 것입니다. 콘크리트 블록 장갑에 대한 파동의 상승 값은 파단 매개변수( 0.5 < ξ m – 1 , 0 < 3.3 )가 증가할 때까지(R u 2 % H m 0 = 1.6) ) 최대값에 도달합니다. 따라서 차단파라미터를 증가시키고 파괴파(ξ m − 1 , 0 > 3.3 ) 유형을 붕괴파/해일파로 변경함으로써 콘크리트 블록 호안의 상대파 상승 변화 경향이 점차 증가합니다. 파동(0.5 < ξ m − 1 , 0 < 3.3 )의 경우 차단기 지수(표면 유사성 매개변수)를 높이면 상대파 런다운의 낮은 값이 크게 감소합니다. 또한, 천이영역에서는 파단파동이 쇄도파에서 붕괴/서징으로의 변화( 3.3 < ξ m – 1 , 0 < 5.0 )에서 상대적 런다운 과정이 더 적은 강도로 발생합니다.

    Shoreline protection remains a global priority. Typically, coastal areas are protected by armoring them with hard, non-native, and non-sustainable materials such as limestone. To increase the execution speed and environmental friendliness and reduce the weight of individual concrete blocks and reinforcements, concrete blocks can be designed and implemented as Articulated Concrete Block Mattress (ACB Mat). These structures act as an integral part and can be used as a revetment on the breakwater body or shoreline protection. Physical models are one of the key tools for estimating and investigating the phenomena in coastal structures. However, it does have limitations and obstacles; consequently, in this study, numerical modeling of waves on these structures has been utilized to simulate wave propagation on the breakwater, via Flow-3D software with VOF. Among the factors affecting the instability of ACB Mat are breaking waves as well as the shaking of the revetment and the displacement of the armor due to the uplift force resulting from the failure. The most important purpose of the present study is to investigate the ability of numerical Flow-3D model to simulate hydrodynamic parameters in coastal revetment. The run-up values of the waves on the concrete block armoring will multiply with increasing break parameter ( 0.5 < ξ m − 1 , 0 < 3.3 ) due to the existence of plunging waves until it ( R u 2 % H m 0 = 1.6 ) reaches maximum. Hence, by increasing the breaker parameter and changing breaking waves ( ξ m − 1 , 0 > 3.3 ) type to collapsing waves/surging waves, the trend of relative wave run-up changes on concrete block revetment increases gradually. By increasing the breaker index (surf similarity parameter) in the case of plunging waves ( 0.5 < ξ m − 1 , 0 < 3.3 ), the low values on the relative wave run-down are greatly reduced. Additionally, in the transition region, the change of breaking waves from plunging waves to collapsing/surging ( 3.3 < ξ m − 1 , 0 < 5.0 ), the relative run-down process occurs with less intensity.

    Figure 1. Armor geometric characteristics and drawing three-dimensional geometry of a breakwater section in SolidWorks software.
    Figure 1. Armor geometric characteristics and drawing three-dimensional geometry of a breakwater section in SolidWorks software.
    Figure 5. Wave overtopping on concrete block mattress in (a) laboratory and (b) numerical model.
    Figure 5. Wave overtopping on concrete block mattress in (a) laboratory and (b) numerical model.
    Figure 7. Mesh block for calibrated numerical model with 686,625 cells and utilization of FAVOR tab to assess figure geometry.
    Figure 7. Mesh block for calibrated numerical model with 686,625 cells and utilization of FAVOR tab to assess figure geometry.
    Figure 10. How to place different layers (core, filter, and revetment) of the structure on slope.
    Figure 10. How to place different layers (core, filter, and revetment) of the structure on slope.

    Suggested Citation

    Figure 11. Wave run-up on ACB Mat blocks in (a) laboratory model and (b) numerical modeling.
    Figure 11. Wave run-up on ACB Mat blocks in (a) laboratory model and (b) numerical modeling.
    Figure 15. Localized deformations on revetment due to run-down and sliding of armor from body laboratory model (left) and numerical modeling (right).
    Figure 15. Localized deformations on revetment due to run-down and sliding of armor from body laboratory model (left) and numerical modeling (right).

    References

    1. Capobianco, V.; Robinson, K.; Kalsnes, B.; Ekeheien, C.; Høydal, Ø. Hydro-Mechanical Effects of Several Riparian Vegetation Combinations on the Streambank Stability—A Benchmark Case in Southeastern Norway. Sustainability 2021, 13, 4046. [CrossRef]
    2. MarCom Working Group 113. PIANC Report No 113: The Application of Geosynthetics in Waterfront Areas; PIANC: Brussels, Belgium, 2011; p. 113, ISBN 978-2-87223-188-1.
    3. Hunt, W.F.; Collins, K.A.; Hathaway, J.M. Hydrologic and Water Quality Evaluation of Four Permeable Pavements in North Carolina, USA. In Proceedings of the 9th International Conference on Concrete Block Paving, Buenos Aires, Argentina, 18–21 October 2009.
    4. Kirkpatrick, R.; Campbell, R.; Smyth, J.; Murtagh, J.; Knapton, J. Improvement of Water Quality by Coarse Graded Aggregates in Permeable Pavements. In Proceedings of the 9th International Conference on Concrete Block Paving, Buenos Aires, Argentina, 18–21 October 2009.
    5. Chinowsky, P.; Helman, J. Protecting Infrastructure and Public Buildings against Sea Level Rise and Storm Surge. Sustainability 2021, 13, 10538. [CrossRef]
    6. Breteler, M.K.; Pilarczyk, K.W.; Stoutjesdijk, T. Design of alternative revetments. Coast. Eng. 1998 1999, 1587–1600. [CrossRef]
    7. Pilarczyk, K.W. Design of Revetments; Dutch Public Works Department (Rws), Hydraulic Engineering Division: Delft, The Netherlands, 2003.
    8. Hughes, S.A. Combined Wave and Surge Overtopping of Levees: Flow Hydrodynamics and Articulated Concrete Mat Stability; Engineer Research and Development Center Vicksburg Ms Coastal and Hydraulics Lab: Vicksburg, MS, USA, 2008.
    9. Gier, F.; Schüttrumpf, H.; Mönnich, J.; Van Der Meer, J.; Kudella, M.; Rubin, H. Stability of Interlocked Pattern Placed Block Revetments. Coast. Eng. Proc. 2012, 1, Structures-46. [CrossRef]
    10. Najafi, J.A.; Monshizadeh, M. Laboratory Investigations on Wave Run-up and Transmission over Breakwaters Covered by Antifer Units; Scientia Iranica: Tehran, Iran, 2010.
    11. Oumeraci, H.; Staal, T.; Pförtner, S.; Ludwigs, G.; Kudella, M. Hydraulic Performance, Wave Loading and Response of Elastocoast Revetments and their Foundation—A Large Scale Model Study; Leichtweiß Institut für Wasserbau: Braunschweig, Germany, 2010.
    12. Tripathy, S.K. Significance of Traditional and Advanced Morphometry to Fishery Science. J. Hum. Earth Future 2020, 1, 153–166. [CrossRef]
    13. Nut, N.; Mihara, M.; Jeong, J.; Ngo, B.; Sigua, G.; Prasad, P.V.V.; Reyes, M.R. Land Use and Land Cover Changes and Its Impact on Soil Erosion in Stung Sangkae Catchment of Cambodia. Sustainability 2021, 13, 9276. [CrossRef]
    14. Xu, C.; Pu, L.; Kong, F.; Li, B. Spatio-Temporal Change of Land Use in a Coastal Reclamation Area: A Complex Network Approach. Sustainability 2021, 13, 8690. [CrossRef]
    15. Mousavi, S.; Kavianpour, H.M.R.; Yamini, O.A. Experimental analysis of breakwater stability with antifer concrete block. Mar. Georesour. Geotechnol. 2017, 35, 426–434. [CrossRef]
    16. Yamini, O.; Aminoroayaie, S.; Mousavi, H.; Kavianpour, M.R. Experimental Investigation of Using Geo-Textile Filter Layer In Articulated Concrete Block Mattress Revetment On Coastal Embankment. J. Ocean Eng. Mar. Energy 2019, 5, 119–133. [CrossRef]
    17. Ghasemi, A.; Far, M.S.; Panahi, R. Numerical Simulation of Wave Overtopping From Armour Breakwater by Considering Porous Effect. J. Mar. Eng. 2015, 11, 51–60. Available online: http://dorl.net/dor/20.1001.1.17357608.1394.11.22.8.4 (accessed on 21 October 2021).
    18. Nourani, O.; Askar, M.B. Comparison of the Effect of Tetrapod Block and Armor X block on Reducing Wave Overtopping in Breakwaters. Open J. Mar. Sci. 2017, 7, 472–484. [CrossRef]
    19. Aminoroaya, A.O.; Kavianpour, M.R.; Movahedi, A. Performance of Hydrodynamics Flow on Flip Buckets Spillway for Flood Control in Large Dam Reservoirs. J. Hum. Earth Future 2020, 1, 39–47.
    20. Milanian, F.; Niri, M.Z.; Najafi-Jilani, A. Effect of hydraulic and structural parameters on the wave run-up over the berm breakwaters. Int. J. Nav. Archit. Ocean Eng. 2017, 9, 282–291. [CrossRef]
    21. Yamini, O.A.; Kavianpour, M.R.; Mousavi, S.H. Experimental investigation of parameters affecting the stability of articulated concrete block mattress under wave attack. Appl. Ocean Res. 2017, 64, 184–202. [CrossRef]
    22. Yakhot, V.; Orszag, S.A.; Thangam, S.; Gatski, T.B.; Speziale, C.G. Development of turbulence models for shear flows by a double expansion technique. Phys. Fluids 1992, 4, 1510–1520. [CrossRef]
    23. Bayon, A.; Valero, D.; García-Bartual, R.; López-Jiménez, P.A. Performance assessment of OpenFOAM and FLOW-3D in the numerical modeling of a low Reynolds number hydraulic jump. Environ. Model. Softw. 2016, 80, 322–335. [CrossRef]
    24. Jin, J.; Meng, B. Computation of wave loads on the superstructures of coastal highway bridges. Ocean Eng. 2011, 38, 2185–2200. [CrossRef]
    25. Yang, S.; Yang, W.; Qin, S.; Li, Q.; Yang, B. Numerical study on characteristics of dam-break wave. Ocean Eng. 2018, 159, 358–371. [CrossRef]
    26. Ersoy, H.; Karahan, M.; Geli¸sli, K.; Akgün, A.; Anılan, T.; Sünnetci, M.O.; Yah¸si, B.K. Modelling of the landslide-induced impulse waves in the Artvin Dam reservoir by empirical approach and 3D numerical simulation. Eng. Geol. 2019, 249, 112–128. [CrossRef]
    27. Zhan, J.M.; Dong, Z.; Jiang, W.; Li, Y.S. Numerical simulation of wave transformation and runup incorporating porous media wave absorber and turbulence models. Ocean Eng. 2010, 37, 1261–1272. [CrossRef]
    28. Owen, M.W. The Hydroulic Design of Seawall Profiles, Proceedings Conference on Shoreline Protection; ICE: London, UK, 1980; pp. 185–192.
    29. Pilarczyk, K.W. Geosythetics and Geosystems in Hydraulic and Coastal Engineering; CRC Press: Balkema, FL, USA, 2000; p. 913, ISBN 90.5809.302.6.
    30. Van der Meer, J.W.; Allsop, N.W.H.; Bruce, T.; De Rouck, J.; Kortenhaus, A.; Pullen, T.; Schüttrumpf, H.; Troch, P.; Zanuttigh, B. (Eds.) Manual on Wave Overtopping of Sea Defences and Related Structures–Assessment Manual; EurOtop.: London, UK, 2016; Available online: www.Overtopping-manual.com (accessed on 21 October 2021).
    31. Battjes, J.A. Computation of Set-up, Longshore Currents, Run-up and Overtopping Due to Wind-Generated Waves; TU Delft Library: Delft, The Netherlands, 1974.
    32. Van der Meer, J.W. Rock Slopes and Gravel Beaches under Wave Attack; Delft Hydraulics: Delft, The Netherlands, 1988.
    33. Ten Oever, E. Theoretical and Experimental Study on the Placement of Xbloc; Delft Hydraulics: Delft, The Netherlands, 2006.
    34. Flow Science, Inc. FLOW-3D User Manual Version 9.3; Flow Science, Inc.: Santa Fe, NM, USA, 2008.
    35. Lebaron, J.W. Stability of A-Jacksarmored Rubble-Mound Break Waters Subjected to Breaking and Non-Breaking Waves with No Overtopping; Master of Science in Civil Engineering, Oregon State University: Corvallis, OR, USA, 1999.
    36. McLaren RW, G.; Chin, C.; Weber, J.; Binns, J.; McInerney, J.; Allen, M. Articulated Concrete Mattress block size stability comparison in omni-directional current. In Proceedings of the OCEANS 2016 MTS/IEEE Monterey, Monterey, CA, USA, 19–23 September 2016; pp. 1–6. [CrossRef]
    PDF View
    Flow velocity profiles for canals with a depth of 3 m and flow velocities of 5–5.3 m/s.

    Optimization Algorithms and Engineering: Recent Advances and Applications

    Mahdi Feizbahr,1 Navid Tonekaboni,2Guang-Jun Jiang,3,4 and Hong-Xia Chen3,4Show moreAcademic Editor: Mohammad YazdiReceived08 Apr 2021Revised18 Jun 2021Accepted17 Jul 2021Published11 Aug 2021

    Abstract

    Vegetation along the river increases the roughness and reduces the average flow velocity, reduces flow energy, and changes the flow velocity profile in the cross section of the river. Many canals and rivers in nature are covered with vegetation during the floods. Canal’s roughness is strongly affected by plants and therefore it has a great effect on flow resistance during flood. Roughness resistance against the flow due to the plants depends on the flow conditions and plant, so the model should simulate the current velocity by considering the effects of velocity, depth of flow, and type of vegetation along the canal. Total of 48 models have been simulated to investigate the effect of roughness in the canal. The results indicated that, by enhancing the velocity, the effect of vegetation in decreasing the bed velocity is negligible, while when the current has lower speed, the effect of vegetation on decreasing the bed velocity is obviously considerable.


    강의 식생은 거칠기를 증가시키고 평균 유속을 감소시키며, 유속 에너지를 감소시키고 강의 단면에서 유속 프로파일을 변경합니다. 자연의 많은 운하와 강은 홍수 동안 초목으로 덮여 있습니다. 운하의 조도는 식물의 영향을 많이 받으므로 홍수시 유동저항에 큰 영향을 미칩니다. 식물로 인한 흐름에 대한 거칠기 저항은 흐름 조건 및 식물에 따라 다르므로 모델은 유속, 흐름 깊이 및 운하를 따라 식생 유형의 영향을 고려하여 현재 속도를 시뮬레이션해야 합니다. 근관의 거칠기의 영향을 조사하기 위해 총 48개의 모델이 시뮬레이션되었습니다. 결과는 유속을 높임으로써 유속을 감소시키는 식생의 영향은 무시할 수 있는 반면, 해류가 더 낮은 유속일 때 유속을 감소시키는 식생의 영향은 분명히 상당함을 나타냈다.

    1. Introduction

    Considering the impact of each variable is a very popular field within the analytical and statistical methods and intelligent systems [1–14]. This can help research for better modeling considering the relation of variables or interaction of them toward reaching a better condition for the objective function in control and engineering [15–27]. Consequently, it is necessary to study the effects of the passive factors on the active domain [28–36]. Because of the effect of vegetation on reducing the discharge capacity of rivers [37], pruning plants was necessary to improve the condition of rivers. One of the important effects of vegetation in river protection is the action of roots, which cause soil consolidation and soil structure improvement and, by enhancing the shear strength of soil, increase the resistance of canal walls against the erosive force of water. The outer limbs of the plant increase the roughness of the canal walls and reduce the flow velocity and deplete the flow energy in vicinity of the walls. Vegetation by reducing the shear stress of the canal bed reduces flood discharge and sedimentation in the intervals between vegetation and increases the stability of the walls [38–41].

    One of the main factors influencing the speed, depth, and extent of flood in this method is Manning’s roughness coefficient. On the other hand, soil cover [42], especially vegetation, is one of the most determining factors in Manning’s roughness coefficient. Therefore, it is expected that those seasonal changes in the vegetation of the region will play an important role in the calculated value of Manning’s roughness coefficient and ultimately in predicting the flood wave behavior [43–45]. The roughness caused by plants’ resistance to flood current depends on the flow and plant conditions. Flow conditions include depth and velocity of the plant, and plant conditions include plant type, hardness or flexibility, dimensions, density, and shape of the plant [46]. In general, the issue discussed in this research is the optimization of flood-induced flow in canals by considering the effect of vegetation-induced roughness. Therefore, the effect of plants on the roughness coefficient and canal transmission coefficient and in consequence the flow depth should be evaluated [47, 48].

    Current resistance is generally known by its roughness coefficient. The equation that is mainly used in this field is Manning equation. The ratio of shear velocity to average current velocity  is another form of current resistance. The reason for using the  ratio is that it is dimensionless and has a strong theoretical basis. The reason for using Manning roughness coefficient is its pervasiveness. According to Freeman et al. [49], the Manning roughness coefficient for plants was calculated according to the Kouwen and Unny [50] method for incremental resistance. This method involves increasing the roughness for various surface and plant irregularities. Manning’s roughness coefficient has all the factors affecting the resistance of the canal. Therefore, the appropriate way to more accurately estimate this coefficient is to know the factors affecting this coefficient [51].

    To calculate the flow rate, velocity, and depth of flow in canals as well as flood and sediment estimation, it is important to evaluate the flow resistance. To determine the flow resistance in open ducts, Manning, Chézy, and Darcy–Weisbach relations are used [52]. In these relations, there are parameters such as Manning’s roughness coefficient (n), Chézy roughness coefficient (C), and Darcy–Weisbach coefficient (f). All three of these coefficients are a kind of flow resistance coefficient that is widely used in the equations governing flow in rivers [53].

    The three relations that express the relationship between the average flow velocity (V) and the resistance and geometric and hydraulic coefficients of the canal are as follows:where n, f, and c are Manning, Darcy–Weisbach, and Chézy coefficients, respectively. V = average flow velocity, R = hydraulic radius, Sf = slope of energy line, which in uniform flow is equal to the slope of the canal bed,  = gravitational acceleration, and Kn is a coefficient whose value is equal to 1 in the SI system and 1.486 in the English system. The coefficients of resistance in equations (1) to (3) are related as follows:

    Based on the boundary layer theory, the flow resistance for rough substrates is determined from the following general relation:where f = Darcy–Weisbach coefficient of friction, y = flow depth, Ks = bed roughness size, and A = constant coefficient.

