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 [114]. 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 [1527]. Consequently, it is necessary to study the effects of the passive factors on the active domain [2836]. 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 [3841].

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 [4345]. 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 [4748].

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 nf, 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 [4555].

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 [475758] and the other method is to increase the canal bed roughness (Manning-Strickler coefficient) [455961]. 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 [496366] 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 xyz and  are viscosity accelerations in the directions xyz 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 [7077]. Determination and true implementation of such parameters is one of the key steps of any simulation [237881] and computing procedure [8286]. 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 [8788]. 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 414 ). 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 510, 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 58 and 1011), 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)
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(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)
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(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 [27815188994]. In future, we can also apply the simulation logic and software of this research for other domains such as power engineering [9599].(a)
(a)(b)
(b)(c)
(c)(a)
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(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.

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Fig. 1. Hydraulic jump flow structure.

Performance assessment of OpenFOAM and FLOW-3D in the numerical modeling of a low Reynolds number hydraulic jump

낮은 레이놀즈 수 유압 점프의 수치 모델링에서 OpenFOAM 및 FLOW-3D의 성능 평가

ArnauBayona DanielValerob RafaelGarcía-Bartuala Francisco ​JoséVallés-Morána P. AmparoLópez-Jiméneza

Abstract

A comparative performance analysis of the CFD platforms OpenFOAM and FLOW-3D is presented, focusing on a 3D swirling turbulent flow: a steady hydraulic jump at low Reynolds number. Turbulence is treated using RANS approach RNG k-ε. A Volume Of Fluid (VOF) method is used to track the air–water interface, consequently aeration is modeled using an Eulerian–Eulerian approach. Structured meshes of cubic elements are used to discretize the channel geometry. The numerical model accuracy is assessed comparing representative hydraulic jump variables (sequent depth ratio, roller length, mean velocity profiles, velocity decay or free surface profile) to experimental data. The model results are also compared to previous studies to broaden the result validation. Both codes reproduced the phenomenon under study concurring with experimental data, although special care must be taken when swirling flows occur. Both models can be used to reproduce the hydraulic performance of energy dissipation structures at low Reynolds numbers.

CFD 플랫폼 OpenFOAM 및 FLOW-3D의 비교 성능 분석이 3D 소용돌이치는 난류인 낮은 레이놀즈 수에서 안정적인 유압 점프에 초점을 맞춰 제시됩니다. 난류는 RANS 접근법 RNG k-ε을 사용하여 처리됩니다.

VOF(Volume Of Fluid) 방법은 공기-물 계면을 추적하는 데 사용되며 결과적으로 Eulerian-Eulerian 접근 방식을 사용하여 폭기가 모델링됩니다. 입방체 요소의 구조화된 메쉬는 채널 형상을 이산화하는 데 사용됩니다. 수치 모델 정확도는 대표적인 유압 점프 변수(연속 깊이 비율, 롤러 길이, 평균 속도 프로파일, 속도 감쇠 또는 자유 표면 프로파일)를 실험 데이터와 비교하여 평가됩니다.

모델 결과는 또한 결과 검증을 확장하기 위해 이전 연구와 비교됩니다. 소용돌이 흐름이 발생할 때 특별한 주의가 필요하지만 두 코드 모두 실험 데이터와 일치하는 연구 중인 현상을 재현했습니다. 두 모델 모두 낮은 레이놀즈 수에서 에너지 소산 구조의 수리 성능을 재현하는 데 사용할 수 있습니다.

Keywords

CFDRANS, OpenFOAM, FLOW-3D ,Hydraulic jump, Air–water flow, Low Reynolds number

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Conflict resolution in the multi-stakeholder stepped spillway design under uncertainty by machine learning techniques

기계 학습 기술에 의한 불확실성 하에서 다중 이해 관계자 계단형 배수로 설계의 충돌 해결

Conflict resolution in the multi-stakeholder stepped spillway design under uncertainty by machine learning techniques

Mehrdad GhorbaniMooseluaMohammad RezaNikoobParnian HashempourBakhtiaribNooshin BakhtiariRayanicAzizallahIzadyd
aDepartment of Engineering Sciences, University of Agder, Norway
bDepartment of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran
cSchool of Engineering, Department of Civil and Environmental Engineering, Shiraz University, Shiraz, IrandWater Research Center, Sultan Qaboos University, Muscat, Oman

Abstract

The optimal spillway design is of great significance since these structures can reduce erosion downstream of the dams. This study proposes a risk-based optimization framework for a stepped spillway to achieve an economical design scenario with the minimum loss in hydraulic performance. Accordingly, the stepped spillway was simulated in the FLOW-3D® model, and the validated model was repeatedly performed for various geometric states.

The results were used to form a Multilayer Perceptron artificial neural network (MLP-ANN) surrogate model. Then, a risk-based optimization model was formed by coupling the MLP-ANN and NSGA-II. The concept of conditional value at risk (CVaR) was utilized to reduce the risk of the designed spillway malfunctions in high flood flow rates, while minimizing the construction cost and the loss in hydraulic performance.

Lastly, given the conflicting objectives of stakeholders, the non-cooperative graph model for conflict resolution (GMCR) was applied to achieve a compromise on the Pareto optimal solutions. Applicability of the suggested approach in the Jarreh Dam, Iran, resulted in a practical design scenario, which simultaneously minimizes the loss in hydraulic performance and the project cost and satisfies the priorities of decision-makers.

Keywords

Stepped spillway, FLOW-3D® ,CVaR-based optimization model, GMCR-plus, NSGA-II

최적의 배수로 설계는 이러한 구조가 댐 하류의 침식을 줄일 수 있기 때문에 매우 중요합니다. 본 연구에서는 유압 성능 손실을 최소화하면서 경제적인 설계 시나리오를 달성하기 위해 계단형 여수로에 대한 위험 기반 최적화 프레임워크를 제안합니다. 따라서 FLOW-3D® 모델에서 계단식 배수로를 시뮬레이션하고 다양한 기하학적 상태에 대해 검증된 모델을 반복적으로 수행했습니다.

결과는 다층 퍼셉트론 인공 신경망(MLP-ANN) 대리 모델을 형성하는 데 사용되었습니다. 그런 다음 MLP-ANN과 NSGA-II를 결합하여 위험 기반 최적화 모델을 구성했습니다. 위험 조건부 값(CVaR)의 개념은 높은 홍수 유량에서 설계된 방수로 오작동의 위험을 줄이는 동시에 건설 비용과 수리 성능 손실을 최소화하기 위해 활용되었습니다.

마지막으로 이해관계자의 상충되는 목표를 고려하여 파레토 최적해에 대한 절충안을 달성하기 위해 갈등 해결을 위한 비협조적 그래프 모델(GMCR)을 적용하였다. 이란 Jarreh 댐에서 제안된 접근 방식의 적용 가능성은 수력 성능 손실과 프로젝트 비용을 동시에 최소화하고 의사 결정자의 우선 순위를 만족시키는 실용적인 설계 시나리오로 귀결되었습니다.

electromagnetic metal casting computation designs Fig1

A survey of electromagnetic metal casting computation designs, present approaches, future possibilities, and practical issues

The European Physical Journal Plus volume 136, Article number: 704 (2021) Cite this article

Abstract

Electromagnetic metal casting (EMC) is a casting technique that uses electromagnetic energy to heat metal powders. It is a faster, cleaner, and less time-consuming operation. Solid metals create issues in electromagnetics since they reflect the electromagnetic radiation rather than consume it—electromagnetic energy processing results in sounded pieces with higher-ranking material properties and a more excellent microstructure solution. For the physical production of the electromagnetic casting process, knowledge of electromagnetic material interaction is critical. Even where the heated material is an excellent electromagnetic absorber, the total heating quality is sometimes insufficient. Numerical modelling works on finding the proper coupled effects between properties to bring out the most effective operation. The main parameters influencing the quality of output of the EMC process are: power dissipated per unit volume into the material, penetration depth of electromagnetics, complex magnetic permeability and complex dielectric permittivity. The contact mechanism and interference pattern also, in turn, determines the quality of the process. Only a few parameters, such as the environment’s temperature, the interference pattern, and the rate of metal solidification, can be controlled by AI models. Neural networks are used to achieve exact outcomes by stimulating the neurons in the human brain. Additive manufacturing (AM) is used to design mold and cores for metal casting. The models outperformed the traditional DFA optimization approach, which is susceptible to local minima. The system works only offline, so real-time analysis and corrections are not yet possible.

Korea Abstract

전자기 금속 주조 (EMC)는 전자기 에너지를 사용하여 금속 분말을 가열하는 주조 기술입니다. 더 빠르고 깨끗하며 시간이 덜 소요되는 작업입니다.

고체 금속은 전자기 복사를 소비하는 대신 반사하기 때문에 전자기학에서 문제를 일으킵니다. 전자기 에너지 처리는 더 높은 등급의 재료 특성과 더 우수한 미세 구조 솔루션을 가진 사운드 조각을 만듭니다.