    On the other hand, the relationship between the Darcy–Weisbach coefficient of friction and the shear velocity of the flow is as follows:

    By using equation (6), equation (5) is converted as follows:

    Investigation on the effect of vegetation arrangement on shear velocity of flow in laboratory conditions showed that, with increasing the shear Reynolds number (), the numerical value of the  ratio also increases; in other words the amount of roughness coefficient increases with a slight difference in the cases without vegetation, checkered arrangement, and cross arrangement, respectively [54].

    Roughness in river vegetation is simulated in mathematical models with a variable floor slope flume by different densities and discharges. The vegetation considered submerged in the bed of the flume. Results showed that, with increasing vegetation density, canal roughness and flow shear speed increase and with increasing flow rate and depth, Manning’s roughness coefficient decreases. Factors affecting the roughness caused by vegetation include the effect of plant density and arrangement on flow resistance, the effect of flow velocity on flow resistance, and the effect of depth [45, 55].

    One of the works that has been done on the effect of vegetation on the roughness coefficient is Darby [56] study, which investigates a flood wave model that considers all the effects of vegetation on the roughness coefficient. There are currently two methods for estimating vegetation roughness. One method is to add the thrust force effect to Manning’s equation [47, 57, 58] and the other method is to increase the canal bed roughness (Manning-Strickler coefficient) [45, 59–61]. These two methods provide acceptable results in models designed to simulate floodplain flow. Wang et al. [62] simulate the floodplain with submerged vegetation using these two methods and to increase the accuracy of the results, they suggested using the effective height of the plant under running water instead of using the actual height of the plant. Freeman et al. [49] provided equations for determining the coefficient of vegetation roughness under different conditions. Lee et al. [63] proposed a method for calculating the Manning coefficient using the flow velocity ratio at different depths. Much research has been done on the Manning roughness coefficient in rivers, and researchers [49, 63–66] sought to obtain a specific number for n to use in river engineering. However, since the depth and geometric conditions of rivers are completely variable in different places, the values of Manning roughness coefficient have changed subsequently, and it has not been possible to choose a fixed number. In river engineering software, the Manning roughness coefficient is determined only for specific and constant conditions or normal flow. Lee et al. [63] stated that seasonal conditions, density, and type of vegetation should also be considered. Hydraulic roughness and Manning roughness coefficient n of the plant were obtained by estimating the total Manning roughness coefficient from the matching of the measured water surface curve and water surface height. The following equation is used for the flow surface curve:where  is the depth of water change, S0 is the slope of the canal floor, Sf is the slope of the energy line, and Fr is the Froude number which is obtained from the following equation:where D is the characteristic length of the canal. Flood flow velocity is one of the important parameters of flood waves, which is very important in calculating the water level profile and energy consumption. In the cases where there are many limitations for researchers due to the wide range of experimental dimensions and the variety of design parameters, the use of numerical methods that are able to estimate the rest of the unknown results with acceptable accuracy is economically justified.

    FLOW-3D software uses Finite Difference Method (FDM) for numerical solution of two-dimensional and three-dimensional flow. This software is dedicated to computational fluid dynamics (CFD) and is provided by Flow Science [67]. The flow is divided into networks with tubular cells. For each cell there are values of dependent variables and all variables are calculated in the center of the cell, except for the velocity, which is calculated at the center of the cell. In this software, two numerical techniques have been used for geometric simulation, FAVOR™ (Fractional-Area-Volume-Obstacle-Representation) and the VOF (Volume-of-Fluid) method. The equations used at this model for this research include the principle of mass survival and the magnitude of motion as follows. The fluid motion equations in three dimensions, including the Navier–Stokes equations with some additional terms, are as follows:where  are mass accelerations in the directions x, y, z and  are viscosity accelerations in the directions x, y, z and are obtained from the following equations:

    Shear stresses  in equation (11) are obtained from the following equations:

    The standard model is used for high Reynolds currents, but in this model, RNG theory allows the analytical differential formula to be used for the effective viscosity that occurs at low Reynolds numbers. Therefore, the RNG model can be used for low and high Reynolds currents.

    Weather changes are high and this affects many factors continuously. The presence of vegetation in any area reduces the velocity of surface flows and prevents soil erosion, so vegetation will have a significant impact on reducing destructive floods. One of the methods of erosion protection in floodplain watersheds is the use of biological methods. The presence of vegetation in watersheds reduces the flow rate during floods and prevents soil erosion. The external organs of plants increase the roughness and decrease the velocity of water flow and thus reduce its shear stress energy. One of the important factors with which the hydraulic resistance of plants is expressed is the roughness coefficient. Measuring the roughness coefficient of plants and investigating their effect on reducing velocity and shear stress of flow is of special importance.

    Roughness coefficients in canals are affected by two main factors, namely, flow conditions and vegetation characteristics [68]. So far, much research has been done on the effect of the roughness factor created by vegetation, but the issue of plant density has received less attention. For this purpose, this study was conducted to investigate the effect of vegetation density on flow velocity changes.

    In a study conducted using a software model on three density modes in the submerged state effect on flow velocity changes in 48 different modes was investigated (Table 1).Table 1 The studied models.

    The number of cells used in this simulation is equal to 1955888 cells. The boundary conditions were introduced to the model as a constant speed and depth (Figure 1). At the output boundary, due to the presence of supercritical current, no parameter for the current is considered. Absolute roughness for floors and walls was introduced to the model (Figure 1). In this case, the flow was assumed to be nonviscous and air entry into the flow was not considered. After  seconds, this model reached a convergence accuracy of .

    Figure 1 The simulated model and its boundary conditions.

    Due to the fact that it is not possible to model the vegetation in FLOW-3D software, in this research, the vegetation of small soft plants was studied so that Manning’s coefficients can be entered into the canal bed in the form of roughness coefficients obtained from the studies of Chow [69] in similar conditions. In practice, in such modeling, the effect of plant height is eliminated due to the small height of herbaceous plants, and modeling can provide relatively acceptable results in these conditions.

    48 models with input velocities proportional to the height of the regular semihexagonal canal were considered to create supercritical conditions. Manning coefficients were applied based on Chow [69] studies in order to control the canal bed. Speed profiles were drawn and discussed.

    Any control and simulation system has some inputs that we should determine to test any technology [70–77]. Determination and true implementation of such parameters is one of the key steps of any simulation [23, 78–81] and computing procedure [82–86]. The input current is created by applying the flow rate through the VFR (Volume Flow Rate) option and the output flow is considered Output and for other borders the Symmetry option is considered.

    Simulation of the models and checking their action and responses and observing how a process behaves is one of the accepted methods in engineering and science [87, 88]. For verification of FLOW-3D software, the results of computer simulations are compared with laboratory measurements and according to the values of computational error, convergence error, and the time required for convergence, the most appropriate option for real-time simulation is selected (Figures 2 and 3 ).

    Figure 2 Modeling the plant with cylindrical tubes at the bottom of the canal.

    Figure 3 Velocity profiles in positions 2 and 5.

    The canal is 7 meters long, 0.5 meters wide, and 0.8 meters deep. This test was used to validate the application of the software to predict the flow rate parameters. In this experiment, instead of using the plant, cylindrical pipes were used in the bottom of the canal.

    The conditions of this modeling are similar to the laboratory conditions and the boundary conditions used in the laboratory were used for numerical modeling. The critical flow enters the simulation model from the upstream boundary, so in the upstream boundary conditions, critical velocity and depth are considered. The flow at the downstream boundary is supercritical, so no parameters are applied to the downstream boundary.

    The software well predicts the process of changing the speed profile in the open canal along with the considered obstacles. The error in the calculated speed values can be due to the complexity of the flow and the interaction of the turbulence caused by the roughness of the floor with the turbulence caused by the three-dimensional cycles in the hydraulic jump. As a result, the software is able to predict the speed distribution in open canals.

    2. Modeling Results

    After analyzing the models, the results were shown in graphs (Figures 4–14 ). The total number of experiments in this study was 48 due to the limitations of modeling.(a)
    (a)(b)
    (b)(c)
    (c)(d)
    (d)(a)
    (a)(b)
    (b)(c)
    (c)(d)
    (d)Figure 4 Flow velocity profiles for canals with a depth of 1 m and flow velocities of 3–3.3 m/s. Canal with a depth of 1 meter and a flow velocity of (a) 3 meters per second, (b) 3.1 meters per second, (c) 3.2 meters per second, and (d) 3.3 meters per second.

    Figure 5 Canal diagram with a depth of 1 meter and a flow rate of 3 meters per second.

    Figure 6 Canal diagram with a depth of 1 meter and a flow rate of 3.1 meters per second.

    Figure 7 Canal diagram with a depth of 1 meter and a flow rate of 3.2 meters per second.

    Figure 8 Canal diagram with a depth of 1 meter and a flow rate of 3.3 meters per second.(a)
    (a)(b)
    (b)(c)
    (c)(d)
    (d)(a)
    (a)(b)
    (b)(c)
    (c)(d)
    (d)Figure 9 Flow velocity profiles for canals with a depth of 2 m and flow velocities of 4–4.3 m/s. Canal with a depth of 2 meters and a flow rate of (a) 4 meters per second, (b) 4.1 meters per second, (c) 4.2 meters per second, and (d) 4.3 meters per second.

    Figure 10 Canal diagram with a depth of 2 meters and a flow rate of 4 meters per second.

    Figure 11 Canal diagram with a depth of 2 meters and a flow rate of 4.1 meters per second.

    Figure 12 Canal diagram with a depth of 2 meters and a flow rate of 4.2 meters per second.

    Figure 13 Canal diagram with a depth of 2 meters and a flow rate of 4.3 meters per second.(a)
    (a)(b)
    (b)(c)
    (c)(d)
    (d)(a)
    (a)(b)
    (b)(c)
    (c)(d)
    (d)Figure 14 Flow velocity profiles for canals with a depth of 3 m and flow velocities of 5–5.3 m/s. Canal with a depth of 2 meters and a flow rate of (a) 4 meters per second, (b) 4.1 meters per second, (c) 4.2 meters per second, and (d) 4.3 meters per second.

    To investigate the effects of roughness with flow velocity, the trend of flow velocity changes at different depths and with supercritical flow to a Froude number proportional to the depth of the section has been obtained.

    According to the velocity profiles of Figure 5, it can be seen that, with the increasing of Manning’s coefficient, the canal bed speed decreases.

    According to Figures 5 to 8, it can be found that, with increasing the Manning’s coefficient, the canal bed speed decreases. But this deceleration is more noticeable than the deceleration of the models 1 to 12, which can be justified by increasing the speed and of course increasing the Froude number.

    According to Figure 10, we see that, with increasing Manning’s coefficient, the canal bed speed decreases.

    According to Figure 11, we see that, with increasing Manning’s coefficient, the canal bed speed decreases. But this deceleration is more noticeable than the deceleration of Figures 5–10, which can be justified by increasing the speed and, of course, increasing the Froude number.

    With increasing Manning’s coefficient, the canal bed speed decreases (Figure 12). But this deceleration is more noticeable than the deceleration of the higher models (Figures 5–8 and 10, 11), which can be justified by increasing the speed and, of course, increasing the Froude number.

    According to Figure 13, with increasing Manning’s coefficient, the canal bed speed decreases. But this deceleration is more noticeable than the deceleration of Figures 5 to 12, which can be justified by increasing the speed and, of course, increasing the Froude number.

    According to Figure 15, with increasing Manning’s coefficient, the canal bed speed decreases.

    Figure 15 Canal diagram with a depth of 3 meters and a flow rate of 5 meters per second.

    According to Figure 16, with increasing Manning’s coefficient, the canal bed speed decreases. But this deceleration is more noticeable than the deceleration of the higher model, which can be justified by increasing the speed and, of course, increasing the Froude number.

    Figure 16 Canal diagram with a depth of 3 meters and a flow rate of 5.1 meters per second.

    According to Figure 17, it is clear that, with increasing Manning’s coefficient, the canal bed speed decreases. But this deceleration is more noticeable than the deceleration of the higher models, which can be justified by increasing the speed and, of course, increasing the Froude number.

    Figure 17 Canal diagram with a depth of 3 meters and a flow rate of 5.2 meters per second.

    According to Figure 18, with increasing Manning’s coefficient, the canal bed speed decreases. But this deceleration is more noticeable than the deceleration of the higher models, which can be justified by increasing the speed and, of course, increasing the Froude number.

    Figure 18 Canal diagram with a depth of 3 meters and a flow rate of 5.3 meters per second.

    According to Figure 19, it can be seen that the vegetation placed in front of the flow input velocity has negligible effect on the reduction of velocity, which of course can be justified due to the flexibility of the vegetation. The only unusual thing is the unexpected decrease in floor speed of 3 m/s compared to higher speeds.(a)
    (a)(b)
    (b)(c)
    (c)(a)
    (a)(b)
    (b)(c)
    (c)Figure 19 Comparison of velocity profiles with the same plant densities (depth 1 m). Comparison of velocity profiles with (a) plant densities of 25%, depth 1 m; (b) plant densities of 50%, depth 1 m; and (c) plant densities of 75%, depth 1 m.

    According to Figure 20, by increasing the speed of vegetation, the effect of vegetation on reducing the flow rate becomes more noticeable. And the role of input current does not have much effect in reducing speed.(a)
    (a)(b)
    (b)(c)
    (c)(a)
    (a)(b)
    (b)(c)
    (c)Figure 20 Comparison of velocity profiles with the same plant densities (depth 2 m). Comparison of velocity profiles with (a) plant densities of 25%, depth 2 m; (b) plant densities of 50%, depth 2 m; and (c) plant densities of 75%, depth 2 m.

    According to Figure 21, it can be seen that, with increasing speed, the effect of vegetation on reducing the bed flow rate becomes more noticeable and the role of the input current does not have much effect. In general, it can be seen that, by increasing the speed of the input current, the slope of the profiles increases from the bed to the water surface and due to the fact that, in software, the roughness coefficient applies to the channel floor only in the boundary conditions, this can be perfectly justified. Of course, it can be noted that, due to the flexible conditions of the vegetation of the bed, this modeling can show acceptable results for such grasses in the canal floor. In the next directions, we may try application of swarm-based optimization methods for modeling and finding the most effective factors in this research [2, 7, 8, 15, 18, 89–94]. In future, we can also apply the simulation logic and software of this research for other domains such as power engineering [95–99].(a)
    (a)(b)
    (b)(c)
    (c)(a)
    (a)(b)
    (b)(c)
    (c)Figure 21 Comparison of velocity profiles with the same plant densities (depth 3 m). Comparison of velocity profiles with (a) plant densities of 25%, depth 3 m; (b) plant densities of 50%, depth 3 m; and (c) plant densities of 75%, depth 3 m.

    3. Conclusion

    The effects of vegetation on the flood canal were investigated by numerical modeling with FLOW-3D software. After analyzing the results, the following conclusions were reached:(i)Increasing the density of vegetation reduces the velocity of the canal floor but has no effect on the velocity of the canal surface.(ii)Increasing the Froude number is directly related to increasing the speed of the canal floor.(iii)In the canal with a depth of one meter, a sudden increase in speed can be observed from the lowest speed and higher speed, which is justified by the sudden increase in Froude number.(iv)As the inlet flow rate increases, the slope of the profiles from the bed to the water surface increases.(v)By reducing the Froude number, the effect of vegetation on reducing the flow bed rate becomes more noticeable. And the input velocity in reducing the velocity of the canal floor does not have much effect.(vi)At a flow rate between 3 and 3.3 meters per second due to the shallow depth of the canal and the higher landing number a more critical area is observed in which the flow bed velocity in this area is between 2.86 and 3.1 m/s.(vii)Due to the critical flow velocity and the slight effect of the roughness of the horseshoe vortex floor, it is not visible and is only partially observed in models 1-2-3 and 21.(viii)As the flow rate increases, the effect of vegetation on the rate of bed reduction decreases.(ix)In conditions where less current intensity is passing, vegetation has a greater effect on reducing current intensity and energy consumption increases.(x)In the case of using the flow rate of 0.8 cubic meters per second, the velocity distribution and flow regime show about 20% more energy consumption than in the case of using the flow rate of 1.3 cubic meters per second.

    Nomenclature

    n:Manning’s roughness coefficient
    C:Chézy roughness coefficient
    f:Darcy–Weisbach coefficient
    V:Flow velocity
    R:Hydraulic radius
    g:Gravitational acceleration
    y:Flow depth
    Ks:Bed roughness
    A:Constant coefficient
    :Reynolds number
    ∂y/∂x:Depth of water change
    S0:Slope of the canal floor
    Sf:Slope of energy line
    Fr:Froude number
    D:Characteristic length of the canal
    G:Mass acceleration
    :Shear stresses.

    Data Availability

    All data are included within the paper.

    Conflicts of Interest

    The authors declare that they have no conflicts of interest.

    Acknowledgments

    This work was partially supported by the National Natural Science Foundation of China under Contract no. 71761030 and Natural Science Foundation of Inner Mongolia under Contract no. 2019LH07003.