전자기 주조 공정의 물리적 생산을 위해서는 전자기 물질 상호 작용에 대한 지식이 중요합니다. 가열된 물질이 우수한 전자기 흡수재인 경우에도 전체 가열 품질이 때때로 불충분합니다. 수치 모델링은 가장 효과적인 작업을 이끌어 내기 위해 속성 간의 적절한 결합 효과를 찾는데 사용됩니다.

EMC 공정의 출력 품질에 영향을 미치는 주요 매개 변수는 단위 부피당 재료로 분산되는 전력, 전자기의 침투 깊이, 복합 자기 투과성 및 복합 유전율입니다. 접촉 메커니즘과 간섭 패턴 또한 공정의 품질을 결정합니다. 환경 온도, 간섭 패턴 및 금속 응고 속도와 같은 몇 가지 매개 변수 만 AI 모델로 제어 할 수 있습니다.

신경망은 인간 뇌의 뉴런을 자극하여 정확한 결과를 얻기 위해 사용됩니다. 적층 제조 (AM)는 금속 주조용 몰드 및 코어를 설계하는 데 사용됩니다. 모델은 로컬 최소값에 영향을 받기 쉬운 기존 DFA 최적화 접근 방식을 능가했습니다. 이 시스템은 오프라인에서만 작동하므로 실시간 분석 및 수정은 아직 불가능합니다.

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Figure 6. Evolution of melt pool in the overhang region (θ = 45°, P = 100 W, v = 1000 mm/s, the streamlines are shown by arrows).

Experimental and numerical investigation of the origin of surface roughness in laser powder bed fused overhang regions

레이저 파우더 베드 융합 오버행 영역에서 표면 거칠기의 원인에 대한 실험 및 수치 조사

Shaochuan Feng,Amar M. Kamat,Soheil Sabooni &Yutao PeiPages S66-S84 | Received 18 Jan 2021, Accepted 25 Feb 2021, Published online: 10 Mar 2021

ABSTRACT

Surface roughness of laser powder bed fusion (L-PBF) printed overhang regions is a major contributor to deteriorated shape accuracy/surface quality. This study investigates the mechanisms behind the evolution of surface roughness (Ra) in overhang regions. The evolution of surface morphology is the result of a combination of border track contour, powder adhesion, warp deformation, and dross formation, which is strongly related to the overhang angle (θ). When 0° ≤ θ ≤ 15°, the overhang angle does not affect Ra significantly since only a small area of the melt pool boundaries contacts the powder bed resulting in slight powder adhesion. When 15° < θ ≤ 50°, powder adhesion is enhanced by the melt pool sinking and the increased contact area between the melt pool boundary and powder bed. When θ > 50°, large waviness of the overhang contour, adhesion of powder clusters, severe warp deformation and dross formation increase Ra sharply.

레이저 파우더 베드 퓨전 (L-PBF) 프린팅 오버행 영역의 표면 거칠기는 형상 정확도 / 표면 품질 저하의 주요 원인입니다. 이 연구 는 오버행 영역에서 표면 거칠기 (Ra ) 의 진화 뒤에 있는 메커니즘을 조사합니다 . 표면 형태의 진화는 오버행 각도 ( θ ) 와 밀접한 관련이있는 경계 트랙 윤곽, 분말 접착, 뒤틀림 변형 및 드로스 형성의 조합의 결과입니다 . 0° ≤  θ  ≤ 15° 인 경우 , 용융풀 경계의 작은 영역 만 분말 베드와 접촉하여 약간의 분말 접착이 발생하기 때문에 오버행 각도가 R a에 큰 영향을 주지 않습니다 . 15° < θ 일 때  ≤ 50°, 용융 풀 싱킹 및 용융 풀 경계와 분말 베드 사이의 증가된 접촉 면적으로 분말 접착력이 향상됩니다. θ  > 50° 일 때 오버행 윤곽의 큰 파형, 분말 클러스터의 접착, 심한 휨 변형 및 드 로스 형성이 Ra 급격히 증가 합니다.

KEYWORDS: Laser powder bed fusion (L-PBF), melt pool dynamics, overhang region, shape deviation, surface roughness

1. Introduction

레이저 분말 베드 융합 (L-PBF)은 첨단 적층 제조 (AM) 기술로, 집중된 레이저 빔을 사용하여 금속 분말을 선택적으로 융합하여 슬라이스 된 3D 컴퓨터 지원에 따라 층별로 3 차원 (3D) 금속 부품을 구축합니다. 설계 (CAD) 모델 (Chatham, Long 및 Williams 2019 ; Tan, Zhu 및 Zhou 2020 ). 재료가 인쇄 층 아래에 ​​존재하는지 여부에 따라 인쇄 영역은 각각 솔리드 영역 또는 돌출 영역으로 분류 될 수 있습니다. 따라서 오버행 영역은 고체 기판이 아니라 분말 베드 바로 위에 건설되는 특수 구조입니다 (Patterson, Messimer 및 Farrington 2017). 오버행 영역은지지 구조를 포함하거나 포함하지 않고 구축 할 수 있으며, 지지대가있는 돌출 영역의 L-PBF는 지지체가 더 낮은 밀도로 구축된다는 점을 제외 하고 (Wang and Chou 2018 ) 고체 기판의 공정과 유사합니다 (따라서 기계적 강도가 낮기 때문에 L-PBF 공정 후 기계적으로 쉽게 제거 할 수 있습니다. 따라서지지 구조로 인쇄 된 오버행 영역은 L-PBF 공정 후 지지물 제거, 연삭 및 연마와 같은 추가 후 처리 단계가 필요합니다.

수평 내부 채널의 제작과 같은 일부 특정 경우에는 공정 후 지지대를 제거하기가 어려우므로 채널 상단 절반의 돌출부 영역을 지지대없이 건설해야합니다 (Hopkinson and Dickens 2000 ). 수평 내부 채널에 사용할 수없는지지 구조 외에도 내부 표면, 특히 등각 냉각 채널 (Feng, Kamat 및 Pei 2021 ) 에서 발생하는 복잡한 3D 채널 네트워크의 경우 표면 마감 프로세스를 구현하는 것도 어렵습니다 . 결과적으로 오버행 영역은 (i) 잔류 응력에 의한 변형, (ii) 계단 효과 (Kuo et al. 2020 ; Li et al. 2020 )로 인해 설계된 모양에서 벗어날 수 있습니다 .) 및 (iii) 원하지 않는 분말 소결로 인한 향상된 표면 거칠기; 여기서, 앞의 두 요소는 일반적으로 mm 길이 스케일에서 ‘매크로’편차로 분류되고 후자는 일반적으로 µm 길이 스케일에서 ‘마이크로’편차로 인식됩니다.

열 응력에 의한 변형은 오버행 영역에서 발생하는 중요한 문제입니다 (Patterson, Messimer 및 Farrington 2017 ). 국부적 인 용융 / 냉각은 용융 풀 내부 및 주변에서 큰 온도 구배를 유도하여 응고 된 층에 집중적 인 열 응력을 유발합니다. 열 응력에 의한 뒤틀림은 고체 영역을 현저하게 변형하지 않습니다. 이러한 영역은 아래의 여러 레이어에 의해 제한되기 때문입니다. 반면에 오버행 영역은 구속되지 않고 공정 중 응력 완화로 인해 상당한 변형이 발생합니다 (Kamat 및 Pei 2019 ). 더욱이 용융 깊이는 레이어 두께보다 큽니다 (이전 레이어도 재용 해되어 빌드 된 레이어간에 충분한 결합을 보장하기 때문입니다 [Yadroitsev et al. 2013 ; Kamath et al.2014 ]),응고 된 두께가 설계된 두께보다 크기 때문에형태 편차 (예 : 드 로스 [Charles et al. 2020 ; Feng et al. 2020 ])가 발생합니다. 마이크로 스케일에서 인쇄 된 표면 (R a 및 S a ∼ 10 μm)은 기계적으로 가공 된 표면보다 거칠다 (Duval-Chaneac et al. 2018 ; Wen et al. 2018 ). 이 문제는고형화 된 용융 풀의 가장자리에 부착 된 용융되지 않은 분말의 결과로 표면 거칠기 (R a )가 일반적으로 약 20 μm인 오버행 영역에서 특히 심각합니다 (Mazur et al. 2016 ; Pakkanen et al. 2016 ).

오버행 각도 ( θ , 빌드 방향과 관련하여 측정)는 오버행 영역의 뒤틀림 편향과 표면 거칠기에 영향을 미치는 중요한 매개 변수입니다 (Kamat and Pei 2019 ; Mingear et al. 2019 ). θ ∼ 45 ° 의 오버행 각도 는 일반적으로지지 구조없이 오버행 영역을 인쇄 할 수있는 임계 값으로 합의됩니다 (Pakkanen et al. 2016 ; Kadirgama et al. 2018 ). θ 일 때이 임계 값보다 크면 오버행 영역을 허용 가능한 표면 품질로 인쇄 할 수 없습니다. 오버행 각도 외에도 레이저 매개 변수 (레이저 에너지 밀도와 관련된)는 용융 풀의 모양 / 크기 및 용융 풀 역학에 영향을줌으로써 오버행 영역의 표면 거칠기에 영향을줍니다 (Wang et al. 2013 ; Mingear et al . 2019 ).

용융 풀 역학은 고체 (Shrestha 및 Chou 2018 ) 및 오버행 (Le et al. 2020 ) 영역 모두에서 수행되는 L-PBF 공정을 포함한 레이저 재료 가공의 일반적인 물리적 현상입니다 . 용융 풀 모양, 크기 및 냉각 속도는 잔류 응력으로 인한 변형과 ​​표면 거칠기에 모두 영향을 미치므로 처리 매개 변수와 표면 형태 / 품질 사이의 다리 역할을하며 용융 풀을 이해하기 위해 수치 시뮬레이션을 사용하여 추가 조사를 수행 할 수 있습니다. 거동과 표면 거칠기에 미치는 영향. 현재까지 고체 영역의 L-PBF 동안 용융 풀 동작을 시뮬레이션하기 위해 여러 연구가 수행되었습니다. 유한 요소 방법 (FEM)과 같은 시뮬레이션 기술 (Roberts et al. 2009 ; Du et al.2019 ), 유한 차분 법 (FDM) (Wu et al. 2018 ), 전산 유체 역학 (CFD) (Lee and Zhang 2016 ), 임의의 Lagrangian-Eulerian 방법 (ALE) (Khairallah and Anderson 2014 )을 사용하여 증발 반동 압력 (Hu et al. 2018 ) 및 Marangoni 대류 (Zhang et al. 2018 ) 현상을포함하는 열 전달 (온도 장) 및 물질 전달 (용융 흐름) 프로세스. 또한 이산 요소법 (DEM)을 사용하여 무작위 분산 분말 베드를 생성했습니다 (Lee and Zhang 2016 ; Wu et al. 2018 ). 이 모델은 분말 규모의 L-PBF 공정을 시뮬레이션했습니다 (Khairallah et al. 2016) 메조 스케일 (Khairallah 및 Anderson 2014 ), 단일 트랙 (Leitz et al. 2017 )에서 다중 트랙 (Foroozmehr et al. 2016 ) 및 다중 레이어 (Huang, Khamesee 및 Toyserkani 2019 )로.

그러나 결과적인 표면 거칠기를 결정하는 오버행 영역의 용융 풀 역학은 문헌에서 거의 관심을받지 못했습니다. 솔리드 영역의 L-PBF에 대한 기존 시뮬레이션 모델이 어느 정도 참조가 될 수 있지만 오버행 영역과 솔리드 영역 간의 용융 풀 역학에는 상당한 차이가 있습니다. 오버행 영역에서 용융 금속은 분말 입자 사이의 틈새로 아래로 흘러 용융 풀이 다공성 분말 베드가 제공하는 약한 지지체 아래로 가라 앉습니다. 이것은 중력과 표면 장력의 영향이 용융 풀의 결과적인 모양 / 크기를 결정하는 데 중요하며, 결과적으로 오버행 영역의 마이크로 스케일 형태의 진화에 중요합니다. 또한 분말 입자 사이의 공극, 열 조건 (예 : 에너지 흡수,2019 ; Karimi et al. 2020 ; 노래와 영 2020 ). 표면 거칠기는 (마이크로) 형상 편차를 증가시킬뿐만 아니라 주기적 하중 동안 미세 균열의 시작 지점 역할을함으로써 기계적 강도를 저하시킵니다 (Günther et al. 2018 ). 오버행 영역의 높은 표면 거칠기는 (마이크로) 정확도 / 품질에 대한 엄격한 요구 사항이있는 부품 제조에서 L-PBF의 적용을 제한합니다.

본 연구는 실험 및 시뮬레이션 연구를 사용하여 오버행 영역 (지지물없이 제작)의 미세 형상 편차 형성 메커니즘과 표면 거칠기의 기원을 체계적이고 포괄적으로 조사합니다. 결합 된 DEM-CFD 시뮬레이션 모델은 경계 트랙 윤곽, 분말 접착 및 뒤틀림 변형의 효과를 고려하여 오버행 영역의 용융 풀 역학과 표면 형태의 형성 메커니즘을 나타 내기 위해 개발되었습니다. 표면 거칠기 R의 시뮬레이션 및 단일 요인 L-PBF 인쇄 실험을 사용하여 오버행 각도의 함수로 연구됩니다. 용융 풀의 침몰과 관련된 오버행 영역에서 분말 접착의 세 가지 메커니즘이 식별되고 자세히 설명됩니다. 마지막으로, 인쇄 된 오버행 영역에서 높은 표면 거칠기 문제를 완화 할 수 있는 잠재적 솔루션에 대해 간략하게 설명합니다.

The shape and size of the L-PBF printed samples are illustrated in Figure 1
The shape and size of the L-PBF printed samples are illustrated in Figure 1
Figure 2. Borders in the overhang region depending on the overhang angle θ
Figure 2. Borders in the overhang region depending on the overhang angle θ
Figure 3. (a) Profile of the volumetric heat source, (b) the model geometry of single-track printing on a solid substrate (unit: µm), and (c) the comparison of melt pool dimensions obtained from the experiment (right half) and simulation (left half) for a calibrated optical penetration depth of 110 µm (laser power 200 W and scan speed 800 mm/s, solidified layer thickness 30 µm, powder size 10–45 µm).
Figure 3. (a) Profile of the volumetric heat source, (b) the model geometry of single-track printing on a solid substrate (unit: µm), and (c) the comparison of melt pool dimensions obtained from the experiment (right half) and simulation (left half) for a calibrated optical penetration depth of 110 µm (laser power 200 W and scan speed 800 mm/s, solidified layer thickness 30 µm, powder size 10–45 µm).
Figure 4. The model geometry of an overhang being L-PBF processed: (a) 3D view and (b) right view.
Figure 4. The model geometry of an overhang being L-PBF processed: (a) 3D view and (b) right view.
Figure 5. The cross-sectional contour of border tracks in a 45° overhang region.
Figure 5. The cross-sectional contour of border tracks in a 45° overhang region.
Figure 6. Evolution of melt pool in the overhang region (θ = 45°, P = 100 W, v = 1000 mm/s, the streamlines are shown by arrows).
Figure 6. Evolution of melt pool in the overhang region (θ = 45°, P = 100 W, v = 1000 mm/s, the streamlines are shown by arrows).
Figure 7. The overhang contour is contributed by (a) only outer borders when θ ≤ 60° (b) both inner borders and outer borders when θ > 60°.
Figure 7. The overhang contour is contributed by (a) only outer borders when θ ≤ 60° (b) both inner borders and outer borders when θ > 60°.
Figure 8. Schematic of powder adhesion on a 45° overhang region.
Figure 8. Schematic of powder adhesion on a 45° overhang region.
Figure 9. The L-PBF printed samples with various overhang angle (a) θ = 0° (cube), (b) θ = 30°, (c) θ = 45°, (d) θ = 55° and (e) θ = 60°.
Figure 9. The L-PBF printed samples with various overhang angle (a) θ = 0° (cube), (b) θ = 30°, (c) θ = 45°, (d) θ = 55° and (e) θ = 60°.
Figure 10. Two mechanisms of powder adhesion related to the overhang angle: (a) simulation-predicted, θ = 45°; (b) simulation-predicted, θ = 60°; (c, e) optical micrographs, θ = 45°; (d, f) optical micrographs, θ = 60°. (e) and (f) are partial enlargement of (c) and (d), respectively.
Figure 10. Two mechanisms of powder adhesion related to the overhang angle: (a) simulation-predicted, θ = 45°; (b) simulation-predicted, θ = 60°; (c, e) optical micrographs, θ = 45°; (d, f) optical micrographs, θ = 60°. (e) and (f) are partial enlargement of (c) and (d), respectively.
Figure 11. Simulation-predicted surface morphology in the overhang region at different overhang angle: (a) θ = 15°, (b) θ = 30°, (c) θ = 45°, (d) θ = 60° and (e) θ = 80° (Blue solid lines: simulation-predicted contour; red dashed lines: the planar profile of designed overhang region specified by the overhang angles).
Figure 11. Simulation-predicted surface morphology in the overhang region at different overhang angle: (a) θ = 15°, (b) θ = 30°, (c) θ = 45°, (d) θ = 60° and (e) θ = 80° (Blue solid lines: simulation-predicted contour; red dashed lines: the planar profile of designed overhang region specified by the overhang angles).
Figure 12. Effect of overhang angle on surface roughness Ra in overhang regions
Figure 12. Effect of overhang angle on surface roughness Ra in overhang regions
Figure 13. Surface morphology of L-PBF printed overhang regions with different overhang angle: (a) θ = 15°, (b) θ = 30°, (c) θ = 45° and (d) θ = 60° (overhang border parameters: P = 100 W, v = 1000 mm/s).
Figure 13. Surface morphology of L-PBF printed overhang regions with different overhang angle: (a) θ = 15°, (b) θ = 30°, (c) θ = 45° and (d) θ = 60° (overhang border parameters: P = 100 W, v = 1000 mm/s).
Figure 14. Effect of (a) laser power (scan speed = 1000 mm/s) and (b) scan speed (lase power = 100 W) on surface roughness Ra in overhang regions (θ = 45°, laser power and scan speed referred to overhang border parameters, and the other process parameters are listed in Table 2).
Figure 14. Effect of (a) laser power (scan speed = 1000 mm/s) and (b) scan speed (lase power = 100 W) on surface roughness Ra in overhang regions (θ = 45°, laser power and scan speed referred to overhang border parameters, and the other process parameters are listed in Table 2).