    References

    1. H. Yu, L. Jie, W. Gui et al., “Dynamic Gaussian bare-bones fruit fly optimizers with abandonment mechanism: method and analysis,” Engineering with Computers, vol. 20, pp. 1–29, 2020.View at: Publisher Site | Google Scholar
    2. X. Zhao, D. Li, B. Yang, C. Ma, Y. Zhu, and H. Chen, “Feature selection based on improved ant colony optimization for online detection of foreign fiber in cotton,” Applied Soft Computing, vol. 24, pp. 585–596, 2014.View at: Publisher Site | Google Scholar
    3. J. Hu, H. Chen, A. A. Heidari et al., “Orthogonal learning covariance matrix for defects of grey wolf optimizer: insights, balance, diversity, and feature selection,” Knowledge-Based Systems, vol. 213, Article ID 106684, 2021.View at: Publisher Site | Google Scholar
    4. C. Yu, M. Chen, K. Chen et al., “SGOA: annealing-behaved grasshopper optimizer for global tasks,” Engineering with Computers, vol. 4, pp. 1–28, 2021.View at: Publisher Site | Google Scholar
    5. W. Shan, Z. Qiao, A. A. Heidari, H. Chen, H. Turabieh, and Y. Teng, “Double adaptive weights for stabilization of moth flame optimizer: balance analysis, engineering cases, and medical diagnosis,” Knowledge-Based Systems, vol. 8, Article ID 106728, 2020.View at: Google Scholar
    6. J. Tu, H. Chen, J. Liu et al., “Evolutionary biogeography-based whale optimization methods with communication structure: towards measuring the balance,” Knowledge-Based Systems, vol. 212, Article ID 106642, 2021.View at: Publisher Site | Google Scholar
    7. Y. Zhang, R. Liu, X. Wang et al., “Towards augmented kernel extreme learning models for bankruptcy prediction: algorithmic behavior and comprehensive analysis,” Neurocomputing, vol. 430, 2020.View at: Google Scholar
    8. H.-L. Chen, G. Wang, C. Ma, Z.-N. Cai, W.-B. Liu, and S.-J. Wang, “An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson׳s disease,” Neurocomputing, vol. 184, pp. 131–144, 2016.View at: Publisher Site | Google Scholar
    9. J. Xia, H. Chen, Q. Li et al., “Ultrasound-based differentiation of malignant and benign thyroid Nodules: an extreme learning machine approach,” Computer Methods and Programs in Biomedicine, vol. 147, pp. 37–49, 2017.View at: Publisher Site | Google Scholar
    10. C. Li, L. Hou, B. Y. Sharma et al., “Developing a new intelligent system for the diagnosis of tuberculous pleural effusion,” Computer Methods and Programs in Biomedicine, vol. 153, pp. 211–225, 2018.View at: Publisher Site | Google Scholar
    11. X. Xu and H.-L. Chen, “Adaptive computational chemotaxis based on field in bacterial foraging optimization,” Soft Computing, vol. 18, no. 4, pp. 797–807, 2014.View at: Publisher Site | Google Scholar
    12. M. Wang, H. Chen, B. Yang et al., “Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses,” Neurocomputing, vol. 267, pp. 69–84, 2017.View at: Publisher Site | Google Scholar
    13. L. Chao, K. Zhang, Z. Li, Y. Zhu, J. Wang, and Z. Yu, “Geographically weighted regression based methods for merging satellite and gauge precipitation,” Journal of Hydrology, vol. 558, pp. 275–289, 2018.View at: Publisher Site | Google Scholar
    14. F. J. Golrokh, G. Azeem, and A. Hasan, “Eco-efficiency evaluation in cement industries: DEA malmquist productivity index using optimization models,” ENG Transactions, vol. 1, 2020.View at: Google Scholar
    15. D. Zhao, L. Lei, F. Yu et al., “Chaotic random spare ant colony optimization for multi-threshold image segmentation of 2D Kapur entropy,” Knowledge-Based Systems, vol. 8, Article ID 106510, 2020.View at: Google Scholar
    16. Y. Zhang, R. Liu, X. Wang, H. Chen, and C. Li, “Boosted binary Harris hawks optimizer and feature selection,” Engineering with Computers, vol. 517, pp. 1–30, 2020.View at: Publisher Site | Google Scholar
    17. L. Hu, G. Hong, J. Ma, X. Wang, and H. Chen, “An efficient machine learning approach for diagnosis of paraquat-poisoned patients,” Computers in Biology and Medicine, vol. 59, pp. 116–124, 2015.View at: Publisher Site | Google Scholar
    18. L. Shen, H. Chen, Z. Yu et al., “Evolving support vector machines using fruit fly optimization for medical data classification,” Knowledge-Based Systems, vol. 96, pp. 61–75, 2016.View at: Publisher Site | Google Scholar
    19. X. Zhao, X. Zhang, Z. Cai et al., “Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients,” Computational Biology and Chemistry, vol. 78, pp. 481–490, 2019.View at: Publisher Site | Google Scholar
    20. Y. Xu, H. Chen, J. Luo, Q. Zhang, S. Jiao, and X. Zhang, “Enhanced Moth-flame optimizer with mutation strategy for global optimization,” Information Sciences, vol. 492, pp. 181–203, 2019.View at: Publisher Site | Google Scholar
    21. M. Wang and H. Chen, “Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis,” Applied Soft Computing Journal, vol. 88, Article ID 105946, 2020.View at: Publisher Site | Google Scholar
    22. Y. Chen, J. Li, H. Lu, and P. Yan, “Coupling system dynamics analysis and risk aversion programming for optimizing the mixed noise-driven shale gas-water supply chains,” Journal of Cleaner Production, vol. 278, Article ID 123209, 2020.View at: Google Scholar
    23. H. Tang, Y. Xu, A. Lin et al., “Predicting green consumption behaviors of students using efficient firefly grey wolf-assisted K-nearest neighbor classifiers,” IEEE Access, vol. 8, pp. 35546–35562, 2020.View at: Publisher Site | Google Scholar
    24. H.-J. Ma and G.-H. Yang, “Adaptive fault tolerant control of cooperative heterogeneous systems with actuator faults and unreliable interconnections,” IEEE Transactions on Automatic Control, vol. 61, no. 11, pp. 3240–3255, 2015.View at: Google Scholar
    25. H.-J. Ma and L.-X. Xu, “Decentralized adaptive fault-tolerant control for a class of strong interconnected nonlinear systems via graph theory,” IEEE Transactions on Automatic Control, vol. 66, 2020.View at: Google Scholar
    26. H. J. Ma, L. X. Xu, and G. H. Yang, “Multiple environment integral reinforcement learning-based fault-tolerant control for affine nonlinear systems,” IEEE Transactions on Cybernetics, vol. 51, pp. 1–16, 2019.View at: Publisher Site | Google Scholar
    27. J. Hu, M. Wang, C. Zhao, Q. Pan, and C. Du, “Formation control and collision avoidance for multi-UAV systems based on Voronoi partition,” Science China Technological Sciences, vol. 63, no. 1, pp. 65–72, 2020.View at: Publisher Site | Google Scholar
    28. C. Zhang, H. Li, Y. Qian, C. Chen, and X. Zhou, “Locality-constrained discriminative matrix regression for robust face identification,” IEEE Transactions on Neural Networks and Learning Systems, vol. 99, pp. 1–15, 2020.View at: Publisher Site | Google Scholar
    29. X. Zhang, D. Wang, Z. Zhou, and Y. Ma, “Robust low-rank tensor recovery with rectification and alignment,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 1, pp. 238–255, 2019.View at: Google Scholar
    30. X. Zhang, J. Wang, T. Wang, R. Jiang, J. Xu, and L. Zhao, “Robust feature learning for adversarial defense via hierarchical feature alignment,” Information Sciences, vol. 560, 2020.View at: Google Scholar
    31. X. Zhang, R. Jiang, T. Wang, and J. Wang, “Recursive neural network for video deblurring,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 03, p. 1, 2020.View at: Publisher Site | Google Scholar
    32. X. Zhang, T. Wang, J. Wang, G. Tang, and L. Zhao, “Pyramid channel-based feature attention network for image dehazing,” Computer Vision and Image Understanding, vol. 197-198, Article ID 103003, 2020.View at: Publisher Site | Google Scholar
    33. X. Zhang, T. Wang, W. Luo, and P. Huang, “Multi-level fusion and attention-guided CNN for image dehazing,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 3, p. 1, 2020.View at: Publisher Site | Google Scholar
    34. L. He, J. Shen, and Y. Zhang, “Ecological vulnerability assessment for ecological conservation and environmental management,” Journal of Environmental Management, vol. 206, pp. 1115–1125, 2018.View at: Publisher Site | Google Scholar
    35. Y. Chen, W. Zheng, W. Li, and Y. Huang, “Large group Activity security risk assessment and risk early warning based on random forest algorithm,” Pattern Recognition Letters, vol. 144, pp. 1–5, 2021.View at: Publisher Site | Google Scholar
    36. J. Hu, H. Zhang, Z. Li, C. Zhao, Z. Xu, and Q. Pan, “Object traversing by monocular UAV in outdoor environment,” Asian Journal of Control, vol. 25, 2020.View at: Google Scholar
    37. P. Tian, H. Lu, W. Feng, Y. Guan, and Y. Xue, “Large decrease in streamflow and sediment load of Qinghai-Tibetan Plateau driven by future climate change: a case study in Lhasa River Basin,” Catena, vol. 187, Article ID 104340, 2020.View at: Publisher Site | Google Scholar
    38. A. Stokes, C. Atger, A. G. Bengough, T. Fourcaud, and R. C. Sidle, “Desirable plant root traits for protecting natural and engineered slopes against landslides,” Plant and Soil, vol. 324, no. 1, pp. 1–30, 2009.View at: Publisher Site | Google Scholar
    39. T. B. Devi, A. Sharma, and B. Kumar, “Studies on emergent flow over vegetative channel bed with downward seepage,” Hydrological Sciences Journal, vol. 62, no. 3, pp. 408–420, 2017.View at: Google Scholar
    40. G. Ireland, M. Volpi, and G. Petropoulos, “Examining the capability of supervised machine learning classifiers in extracting flooded areas from Landsat TM imagery: a case study from a Mediterranean flood,” Remote Sensing, vol. 7, no. 3, pp. 3372–3399, 2015.View at: Publisher Site | Google Scholar
    41. L. Goodarzi and S. Javadi, “Assessment of aquifer vulnerability using the DRASTIC model; a case study of the Dezful-Andimeshk Aquifer,” Computational Research Progress in Applied Science & Engineering, vol. 2, no. 1, pp. 17–22, 2016.View at: Google Scholar
    42. K. Zhang, Q. Wang, L. Chao et al., “Ground observation-based analysis of soil moisture spatiotemporal variability across a humid to semi-humid transitional zone in China,” Journal of Hydrology, vol. 574, pp. 903–914, 2019.View at: Publisher Site | Google Scholar
    43. L. De Doncker, P. Troch, R. Verhoeven, K. Bal, P. Meire, and J. Quintelier, “Determination of the Manning roughness coefficient influenced by vegetation in the river Aa and Biebrza river,” Environmental Fluid Mechanics, vol. 9, no. 5, pp. 549–567, 2009.View at: Publisher Site | Google Scholar
    44. M. Fathi-Moghadam and K. Drikvandi, “Manning roughness coefficient for rivers and flood plains with non-submerged vegetation,” International Journal of Hydraulic Engineering, vol. 1, no. 1, pp. 1–4, 2012.View at: Google Scholar
    45. F.-C. Wu, H. W. Shen, and Y.-J. Chou, “Variation of roughness coefficients for unsubmerged and submerged vegetation,” Journal of Hydraulic Engineering, vol. 125, no. 9, pp. 934–942, 1999.View at: Publisher Site | Google Scholar
    46. M. K. Wood, “Rangeland vegetation-hydrologic interactions,” in Vegetation Science Applications for Rangeland Analysis and Management, vol. 3, pp. 469–491, Springer, 1988.View at: Publisher Site | Google Scholar
    47. C. Wilson, O. Yagci, H.-P. Rauch, and N. Olsen, “3D numerical modelling of a willow vegetated river/floodplain system,” Journal of Hydrology, vol. 327, no. 1-2, pp. 13–21, 2006.View at: Publisher Site | Google Scholar
    48. R. Yazarloo, M. Khamehchian, and M. R. Nikoodel, “Observational-computational 3d engineering geological model and geotechnical characteristics of young sediments of golestan province,” Computational Research Progress in Applied Science & Engineering (CRPASE), vol. 03, 2017.View at: Google Scholar
    49. G. E. Freeman, W. H. Rahmeyer, and R. R. Copeland, “Determination of resistance due to shrubs and woody vegetation,” International Journal of River Basin Management, vol. 19, 2000.View at: Google Scholar
    50. N. Kouwen and T. E. Unny, “Flexible roughness in open channels,” Journal of the Hydraulics Division, vol. 99, no. 5, pp. 713–728, 1973.View at: Publisher Site | Google Scholar
    51. S. Hosseini and J. Abrishami, Open Channel Hydraulics, Elsevier, Amsterdam, Netherlands, 2007.
    52. C. S. James, A. L. Birkhead, A. A. Jordanova, and J. J. O’Sullivan, “Flow resistance of emergent vegetation,” Journal of Hydraulic Research, vol. 42, no. 4, pp. 390–398, 2004.View at: Publisher Site | Google Scholar
    53. F. Huthoff and D. Augustijn, “Channel roughness in 1D steady uniform flow: Manning or Chézy?,,” NCR-days, vol. 102, 2004.View at: Google Scholar
    54. M. S. Sabegh, M. Saneie, M. Habibi, A. A. Abbasi, and M. Ghadimkhani, “Experimental investigation on the effect of river bank tree planting array, on shear velocity,” Journal of Watershed Engineering and Management, vol. 2, no. 4, 2011.View at: Google Scholar
    55. A. Errico, V. Pasquino, M. Maxwald, G. B. Chirico, L. Solari, and F. Preti, “The effect of flexible vegetation on flow in drainage channels: estimation of roughness coefficients at the real scale,” Ecological Engineering, vol. 120, pp. 411–421, 2018.View at: Publisher Site | Google Scholar
    56. S. E. Darby, “Effect of riparian vegetation on flow resistance and flood potential,” Journal of Hydraulic Engineering, vol. 125, no. 5, pp. 443–454, 1999.View at: Publisher Site | Google Scholar
    57. V. Kutija and H. Thi Minh Hong, “A numerical model for assessing the additional resistance to flow introduced by flexible vegetation,” Journal of Hydraulic Research, vol. 34, no. 1, pp. 99–114, 1996.View at: Publisher Site | Google Scholar
    58. T. Fischer-Antze, T. Stoesser, P. Bates, and N. R. B. Olsen, “3D numerical modelling of open-channel flow with submerged vegetation,” Journal of Hydraulic Research, vol. 39, no. 3, pp. 303–310, 2001.View at: Publisher Site | Google Scholar
    59. U. Stephan and D. Gutknecht, “Hydraulic resistance of submerged flexible vegetation,” Journal of Hydrology, vol. 269, no. 1-2, pp. 27–43, 2002.View at: Publisher Site | Google Scholar
    60. F. G. Carollo, V. Ferro, and D. Termini, “Flow resistance law in channels with flexible submerged vegetation,” Journal of Hydraulic Engineering, vol. 131, no. 7, pp. 554–564, 2005.View at: Publisher Site | Google Scholar
    61. W. Fu-sheng, “Flow resistance of flexible vegetation in open channel,” Journal of Hydraulic Engineering, vol. S1, 2007.View at: Google Scholar
    62. P.-f. Wang, C. Wang, and D. Z. Zhu, “Hydraulic resistance of submerged vegetation related to effective height,” Journal of Hydrodynamics, vol. 22, no. 2, pp. 265–273, 2010.View at: Publisher Site | Google Scholar
    63. J. K. Lee, L. C. Roig, H. L. Jenter, and H. M. Visser, “Drag coefficients for modeling flow through emergent vegetation in the Florida Everglades,” Ecological Engineering, vol. 22, no. 4-5, pp. 237–248, 2004.View at: Publisher Site | Google Scholar
    64. G. J. Arcement and V. R. Schneider, Guide for Selecting Manning’s Roughness Coefficients for Natural Channels and Flood Plains, US Government Printing Office, Washington, DC, USA, 1989.
    65. Y. Ding and S. S. Y. Wang, “Identification of Manning’s roughness coefficients in channel network using adjoint analysis,” International Journal of Computational Fluid Dynamics, vol. 19, no. 1, pp. 3–13, 2005.View at: Publisher Site | Google Scholar
    66. E. T. Engman, “Roughness coefficients for routing surface runoff,” Journal of Irrigation and Drainage Engineering, vol. 112, no. 1, pp. 39–53, 1986.View at: Publisher Site | Google Scholar
    67. M. Feizbahr, C. Kok Keong, F. Rostami, and M. Shahrokhi, “Wave energy dissipation using perforated and non perforated piles,” International Journal of Engineering, vol. 31, no. 2, pp. 212–219, 2018.View at: Publisher Site | Google Scholar
    68. M. Farzadkhoo, A. Keshavarzi, H. Hamidifar, and M. Javan, “Sudden pollutant discharge in vegetated compound meandering rivers,” Catena, vol. 182, Article ID 104155, 2019.View at: Publisher Site | Google Scholar
    69. V. T. Chow, Open-channel Hydraulics, Mcgraw-Hill Civil Engineering Series, Chennai, TN, India, 1959.
    70. X. Zhang, R. Jing, Z. Li, Z. Li, X. Chen, and C.-Y. Su, “Adaptive pseudo inverse control for a class of nonlinear asymmetric and saturated nonlinear hysteretic systems,” IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 4, pp. 916–928, 2020.View at: Google Scholar
    71. C. Zuo, Q. Chen, L. Tian, L. Waller, and A. Asundi, “Transport of intensity phase retrieval and computational imaging for partially coherent fields: the phase space perspective,” Optics and Lasers in Engineering, vol. 71, pp. 20–32, 2015.View at: Publisher Site | Google Scholar
    72. C. Zuo, J. Sun, J. Li, J. Zhang, A. Asundi, and Q. Chen, “High-resolution transport-of-intensity quantitative phase microscopy with annular illumination,” Scientific Reports, vol. 7, no. 1, pp. 7654–7722, 2017.View at: Publisher Site | Google Scholar
    73. B.-H. Li, Y. Liu, A.-M. Zhang, W.-H. Wang, and S. Wan, “A survey on blocking technology of entity resolution,” Journal of Computer Science and Technology, vol. 35, no. 4, pp. 769–793, 2020.View at: Publisher Site | Google Scholar
    74. Y. Liu, B. Zhang, Y. Feng et al., “Development of 340-GHz transceiver front end based on GaAs monolithic integration technology for THz active imaging array,” Applied Sciences, vol. 10, no. 21, p. 7924, 2020.View at: Publisher Site | Google Scholar
    75. J. Hu, H. Zhang, L. Liu, X. Zhu, C. Zhao, and Q. Pan, “Convergent multiagent formation control with collision avoidance,” IEEE Transactions on Robotics, vol. 36, no. 6, pp. 1805–1818, 2020.View at: Publisher Site | Google Scholar
    76. M. B. Movahhed, J. Ayoubinejad, F. N. Asl, and M. Feizbahr, “The effect of rain on pedestrians crossing speed,” Computational Research Progress in Applied Science & Engineering (CRPASE), vol. 6, no. 3, 2020.View at: Google Scholar
    77. A. Li, D. Spano, J. Krivochiza et al., “A tutorial on interference exploitation via symbol-level precoding: overview, state-of-the-art and future directions,” IEEE Communications Surveys & Tutorials, vol. 22, no. 2, pp. 796–839, 2020.View at: Publisher Site | Google Scholar
    78. W. Zhu, C. Ma, X. Zhao et al., “Evaluation of sino foreign cooperative education project using orthogonal sine cosine optimized kernel extreme learning machine,” IEEE Access, vol. 8, pp. 61107–61123, 2020.View at: Publisher Site | Google Scholar
    79. G. Liu, W. Jia, M. Wang et al., “Predicting cervical hyperextension injury: a covariance guided sine cosine support vector machine,” IEEE Access, vol. 8, pp. 46895–46908, 2020.View at: Publisher Site | Google Scholar
    80. Y. Wei, H. Lv, M. Chen et al., “Predicting entrepreneurial intention of students: an extreme learning machine with Gaussian barebone harris hawks optimizer,” IEEE Access, vol. 8, pp. 76841–76855, 2020.View at: Publisher Site | Google Scholar
    81. A. Lin, Q. Wu, A. A. Heidari et al., “Predicting intentions of students for master programs using a chaos-induced sine cosine-based fuzzy K-Nearest neighbor classifier,” Ieee Access, vol. 7, pp. 67235–67248, 2019.View at: Publisher Site | Google Scholar
    82. Y. Fan, P. Wang, A. A. Heidari et al., “Rationalized fruit fly optimization with sine cosine algorithm: a comprehensive analysis,” Expert Systems with Applications, vol. 157, Article ID 113486, 2020.View at: Publisher Site | Google Scholar
    83. E. Rodríguez-Esparza, L. A. Zanella-Calzada, D. Oliva et al., “An efficient Harris hawks-inspired image segmentation method,” Expert Systems with Applications, vol. 155, Article ID 113428, 2020.View at: Publisher Site | Google Scholar
    84. S. Jiao, G. Chong, C. Huang et al., “Orthogonally adapted Harris hawks optimization for parameter estimation of photovoltaic models,” Energy, vol. 203, Article ID 117804, 2020.View at: Publisher Site | Google Scholar
    85. Z. Xu, Z. Hu, A. A. Heidari et al., “Orthogonally-designed adapted grasshopper optimization: a comprehensive analysis,” Expert Systems with Applications, vol. 150, Article ID 113282, 2020.View at: Publisher Site | Google Scholar
    86. A. Abbassi, R. Abbassi, A. A. Heidari et al., “Parameters identification of photovoltaic cell models using enhanced exploratory salp chains-based approach,” Energy, vol. 198, Article ID 117333, 2020.View at: Publisher Site | Google Scholar
    87. M. Mahmoodi and K. K. Aminjan, “Numerical simulation of flow through sukhoi 24 air inlet,” Computational Research Progress in Applied Science & Engineering (CRPASE), vol. 03, 2017.View at: Google Scholar
    88. F. J. Golrokh and A. Hasan, “A comparison of machine learning clustering algorithms based on the DEA optimization approach for pharmaceutical companies in developing countries,” ENG Transactions, vol. 1, 2020.View at: Google Scholar
    89. H. Chen, A. A. Heidari, H. Chen, M. Wang, Z. Pan, and A. H. Gandomi, “Multi-population differential evolution-assisted Harris hawks optimization: framework and case studies,” Future Generation Computer Systems, vol. 111, pp. 175–198, 2020.View at: Publisher Site | Google Scholar
    90. J. Guo, H. Zheng, B. Li, and G.-Z. Fu, “Bayesian hierarchical model-based information fusion for degradation analysis considering non-competing relationship,” IEEE Access, vol. 7, pp. 175222–175227, 2019.View at: Publisher Site | Google Scholar
    91. J. Guo, H. Zheng, B. Li, and G.-Z. Fu, “A Bayesian approach for degradation analysis with individual differences,” IEEE Access, vol. 7, pp. 175033–175040, 2019.View at: Publisher Site | Google Scholar
    92. M. M. A. Malakoutian, Y. Malakoutian, P. Mostafapour, and S. Z. D. Abed, “Prediction for monthly rainfall of six meteorological regions and TRNC (case study: north Cyprus),” ENG Transactions, vol. 2, no. 2, 2021.View at: Google Scholar
    93. H. Arslan, M. Ranjbar, and Z. Mutlum, “Maximum sound transmission loss in multi-chamber reactive silencers: are two chambers enough?,,” ENG Transactions, vol. 2, no. 1, 2021.View at: Google Scholar
    94. N. Tonekaboni, M. Feizbahr, N. Tonekaboni, G.-J. Jiang, and H.-X. Chen, “Optimization of solar CCHP systems with collector enhanced by porous media and nanofluid,” Mathematical Problems in Engineering, vol. 2021, Article ID 9984840, 12 pages, 2021.View at: Publisher Site | Google Scholar
    95. Z. Niu, B. Zhang, J. Wang et al., “The research on 220GHz multicarrier high-speed communication system,” China Communications, vol. 17, no. 3, pp. 131–139, 2020.View at: Publisher Site | Google Scholar
    96. B. Zhang, Z. Niu, J. Wang et al., “Four‐hundred gigahertz broadband multi‐branch waveguide coupler,” IET Microwaves, Antennas & Propagation, vol. 14, no. 11, pp. 1175–1179, 2020.View at: Publisher Site | Google Scholar
    97. Z.-Q. Niu, L. Yang, B. Zhang et al., “A mechanical reliability study of 3dB waveguide hybrid couplers in the submillimeter and terahertz band,” Journal of Zhejiang University Science, vol. 1, no. 1, 1998.View at: Google Scholar
    98. B. Zhang, D. Ji, D. Fang, S. Liang, Y. Fan, and X. Chen, “A novel 220-GHz GaN diode on-chip tripler with high driven power,” IEEE Electron Device Letters, vol. 40, no. 5, pp. 780–783, 2019.View at: Publisher Site | Google Scholar
    99. M. Taleghani and A. Taleghani, “Identification and ranking of factors affecting the implementation of knowledge management engineering based on TOPSIS technique,” ENG Transactions, vol. 1, no. 1, 2020.View at: Google Scholar
    Figure 1. The bathymetry provided with the benchmark problem.