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Figure 1. The push barge model in 1:20 geometrical scale during field experiments.

Experimental Method for the Measurements and Numerical Investigations of Force Generated on the Rotating Cylinder under Water Flow

by Teresa Abramowicz-Gerigk 1,*,Zbigniew Burciu 1,Jacek Jachowski 1,Oskar Kreft 2,Dawid Majewski 3,Barbara Stachurska 3,Wojciech Sulisz 3 andPiotr Szmytkiewicz 3

1Faculty of Navigation, Gdynia Maritime University, 81-225 Gdynia, Poland
2AREX Ltd., 81-212 Gdynia, Poland
3Institute of Hydro-Engineering of Polish Academy of Sciences, 80-328 Gdansk, Poland
*Author to whom correspondence should be addressed.
Academic Editor: Remco J. WiegerinkSensors202121(6), 2216; https://doi.org/10.3390/s21062216
Received: 20 January 2021 / Revised: 9 March 2021 / Accepted: 18 March 2021 / Published: 22 March 2021(This article belongs to the Special Issue Sensing in Flow Analysis)

Abstract

본 논문은 자유 표면 효과를 포함한 균일한 흐름 하에서 회전하는 실린더 (로터)에 발생하는 유체 역학적 힘의 실험 테스트 설정 및 측정 방법을 제시합니다. 실험 테스트 설정은 고급 유량 생성 및 측정 시스템을 갖춘 수로 탱크에 설치된 고유 한 구조였습니다.

테스트 설정은 로터 드라이브가 있는 베어링 장착 플랫폼과 유체 역학적 힘을 측정하는 센서로 구성되었습니다. 낮은 길이 대 직경 비율 실린더는 얕은 흘수 강 바지선의 선수 로터 방향타 모델로 선택되었습니다. 로터 역학은 최대 550rpm의 회전 속도와 최대 0.85m / s의 수류 속도에 대해 테스트되었습니다.

실린더의 낮은 종횡비와 자유 표면 효과는 생성 된 유체 역학적 힘에 영향을 미치는 현상에 상당한 영향을 미쳤습니다. 회전자 길이 대 직경 비율, 회전 속도 대 유속 비율 및 양력에 대한 레이놀즈 수의 영향을 분석했습니다. 실험 결과에 대한 계산 모델의 유효성이 표시됩니다. 결과는 시뮬레이션 및 실험에 대한 결과의 유사한 경향을 보여줍니다.

The paper presents the experimental test setup and measurement method of hydrodynamic force generated on the rotating cylinder (rotor) under uniform flow including the free surface effect. The experimental test setup was a unique construction installed in the flume tank equipped with advanced flow generating and measuring systems.

The test setup consisted of a bearing mounted platform with rotor drive and sensors measuring the hydrodynamic force. The low length to diameter ratio cylinders were selected as models of bow rotor rudders of a shallow draft river barge. The rotor dynamics was tested for the rotational speeds up to 550 rpm and water current velocity up to 0.85 m/s. The low aspect ratio of the cylinder and free surface effect had significant impacts on the phenomena influencing the generated hydrodynamic force. The effects of the rotor length to diameter ratio, rotational velocity to flow velocity ratio, and the Reynolds number on the lift force were analyzed. The validation of the computational model against experimental results is presented. The results show a similar trend of results for the simulation and experiment.

Keywords: rotating cylinderforce sensor with built-in amplifierstrain gauge sensorCFD analysis

Figure 1. The push barge model in 1:20 geometrical scale during field experiments.
Figure 1. The push barge model in 1:20 geometrical scale during field experiments.
Figure 2. Scheme of the measurement area.
Figure 2. Scheme of the measurement area.
Figure 3. The force measuring part of the experimental test setup: (a) side view: 1—bearing-mounted platform, 2—drive system, 3—cylinder, 4—support frame, 5—force sensors, and 6—adjusting screw; (b) top view.
Figure 3. The force measuring part of the experimental test setup: (a) side view: 1—bearing-mounted platform, 2—drive system, 3—cylinder, 4—support frame, 5—force sensors, and 6—adjusting screw; (b) top view.
Figure 4. Location of the rotor, rotor drive, and supporting frame in the wave flume.
Figure 4. Location of the rotor, rotor drive, and supporting frame in the wave flume.
Figure 5. Lift force obtained from the measurements in the wave flume for different flow velocities and cylinder diameters.
Figure 5. Lift force obtained from the measurements in the wave flume for different flow velocities and cylinder diameters.
Figure 6. Variation of the lift coefficient with rotation rate for various free stream velocities and various cylinder diameters—experimental results.
Figure 6. Variation of the lift coefficient with rotation rate for various free stream velocities and various cylinder diameters—experimental results.
Figure 7. Boundary conditions for rotor-generated flow field simulation—computing domain with free surface level.
Figure 7. Boundary conditions for rotor-generated flow field simulation—computing domain with free surface level.
Figure 8. General view and the close-up of the rotor wall sector applied for the rotor simulation.
Figure 8. General view and the close-up of the rotor wall sector applied for the rotor simulation.
Figure 9. Structured mesh used in FLOW-3D and the FAVORTM technique—the original shape of the rotor and the shape of the object after FAVOR discretization technique for 3 mesh densities.
Figure 9. Structured mesh used in FLOW-3D and the FAVORTM technique—the original shape of the rotor and the shape of the object after FAVOR discretization technique for 3 mesh densities.
Figure 10. Parameter y+ for the studied turbulence models and meshes.
Figure 10. Parameter y+ for the studied turbulence models and meshes.
Figure 11. Results of numerical computations in time for the cylinder with D2 diameter at 500 rpm rotational speed and current speed V = 0.82 m/s using LES model in dependence of mesh density: (a) FX and (b) FY
Figure 11. Results of numerical computations in time for the cylinder with D2 diameter at 500 rpm rotational speed and current speed V = 0.82 m/s using LES model in dependence of mesh density: (a) FX and (b) FY
Figure 12. Results of 3D flow simulation for V = 0.40 m/s: (a) perspective view of velocity field on the free surface, (b) top view of velocity field on the free surface, (c) velocity field in the horizontal plane at half-length section of the rotor, and (d) velocity field in the rotor symmetry plane.
Figure 12. Results of 3D flow simulation for V = 0.40 m/s: (a) perspective view of velocity field on the free surface, (b) top view of velocity field on the free surface, (c) velocity field in the horizontal plane at half-length section of the rotor, and (d) velocity field in the rotor symmetry plane.
Figure 13. Results of 3D flow simulation for V = 0.50 m/s: (a) perspective view of velocity field on the free surface, (b) top view of velocity field on the free surface, (c) velocity field in the horizontal plane at half-length section of the rotor, and (d) velocity field in the rotor symmetry plane.
Figure 13. Results of 3D flow simulation for V = 0.50 m/s: (a) perspective view of velocity field on the free surface, (b) top view of velocity field on the free surface, (c) velocity field in the horizontal plane at half-length section of the rotor, and (d) velocity field in the rotor symmetry plane.
Figure 14. Results of 3D flow simulation for V = 0.82 m/s: (a) perspective view of velocity field on the free surface, (b) top view of velocity field on the free surface, (c) velocity field in the horizontal plane at half-length section of the rotor, and (d) velocity field in the rotor symmetry plane.
Figure 14. Results of 3D flow simulation for V = 0.82 m/s: (a) perspective view of velocity field on the free surface, (b) top view of velocity field on the free surface, (c) velocity field in the horizontal plane at half-length section of the rotor, and (d) velocity field in the rotor symmetry plane.
Figure 15. Flow chart of validation of the computational model against experimental results.
Figure 15. Flow chart of validation of the computational model against experimental results.
Figure 16. Measured (EXP) and computed (CFD) lift force values.
Figure 16. Measured (EXP) and computed (CFD) lift force values.

결론

결론은 다음과 같습니다.
계산 결과가 일반적으로 실험 데이터와 일치하는 경우 계산 결과는 검증 된 것으로 간주되며 추가 예측에 사용할 수 있습니다. 검증 실험을 통해 메쉬 밀도와 난류 모델을 결정할 수있었습니다.
작은 전류 속도 0.4m / s 및 0.5m / s에서 직경 D3의 로터에 대해 계산 된 양력 값은 회전 속도가 200rpm 이상일 때의 실험 값과 달랐습니다. 그 이유는 실험 중에 관찰 된 강한 진동과 수치 시뮬레이션에서 모델링되지 않은 유동 분리 때문이었습니다.
D2 직경을 가진 로터의 경우 작은 rpm에서 양력의 반대 부호가 관찰되었습니다. 이 현상은 시뮬레이션 중에 관찰되지 않았습니다.
제시된 실험 테스트 설정은 드라이브,지지 구조물 및 측정 장치에 손상을 주지 않고 진동을 포함한 모든 현상을 관찰 할 수 있도록 구성되었습니다. Wang et al. [14]는 동일한 α 값에서 실린더 종횡비가 증가함에 따라 와류 유발 진동이 증가하는 것을 관찰했습니다.
실험의 원활한 진행은 장치 손상 가능성과 함께 약 4의 α에 영향을 미쳤습니다. 본 연구에서는 α = 4.8에서 시작하는 가장 큰 직경의 실린더에서 가장 강한 진동이 관찰되었습니다.
제시된 연구는 로터 생성 흐름의 능동적 제어에 대한 추가 연구의 첫 번째 부분으로 유체 역학적 힘의 신뢰할 수 있는 실험적 예측 방법을 설명했습니다 [22]. , 바람, 파도 [23].
논문의 참신함은 저상 실린더에 대해 회 전자에서 생성 된 유체 역학적 힘을 모델링 할 수있는 가능성에 대한 조사입니다.
이 방법의 주요 장점은 자유 표면 효과 및 유동 유도 회 전자 진동과 관련된 현상을 포함하여 회 전자 생성 유동장 및 유체 역학적 힘을 관찰 할 수 있다는 것입니다. 제안 된 테스트 설정 구성은 유체 역학적 힘의 매개 변수 연구, 스케일 효과 조사 및 낮은 전류 속도와 큰 회전 속도에서 큰 불일치가 확인 된 CFD 시뮬레이션 모델의 검증에 사용될 것입니다.

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Figure 1. (a) Top view of the microfluidic-magnetophoretic device, (b) Schematic representation of the channel cross-sections studied in this work, and (c) the magnet position relative to the channel location (Sepy and Sepz are the magnet separation distances in y and z, respectively).

Continuous-Flow Separation of Magnetic Particles from Biofluids: How Does the Microdevice Geometry Determine the Separation Performance?

1Department of Chemical and Biomolecular Engineering, ETSIIT, University of Cantabria, Avda. Los Castros s/n, 39005 Santander, Spain
2William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, 151 W. Woodruff Ave., Columbus, OH 43210, USA
*Author to whom correspondence should be addressed.
Sensors 202020(11), 3030; https://doi.org/10.3390/s20113030
Received: 16 April 2020 / Revised: 21 May 2020 / Accepted: 25 May 2020 / Published: 27 May 2020
(This article belongs to the Special Issue Lab-on-a-Chip and Microfluidic Sensors)

Abstract

The use of functionalized magnetic particles for the detection or separation of multiple chemicals and biomolecules from biofluids continues to attract significant attention. After their incubation with the targeted substances, the beads can be magnetically recovered to perform analysis or diagnostic tests. Particle recovery with permanent magnets in continuous-flow microdevices has gathered great attention in the last decade due to the multiple advantages of microfluidics. As such, great efforts have been made to determine the magnetic and fluidic conditions for achieving complete particle capture; however, less attention has been paid to the effect of the channel geometry on the system performance, although it is key for designing systems that simultaneously provide high particle recovery and flow rates. Herein, we address the optimization of Y-Y-shaped microchannels, where magnetic beads are separated from blood and collected into a buffer stream by applying an external magnetic field. The influence of several geometrical features (namely cross section shape, thickness, length, and volume) on both bead recovery and system throughput is studied. For that purpose, we employ an experimentally validated Computational Fluid Dynamics (CFD) numerical model that considers the dominant forces acting on the beads during separation. Our results indicate that rectangular, long devices display the best performance as they deliver high particle recovery and high throughput. Thus, this methodology could be applied to the rational design of lab-on-a-chip devices for any magnetically driven purification, enrichment or isolation.

Keywords: particle magnetophoresisCFDcross sectionchip fabrication

Korea Abstract

생체 유체에서 여러 화학 물질과 생체 분자의 검출 또는 분리를위한 기능화 된 자성 입자의 사용은 계속해서 상당한 관심을 받고 있습니다. 표적 물질과 함께 배양 한 후 비드를 자기 적으로 회수하여 분석 또는 진단 테스트를 수행 할 수 있습니다. 연속 흐름 마이크로 장치에서 영구 자석을 사용한 입자 회수는 마이크로 유체의 여러 장점으로 인해 지난 10 년 동안 큰 관심을 모았습니다. 

따라서 완전한 입자 포획을 달성하기 위한 자기 및 유체 조건을 결정하기 위해 많은 노력을 기울였습니다. 그러나 높은 입자 회수율과 유속을 동시에 제공하는 시스템을 설계하는 데있어 핵심이기는 하지만 시스템 성능에 대한 채널 형상의 영향에 대해서는 덜주의를 기울였습니다. 

여기에서 우리는 자기 비드가 혈액에서 분리되고 외부 자기장을 적용하여 버퍼 스트림으로 수집되는 YY 모양의 마이크로 채널의 최적화를 다룹니다. 비드 회수 및 시스템 처리량에 대한 여러 기하학적 특징 (즉, 단면 형상, 두께, 길이 및 부피)의 영향을 연구합니다. 

이를 위해 분리 중에 비드에 작용하는 지배적인 힘을 고려하는 실험적으로 검증 된 CFD (Computational Fluid Dynamics) 수치 모델을 사용합니다. 우리의 결과는 직사각형의 긴 장치가 높은 입자 회수율과 높은 처리량을 제공하기 때문에 최고의 성능을 보여줍니다. 

따라서 이 방법론은 자기 구동 정제, 농축 또는 분리를 위한 랩온어 칩 장치의 합리적인 설계에 적용될 수 있습니다.

Figure 1. (a) Top view of the microfluidic-magnetophoretic device, (b) Schematic representation of the channel cross-sections studied in this work, and (c) the magnet position relative to the channel location (Sepy and Sepz are the magnet separation distances in y and z, respectively).
Figure 1. (a) Top view of the microfluidic-magnetophoretic device, (b) Schematic representation of the channel cross-sections studied in this work, and (c) the magnet position relative to the channel location (Sepy and Sepz are the magnet separation distances in y and z, respectively).
Figure 2. (a) Channel-magnet configuration and (b–d) magnetic force distribution in the channel midplane for 2 mm, 5 mm and 10 mm long rectangular (left) and U-shaped (right) devices.
Figure 2. (a) Channel-magnet configuration and (b–d) magnetic force distribution in the channel midplane for 2 mm, 5 mm and 10 mm long rectangular (left) and U-shaped (right) devices.
Figure 3. (a) Velocity distribution in a section perpendicular to the flow for rectangular (left) and U-shaped (right) cross section channels, and (b) particle location in these cross sections.
Figure 3. (a) Velocity distribution in a section perpendicular to the flow for rectangular (left) and U-shaped (right) cross section channels, and (b) particle location in these cross sections.
Figure 4. Influence of fluid flow rate on particle recovery when the applied magnetic force is (a) different and (b) equal in U-shaped and rectangular cross section microdevices.
Figure 4. Influence of fluid flow rate on particle recovery when the applied magnetic force is (a) different and (b) equal in U-shaped and rectangular cross section microdevices.
Figure 5. Magnetic bead capture as a function of fluid flow rate for all of the studied geometries.
Figure 5. Magnetic bead capture as a function of fluid flow rate for all of the studied geometries.
Figure 6. Influence of (a) magnetic and fluidic forces (J parameter) and (b) channel geometry (θ parameter) on particle recovery. Note that U-2mm does not accurately fit a line.
Figure 6. Influence of (a) magnetic and fluidic forces (J parameter) and (b) channel geometry (θ parameter) on particle recovery. Note that U-2mm does not accurately fit a line.
Figure 7. Dependence of bead capture on the (a) functional channel volume and (b) particle residence time (tres). Note that in the curve fitting expressions V represents the functional channel volume and that U-2mm does not accurately fit a line.
Figure 7. Dependence of bead capture on the (a) functional channel volume and (b) particle residence time (tres). Note that in the curve fitting expressions V represents the functional channel volume and that U-2mm does not accurately fit a line.

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Fluid velocity magnitude including velocity vectors and blood volumetric fraction contours for scenario 3: (a,b) Magnet distance d = 0; (c,d) Magnet distance d = 1 mm.

Numerical Analysis of Bead Magnetophoresis from Flowing Blood in a Continuous-Flow Microchannel: Implications to the Bead-Fluid Interactions

Scientific Reports volume 9, Article number: 7265 (2019) Cite this article

Abstract

이 연구에서는 비드 운동과 유체 흐름에 미치는 영향에 대한 자세한 분석을 제공하기 위해 연속 흐름 마이크로 채널 내부의 비드 자기 영동에 대한 수치 흐름 중심 연구를 보고합니다.

수치 모델은 Lagrangian 접근 방식을 포함하며 영구 자석에 의해 생성 된 자기장의 적용에 의해 혈액에서 비드 분리 및 유동 버퍼로의 수집을 예측합니다.

다음 시나리오가 모델링됩니다. (i) 운동량이 유체에서 점 입자로 처리되는 비드로 전달되는 단방향 커플 링, (ii) 비드가 점 입자로 처리되고 운동량이 다음으로부터 전달되는 양방향 결합 비드를 유체로 또는 그 반대로, (iii) 유체 변위에서 비드 체적의 영향을 고려한 양방향 커플 링.