    Performance Assessment of NAMI DANCE in Tsunami Evolution and Currents Using a Benchmark Problem

    by 
    Deniz Velioglu
     1,*,
    Rozita Kian
     2,
    Ahmet Cevdet Yalciner
     3 and
    Andrey Zaytsev
     4
    1Civil Engineering Department, Middle East Technical University, Ankara 06800, Turkey
    2Ocean Engineering Department, University of Rhode Island, Narragansett, RI 02882, USA
    3Civil Engineering Department, Middle East Technical University, Ankara 06800, Turkey
    4Department of Applied Mathematics, Nizhny Novgorod State Technical University, Nizhny Novgorod 603950, Russia
    *
    Author to whom correspondence should be addressed.
    Academic Editor: Richard P. Signell
    J. Mar. Sci. Eng. 2016, 4(3), 49; https://doi.org/10.3390/jmse4030049
    Received: 5 July 2016 / Revised: 2 August 2016 / Accepted: 12 August 2016 / Published: 18 August 2016
    (This article belongs to the Special Issue Selected Papers from the 14th Estuarine and Coastal Modeling Conference)

    Abstract

    쓰나미 진화, 전파 및 침수의 수치 모델링은 현상에 관련된 수많은 매개 변수로 인해 복잡합니다. 쓰나미 모션을 해결하는 숫자 코드의 성능과 흐름 및 속도 패턴을 평가하는 것이 중요합니다. NAMI DANCE는 긴 파도 모델링을 위해 개발된 계산 도구입니다.

    쓰나미 생성, 전파 및 침수 메커니즘의 수치 모델링 및 효율적인 시각화를 제공하고 쓰나미 매개 변수를 계산합니다. 긴 파도 이론에서, 물 입자의 수직 움직임은 압력 분포에 영향을 미치지 않습니다.

    이러한 근사치와 소홀히 하는 수직 가속을 기반으로 질량 보존 및 모멘텀 방정식은 2차원 깊이 평균 방정식으로 줄어듭니다. NAMI DANCE는 유한차 계산 방법을 사용하여 긴 파도 문제에서 선형 및 비선형 형태의 깊이 평균 얕은 수식을 해결합니다.

    이 연구에서 NAMI DANCE는 미국 포틀랜드에서 열린 2015 년 국립 쓰나미 위험 완화 프로그램 (NTHMP) 연례 회의에서 논의된 벤치 마크 문제에 적용됩니다.

    벤치마크 문제는 하나의 독방 파도가 해양 섬 특징이 있는 삼각형 모양의 선반을 전파하는 일련의 실험을 특징으로 합니다. 이 문제는 섬 부근에서 상세한 무료 표면 고도 및 속도 의 타임 시리즈를 제공합니다. 결과를 비교한 결과, NAMI DANCE는 긴 파도 진화, 전파, 증폭 및 쓰나미 전류를 만족스럽게 예측할 수 있음을 보여주었습니다.

    키워드: 수치 모델링;쓰나미 전류;깊이 평균 방정식;벤치마크,numerical modeling; tsunami currents; depth-averaged equation; benchmark

    쓰나미는 해저 지진, 수중 산사태, 화산 폭발 또는 큰 운석 파업으로 인한 해저의 갑작스런 움직임에 의해 생성되는 큰 파도입니다. 쓰나미 파도는이 현상의 가장 파괴적인 매개 변수로 받아 들여진다; 그러나 큰 파도 움직임에 의해 트리거되는 전류는 경우에 따라 매우 치명적일 수 있습니다.

    분지 공명 및 기하학적 증폭은 폐쇄 된 분지에서 쓰나미 영향의 지역 배율에 대한 두 가지 합리적으로 잘 이해된 메커니즘이며, 일반적으로 항구 또는 항구에서 쓰나미 위험 잠재력을 추정 할 때 조사 되는 메커니즘입니다. 반면에 전류에 대한 이해력과 예측 능력은부족하다[1]. 

    이 연구는 수치 도구를 사용하여 쓰나미 진화, 전파 및 증폭뿐만 아니라 쓰나미 전류의 추정에 2 차원 깊이 평균 천수(shallow water)모델 방정식의 충분성을 조사하는 것을 목표로; 즉 나미 댄스. 1970 년대 이후, 독방 파도는 일반적으로 실험 및 수학 연구에서, 쓰나미를 모델링하는 데 사용되었습니다[2]. 

    이러한 점에서 수치 코드는 복잡한 목욕을 통해 단일 독방 파도의 진화와 전파에 초점을 맞춘 벤치마크 문제에 적용됩니다. 이 문제는 선반의 근해에 위치한 섬 특징이 있는 삼각형 모양의 선반을 전파할 때 단일 고독한 파도의 변형을 분석하는 일련의 실험을 설명합니다. 섬 부근에 형성되는 해류도 실험에서 조사된다.

    이 연구에 사용된 벤치마크 문제는 미국 포틀랜드에서 개최된 2015 년 국립 쓰나미 위험 완화 프로그램 (NTHMP) 워크샵의 벤치마크 문제 #5.3]. 벤치마크 데이터와 수치 결과를 비교하여 2차원 깊이 평균 얕은 수식은 쓰나미 파도 진화와 해류에 대해 만족스러운 결과를 제공하므로 쓰나미 완화 전략을 결정하는 동안 사용하기에 충분한 도구임을 관찰합니다.

    Figure 1. The bathymetry provided with the benchmark problem.
    Figure 1. The bathymetry provided with the benchmark problem.
    Figure 2. Model parameters: (a) bathymetry of the numerical model, NAMI DANCE; (b) incoming wave.
    Figure 2. Model parameters: (a) bathymetry of the numerical model, NAMI DANCE; (b) incoming wave.
    Figure 3. Comparison of free surface elevation (FSE) results: (a) X = 7.5 m and Y = 0.0 m at Gage 1; (b) X = 13.0 m and Y = 0.0 m at Gage 2; (c) X = 21.0 m and Y = 0.0 m at Gage 3; (d) X = 7.5 m and Y = 5.0 m at Gage 4; (e) X = 13.0 m and Y = 5.0 m at Gage 5; (f) X = 21.0 m and Y = 5.0 m at Gage 6; (g) X = 25.0 m and Y = 0.0 m at Gage 7; (h) X = 25.0 m and Y = 5.0 m at Gage 8. Black line represents benchmark data, red line represents numerical results.
    Figure 3. Comparison of free surface elevation (FSE) results: (a) X = 7.5 m and Y = 0.0 m at Gage 1; (b) X = 13.0 m and Y = 0.0 m at Gage 2; (c) X = 21.0 m and Y = 0.0 m at Gage 3; (d) X = 7.5 m and Y = 5.0 m at Gage 4; (e) X = 13.0 m and Y = 5.0 m at Gage 5; (f) X = 21.0 m and Y = 5.0 m at Gage 6; (g) X = 25.0 m and Y = 0.0 m at Gage 7; (h) X = 25.0 m and Y = 5.0 m at Gage 8. Black line represents benchmark data, red line represents numerical results.
    Figure 4. Comparison of results: (a) horizontal velocity in x-direction, U, recorded at X = 13.0 m, Y = 0.0 m and Z = 0.75 m at Gage 2; (b) horizontal velocity in y-direction, V, recorded at X = 13.0 m, Y = 0.0 m and Z = 0.75 m at Gage 2; (c) horizontal velocity in x-direction, U, recorded at X = 21.0 m, Y = −5.0 m and Z = 0.77 m at Gage 9; (d) horizontal velocity in y-direction, V, recorded at X = 21.0 m, Y = −5.0 m and Z = 0.77 m at Gage 9. Black line represents benchmark data, red line represents numerical results.
    Figure 4. Comparison of results: (a) horizontal velocity in x-direction, U, recorded at X = 13.0 m, Y = 0.0 m and Z = 0.75 m at Gage 2; (b) horizontal velocity in y-direction, V, recorded at X = 13.0 m, Y = 0.0 m and Z = 0.75 m at Gage 2; (c) horizontal velocity in x-direction, U, recorded at X = 21.0 m, Y = −5.0 m and Z = 0.77 m at Gage 9; (d) horizontal velocity in y-direction, V, recorded at X = 21.0 m, Y = −5.0 m and Z = 0.77 m at Gage 9. Black line represents benchmark data, red line represents numerical results.

    References

    1. Lynett, P.J.; Borrero, J.C.; Weiss, R.; Son, S.; Greer, D.; Renteria, W. Observations and modeling of tsunami-induced currents in ports and harbors. EPSL 2012, 327, 68–74. [Google Scholar]
    2. Madsen, P.A.; Fuhrman, D.R.; Schaffer, H.A. On the solitary wave paradigm for tsunamis. J. Geophys. Res. 2008, 113. [Google Scholar] [CrossRef]
    3. NTHMP Mapping & Modeling Benchmarking Workshop: Tsunami Currents. Benchmark #5. Available online: http://coastal.usc.edu/currents_workshop/problems/prob5.html (accessed on 2 August 2016).
    4. Onat, Y.; Yalciner, A.C. Initial stage of database development for tsunami warning system along Turkish coasts. Ocean Eng. 2013, 74, 141–154. [Google Scholar] [CrossRef]
    5. Kian, R.; Yalciner, A.C.; Aytore, B.; Zaytsev, A. Wave Amplification and Resonance in Enclosed Basins; A Case Study in Haydarpasa Port of Istanbul. In Proceedings of the 2015 IEEE/OES Eleventh Current, Waves and Turbulence Measurement, St. Petersburg, VA, USA, 2–6 March 2015; Volume 11, pp. 1–7.
    6. Patel, V.M.; Dholakia, M.B.; Singh, A.P. Emergency preparedness in the case of Makran tsunami: A case study on tsunami risk visualization for the western parts of Gujarat, India. Geomat. Nat. Hazards Risk 2016, 7, 826–842. [Google Scholar] [CrossRef]
    7. Yalciner, A.C.; Pelinovsky, E.; Zaytsev, A.; Kurkin, A.; Ozer, C.; Karakus, H.; Ozyurt, G. Modeling and visualization of tsunamis: Mediterranean examples. In Tsunami and Nonlinear Waves, 1st ed.; Kundu, A., Ed.; Springer: Berlin, Germany, 2007; pp. 273–283. [Google Scholar]
    8. Synolakis, C.E.; Bernard, E.N.; Titov, V.; Kanoglu, U.; Gonzalez, F. Validation and verification of tsunami numerical models. PAGEOPH 2008, 165, 2197–2228. [Google Scholar] [CrossRef]
    9. Yalciner, A.C.; Zaytsev, A.; Kanoglu, U.; Velioglu, D.; Dogan, G.G.; Kian, R.; Sharghivand, N.; Aytore, B. NTHMP Mapping and Modeling Benchmarking Workshop: Tsunami Currents. Available online: http://coastal.usc.edu/currents_workshop/presentations/Yalciner.pdf (accessed on 2 August 2016).
    10. Ozer, C.; Yalciner, A.C. Sensitivity study of hydrodynamic parameters during numerical simulations of tsunami inundation. PAGEOPH 2011, 168, 2083–2095. [Google Scholar]
    11. Sozdinler, C.O.; Yalciner, A.C.; Zaytsev, A. Investigation of tsunami hydrodynamic parameters in inundation zones with different structural layouts. PAGEOPH 2014, 172, 931–952. [Google Scholar] [CrossRef]
    12. Sozdinler, C.O.; Yalciner, A.C.; Zaytsev, A.; Suppasri, A.; Imamura, F. Investigation of hydrodynamic parameters and the effects of breakwaters during the 2011 Great East Japan Tsunami in Kamaishi Bay. PAGEOPH 2015, 172, 3473–3491. [Google Scholar] [CrossRef]
    13. Velioglu, D.; Kian, R.; Yalciner, A.C.; Zaytsev, A. Validation and Performance Comparison of Numerical Codes for Tsunami Inundation. In Proceedings of the 2015 American Geophysical Union Fall Meeting, San Francisco, CA, USA, 14–18 December 2015.
    14. Velioglu, D.; Kian, R.; Yalciner, A.C.; Zaytsev, A. Validation and Comparison of 2D and 3D Codes for Nearshore Motion of Long Waves Using Benchmark Problems. In Proceedings of the 2016 European Geosciences Union, Vienna, Austria, 17–22 April 2016.
    15. Dilmen, D.I.; Kemec, S.; Yalciner, A.C.; Düzgün, S.; Zaytsev, A. Development of a tsunami inundation map in detecting tsunami risk in Gulf of Fethiye, Turkey. PAGEOPH 2015, 172. [Google Scholar] [CrossRef]
    16. Heidarzadeh, M.; Krastel, S.; Yalciner, A.C. The state-of-the-art numerical tools for modeling landslide tsunamis: A short review. In Submarine Mass Movements and Their Consequences, 6th ed.; Sebastian, K., Jan-Hinrich, B., David, V., Michael, S., Christian, B., Roger, U., Jason, C., Katrin, H., Michael, S., Carl, B.H., Eds.; Springer: Bern, Switzerland, 2013; Volume 37, pp. 483–495. [Google Scholar]
    17. Yalciner, A.C.; Gülkan, P.; Dilmen, D. I.; Aytore, B.; Ayca, A.; Insel, I.; Zaytsev, A. Evaluation of tsunami scenarios for western Peloponnese, Greece. Boll. Geofis. Teor. Appl. 2014, 55, 485–500. [Google Scholar]
    18. Zahibo, N.; Pelinovsky, E.; Kurkin, A.; Kozelkov, A. Estimation of far-field tsunami potential for the Caribbean Coast based on numerical simulation. Sci. Tsunami Hazards 2003, 21, 202–222. [Google Scholar]
    19. Swigler, D.T. Laboratory Study Investigating the Three-dimensıonal Turbulence and Kinematic Properties Associated with a Breaking Solitary Wave. Master’s Thesis, Texas A&M University, College Station, TX, USA, August 2009. [Google Scholar]
    20. National Tsunami Hazard Mitigation Program. Proceedings and Results of the 2011 NTHMP Model Benchmarking Workshop. Available online: http://nws.weather.gov/nthmp/documents/nthmpWorkshopProcMerged.pdf (accessed on 21 July 2016).
    논문 원문 바로가기
    Sketch of a subaerial landslide-induced tsunami wave

    NUMERICAL SIMULATION OF THREE-DIMENSIONAL TSUNAMI GENERATION BY SUBAERIAL LANDSLIDES

    SUBAERIAL LANDSLIDES에 의한 3 차원 쓰나미 생성의 수치 시뮬레이션

    A Thesis by GYEONG-BO KIM
    Submitted to the Office of Graduate Studies of
    Texas A&M University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE

    초록

    쓰나미는 해저 지진으로 인해 종종 발생하는 해안 지역에 영향을 미치는 가장 치명적인 자연 현상 중 하나입니다. 그럼에도 불구하고 밀폐된 분지, 즉 피요르드, 저수지 및 호수에서, 수중 또는 해저 산사태는 유사한 결과로 파괴적인 쓰나미를 일으킬 수 있습니다. 큰 수역에 충돌하는 수중 또는 해저 산사태가 쓰나미를 발생시킬 수 있지만, 해저 산사태는 대응하는 것보다 훨씬 더 위협적인 쓰나미 발생원입니다.

    이 연구에서 우리는 지하 산사태에 의한 쓰나미 발생에 대한 실험실 규모의 실험을 수치 모델과 통합하는 것을 목표로 합니다. 이 작업은 2 개의 3 차원 Navier-Stokes (3D-NS) 모델, FLOW-3D 및 당사가 개발 한 모델 TSUNAMI3D의 수치 검증에 중점을 둡니다.

    이 모델은 Georgia Institute of Technology의 Hermann Fritz 박사가 이끄는 쓰나미 연구팀이 수행 한 이전의 대규모 실험실 실험을 기반으로 검증되었습니다. 일련의 실험실 실험에서 세 가지 대규모 산사태 시나리오, 즉 피요르드 유사, 곶 및 원거리 해안선이 선택되었습니다. 이러한 시나리오는 복잡한 파도 장이 지하 산사태에 의해 생성 될 수 있음을 보여주었습니다.

    파동 장의 정확한 정의와 진화는 뒤 따르는 쓰나미와 해안 지역에서의 영향을 정확하게 모델링하는 데 중요합니다. 이 연구에서는 수치 결과와 실험실 실험을 비교합니다. 토양 유변학에 대한 방법론과 주요 매개 변수는 모델 검증을 위해 정의됩니다. 모델의 결과는 쓰나미 수치 모델의 검증을 위해 National Tsunami Hazard Mitigation Program (NTHMP), National Oceanic and Atmospheric Administration (NOAA) 지침에 명시된 허용 오차 미만일 것으로 예상됩니다.

    이 연구의 궁극적 인 목표는 멕시코만과 카리브해 지역의 침수지도를 구축하는 데 필요한 해저 산사태 쓰나미에 대한 3D 모델의 실제 적용을 위한 더 나은 쓰나미 계산 도구를 얻는 것입니다.

    주요 분석 이미지

    Sketch of a subaerial landslide-induced tsunami wave
    Figure 1.4: Sketch of a subaerial landslide-induced tsunami wave: (a) cross section
    defining parameters in the direction of slide motion; (b) plan view defining coordinate
    system to reference and quantify the generated tsunami wave
    A typical computational domain with moving and stationary objects
    Figure 2.1: A typical computational domain with moving and stationary objects. Courtesy Dr. Juan J. Horrillo, Texas A&M at Galveston.
    A typical tsunami computational domain
    Figure 2.2: A typical tsunami computational domain: (a) Location of variables in a computational cell. The horizontal (ui,j ) and vertical (vi,j ) velocity components are located at the right cell face and top cell faces, respectively. The pressure pi,j and VOF function Fi,j are located at the cell center; (b) Volume and side cell apertures. Courtesy Dr. Juan J. Horrillo, Texas A&M at Galveston.
    Figure A.1: Configurations of boundary conditions for fjord case: FLOW-3D
    Figure A.1: Configurations of boundary conditions for fjord case: FLOW-3D

    <자료 안내>

    원문 다운로드

    Water & Environmental 논문 자료보기

    Landslide-Induced Wave Hazard

    Landslide-Induced Wave Hazard 

    Figure 1. The outskirts of Chungtangh village

    인도 Sikkim에 위치한 The Teesta III Hydropower Project는 가파르고 좁은 히말라야 계곡에 위치한 60m의 Concrete Face Rockfill Dam (CFRD)이 포함되어 있습니다. 이 계곡은 지진 활동이 활발하며 가파른 경사면은 산사태를 발생시킬 수 있습니다. 댐 상류 저수지의 산사태로 CFRD를 범람할 수 있다는 우려가 있었습니다. 몇 초 이상 과도하게 지속되면 오버플로우로 인해 CFRD가 잘못될 수 있습니다. 비록 댐이 무너지지 않았지만, 여전히 Chungtangh에 있는 상류쪽 작은 마을은 홍수가 날 것이라는 우려가 있었습니다.

    Teesta강 계곡의 가장 가파른 경사면은 댐의 바로 상류에 위치해 있는데, 댐의 산사태가 가장 일어날 가능성이 높은 지역입니다. 이 분석의 목적은 저수지에 대한 산사태를 시뮬레이션하고 그 결과로 발생하는 파도가 댐에 넘치는지 여부를 결정하는 것이었습니다.

    Moving Objects Model Used to Simulate Landslide                                      

    Tecsult는 저수지의 침전물과 퇴적물을 모델링하는데 성공적이었기 때문에 FLOW-3D를 선택하여 이를 시뮬레이션하였습니다. 저수지의 시뮬레이션은 시작점으로 사용되었습니다. FLOW-3D의 Moving Objects모델은 산사태를 시뮬레이션하는데 사용되었으며 VOF모델은 웨이브 생성을 시뮬레이션하는 데 사용되었습니다.

    저수지의 산사태를 추정하기 위해서는 여러가지 방법이 고려되었습니다. 경험적 방법은 흔히 산사태가 발생한 파도를 평가하는데 사용되지만, 이러한 방법은 여러가지 면에서 부족합니다. 이러한 방법은 근접 필드 또는 스플래시 영역에 대한 정보를 제공하지 않습니다. 댐은 슬라이드 면과 매우 가깝기 때문에 스플래시 영역을 아는 것이 중요했습니다. CFRD는 몇 초 이상 overflow를 견딜 수 없었습니다. FLOW-3D는 미끄러운 지형 질량과 물 사이의 완전 결합된 상호 작용을 계산하여 시나리오를 3 차원에서 시뮬레이션하는 방법을 제공합니다.

    이 문제를 시뮬레이션하기 위해 간단하고 작은 크기의 자유 낙하 블록으로 구성된 실험과 비교하였습니다. 이 경우는 아래 동영상에 나와 있습니다. 그 결과로 생긴 파도 높이는 그 실험과 잘 맞았습니다.

    이 모델의 STL파일은 FLOW-3D로 직접 가져옵니다. 예상 산사태 지역의 크기는 지질 정보와 주변 산사태 관측치를 바탕으로 결정되었습니다. 30,000m³, 100m높이의 산사태가 310만 셀의 메쉬로 시뮬레이션 되었습니다. 높이가 1m인 측면 3m의 균일한 셀을 사용했습니다. 최대 슬라이딩 속도는 진입 지점에서 23m/s에 도달했습니다. 파도는 높이 8m, 속도 10m/s로 댐에 도달하여 몇 초 동안 범람했습니다. 그 결과로 상류 마을에서는 홍수가 나타나지 않았습니다.

    Figure 3. Prediction of wave height in the splash zone and near field in a small reservoir, with refraction.

    Figure 4. Wave heights plotted against each other

    Figure 5. Downstream view of TEEST III dam and water intake CATIA model

    Conclusions

    이 작업의 주된 관심사는 댐의 범람으로 인해 댐과 Chungtangh 마을이 파괴될 수 있었다는 것입니다. 그러나 시뮬레이션에 따르면 댐은 잠시 동안만 범람했고 파도는 마을에 닿지 않았습니다. Chungtangh마을은 강 위에 충분히 높기 때문에, 그것을 범람시키기 위해서는 상당한 파도의 높이가 필요할 것입니다.

     

    Aerial Landslide Generated Wave Simulations

    Aerial Landslide Generated Wave Simulations

    Aerial Landslide Generated Wave(ALGW)는 수역에 영향을 미치는 빠른 슬라이드의 결과이다. 이것은 암석에 의해 생성된 작은 파도 이거나, 3000만 입방 미터의 암석으로 인한 500m를 초과하는 파도 일 수도 있다.
    공학적 관점에서 보면 ALGW는 근접한 해안을 따라 인간이 거주하는 인구/자산이 있는 수역에서 발생할 때 큰 관심을 가진다. 여기서 파동이 발생하면 해안선이 파손되고 홍수가 날수 있으며, 댐붕괴로 인한 사망까지 일으킬 수 있다(Müller-Salzburg, 1987). 결과적으로, ALGW에 의해 야기되는 최대 파도 상승을 예측하는 것은 경제적, 환경적, 안전상의 이유로 매우 중요합니다.
    안타깝게도 분석적인 솔루션이 없는 매우 복잡한 문제로, 유체 역학적인 측면에서뿐만 아니라 지질학적인 관점(즉, 크기/기하학적인 슬라이드의 밀도 프로파일)에서도 마찬가지입니다. 이와 같이, 대부분의 현장 별 ALGW 최대 파형 예측은 확장된 물리적 모델을 사용하여 평가되었다. 일부는 전산유체역학(CFD) 소프트웨어를 기반으로 할 수도 있지만 비용이 많이 들며, 특히 풀 스케일 3차원 문제의 경우 정확성에 대한 논쟁의 대상이 되고 있습니다.
    그러나 컴퓨터 하드웨어와 CFD소프트웨어가 계속 발전함에 따라 이제 CFD를 사용하여 ALGW를 실제로 시뮬레이션할 수 있게 되었습니다. 이와 같이 본 연구는 고 충실도의 물리적 모델 데이터를 FLOW-3D와 비교하여 ALGW를 CFD시뮬레이션을 검증하기 위한 지속적인 노력으로 진척시키는 것을 목표로 한다.
    다음 절에서는 실제 및 수치 모델 설정에 대한 개요를 제공한다. 뿐만 아니라, 생성된 데이터와 간단한 비교를 제공한다.

    Experimental Setup
    물리적 실험은 Northwest Hydraulic Consultants 노스 밴쿠버, 캐나다 실험실에서 만들어졌고 실험을 거쳤다. 그것은 30° 경사의 서쪽 벽을 가진 0.5미터 폭의 수로, 45°의 경사진 동쪽 벽, 그리고 두개의 북쪽과 남쪽 측면에 수직 벽, 그리고 1.025m의 수평 단면을 가진 0.610m 너비의 수로로 구성되었다. ALGW를 생성하고 평가하기 위해, 45° 경사 노즈를 가진 0.177×0.305×0.305m의 아크릴 박스를 사용한 6초 시험을 사용했다.
    이 슬라이드를 놓았을 때, 슬라이드는 (중력에 의해) 0.607m 심층수에 충돌하기 전에 서쪽 경사면에서 0.768m 아래로 이동했다. 그 후, 물을 통해 또 다른 1.05m를 이동하여 정지 블록을 치기 시작했다. 슬라이드 가속 및 변위뿐만 아니라 파고 높이는 6 초 실험 전체에 대해 100Hz의 주파수에서 기록되었다. 이 데이터를 수집하는 데 사용 된 도구는 다음과 같다.

    • 컴퓨터화된 데이터 수집 시스템
    • 슬라이드의 시간에 따라 이동 한 거리를 측정하는 문자열 가변 저항기
    • 슬라이드 가속도를 측정하는 1 차원 가속도계
    • 물의 주요 본체 내에 배치 된 3 개의 1 차원 커패시턴스 웨이브 – 프로브
    • 웨이브 런업을 캡처하기 위해 동쪽 경사면을 따라 사용되는 저항 사다리꼴 웨이브 프로브
    • 타이밍 스위치 캡처 슬라이드 릴리스 시간 사용
    • 흑백 비디오 카메라

    테스트가 반복 가능하고 오작동이 발생하지 않았는지 확인하기 위해 테스트를 5 번 반복하고 각 장비에 대해 평균을 구했다.

    Numerical Model Setup
    물리적 실험의 전산화 된 3 차원 모델을 제작한 STL 파일을 FLOW-3D로 가져왔다. 일단 FLOW-3D에 들어간 3D 모델은 약 1,370 만개의 0.0075m 크기의 정사각형 셀로 이산화되었고, 벽을 둘러싸고있는 6 개의면 각각에 ‘wall’경계가 사용되었다.
    슬라이드를 일반적인 이동 물체로 설정하고, 물리 모델로부터 수집 된 데이터(즉, 가속 및 변위 데이터의 후 처리)에 기초하여 속도가 주어졌다. 동서면 경사면의 표면 거칠기는 0.00025m으로 설정되었다. 모델링 된 유체는 293k의 물이었고, 동적 RNG 난류 모델이 기본 설정과 함께 사용되었다(implicit pressure solve; and, explicit viscous stress, free surface pressure, advection, moving object/fluid coupling solvers).
    물리적 모델과 마찬가지로 FLOW-3D는 6 초의 시간을 시뮬레이트하지만 실제 모델과 같이 매 0.01 초가 아닌 0.02 초마다 데이터를 저장하였다(데이터 관리 관점에서 선택하였음).

    Result

    FLOW-3D 실험의 결과는 그림에 나와 있다. 4개의 웨이브 각각에 대해 실험 시간 동안 파고를 보여준다. 이와 같이, 제시된 파도 높이는 단순히 flume을 통해 전파되는 파도의 구현(즉, 2 차원의 경우에서 볼 수있는 것)이 아니라 오히려 여러 파도의 상호 작용으로 인한 파도 높이를 초래한다.

    • 슬라이드 충격시 발생하는 충격파(1차 신호)
    • 슬라이드 뒤의 충격파 충돌(2차 신호)
    • 북쪽, 동쪽, 서쪽 및 남쪽 벽에서의 웨이브 반사(3차 신호)

    또한 길이 방향의 FLOW-3D 데이터(중심선에서)를 실제 모델 비디오 위에 겹쳐서 자유 표면의 FLOW-3D 글로벌 예측을 평가했다. 이것은 아래의 동영상에서 볼 수 있다.
    그림과 위의 비디오를 보면 FLOW-3D 데이터가 웨이브 프로브 1, 2 및 3의 경우 물리적 데이터를 매우 잘 일치한다는 것을 알 수 있다. 하지만 웨이브 프로브 4에 대해서는 정확도가 떨어진다.
    FLOW-3D 시간 데이터와 관련된 오류는 각 웨이브 프로브에 대한 RMSE (root-mean-square-error)를 취하여 평가된다.

    Discussion
    이 조사에서 실제 모델의 고 충실도 데이터는 ALGW로 최대 파도 상승에 대한 FLOW-3D 예측과 비교되었다. RNG 모형의 기본 설정을 사용하여 FLOW-3D는 주요 수역 내에서 파고를 정확하게 재현 할 수 있었다. 그러나 최대 파동은 약 43%가 넘었다.
    최대 웨이브 런업을 줄이기 위해 몇 가지 대안인 FLOW-3D 물리 설정이 사용되었다. 그러나 43 % 이하로 떨어지는 것은 불가능했다. 이러한 대체 시뮬레이션에 대한 주목할만한 관찰은 다음과 같다.

    • first-order momentum advection scheme의 0.01m 메쉬는 최대 파동 상승 오차가 96% 인 반면 동일하게 0.0075m 메쉬의 오차는 130%였다. 그러나 second-order로 변경하면 0.01 m 및 0.0075 m 메시의 경우 각각 55% 및 43%의 오차가 발생한다. 또한 메쉬 셀 크기를 0.005m으로 줄이면 80%의 오차가 발생한다.
    • 이 테스트 케이스에서 가장 중요한 매개 변수는 momentum advection scheme이다. 평균적으로 second-order를 사용하면 first-order대비 오차가 약 50% 감소한다.
    • FLOW-3D의 MP 버전을 사용하여 0.005m의 메쉬 셀 크기를 사용해야 한다. 해석 시 CPU 시간은 33 시간이었다. 비교를 위해 FLOW-3D의 SMP 버전은 0.0075m의 메쉬 셀 크기로 시뮬레이션을 실행하는 데 26시간이 필요했지만 MP 버전은 4.5시간 밖에 걸리지 않았다.

    [1] 3.5GHz 8 코어 AMD FX-8320 프로세서에서 약 6초의 시뮬레이션 시간이 대략 26시간 소요되었다.

    References
    Fritz, H. M., Hager, W. H., & Minor, H.-E. (2004). Near Field Characteristics of Landslide Generated Impulse Waves. Journal of Waterway, Port, Coastal & Ocean Engineering, 130(6), 287–302. doi:10.1061/(ASCE)0733-950X(2004)130:6(287)
    Miller, D. J. (1960). Giant Waves in Lituya Bay Alaska (Geological Survey Professional Paper No. 354-C). Washington, D.C.: United States Government Printing Office.
    Müller-Salzburg, L. (1987). The Vajont catastrophe— A personal review. Engineering Geology, 24(1–4), 423–444. doi:10.1016/0013-7952(87)90078-0

    조선/해양 분야

    Coastal & Maritime

    FLOW-3D 는 선박설계, 슬로싱 동역학, 파도에 미치는 영향 및 환기를 포함하여 해안 및 해양 관련 분야에 사용할 수 있는 이상적인 소프트웨어입니다.