결과는 세 가지 시나리오에서 비드 궤적에 약간의 차이가 있지만 특히 높은 자기력이 비드에 적용될 때 유동장에 상당한 변화가 있음을 나타냅니다.

따라서 높은 자기력을 사용할 때 비드 운동과 유동장의 체적 효과를 고려한 정확한 전체 유동 중심 모델을 해결해야 합니다. 그럼에도 불구하고 비드가 중간 또는 낮은 자기력을 받을 때 계산적으로 저렴한 모델을 안전하게 사용하여 자기 영동을 모델링 할 수 있습니다.

Sketch of the magnetophoresis process in the continuous-flow microdevice.
Sketch of the magnetophoresis process in the continuous-flow microdevice.
Schematic view of the microdevice showing the working conditions set in the simulations.
Schematic view of the microdevice showing the working conditions set in the simulations.
Bead trajectories for different magnetic field conditions, magnet placed at different distances “d” from the channel: (a) d = 0; (b) d = 1 mm; (c) d = 1.5 mm; (d) d = 2 mm
Bead trajectories for different magnetic field conditions, magnet placed at different distances “d” from the channel: (a) d = 0; (b) d = 1 mm; (c) d = 1.5 mm; (d) d = 2 mm
Separation efficacy as a function of the magnet distance. Comparison between one-way and two-way coupling.
Separation efficacy as a function of the magnet distance. Comparison between one-way and two-way coupling.
(a) Fluid velocity magnitude including velocity vectors and (b) blood volumetric fraction contours with magnet distance d = 0 mm for scenario 1 (t = 0.25 s).
(a) Fluid velocity magnitude including velocity vectors and (b) blood volumetric fraction contours with magnet distance d = 0 mm for scenario 1 (t = 0.25 s).
luid velocity magnitude including velocity vectors and blood volumetric fraction contours for scenario 2: (a,b) Magnet distance d = 0 mm at t = 0.4 s; (c,d) Magnet distance d = 1 mm at t = 0.4 s.
luid velocity magnitude including velocity vectors and blood volumetric fraction contours for scenario 2: (a,b) Magnet distance d = 0 mm at t = 0.4 s; (c,d) Magnet distance d = 1 mm at t = 0.4 s.
Fluid velocity magnitude including velocity vectors and blood volumetric fraction contours for scenario 3: (a,b) Magnet distance d = 0; (c,d) Magnet distance d = 1 mm.
Fluid velocity magnitude including velocity vectors and blood volumetric fraction contours for scenario 3: (a,b) Magnet distance d = 0; (c,d) Magnet distance d = 1 mm.
Blood volumetric fraction contours. Scenario 1: (a) Magnet distance d = 0 and (b) Magnet distance d = 1 mm; Scenario 2: (c) Magnet distance d = 0 and (d) Magnet distance d = 1 mm; and Scenario 3: (e) Magnet distance d = 0 and (f) Magnet distance d = 1 mm.
Blood volumetric fraction contours. Scenario 1: (a) Magnet distance d = 0 and (b) Magnet distance d = 1 mm; Scenario 2: (c) Magnet distance d = 0 and (d) Magnet distance d = 1 mm; and Scenario 3: (e) Magnet distance d = 0 and (f) Magnet distance d = 1 mm.

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Author information

  1. Edward P. Furlani is deceased.

Affiliations

  1. Department of Chemical and Biomolecular Engineering, ETSIIT, University of Cantabria, Avda. Los Castros s/n, 39005, Santander, SpainJenifer Gómez-Pastora, Eugenio Bringas & Inmaculada Ortiz
  2. Flow Science, Inc, Santa Fe, New Mexico, 87505, USAIoannis H. Karampelas
  3. Department of Chemical and Biological Engineering, University at Buffalo (SUNY), Buffalo, New York, 14260, USAEdward P. Furlani
  4. Department of Electrical Engineering, University at Buffalo (SUNY), Buffalo, New York, 14260, USAEdward P. Furlani
The Simulation of Droplet Impact on the Super-Hydrophobic Surface with Micro-Pillar Arrays Fabricated by Laser Irradiation and Silanization Processes

The simulation of droplet impact on the super-hydrophobic surface with micro-pillar arrays fabricated by laser irradiation and silanization processes

레이저 조사 및 silanization 공정으로 제작된 micro-pillar arrays를 사용하여 초 소수성 표면에 대한 액적 영향 시뮬레이션

ZhenyanXiaa YangZhaoa ZhenYangabc ChengjuanYangab LinanLia ShibinWanga MengWangab
aSchool of Mechanical Engineering, Tianjin University, Tianjin, 300054, China
bKey Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin, 300072, Chinac
School of Engineering, University of Warwick, Coventry, CV4 7AL, UK

Received 23 September 2020, Revised 17 November 2020, Accepted 26 November 2020, Available online 11 December 2020.

Abstract

Super-hydrophobicity is one of the significant natural phenomena, which has inspired researchers to fabricate artificial smart materials using advanced manufacturing techniques. In this study, a super-hydrophobic aluminum surface was prepared by nanosecond laser texturing and FAS modification in sequence. The surface wettability turned from original hydrophilicity to super-hydrophilicity immediately after laser treatment. Then it changed to super-hydrophobicity showing a WCA of 157.6 ± 1.2° with a SA of 1.7 ± 0.7° when the laser-induced rough surface being coated with a layer of FAS molecules. The transforming mechanism was further explored from physical and chemical aspects based on the analyses of surface morphology and surface chemistry. Besides, the motion process of droplet impacting super-hydrophobic surface was systematically analyzed via the optimization of simulation calculation grid and the simulation method of volume of fluid (VOF). Based on this simulation method, the morphological changes, the inside pressure distribution and velocity of the droplet were further investigated. And the motion mechanism of the droplet on super-hydrophobic surface was clearly revealed in this paper. The simulation results and the images captured by high-speed camera were highly consistent, which indicated that the computational fluid dynamics (CFD) is an effective method to predict the droplet motion on super- hydrophobic surfaces. This paper can provide an explicit guidance for the selection of suitable methods for functional surfaces with different requirements in the industry.

Korea Abstract

초 소수성은 연구원들이 첨단 제조 기술을 사용하여 인공 스마트 재료를 제작하도록 영감을 준 중요한 자연 현상 중 하나 입니다. 이 연구에서 초 소수성 알루미늄 표면은 나노초 레이저 텍스처링과 FAS 수정에 의해 순서대로 준비되었습니다.

레이저 처리 직후 표면 습윤성은 원래의 친수성에서 초 친수성으로 바뀌 었습니다. 그런 다음 레이저 유도 거친 표면을 FAS 분자 층으로 코팅했을 때 WCA가 157.6 ± 1.2 °이고 SA가 1.7 ± 0.7 ° 인 초 소수성으로 변경되었습니다.

변형 메커니즘은 표면 형태 및 표면 화학 분석을 기반으로 물리적 및 화학적 측면에서 추가로 탐구 되었습니다. 또한, 초 소수성 표면에 영향을 미치는 물방울의 운동 과정은 시뮬레이션 계산 그리드의 최적화와 유체 부피 (VOF) 시뮬레이션 방법을 통해 체계적으로 분석되었습니다.

이 시뮬레이션 방법을 바탕으로 형태학적 변화, 내부 압력 분포 및 액 적의 속도를 추가로 조사했습니다. 그리고 초 소수성 표면에 있는 물방울의 운동 메커니즘이 이 논문에서 분명하게 드러났습니다.

시뮬레이션 결과와 고속 카메라로 캡처한 이미지는 매우 일관적 이었습니다. 이는 전산 유체 역학 (CFD)이 초 소수성 표면에서 액적 움직임을 예측하는 효과적인 방법임을 나타냅니다.

이 백서는 업계의 다양한 요구 사항을 가진 기능 표면에 적합한 방법을 선택하기 위한 명시적인 지침을 제공 할 수 있습니다.

Keywords: Laser irradiation; Wettability; Droplet impact; Simulation; VOF

Introduction

서식지에 적응하기 위해 많은 자연 식물과 동물에서 특별한 습윤 표면이 진화되었습니다 [1-3]. 연잎은 먼지에 의한 오염으로부터 스스로를 보호하기 위해 우수한 자가 청소 특성을 나타냅니다 [4]. 사막 딱정벌레는 공기에서 물을 수확할 수 있는 기능적 표면 때문에 건조한 사막에서 생존 할 수 있습니다 [5].

자연 세계에서 영감을 받아 고체 기질의 표면 습윤성을 수정하는데 더 많은 관심이 집중되었습니다 [6-7]. 기능성 표면의 우수한 성능은 고유 한 표면 습윤성에 기인하며, 이는 고체 표면에서 액체의 확산 능력을 반영하는 중요한 특성 중 하나입니다 [8].

일반적으로 물 접촉각 (WCA) 값에 따라 90 °는 친수성과 소수성의 경계로 간주됩니다. WCA가 90 ° 이상인 소수성 표면, WCA가 90 ° 미만인 친수성 표면 [9 ]. 특히 고체 표면은 WCA가 10 ° 미만의 슬라이딩 각도 (SA)에서 150 °를 초과 할 때 특별한 초 소수성을 나타냅니다 [10-11].