    자유 표면 유체 역학, 파동 생성, 움직이는 물체, 계선 및 용접 공정과 관련한 FLOW-3D 의 기능은 해양 및 해양 산업에서 CFD 공정을 모델링하는 데 매우 적합한 도구입니다. 해안 응용 분야의 경우  FLOW-3D  해안 응용 분야의 경우 FLOW-3D  는 해안 구조물에 대한 심한 폭풍 및 쓰나미 파동의 세부 사항을 정확하게 예측하고 돌발 홍수 및 중요 구조물 홍수 및 피해 분석에 사용됩니다. 기능은 다음과 같습니다.

    • 자유 표면 – 파동 유체 역학 및 오버 토핑 : 규칙 및 불규칙파 및 파동 스펙트럼 (Pierson Moskowitz, JONSWAP)
    • Seakeeping – slamming, planing, porpoising 및 선체 선체 변위 : 완전히 결합된 선박 및 수중 차량 유체 역학
    • 선체 – Resistance, stability and dynamics: surging, heaving, pitching and rolling motion (response amplitude operators or RAOs)
    • 슬로싱 – LNG / 밸러스트 탱크
    • 해양 공학 – 파동 에너지 변환기
     

    해안 응용 분야의 경우, FLOW-3D 는 강력한 폭풍과 쓰나미 현상에 의한 해안 구조물이 받는 영향에 대한 세부 사항 예측, 돌발 홍수에 의한 중요한 시설물에 대한 정확한 피해 분석 등을 위해 사용됩니다.

    Mooring Lines, Springs and Ropes

    FLOW-3D (계류선 및 스프링 등)의 특수 물체를 다른 움직이는 물체에 부착하면 엔지니어가 선박 런칭, 부유 장애물 역학, 부표, 파도에너지 변환기 등을 정확하게 포착할 수 있습니다.

    Welding

    FLOW-3D 용접 모듈이 추가되면서 조선업계의 용접분야에서는 다공성 등 용접 결함을 최소화할 수 있어 선체의 품질을 크게 높이는 동시에 생산 시간을 최적화할 수 있습니다.

    Coastal & Maritime Case Studies

    FLOW-3D 사용자들은 연약한 해안선 보호, 구조물에 대한 파장 시뮬레이션, 선체 설계 최적화, 선박 내 환기 연구 등 해안 및 해양 애플리케이션에 FLOW-3D를 사용합니다.

    우리는 보트가 세계 항해를 하면서 마주칠 것 같은 다양한 조건에서 항해를 할 수 있는지를 볼 수 있었습니다. 그리고 속도뿐만 아니라 연료 효율과 안전도 고려하도록 설계를 수정할 수 있었습니다.
    – Pete Bethune, skipper of Earthrace

    Lateral wave impact in waterWave resultsEarthrace vessel
    Validation of Sloshing Simulations in Narrow Tanks / Aerial Landslide Generated Wave Simulations / Earthrace: Speed, Fuel Efficiency and Safety
    Wave impact vertical displacementEmerged breakwater accropodeStokes theory horizontal velocity
    Wave Impact on Offshore StructuresInteraction Between Waves and BreakwatersWave Forces on Coastal Bridges

    기타

    Bibliography

    Models

    • Hybrid Shallow Water/3D Flow
    • Moving Objects
    • Sediment Scour
    • More Modeling Capabilities

    관련 기술자료

    Nozzle Scour

    Study on the Sand-Scouring Characteristics of Pulsed Submerged Jets Based on Experiments and Numerical Methods

    실험과 수치 해석을 기반으로 한 펄스 잠수 제트의 모래 침식 특성 연구 Hongliang Wang, Xuanwen Jia,Chuan Wang, Bo Hu, Weidong ...
    더 보기
    The Fastest Laptops for 2024

    FLOW-3D 수치해석용 노트북 선택 가이드

    2024년 가장 빠른 노트북 PCMag이 테스트하는 방법 소개 : 기사 원본 출처: https://www.pcmag.com/picks/the-fastest-laptops CFD를 수행하기 위한 노트북 선정 기준은 별도로 ...
    더 보기
    river depth

    Ecological inferences on invasive carp survival using hydrodynamics and egg drift models

    수리역학 및 알 이동 모델을 활용한 외래종 잉어 생존에 대한 생태적 추론 Ruichen Xu, Duane C. Chapman, Caroline M. Elliott, ...
    더 보기
    Velocity of pipe

    Dynamic Performance of Suspended Pipelines with Permeable Wrappers under Solitary Waves

    단일 파동 하에서 투과성 포장지가 있는 현수 파이프라인의 동적 성능 Youkou Dong, Enjin Zhao, Lan Cui, Yizhe Li, Yang Wang ...
    더 보기
    Scouring

    Non-Equilibrium Scour Evolution around an Emerged Structure Exposed to a Transient Wave

    일시적인 파도에 노출된 구조에서의 비평형 세굴 결과 Deniz Velioglu Sogut ,Erdinc Sogut ,Ali Farhadzadeh,Tian-Jian Hsu Abstract The present study evaluates ...
    더 보기
    Omega-Liutex Method

    Prediction of the Vortex Evolution and Influence Analysis of Rough Bed in a Hydraulic Jump with the Omega-Liutex Method

    Omega-Luitex법을 이용한 수력점프 발생시 러프 베드의 와류 진화 예측 및 영향 분석 Cong Trieu Tran, Cong Ty Trinh Abstract The ...
    더 보기
    Computational Fluid Dynamics Study of Perforated Monopiles

    Computational Fluid Dynamics Study of Perforated Monopiles

    Mary Kathryn WalkerFlorida Institute of Technology, mwalker2022@my.fit.edu Robert J. Weaver, Ph.D.Associate ProfessorOcean Engineering and Marine SciencesMajor Advisor Chungkuk Jin, Ph.D.Assistant ...
    더 보기
    Numerical Investigation of the Local Scour for Tripod Pile Foundation

    Numerical Investigation of the Local Scour for Tripod Pile Foundation

    Waqed H. Hassan | Zahraa Mohammad Fadhe* | Rifqa F. Thiab | Karrar MahdiCivil Engineering Department, Faculty of Engineering, University of Warith Al-Anbiyaa, Kerbala 56001, IraqCivil ...
    더 보기
    An investigation of the effect of the pulse width and amplitude on sand bed scouring by a vertical submerged pulsed jet

    An investigation of the effect of the pulse width and amplitude on sand bed scouring by a vertical submerged pulsed jet

    수직 수중 펄스 제트에 의한 모래층 정련에 대한 펄스 폭과 진폭의 영향 조사 Chuan Wang abc, Hao Yu b, Yang Yang b, Zhenjun Gao c, Bin Xi b, Hui Wang b, Yulong Yao b aInternational Shipping Research Institute, GongQing Institute ...
    더 보기
    Fig. 1. Protection matt over the scour pit.

    Numerical study of the flow at a vertical pile with net-like scourprotection matt

    그물형 세굴방지 매트를 사용한 수직말뚝의 유동에 대한 수치적 연구 Minxi Zhanga,b, Hanyan Zhaoc, Dongliang Zhao d, Shaolin Yuee, Huan Zhoue,Xudong ...
    더 보기

    Coastal & Maritime Bibliography

    Coastal & Maritime Bibliography

    다음은 연안 및 해양 분야의 기술 문서 모음입니다.
    이 모든 논문은 FLOW-3D  결과를 포함하고 있습니다. FLOW-3D를 사용하여 연안 및 해양 시설물을 성공적으로 시뮬레이션 하는 방법에 대해 자세히 알아보십시오.

    2024년 11월 20일 Update

    119-24 Faris Ali Hamood Al-Towayti, Hee-Min Teh, Zhe Ma, Idris Ahmed Jae, Agusril Syamsir, Ebrahim Hamid Hussein Al-Qadami, Hydrodynamic performance assessment of emerged, alternatively submerged and submerged semicircular breakwater: An experimental and computational study, Journal of Marine Science and Engineering, 12; 1105, 2024. doi.org/10.3390/jmse12071105

    117-24 Dong Zeng, Wuyang Bi, Yi Yu, Yun Yan, Weiqiu Chen, Yong Yao, Cheng Zhang, Tianyu Wu, Prediction of local scouring of offshore wind turbine foundations based on the amplification principle of local seabed shear stress, The 34th International Ocean and Polar Engineering Conference, ISOPE-I-24-125, 2024.

    116-24 Chen-Shan Kung, Ya-Cing You, Pei-Yu Lee, Siu-Yu Pan, The air entrainment effect of pump blades operation under different water depths, The 34th International Ocean and Polar Engineering Conference, ISOPE-I-24-595, 2024.

    114-24 Chen-Shan Kung, Siu-Yu Pan, Pei-Yu Lee, Ya-Cing You, Sediment flushing of different angle on density outflow, The 34th International Ocean and Polar Engineering Conference, ISOPE-I-24-183, 2024.

    102-24 Mary Kathryn Walker, Computational fluid dynamics study of perforated monopiles, Thesis, Florida Institute of Technology, 2024.

    80-24 Deniz Velioglu Sogut, Erdinc Sogut, Ali Farhadzadeh, Tian-Jian Hsu, Non-equilibrium scour evolution around an emerged structure exposed to a transient wave, Journal of Marine Science and Engineering, 12; 946, 2024. doi.org/10.3390/jmse12060946

    79-24 Sujantoko, D.R. Ahidah, W. Wardhana, E.B. Djatmiko, M. Mustain, Numerical modeling of wave reflection and transmission in I-shaped floating breakwater series, IOP Conference Series: Earth and Environmental Science, 1321; 012010, 2024. doi.org/10.1088/1755-1315/1321/1/012010

    75-24 Sahel Sohrabi, Mohamad Ali Lofollahi Yaghin, Alireza Mojtahedi, Mohamad Hosein Aminfar, Mehran Dadashzadeh, Experimental and numerical investigation of a hybrid floating breakwater-WEC system, Ocean Engineering, 303; 117613, 2024. doi.org/10.1016/j.oceaneng.2024.117613

    73-24 Penghui Wang, Chunning Ji, Xiping Sun, Dong Xu, Chao Ying, Development and test of FDEM–FLOW-3D—A CFD–DEM model for the fluid–structure interaction of AccropodeTM blocks under wave loads, Ocean Engineering, 303; 117735, 2024. doi.org/10.1016/j.oceaneng.2024.117735

    67-24 Alexander Schendel, Stefan Schimmels, Mario Welzel, Philippe April-LeQuéré, Abdolmajid Mohammadian, Clemens Krautwald, Jacob Stolle, Ioan Nistor, Nils Goseberg, Spatiotemporal scouring processes around a square column on a sloped beach induced by tsunami bores, Journal of Waterway, Port, Coastal, and Ocean Engineering, 150.3; 2024. https://doi.org/10.1061/JWPED5.WWENG-2052

    65-24 Kaiqi Yu, Elda Miramontes, Matthieu J.B. Cartigny, Yuping Yang, Jingping Xu, The impacts of profile concavity on turbidite deposits: Insights from the submarine canyons on global continental margins, Geomorphology, 454; 109157, 2024. doi.org/10.1016/j.geomorph.2024.109157

    61-24 M.T. Mansouri Kia, H.R. Sheibani, A. Hoback, Initial maintenance notes about the first river ship lock in Iran, Journal of Hydraulic and Water Engineering, 1.2; pp. 143-162, 2024.

    47-24 Cheng Yee Ng, Nauman Riyaz Maldar, Muk Chen Ong, Numerical investigation on performance enhancement in a drag-based hydrokinetic turbine with a diffuser, Ocean Engineering, 298; 117179, 2024. doi.org/10.1016/j.oceaneng.2024.117179

    26-24 Zegao Yin, Guoqing Li, Fei Wu, Zihan Ni, Feifan Li, Experimental and numerical study on hydrodynamic characteristics of a bottom-hinged pitching flap breakwater under regular waves, Ocean Engineering, 293; 116665, 2024. doi.org/10.1016/j.oceaneng.2024.116665

    21-24   Young-Ki Moon, Chang-Ill Yoo, Jong-Min Lee, Sang-Hyub Lee, Han-Sam Yoon, Evaluation of pedestrian safety for wave overtopping by ship-induced waves in waterfront revetment, Journal of Coastal Research, 116; pp.314-318, 2024. doi.org/10.2112/JCR-SI116-064.1

    14-24   Hongliang Wang, Xuanwen Jia, Chuan Wang, Bo Hu, Weidong Cao, Shanshan Li, Hui Wang, Study on the sand-scouring characteristics of pulsed submerged jets based on experiments and numerical models, Journal of Marine Science and Engineering, 12.1; 57, 2024. doi.org/10.3390/jmse12010057

    239-23 Sara Tuozzo, Angela Di Leo, Mariano Buccino, Fabio Dentale, Eugenio Pugliese Carratelli, Mario Calabrese, The effect of wind stress on wave overtopping on vertical seawall, Coastal Engineering Proceedings, 37; 2023. doi.org/10.9753/icce.v37.papers.49

    224-23   Helia Molaei Nodeh, Reza Dezvareh, Mahdi Yousefifard, Numerical analysis of the effects of rubble mound breakwater geometry under the effect of nonlinear wave force, Arabian Journal for Science and Engineering, 2023. doi.org/10.1007/s13369-023-08520-2

    212-23   Feifei Cao, Mingqi Yu, Meng Han, Bing Liu, Zhiwen Wei, Juan Jiang, Huiyuan Tian, Hongda Shi, Yanni Li, WECs microarray effect on the coupled dynamic response and power performance of a floating combined wind and wave energy system, Renewable Energy, 219.2; 119476, 2023. doi.org/10.1016/j.renene.2023.119476

    210-23   H. Omara, Sherif M. Elsayed, Karim Adel Nassar, Reda Diab, Ahmed Tawfik, Hydrodynamic and morphologic investigating of the discrepancy in flow performance between inclined rectangular and oblong piers, Ocean Engineering, 288.2; 116132, 2023. doi.org/10.1016/j.oceaneng.2023.116132

    190-23   M.F. Ahmad, M.I. Ramli, M.A. Musa, S.E.G. Goh, C.W.M.N Che Wan Othman, E.H. Ariffin, N.A. Mokhtar, Numerical simulation for overtopping discharge on tetrapod breakwater, AIP Conference Proceedings, 2746.1; 2023. doi.org/10.1063/5.0153371

    183-23   Youkou Dong, Enjin Zhao, Lan Cui, Yizhe Li, Yang Wang, Dynamic performance of suspended pipelines with permeable wrappers under solitary waves, Journal of Marine Science and Engineering, 11.10; 1872, 2023. doi.org/10.3390/jmse11101872

    176-23   Guoxu Niu, Yaoyong Chen, Jiao Lu, Jing Zhang, Ning Fan, Determination of formulae for the hydrodynamic performance of a fixed box-type free surface breakwater in the intermediate water, Journal of Marine Science and Engineering, 11.9; 1812, 2023. doi.org/10.3390/jmse11091812

    168-23   Yupeng Ren, Huiguang Zhou, Houjie Wang, Xiao Wu, Guohui Xu, Qingsheng Meng, Study on the critical sediment concentration determining the optimal transport capability of submarine sediment flows with different particle size composition, Marine Geology, 464; 107142, 2023. doi.org/10.1016/j.margeo.2023.107142

    163-23   Ahmad Fitriadhy, Sheikh Fakruradzi, Alamsyah Kurniawan, Nita Yuanita, Anuar Abu Bakar, 3D computational fluid dynamic investigation on wave transmission behind low-crested submerged geo-bag breakwater, CFD Letters, 15.10; 2023. doi.org/10.37934/cfdl.15.10.1222

    162-23   Ramtin Sabeti, Landslide-generated tsunami waves-physical and numerical modelling, International Seminar on Tsunami Research, University of Bath, 2023.

    161-23   Duy Linh Du, Study on the optimal location for pile-rock breakwater in reducing wave height in Dong Hai District, Bac Lieu Province, Vietnam, Thesis, Can Tho University, 2023.

    160-23   Duy Linh Du, Dai Bang Pham, Van Duy Dinh, Tan Ngoc Cao, Van Ty Tran, Gia Bao Tran, Hieu Duc Tran, Modelling the wave reduction effectiveness of pile-rock breakwater using FLOW-3D, (in Vietnamese) Journal of Materials and Construction, 13.04; 2023. doi.org/10.54772/jomc.04.2023.537

    151-23 Zhiguo Zhang, Jinpeng Chen, Tong Ye, Zhengguo Zhu, Mengxi Zhang, Yutao Pan, Wave-induced response of seepage pressure around shield tunnel in sand seabed slope, International Journal of Geomechanics, 23.10; 2023. doi.org/10.1061/IJGNAI.GMENG-8072

    147-23 Jiale Li, Jijian Lian, Haijun Wang, Yaohua Guo, Sha Liu, Yutong Zhang, FengWu Zhang, Numerical study of the local scour characteristics of bottom-supported installation platforms during the installation of a monopile, Ships and Offshore Structures, 2023. doi.org/10.1080/17445302.2023.2243700

    144-23 Weixang Liang, Min Lou, Changhong Fan, Deguang Zhao, Xiang Li, Coupling effect of vortex-induced vibration and local scour of double tandem pipelines in steady current, Ocean Engineering, 286.1; 115495, 2023. doi.org/10.1016/j.oceaneng.2023.11549

    136-23 Zegao Yin, Jiahao Li, Yanxu Wang, Haojian Wang, Tianxu Yin, Solitary wave attenuation characteristics of mangroves and multi-parameter prediction model, Ocean Engineering, 285.2; 115372, 2023. doi.org/10.1016/j.oceaneng.2023.115372

    130-23 Sheng Wang, Chaozhe Yuan, Yuchi Hao, Xiaowei Yan, Feasibility analysis of laying and construction of deep-water dredging sinking pipeline, The 33rd International Ocean and Polar Engineering Conference, ISOPE-1-23-030, 2023.

    127-23 Chen-Shan Kung, Ya-Cing You, Pei-Yu Lee, Siu-Yu Pan, Yu-Chun Chen, The air entrainment effect stability on the marine pipeline, The 33rd International Ocean and Polar Engineering Conference, ISOPE-I-23-242, 2023.

    126-23 Yuting Wang, Zhaode Zhang, Yuan Zhang, Numerical simulationa and measurement of artificial flow creation in reclamation projects, The 33rd International Ocean and Polar Engineering Conference, ISOPE-1-23-168, 2023.

    125-23 Chen-Shan Kung, Siu-Yu Pan, Pei-Yu Lee, Ya-Cing You, Yu-Chun Chen, Numerical simulation of wave motion on the submarine HDPE pipe system, The 33rd International Ocean and Polar Engineering Conference, ISOPE-I-23-327, 2023.

    115-23 Qishun Li, Yanpeng Hao, Peng Zhang, Haotian Tan, Wanxing Tian, Linhao Chen, Lin Yang, Numerical study of the local scouring process and influencing factors of semi-exposed submarine cables, Journal of Marine Science and Engineering, 11.7; 1349, 2023. doi.org/10.3390/jmse11071349

    113-23 Minxi Zhang, Hanyan Zhao, Dongliang Zhao, Shaolin Yue, Huan Zhou, Xudong Zhao, Carlo Gualtieri, Guoliang Yu, Numerical study of the flow at a vertical pile with net-like scour protection mat, Journal of Ocean Engineering and Science, 2023. doi.org/10.1016/j.joes.2023.06.002

    108-23 Seyed A. Ghaherinezhad, M. Behdarvandi Askar, Investigating effect of changing vegetation height with irregular layout on reduction of waves using FLOW-3D numerical model, Journal of Hydraulic and Water Engineering, 1.1; pp.55-64, 2023. doi.org/10.22044/JHWE.2023.12844.1004

    92-23 Tongshun Yu, Xingyu Chen, Yuying Tang, Junrong Wang, Yuqiao Wang, Shuting Huang, Numerical modelling of wave run-up heights and loads on multi-degree-of-freedom buoy wave energy converters, Applied Energy, 344; 121255, 2023. doi.org/10.1016/j.apenergy.2023.121255

    85-23   Emilee A. Wissmach, Biomimicry of natural reef hydrodynamics in an artificial spur and groove reef formation, Thesis, Florida Institute of Technology, 2023.

    81-23   Zhi Fan, Feifei Cao, Hongda Shi, Numerical simulation on the energy capture spectrum of heaving buoy wave energy converter, Ocean Engineering, 280; 114475, 2023. doi.org/10.1016/j.oceaneng.2023.114475

    72-23   Zegao Yin, Fei Wu, Yingni Luan, Xuecong Zhang, Xiutao Jiang, Jie Xiong, Hydrodynamic and aeration characteristics of an aerator of a surging water tank with a vertical baffle under a horizontal sinusoidal motion, Ocean Engineering, 287; 114396, 2023. doi.org/10.1016/j.oceaneng.2023.114396

    71-23   Erfan Amini, Mahdieh Nasiri, Navid Salami Pargoo, Zahra Mozhgani, Danial Golbaz, Mehrdad Baniesmaeil, Meysam Majidi Nezhad, Mehdi Neshat, Davide Astiaso Garcia, Georgios Sylaios, Design optimization of ocean renewable energy converter using a combined Bi-level metaheuristic approach, Energy Conversion and Management: X, 19; 100371, 2023. doi.org/10.1016/j.ecmx.2023.100371

    70-23   Ali Ghasemi, Rouholla Amirabadi, Ulrich Reza Kamalian, Numerical investigation of hydrodynamic responses and statistical analysis of imposed forces for various geometries of the crown structure of caisson breakwater, Ocean Engineering, 278; 114358, 2023. doi.org/10.1016/j.oceaneng.2023.114358

    67-23   Aisyah Dwi Puspasari, Jyh-Haw Tang, Numerical simulation of scouring around groups of six cylinders with different flow directions, Journal of the Chinese Institute of Engineers, 46.4; 2023. doi.org/10.1080/02533839.2023.2194919

    62-23   Rob Nairn, Qimiao Lu, Rebecca Quan, Matthew Hoy, Dain Gillen, Data collection and modeling in support of the Mid-Breton Sediment Diversion Project, Coastal Sediments, 2023. doi.org/10.1142/9789811275135_0246

    55-23   Yupeng Ren, Hao Tian, Zhiyuan Chen, Guohui Xu, Lejun Liu, Yibing Li, Two kinds of waves causing the resuspension of deep-sea sediments: excitation and internal solitary waves, Journal of Ocean University of China, 22; pp. 429-440, 2023. doi.org/10.1007/s11802-023-5293-2

    42-23   Antonija Harasti, Gordon Gilja, Simulation of equilibrium scour hole development around riprap sloping structure using the numerical model, EGU General Assembly, 2023. doi.org/10.5194/egusphere-egu23-6811

    25-23   Ke Hu, Xinglan Bai, Murilo A. Vaz, Numerical simulation on the local scour processing and influencing factors of submarine pipeline, Journal of Marine Science and Engineering, 11.1; 234, 2023. doi.org/10.3390/jmse11010234

    12-23   Fan Zhang, Zhipeng Zang, Ming Zhao, Jinfeng Zhang, Numerical investigations on scour and flow around two crossing pipelines on a sandy seabed, Journal of Marine Science and Engineering, 10.12; 2019, 2023. doi.org/10.3390/jmse10122019

    10-23 Wenshe Zhou, Yongzhou Cheng, Zhiyuan Lin, Numerical simulation of long-wave wave dissipation in near-water flat-plate array breakwaters, Ocean Engineering, 268; 113377, 2023. doi.org/10.1016/j.oceaneng.2022.113377

    181-22   Ramtin Sabeti, Mohammad Heidarzadeh, Numerical simulations of water waves generated by subaerial granular and solid-block landslides: Validation, comparison, and predictive equations, Ocean Engineering, 266.3; 112853, 2022. doi.org/10.1016/j.oceaneng.2022.112853 

    167-22 Zhiyong Zhang, Cunhong Pan, Jian Zeng, Fuyuan Chen, Hao Qin, Kun He, Kui Zhu, Enjin Zhao, Hydrodynamics of tidal bore overflow on the spur dike and its infuence on the local scour, Ocean Engineering, 266.4; 113140, 2022. doi.org/10.1016/j.oceaneng.2022.113140

    166-22 Nguyet-Minh Nguyen, Duong Do Van, Duy Tu Le, Quyen Nguyen, Bang Tran, Thanh Cong Nguyen, David Wright, Ahad Hasan Tanim, Phong Nguyen Thanh, Duong Tran Anh, Physical and numerical modeling of four different shapes of breakwaters to test the suspended sediment trapping capacity in the Mekong Delta, Estuarine, Coastal and Shelf Science, 279; 108141, 2022. doi.org/10.1016/j.ecss.2022.108141

    163-22 Sahameddin Mahmoudi Kurdistani, Giuseppe Roberto Tomasicchio, Felice D’Alessandro, Antonio Francone, Formula for wave transmission at submerged homogeneous porous breakwaters, Ocean Engineering, 266.4; 113053, 2022. doi.org/10.1016/j.oceaneng.2022.113053

    162-22 Kai Wei, Xueshuang Yin, Numerical study into configuration of horizontal flanges on hydrodynamic performance of moored box-type floating breakwater, Ocean Engineering, 266.4; 112991, 2022. doi.org/10.1016/j.oceaneng.2022.112991

    161-22 Sung-Chul Jang, Jin-Yong Jeong, Seung-Woo Lee, Dongha Kim, Identifying hydraulic characteristics related to fishery activities using numerical analysis and an automatic identification system of a fishing vessel, Journal of Marine Science and Engineering, 10; 1619, 2022. doi.org/10.3390/jmse10111619

    156-22 Keith Adams, Mohammad Heidarzadeh, Extratropical cyclone damage to the seawall in Dawlish, UK: Eyewitness accounts, sea level analysis and numerical modelling, Natural Hazards, 2022. doi.org/10.1007/s11069-022-05692-2

    155-22 Youxiang Lu, Zhenlu Wang, Zegao Yin, Guoxiang Wu, Bingchen Liang, Experimental and numerical studies on local scour around closely spaced circular piles under the action of steady current, Journal of Marine Science and Engineering, 10; 1569, 2022. doi.org/10.3390/jmse10111569

    152-22 Nauman Riyaz Maldar, Ng Cheng Yee, Elif Oguz, Shwetank Krishna, Performance investigation of a drag-based hydrokinetic turbine considering the effect of deflector, flow velocity, and blade shape, Ocean Engineering, 266.2; 112765, 2022. doi.org/10.1016/j.oceaneng.2022.112765

    148-22   Ramtin Sabeti, Mohammad Heidarzadeh, Numerical simulations of water waves generated by subaerial granular and solid-block landslides: Validation, comparison, and predictive equations, Ocean Engineering, 266.3; 112853, 2022. doi.org/10.1016/j.oceaneng.2022.112853

    145-22   I-Fan Tseng, Chih-Hung Hsu, Po-Hung Yeh, Ting-Chieh Lin, Physical mechanism for seabed scouring around a breakwater—a case study in Mailiao Port, Journal of Marine Science and Engineering, 10; 1386, 2022. doi.org/10.3390/jmse10101386

    144-22   Jiarui Yu, Baozeng Yue, Bole Ma, Isogeometric analysis with level set method for large-amplitude liquid sloshing, Ocean Engineering, 265; 112613, 2022. doi.org/10.1016/j.oceaneng.2022.112613

    141-22   Qi Yang, Peng Yu, Hongjun Liu, Computational investigation of scour characteristics of USAF in multi-specie sand under steady current, Ocean Engineering, 262; 112141, 2022. doi.org/10.1016/j.oceaneng.2022.112141

    128-22   Atish Deoraj, Calvin Wells, Justin Pringle, Derek Stretch, On the reef scale hydrodynamics at Sodwana Bay, South Africa, Environmental Fluid Mechanics, 2022. doi.org/10.1007/s10652-022-09896-9

    108-22   Angela Di Leo, Mariano Buccino, Fabio Dentale, Eugenio Pugliese Carratelli, CFD analysis of wind effect on wave overtopping, 32nd International Ocean and Polar Engineering Conference,  ISOPE-I-22-428, 2022.

    105-22   Pin-Tzu Su, Chen-shan Kung, Effects of currents and sediment flushing on marine pipes, 32nd International Ocean and Polar Engineering Conference, ISOPE-I-22-153, 2022.

    89-22   Kai Wei, Cong Zhou, Bo Xu, Spatial distribution models of horizontal and vertical wave impact pressure on the elevated box structure, Applied Ocean Research, 125; 103245, 2022. doi.org/10.1016/j.apor.2022.103245

    87-22   Tran Thuy Linh, Numerical modelling (3D) of wave interaction with porous structures in the Mekong Delta coastal zone, Thesis, Ho Chi Minh City University of Technology, 2022.

    82-22   Seyyed-Mahmood Ghassemizadeh, Mohammad Javad Ketabdari, Modeling of solitary wave interaction with curved-facing seawalls using numerical method, Advances in Civil Engineering, 5649637, 2022. doi.org/10.1155/2022/5649637

    81-22   Raphael Alwan, Boyin Ding, David M. Skene, Zhaobin Li, Luke G. Bennetts, On the structure of waves radiated by a submerged cylinder undergoing large-amplitude heave motions, 32nd International Ocean and Polar Engineering Conference, Shanghai, China, June 5-10, 2022. doi.org/10.1111/jfr3.12828

    77-22   Weiyun Chen, Linchong Huang, Dan Wang, Chao Liu, Lingyu Xu, Zhi Ding, Effects of siltation and desiltation on the wave-induced stability of foundation trench of immersed tunnel, Soil Dynamics and Earthquake Engineering, 160; 107360, 2022. doi.org/10.1016/j.soildyn.2022.107360

    63-22   Yongzhou Cheng, Zhiyuan Lin, Gan Hu, Xing Lyu, Numerical simulation of the hydrodynamic characteristics of the porous I-type composite breakwater, Journal of Marine Science and Application, 21; pp. 140-150, 2022. doi.org/10.1007/s11804-022-00251-4

    37-22   Ray-Yeng Yang, Chuan-Wen Wang, Chin-Cheng Huang, Cheng-Hsien Chung, Chung-Pang, Chen, Chih-Jung Huang, The 1:20 scaled hydraulic model test and field experiment of barge-type floating offshore wind turbine system, Ocean Engineering, 247.1; 110486, 2022. doi.org/10.1016/j.oceaneng.2021.110486

    35-22   Mingchao Cui, Zhisong Li, Chenglin Zhang, Xiaoyu Guo, Statistical investigation into the flow field of closed aquaculture tanks aboard a platform under periodic oscillation, Ocean Engineering, 248; 110677, 2022. doi.org/10.1016/j.oceaneng.2022.110677

    30-22   Jijian Lian, Jiale Li, Yaohua Guo, Haijun Wang, Xu Yang, Numerical study on local scour characteristics of multi-bucket jacket foundation considering exposed height, Applied Ocean Research, 121; 103092. doi.org/10.1016/j.apor.2022.103092

    19-22   J.J. Wiegerink, T.E. Baldock, D.P. Callaghan, C.M. Wang, Slosh suppression blocks – A concept for mitigating fluid motions in floating closed containment fish pen in high energy environments, Applied Ocean Research, 120; 103068, 2022. doi.org/10.1016/j.apor.2022.103068

    9-22   Amir Bordbar, Soroosh Sharifi, Hassan Hemida, Investigation of scour around two side-by-side piles with different spacing ratios in live-bed, Lecture Notes in Civil Engineering, 208; pp. 302-309, 2022. doi.org/10.1007/978-981-16-7735-9_33

    7-22   Jinzhao Li, Xuan Kong, Yilin Yang, Lu Deng, Wen Xiong, CFD investigations of tsunami-induced scour around bridge piers, Ocean Engineering, 244; 110373, 2022. doi.org/10.1016/j.oceaneng.2021.110373

    3-22   Ana Gomes, José Pinho, Wave loads assessment on coastal structures at inundation risk using CFD modelling, Climate Change and Water Security, 178; pp. 207-218, 2022. doi.org/10.1007/978-981-16-5501-2_17

    2-22   Ramtin Sabeti, Mohammad Heidarzadeh, Numerical simulations of tsunami wave generation by submarine landslides: Validation and sensitivity analysis to landslide parameters, Journal of Waterway, Port, Coastal, and Ocean Engineering, 148.2; 05021016, 2022. doi.org/10.1061/(ASCE)WW.1943-5460.0000694

    146-21   Ming-ming Liu, Hao-cheng Wang, Guo-qiang Tang, Fei-fei Shao, Xin Jin, Investigation of local scour around two vertical piles by using numerical method, Ocean Engineering, 244; 110405, 2021. doi.org/10.1016/j.oceaneng.2021.110405

    135-21   Jian Guo, Jiyi Wu, Tao Wang, Prediction of local scour depth of sea-crossing bridges based on the energy balance theory, Ships and Offshore Structures, 16.10, 2021. doi.org/10.1080/17445302.2021.2005362

    133-21   Sahel Sohrabi, Mohamad Ali Lofollahi Yaghin, Mohamad Hosein Aminfar, Alireza Mojtahedi, Experimental and numerical investigation of hydrodynamic performance of a sloping floating breakwater with and without chain-net, Iranian Journal of Science and Technology: Transactions of Civil Engineering, , 2021. doi.org/10.1007/s40996-021-00780-y

    131-21   Seyed Morteza Marashian, Mehdi Adjami, Ahmad Rezaee Mazyak, Numerical modelling investigation of wave interaction on composite berm breakwater, China Ocean Engineering, 35; pp. 631-645, 2021. doi.org/10.1007/s13344-021-0060-x

    124-21   Ramin Safari Ghaleh, Omid Aminoroayaie Yamini, S. Hooman Mousavi, Mohammad Reza Kavianpour, Numerical modeling of failure mechanisms in articulated concrete block mattress as a sustainable coastal protection structure, Sustainability, 13.22; pp. 1-19, 2021.

    118-21   A. Keshavarz, M. Vaghefi, G. Ahmadi, Investigation of flow patterns around rectangular and oblong peirs with collar located in a 180-degree sharp bend, Scientia Iranica A, 28.5; pp. 2479-2492, 2021.

    109-21   Jacek Jachowski, Edyta Książkiewicz, Izabela Szwoch, Determination of the aerodynamic drag of pneumatic life rafts as a factor for increasing the reliability of rescue operations, Polish Maritime Research, 28.3; p. 128-136, 2021. doi.org/10.2478/pomr-2021-0040

    107-21   Jiay Han, Bing Zhu, Baojie Lu, Hao Ding, Ke Li, Liang Cheng, Bo Huang, The influence of incident angles and length-diameter ratios on the round-ended cylinder under regular wave action, Ocean Engineering, 240; 109980, 2021. doi.org/10.1016/j.oceaneng.2021.109980

    96-21   Andrea Franco, Jasper Moernaut, Barbara Schneider-Muntau, Michael Strasser, Bernhard Gems, Triggers and consequences of landslide-induced impulse waves – 3D dynamic reconstruction of the Taan Fiord 2015 tsunami event, Engineering Geology, 294; 106384, 2021. doi.org/10.1016/j.enggeo.2021.106384

    95-21   Ahmed A. Romya, Hossam M. Moghazy, M.M. Iskander, Ahmed M. Abdelrazek, Performance assessment of corrugated semi-circular breakwaters for coastal protection, Alexandria Engineering Journal, in press, 2021. doi.org/10.1016/j.aej.2021.08.086

    87-21   Ruigeng Hu, Hongjun Liu, Hao Leng, Peng Yu, Xiuhai Wang, Scour characteristics and equilibrium scour depth prediction around umbrella suction anchor foundation under random waves, Journal of Marine Science and Engineering, 9; 886, 2021. doi.org/10.3390/jmse9080886

    78-21   Sahir Asrari, Habib Hakimzadeh, Nazila Kardan, Investigation on the local scour beneath piggyback pipelines under clear-water conditions, China Ocean Engineering, 35; pp. 422-431, 2021. doi.org/10.1007/s13344-021-0039-7

    64-21   Pin-Tzu Su, Chen-shan Kung, Effects of diffusers on discharging jet, 31st International Ocean and Polar Engineering Conference (ISOPE), Rhodes, Greece, June 20-25, 2021.

    62-21   Fei Wu, Wei Li, Shuzhao Li, Xiaopeng Shen, Delong Dong, Numerical simulation of scour of backfill soil by jetting flows on the top of buried caisson, 31st International Ocean and Polar Engineering Conference (ISOPE), Rhodes, Greece, June 20-25, 2021.