<내용 중략> ……

 The Simulation of Droplet Impact on the Super-Hydrophobic Surface with Micro-Pillar Arrays Fabricated by Laser Irradiation and Silanization Processes
The Simulation of Droplet Impact on the Super-Hydrophobic Surface with Micro-Pillar Arrays Fabricated by Laser Irradiation and Silanization Processes

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Figure 5 - Modeling a simple lotus overflow symmetrically in FLOW-3D software

Flow-3D를 이용한 Morning Glory Spillway의 배출 계수에 대한 소용돌이 차단 블레이드 45 도의 효과

Effect of Vortex Breaker Blades 45 Degree on Discharge Coefficient of Morning Glory Spillway Using Flow-3D

Authors

S. Noruzi1
and J. Ahadiyan2*
1– M.Sc. Student, Faculty of Water Sciences Engineering, Shahid Chamran University of Ahvaz, Iran.
2*-Corresponding Author, Associate Professor, Faculty of Water Sciences Engineering, Shahid Chamran
University of Ahvaz, Iran.

Abstract

The discharge coefficient of morning glory spillway is decreased with eddies created by vortex at the inlet part of weir. However, a series of specific blades can reduce vortices which result in the spillway efficiency is increased. Hence, in this research numerical modeling of installed breaker blade on morning glory spillway was evaluated using Flow-3D model. To achieve these purposes, morning glory spillway was modeled without and with blades 3, 4 and 6 blades at 45 degree angle. To simulate the turbulence fluctuations, the modified k-e model (RNG k-e) was used and its results were compared to the experimental data. Results showed that by installing blades, the discharge coefficient increases up to 42 percent with 25 percent decreasing in the upstream water level. Moreover, among the three different arrangements of blades, the six-blade model was found to have more satisfactory results than other models. In comparison to control model, for H/D between 0 to 0.1 and 0.1 to 0.2 the discharge coefficient has been increased 40 and 57 percent for six-blade arrangement, respectively. 

모닝 글로리의 배출 계수는 위어 입구 부분의 와류에 의해 생성된 소용돌이로 감소합니다. 그러나 일련의 특정 블레이드는 와류를 줄여 배수로 효율성을 높일 수 있습니다. 따라서 본 연구에서는 모닝 글로리 여수로에 설치된 브레이커 블레이드의 수치 모델링을 Flow-3D 모델을 사용하여 평가했습니다. 이러한 목적을 달성하기 위해 45도 각도에서 블레이드 3, 4 및 6 블레이드 없이 모닝 글로리 여수로를 모델링 했습니다. 난류 변동을 시뮬레이션하기 위해 수정된 k-e 모델 (RNG k-e)을 사용하고 그 결과를 실험 데이터와 비교했습니다. 결과에 따르면 블레이드를 설치하면 상류 수위가 25 % 감소하면서 배출 계수가 42 %까지 증가합니다. 또한 3 개의 블레이드 배열 중 6 개 블레이드 모델이 다른 모델보다 더 만족스러운 결과를 나타냈다. 제어 모델에 비해 H / D가 0 ~ 0.1 및 0.1 ~ 0.2 인 경우 방전 계수가 6- 블레이드 배열에서 각각 40 % 및 57 % 증가했습니다.

Keywords

Figure 1 - Dimensions of the vortex blade
Figure 1 – Dimensions of the vortex blade
Figure 3 - A (Physical model of lotus overflow without blade, b) Physical model of lotus overflow with eddy blades.
Figure 3 – A (Physical model of lotus overflow without blade, b) Physical model of lotus overflow with eddy blades.
Figure 5 - Modeling a simple lotus overflow symmetrically in FLOW-3D software
Figure 5 – Modeling a simple lotus overflow symmetrically in FLOW-3D software
Figure 7 - Comparison of Ashley flow chart with numerical model and laboratory
Figure 7 – Comparison of Ashley flow chart with numerical model and laboratory
Figure 8 - Comparison of flow coefficient diagram - immersion ratio of numerical model with laboratory: a (overflow without blade, b) overflow with three blades, c (overflow with four blades, d) overflow with six blades
Figure 8 – Comparison of flow coefficient diagram – immersion ratio of numerical model with laboratory: a (overflow without blade, b) overflow with three blades, c (overflow with four blades, d) overflow with six blades

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Fig.2- Richard Dam overflow in America

Studying the effect of shape changes in plan of labyrinth weir on increasing flow discharge coefficient using Flow-3D numerical model

FLOW-3D 수치 모델을 이용하여 미로 위어 평면도의 형상 변화가 유량 계수 증가에 미치는 영향 연구

E. Zamiri 1
, H. Karami 2*
and S. Farzin3
1- M.S. Student, Department of Civil Engineering, Semnan University, Semnan, Iran.
2
*

  • Corresponding Author, Assistant Professor, Department of Civil Engineering, Semnan
    University, Semnan, Iran. (hkarami@semnan.ac.ir).
    3- Assistant Professor, Department of Civil Engineering, Semnan University, Semnan, Iran.

Keywords: : Flood control, Sidewall angle, Predicting discharge coefficient, Computational hydraulic,

Introduction

Weirs are hydraulic structures used to measure, regulate and control the water levels and are
fixed upon open channels and rivers width. Growing magnitude of probable maximum flood
events (PMF) has highlighted the demand for increasing discharge capacity. Application of
labyrinth weir has been suggested as a solution for increasing discharge capacity.
Tullis et al. (1995) evaluated the effective parameters in determining the capacity of a labyrinth
weir. They introduced total head, the effective crest length and the discharge coefficient as
parameters influencing the discharge capacity of a labyrinth weir. Khode et al. (2011)
experimentally studied the parameters of a flow-over labyrinth weir for different side wall angles
(α) from 8 to 30°. They found that discharge coefficient increases by growing side wall angle
values.
Crookston and Tullis (2012a) studied performance of different labyrinth weirs by making
differences between geometric shapes of weirs in plan. The results indicated that discharge
capacity of the arced labyrinth weirs is more than the discharge capacity of horseshoe weirs.
Seo et al. (2016) investigated the effect of weir shapes on discharge of weirs. It was shown that
the discharge of the labyrinth weir had an increase of approximately 71% in comparison with the
linear ogee weir.
In this research, labyrinth weir with sidewall angle equal to 6° was simulated through Flow3D model, using experimental results of previous researchers. After validation, the changes of
discharge coefficient of weir with angles of 45° and 85° and apex shapes of triangular and halfcircular shapes were analyzed.

Weirs는 수위를 측정, 조절 및 제어하는 ​​데 사용되는 수력 구조물이며 열린 수로 및 강 폭에 고정됩니다. 예상되는 최대 홍수 사건 (PMF)의 규모가 커짐에 따라 배출 용량 증가에 대한 요구가 강조되었습니다. 미로 위어 (labyrinth weir)의 적용은 배출 용량을 증가시키기 위한 해결책으로 제안 되었습니다.

Tullis et al. (1995)는 미로 위어의 용량을 결정하는데 효과적인 매개 변수를 평가했습니다. 그들은 미로 위어의 배출 용량에 영향을 미치는 매개 변수로 총 수두, 유효 문장 길이 및 배출 계수를 도입했습니다.

Khode et al. (2011)은 8 ~ 30 °의 다양한 측벽 각도 (α)에 대한 유동-오버 래비 린스 위어의 매개 변수를 실험적으로 연구했습니다.

그들은 측벽 각도 값이 증가함에 따라 방전 계수가 증가한다는 것을 발견했습니다. Crookston과 Tullis (2012a)는 평면에서 위어의 기하학적 모양을 차이를 만들어 서로 다른 미로 위어의 성능을 연구했습니다.

결과는 호형 미로 위어의 배출 용량이 말굽 위어의 배출 용량보다 더 많다는 것을 나타냅니다. Seo et al. (2016)은 위어의 배출에 대한 위어 모양의 영향을 조사했습니다. 미로 위어의 배출량은 선형 오지 위어에 비해 약 71 % 증가한 것으로 나타났습니다.

이 연구에서는 이전 연구자들의 실험 결과를 사용하여 Flow3D 모델을 통해 측벽 각도가 6 ° 인 미로 위어를 시뮬레이션했습니다. 검증 후 각 45 °, 85 °의 위어의 배출 계수 변화와 삼각형 및 반원 형태의 정점 형태를 분석 하였다.