    56-21   Murat Aksel, Oral Yagci, V.S. Ozgur Kirca, Eryilmaz Erdog, Naghmeh Heidari, A comparitive analysis of coherent structures around a pile over rigid-bed and scoured-bottom, Ocean Engineering, 226; 108759, 2021. doi.org/10.1016/j.oceaneng.2021.108759

    52-21   Byeong Wook Lee, Changhoon Lee, Equation for ship wave crests in a uniform current in the entire range of water depths, Coastal Engineering, 167; 103900, 2021. doi.org/10.1016/j.coastaleng.2021.103900

    43-21   Agnieszka Faulkner, Claire E. Bulgin, Christopher J. Merchant, Characterising industrial thermal plumes in coastal regions using 3-D numerical simulations, Environmental Research Communications, 3; 045003, 2021. doi.org/10.1088/2515-7620/abf62e

    39-21   Fan Yang, Yiqi Zhang, Chao Liu, Tieli Wang, Dongin Jiang, Yan Jin, Numerical and experimental investigations of flow pattern and anti-vortex measures of forebay in a multi-unit pumping station, Water, 13.7; 935, 2021. doi.org/10.3390/w13070935

    30-21   Norfadhlina Khalid, Aqil Azraie Che Shamshudin, Megat Khalid Puteri Zarina, Analysis on wave generation and hull: Modification for fishing vessels, Advanced Engineering for Processes and Technologies II: Advanced Structured Materials, 147; pp. 77-89, 2021. doi.org/10.1007/978-3-030-67307-9_9

    28-21   Jae-Sang Jung, Jae-Seon Yoon, Seokkoo Kang, Seokil Jeong, Seung Oh Lee, Yong-Sung Park, Discharge characteristics of drainage gates on Saemangeum tidal dyke, South Korea, KSCE Journal of Engineering, 25; pp. 1308-1325, 2021. doi.org/10.1007/s12205-021-0590-z

    24-21   Ali Temel, Mustafa Dogan, Time dependent investigation of the wave induced scour at the trunk section of a rubble mound breakwater, Ocean Engineering, 221; 108564, 2021. doi.org/10.1016/j.oceaneng.2020.108564

    13-21   P.X. Zou, L.Z. Chen, The coupled tube-mooring system SFT hydrodynamic characteristics under wave excitations, Proceedings, 14th International Conference on Vibration Problems, Crete, Greece, September 1 – 4, 2019, pp. 907-923, 2021. doi.org/10.1007/978-981-15-8049-9_55

    122-20  M.A. Musa, M.F. Roslan, M.F. Ahmad, A.M. Muzathik, M.A. Mustapa, A. Fitriadhy, M.H. Mohd, M.A.A. Rahman, The influence of ramp shape parameters on performance of overtopping breakwater for energy conversion, Journal of Marine Science and Engineering, 8.11; 875, 2020. doi.org/10.3390/jmse8110875

    120-20  Lee Hooi Chie, Ahmad Khairi Abd Wahab, Derivation of engineering design criteria for flow field around intake structure: A numerical simulation study, Journal of Marine Science and Engineering, 8.10; 827, 2020.  doi.org/10.3390/jmse8100827

    109-20  Mario Maiolo, Riccardo Alvise Mel, Salvatore Sinopoli, A stepwise approach to beach restoration at Calabaia Beach, Water, 12.10; 2677, 2020. doi.org/10.3390/w12102677

    107-20  S. Deshpande, P. Sundsbø, S. Das, Ship resistance analysis using CFD simulations in Flow-3D, International Journal of Multiphysics, 14.3; pp. 227-236, 2020. doi.org/10.21152/1750-9548.14.3.227

    103-20   Mahmood Nematollahi, Mohammad Navim Moghid, Numerical simulation of spatial distribution of wave overtopping on non-reshaping berm breakwaters, Journal of Marine Science and Application, 19; pp. 301-316, 2020. doi.org/10.1007/s11804-020-00147-1

    98-20   Lin Zhao, Ning Wang, Qian Li, Analysis of flow characteristics and wave dissipation performances of a new structure, Proceedings, 30th International Ocean and Polar Engineering Conference (ISOPE), Online, October 11-16, ISOPE-I-20-3289, 2020.

    96-20   Xiaoyu Guo, Zhisong Li, Mingchao Cui, Benlong Wang, Numerical investigation on flow characteristics of water in the fish tank on a force-rolling aquaculture platform, Ocean Engineering, 217; 107936, 2020. doi.org/10.1016/j.oceaneng.2020.107936

    92-20   Yong-Jun Cho, Scour controlling effect of hybrid mono-pile as a substructure of offshore wind turbine: A numerical study, Journal of Marine Science and Engineering, 8.9; 637, 2020. doi.org/10.3390/jmse8090637

    89-20   Andrea Franco, Jasper Moernaut, Barbara Schneider-Muntau, Michael Strasser, Bernhard Gems, The
    1958 Lituya Bay tsunami – pre-event bathymetry reconstruction and 3D numerical modelling utilising the computational fluid dynamics software
    Flow-3D    , Natural Hazards and Earth Systems Sciences, 20; pp. 2255–2279, 2020. doi.org/10.5194/nhess-20-2255-2020

    81-20   Eliseo Marchesi, Marco Negri, Stefano Malavasi, Development and analysis of a numerical model for a two-oscillating-body wave energy converter in shallow water, Ocean Engineering, 214; 107765, 2020. doi.org/10.1016/j.oceaneng.2020.107765

    79-20   Zegao Yin, Yanxu Wang, Yong Liu, Wei Zou, Wave attenuation by rigid emergent vegetation under combined wave and current flows, Ocean Engineering, 213; 107632, 2020. doi.org/10.1016/j.oceaneng.2020.107632

    71-20   B. Pan, N. Belyaev, FLOW-3D software for substantiation the layout of the port water area, IOP Conference Series: Materials Science and Engineering, Construction Mechanics, Hydraulics and Water Resources Engineering (CONMECHYDRO), Tashkent, Uzbekistan, 23-25 April, 883; 012020, 2020. doi.org/10.1088/1757-899X/883/1/012020

    51-20       Yupeng Ren, Xingbei Xu, Guohui Xu, Zhiqin Liu, Measurement and calculation of particle trajectory of liquefied soil under wave action, Applied Ocean Research, 101; 102202, 2020. doi.org/10.1016/j.apor.2020.102202

    50-20       C.C. Battiston, F.A. Bombardelli, E.B.C. Schettini, M.G. Marques, Mean flow and turbulence statistics through a sluice gate in a navigation lock system: A numerical study, European Journal of Mechanics – B/Fluids, 84; pp.155-163, 2020. doi.org/10.1016/j.euromechflu.2020.06.003

    49-20     Ahmad Fitriadhy, Nur Amira Adam, Nurul Aqilah Mansor, Mohammad Fadhli Ahmad, Ahmad Jusoh, Noraieni Hj. Mokhtar, Mohd Sofiyan Sulaiman, CFD investigation into the effect of heave plate on vertical motion responses of a floating jetty, CFD Letters, 12.5; pp. 24-35, 2020. doi.org/10.37934/cfdl.12.5.2435

    40-20       P. April Le Quéré, I. Nistor, A. Mohammadian, Numerical modeling of tsunami-induced scouring around a square column: Performance assessment of FLOW-3D and Delft3D, Journal of Coastal Research (preprint), 2020. doi.org/10.2112/JCOASTRES-D-19-00181

    38-20       Sahameddin Mahmoudi Kurdistani, Giuseppe Roberto Tomasicchio, Daniele Conte, Stefano Mascetti, Sensitivity analysis of existing exponential empirical formulas for pore pressure distribution inside breakwater core using numerical modeling, Italian Journal of Engineering Geology and Environment, 1; pp. 65-71, 2020. doi.org/10.4408/IJEGE.2020-01.S-08

    36-20       Mohammadamin Torabi, Bruce Savage, Efficiency improvement of a novel submerged oscillating water column (SOWC) energy harvester, Proceedings, World Environmental and Water Resources Congress (Cancelled), Henderson, Nevada, May 17–21, 2020. doi.org/10.1061/9780784482940.003

    32-20       Adriano Henrique Tognato, Modelagem CFD da interação entre hidrodinâmica costeira e quebra-mar submerso: estudo de caso da Ponta da Praia em Santos, SP (CFD modeling of interaction between sea waves and submerged breakwater at Ponta de Praia – Santos, SP: a case study, Thesis, Universidad Estadual de Campinas, Campinas, Brazil, 2020.

    29-20   Ana Gomes, José L. S. Pinho, Tiago Valente, José S. Antunes do Carmo and Arkal V. Hegde, Performance assessment of a semi-circular breakwater through CFD modelling, Journal of Marine Science and Engineering, 8.3, art. no. 226, 2020. doi.org/10.3390/jmse8030226

    23-20  Qi Yang, Peng Yu, Yifan Liu, Hongjun Liu, Peng Zhang and Quandi Wang, Scour characteristics of an offshore umbrella suction anchor foundation under the combined actions of waves and currents, Ocean Engineering, 202, art. no. 106701, 2020. doi.org/10.1016/j.oceaneng.2019.106701

    04-20  Bingchen Liang, Shengtao Du, Xinying Pan and Libang Zhang, Local scour for vertical piles in steady currents: review of mechanisms, influencing factors and empirical equations, Journal of Marine Science and Engineering, 8.1, art. no. 4, 2020. doi.org/10.3390/jmse8010004

    104-19   A. Fitriadhy, S.F. Abdullah, M. Hairil, M.F. Ahmad and A. Jusoh, Optimized modelling on lateral separation of twin pontoon-net floating breakwater, Journal of Mechanical Engineering and Sciences, 13.4, pp. 5764-5779, 2019. doi.org/10.15282/jmes.13.4.2019.04.0460

    103-19  Ahmad Fitriadhy, Nurul Aqilah Mansor, Nur Adlina Aldin and Adi Maimun, CFD analysis on course stability of an asymmetrical bridle towline model of a towed ship, CFD Letters, 11.12, pp. 43-52, 2019.

    90-19   Eric P. Lemont and Karthik Ramaswamy, Computational fluid dynamics in coastal engineering: Verification of a breakwater design in the Torres Strait, Proceedings, pp. 762-768, Australian Coasts and Ports 2019 Conference, Hobart, Australia, September 10-13, 2019.

    86-19   Mohammed Arab Fatiha, Benoît Augier, François Deniset, Pascal Casari, and Jacques André Astolfi, Morphing hydrofoil model driven by compliant composite structure and internal pressure, Journal of Marine Science and Engineering, 7:423, 2019. doi.org/10.3390/jmse7120423

    83-19   Cong-Uy Nguyen, So-Young Lee, Thanh-Canh Huynh, Heon-Tae Kim, and Jeong-Tae Kim, Vibration characteristics of offshore wind turbine tower with gravity-based foundation under wave excitation, Smart Structures and Systems, 23:5, pp. 405-420, 2019. doi.org/10.12989/sss.2019.23.5.405

    68-19   B.W. Lee and C. Lee, Development of an equation for ship wave crests in a current in whole water depths, Proceedings, 10th International Conference on Asian and Pacific Coasts (APAC 2019), Hanoi, Vietnam, September 25-28, 2019; pp. 207-212, 2019. doi.org/10.1007/978-981-15-0291-0_29

    62-19   Byeong Wook Lee and Changhoon Lee, Equation for ship wave crests in the entire range of water depths, Coastal Engineering, 153:103542, 2019. doi.org/10.1016/j.coastaleng.2019.103542

    23-19     Mariano Buccino, Mohammad Daliri, Fabio Dentale, Angela Di Leo, and Mario Calabrese, CFD experiments on a low crested sloping top caisson breakwater, Part 1: Nature of loadings and global stability, Ocean Engineering, Vol. 182, pp. 259-282, 2019. doi.org/10.1016/j.oceaneng.2019.04.017

    21-19     Mahsa Ghazian Arabi, Deniz Velioglu Sogut, Ali Khosronejad, Ahmet C. Yalciner, and Ali Farhadzadeh, A numerical and experimental study of local hydrodynamics due to interactions between a solitary wave and an impervious structure, Coastal Engineering, Vol. 147, pp. 43-62, 2019. doi.org/10.1016/j.coastaleng.2019.02.004

    15-19     Chencong Liao, Jinjian Chen, and Yizhou Zhang, Accumulation of pore water pressure in a homogeneous sandy seabed around a rocking mono-pile subjected to wave loads, Vol. 173, pp. 810-822, 2019. doi.org/10.1016/j.oceaneng.2018.12.072

    09-19     Yaoyong Chen, Guoxu Niu, and Yuliang Ma, Study on hydrodynamics of a new comb-type floating breakwater fixed on the water surface, 2018 International Symposium on Architecture Research Frontiers and Ecological Environment (ARFEE 2018), Wuhan, China, December 14-16, 2018, E3S Web of Conferences Vol. 79, Art. No. 02003, 2019. doi.org/10.1051/e3sconf/20197902003

    08-19     Hongda Shi, Zhi Han, and Chenyu Zhao, Numerical study on the optimization design of the conical bottom heaving buoy convertor, Ocean Engineering, Vol. 173, pp. 235-243, 2019. doi.org/10.1016/j.oceaneng.2018.12.061

    06-19   S. Hemavathi, R. Manjula and N. Ponmani, Numerical modelling and experimental investigation on the effect of wave attenuation due to coastal vegetation, Proceedings of the Fourth International Conference in Ocean Engineering (ICOE2018), Vol. 2, pp. 99-110, 2019. doi.org/10.1007/978-981-13-3134-3_9

    87-18   Muhammad Syazwan Bazli, Omar Yaakob and Kang Hooi Siang, Validation study of u-oscillating water column device using computational fluid dynamic (CFD) simulation, 11thInternational Conference on Marine Technology, Kuala Lumpur, Malaysia, August 13-14, 2018.

    86-18   Nur Adlina Aldin, Ahmad Fitriadhy, Nurul Aqilah Mansor, and Adi Maimun, CFD analysis on unsteady yaw motion characteristic of a towed ship, 11th International Conference on Marine Technology, Kuala Lumpur, Malaysia, August 13-14, 2018.

    78-18 A.A. Abo Zaid, W.E. Mahmod, A.S. Koraim, E.M. Heikal and H.E. Fath, Wave interaction of partially immersed semicircular breakwater suspended on piles using FLOW-3D, CSME Conference Proceedings, Toronto, Canada, May 27-30, 2018.

    73-18   Jian Zhou and Subhas K. Venayagamoorthy, Near-field mean flow dynamics of a cylindrical canopy patch suspended in deep water, Journal of Fluid Mechanics, Vol. 858, pp. 634-655, 2018. doi.org/10.1017/jfm.2018.775

    69-18   Keisuke Yoshida, Shiro Maeno, Tomihiro Iiboshi and Daisuke Araki, Estimation of hydrodynamic forces acting on concrete blocks of toe protection works for coastal dikes by tsunami overflows, Applied Ocean Research, Vol. 80, pp. 181-196, 2018. doi.org/10.1016/j.apor.2018.09.001

    68-18   Zegao Yin, Yanxu Wang and Xiaoyu Yang, Regular wave run-up attenuation on a slope by emergent rigid vegetation, Journal of Coastal Research (in-press), 2018. doi.org/10.2112/JCOASTRES-D-17-00200.1

    65-18   Dagui Tong, Chencong Liao, Jinjian Chen and Qi Zhang, Numerical simulation of a sandy seabed response to water surface waves propagating on current, Journal of Marine Science and Engineering, Vol. 6, No. 3, 2018. doi.org/10.3390/jmse6030088

    61-18   Manuel Gerardo Verduzco-Zapata, Aramis Olivos-Ortiz, Marco Liñán-Cabello, Christian Ortega-Ortiz, Marco Galicia-Pérez, Chris Matthews, and Omar Cervantes-Rosas, Development of a Desalination System Driven by Low Energy Ocean Surface Waves, Journal of Coastal Research: Special Issue 85 – Proceedings of the 15th International Coastal Symposium, pp. 1321 – 1325, 2018. doi.org/10.2112/SI85-265.1

    37-18   Songsen Xu, Chunshuo Jiao, Meng Ning and Sheng Dong, Analysis of Buoyancy Module Auxiliary Installation Technology Based on Numerical Simulation, Journal of Ocean University of China, vol. 17, no. 2, pp. 267-280, 2018. doi.org/10.1007/s11802-018-3305-4

    36-18   Deniz Velioglu Sogut and Ahmet Cevdet Yalciner, Performance comparison of NAMI DANCE and FLOW-3D® models in tsunami propagation, inundation and currents using NTHMP benchmark problems, Pure and Applied Geophysics, pp. 1-39, 2018. doi.org/10.1007/s00024-018-1907-9

    26-18   Mohammad Sarfaraz and Ali Pak, Numerical investigation of the stability of armour units in low-crested breakwaters using combined SPH–Polyhedral DEM method, Journal of Fluids and Structures, vol. 81, pp. 14-35, 2018. doi.org/10.1016/j.jfluidstructs.2018.04.016

    25-18   Yen-Lung Chen and Shih-Chun Hsiao, Numerical modeling of a buoyant round jet under regular waves, Ocean Engineering, vol. 161, pp. 154-167, 2018. doi.org/10.1016/j.oceaneng.2018.04.093

    13-18   Yizhou Zhang, Chencong Liao, Jinjian Chen, Dagui Tong, and Jianhua Wang, Numerical analysis of interaction between seabed and mono-pile subjected to dynamic wave loadings considering the pile rocking effect, Ocean Engineering, Volume 155, 1 May 2018, Pages 173-188, doi.org/10.1016/j.oceaneng.2018.02.041

    11-18  Ching-Piao Tsai, Chun-Han Ko and Ying-Chi Chen, Investigation on Performance of a Modified Breakwater-Integrated OWC Wave Energy Converter, Open Access Sustainability 2018, 10(3), 643; doi:10.3390/su10030643, © Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2018.

    58-17   Jian Zhou, Claudia Cenedese, Tim Williams and Megan Ball, On the propagation of gravity currents over and through a submerged array of circular cylinders, Journal of Fluid Mechanics, Vol. 831, pp. 394-417, 2017. doi.org/10.1017/jfm.2017.604

    56-17   Yu-Shu Kuo, Chih-Yin Chung, Shih-Chun Hsiao and Yu-Kai Wang, Hydrodynamic characteristics of Oscillating Water Column caisson breakwaters, Renewable Energy, vol. 103, pp. 439-447, 2017. doi.org/10.1016/j.renene.2016.11.028

    47-17   Jae-Nam Cho, Chang-Geun Song, Kyu-Nam Hwang and Seung-Oh Lee, Experimental assessment of suspended sediment concentration changed by solitary wave, Journal of Marine Science and Technology, Vol. 25, No. 6, pp. 649-655 (2017) 649 DOI: 10.6119/JMST-017-1226-04

    45-17   Muhammad Aldhiansyah Rifqi Fauzi, Haryo Dwito Armono, Mahmud Mu