Fig.1- Schematic of trapezoidal, triangular, and rectangular congressional overflow
Fig.1- Schematic of trapezoidal, triangular, and rectangular congressional overflow
Fig.2- Richard Dam overflow in America
Fig.2- Richard Dam overflow in America
Fig.3- Plan of geometric parameters of congressional overflow
Fig.3- Plan of geometric parameters of congressional overflow
Fig. 4- The boundary conditions of the congressional overflow model
Fig. 4- The boundary conditions of the congressional overflow model
Fig.5- View of a simulated congressional overflow
Fig.5- View of a simulated congressional overflow
Fig. 6- Comparison of discharge coefficients resulted from numerical and experimental models
Fig. 6- Comparison of discharge coefficients resulted from numerical and experimental models
Fig.7- The relationship between Cd and Q for different angles of the congressional overflow wall
Fig.7- The relationship between Cd and Q for different angles of the congressional overflow wall
Fig. 8- The relationship between Cd and HT/p for different angles of the congressional overflow wall
Fig. 8- The relationship between Cd and HT/p for different angles of the congressional overflow wall
Table 3- The correlation of Q and HT/p with Cd for different angles of the overflow wall
Table 3- The correlation of Q and HT/p with Cd for different angles of the overflow wall
Fig. 9- The congressional overflow with linear, semicircular and triangular spans
Fig. 9- The congressional overflow with linear, semicircular and triangular spans
Fig. 10- The relationship between Cd and Q for different forms of congressional overflow
Fig. 10- The relationship between Cd and Q for different forms of congressional overflow
Fig. 11- The relationship of Cd and HT/p under different forms of congressional overflow
Fig. 11- The relationship of Cd and HT/p under different forms of congressional overflow
Fig. 12- The relationship Cd other/Cd simple and HT/p in a congressional overflow
Fig. 12- The relationship Cd other/Cd simple and HT/p in a congressional overflow
Fig. 13- Comparison of discharge coefficients resulted from a numerical model and proposed relation
Fig. 13- Comparison of discharge coefficients resulted from a numerical model and proposed relation
Fig. 14- Comparison of Cd from the present study and other studies for 6 angle congressional overflow
Fig. 14- Comparison of Cd from the present study and other studies for 6 angle congressional overflow
Fig. 15- The relationship between the discharge coefficient and HT/p for 6 ◦ angle congressional overflow
Fig. 15- The relationship between the discharge coefficient and HT/p for 6 ◦ angle congressional overflow

Results

오버행의 넘침 흐름을 증가시키는 것이 중요하기 때문에 본 연구에서는 넘침 벽의 돌출부에 6, 45 및 85 도의 세 가지 값을 채점하고 넘침 개구부에 삼각형 및 반원 모양을 제안함으로써 , 오버 플로우의 오버 플로우 계수를 변경하여 3D 숫자 래치를 사용하십시오.

Irene Par Vahsh Bareh에서 얻은 결과는 다음과 같습니다.

1- 흐름을 따라 포병의 범람 벽 각도를 늘리면 방출 계수가 증가합니다. 벽 각도가 85도 및 45 도인 포병의 범람 계수는 벽 각도가 6 도인 범람 계수 평균의 2.28 및 1.24 배입니다.

2-구부러진 양고기를 먹은 상태에서 배수로 모양의 변화는 배출 계수를 증가시킨다. 삼각형과 비 삼각형 개구부가있는 오버플로의 배출 계수는 온대 개구부가있는 오버플로의 배출 계수에 비해 양고기가 50.29 및 4.16 % 증가했습니다.

3- 오버플로 양 (p / HT)의 부하와 함께 부하 부하의 무 차원 비율 값을 늘리면 혼잡 한 오버플로의 방전 계수가 감소합니다. 또한 p <HT / 0.5의 값에서 세 가지 형태의 오버플로 개구에 대한 배출 계수의 값은 서로 가깝고 오버플로 모양의 각 끝은 값에서 동일한 기능을 갖습니다. p / HT <0.5. 4-유량이 증가함에 따라 유량 계수가 감소합니다.

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Figure 2. Experimental setups for the (a) Al/Cu overlap joint and (b) laser welding process.

Investigation on Laser Welding of Al Ribbon to Cu Sheet: Weldability, Microstructure, and Mechanical and Electrical Properties

알루미늄 리본과 구리 시트의 레이저 용접에 대한 조사 : 용접성, 미세 구조, 기계적 및 전기적 특성

Won‐Sang Shin 1,†, Dae‐Won Cho 2,†, Donghyuck Jung 1, Heeshin Kang 3, Jeng O Kim 3, Yoon‐Jun Kim 1,*
and Changkyoo Park 3,*

Al 리본과 Cu 시트의 펄스 레이저 용접은 전력 전자 모듈의 전기적 상호 연결에 대해 조사되었습니다. 결함 없는 Al / Cu 조인트를 얻기 위해 레이저 출력, 스캔 속도 및 열 입력이 서로 다른 다양한 실험 조건이 사용되었습니다. Al / Cu 레이저 용접 중에 금속 간 화합물이 용접 영역에 형성되었습니다. 전자 탐침 마이크로 분석기와 투과 전자 현미경으로 Al4Cu9, Al2Cu, AlCu 등으로 밝혀진 금속 간 화합물의 상을 확인했습니다. 전산 유체 역학 시뮬레이션은 Marangoni 효과가 용융 풀의 순환을 유도하여 혼합물을 생성하는 것으로 나타났습니다. Al과 Cu의 결합과 Al / Cu 조인트에서 소용돌이 모양의 구조 형성. Al / Cu 접합부의 인장 전단강도와 전기 저항을 측정하였으며 용접 면적과 강한 상관 관계를 보였다. Al / Cu 접합부의 용접 면적이 증가함에 따라 기계적 강도의 감소와 전기 저항의 증가가 측정 되었습니다. 또한 무결점 Al / Cu 접합을 위한 공정 창을 개발하고 Al / Cu 레이저 브레이즈 용접을 위한 실험 조건을 조사하여 Al / Cu 접합에서 금속 간 화합물 형성을 최소화했습니다.

Introduction

전기 상호 연결은 전력 전자 모듈을 패키징하는 데 중요합니다. 우수한 기계적 및 전기적 특성을 가진 견고한 전기적 상호 연결은 전력 전자 모듈의 전기적 고장을 방지하는 데 필수적입니다. 저항 스폿 용접, 브레이징, 납땜 및 초음파 용접 (USW)이 전기 상호 연결에 사용되었습니다.

납땜과 납땜 모두 저온 공정으로 인해 접합부에서 한계 변형과 잔류 응력이 발생합니다 [1]. 필러 합금은 두 공정 모두 견고한 전기 접촉을 달성하는 데 필수적입니다. 따라서 조인트는 서로 접촉하는 서로 다른 금속으로 구성됩니다.

결과적으로 조인트는 부식 환경에서 갈바닉 부식에 취약 할 수 있습니다 [2,3]. 더욱이, 비금속과 충전재 사이의 친화도를 고려해야 하기 때문에 제한된 충전재 만 특정 조인트에 사용할 수 있습니다 [1]. USW는 용접 온도가 낮고 용접 시간이 짧기 때문에 접합부의 변형이 비교적 적습니다.

따라서 이는 특히 연질 재료 (예 : Al, Cu, Ag, Au 및 Ni)의 경우 기존 접합 방법을 대체하고 있습니다 [4–6]. 그러나 Cu를위한 USW 공정의 경우, 표면 산화물이 강해 용접성이 저하되는 것을 방지하기 위해 Cu 표면에 Sn 또는 Ni 코팅이 필요하며, 이는 공정 속도를 늦추고 산업적 응용을위한 경제적 측면을 악화시킨다 [7 , 8].

레이저 용접은 쉬운 제어, 고정밀 및 원격 처리의 특성으로 인해 전력 전자 모듈의 전기 연결에 대한 유망한 후보입니다. 열의 영향을 받는 작은 영역과 변형은 전기 접점의 손상을 최소화 할 것으로 예상됩니다 [9-11]. 또한 레이저 용접을 위해 추가 표면 준비가 필요하지 않습니다.

이종 재료의 용접은 산업 응용 분야에서 중요했습니다. 더욱이 그림 1 [12,13]에서 볼 수 있듯이 전기 연결을위한 와이어 또는 리본 본딩에 여러 다른 조인트가 필요하기 때문에 전력 전자 모듈에서 필수적인 기술이되고 있습니다.

전기 접점의 다양한 조합 중에서 Al과 Cu는 높은 전기 전도성으로 인해 전기 연결에 중요한 재료로 종종 간주됩니다 [14]. 그러나 Al과 Cu의 서로 다른 용접은 금속 간 화합물 (IMC)의 형성을 촉진하고 동시에 Al / Cu 조인트의 기계적 및 전기적 특성에 영향을 줍니다. 일반적으로 Al / Cu 조인트 내부에 IMC가 있으면 연성 및 전기 저항에 해를 끼치므로 균열이 쉽게 발생하고 용접을 통한 전기 전도도를 방해합니다 [15,16].

따라서 견고한 Al / Cu 조인트를 얻으려면 IMC의 형성을 피해야합니다. 여러 연구에서 Al 및 Cu 시트의 레이저 빔 용접을 조사했습니다. 연속파 (CW) 레이저가 Al / Cu 조인트에 사용되었습니다 [17-23]. 큰 열 입력과 상당한 IMC 형성으로 인해 용접 영역에서 많은 균열이 관찰되었습니다 [18,19].

CW 레이저 빔의 공간 진동은 Al / Cu 조인트의 용접 품질을 향상시키는 것으로 나타났습니다. 직선 CW 레이저 빔 [18-20]과 비교하여 용접 영역에서 IMC 크기가 더 작은 기공과 균열이 더 적습니다.

Al과 Cu 시트의 겹침 접합에는 CW 단일 모드 파이버 레이저를 사용했으며, IMC 형성을 억제하여 높은 용접 속도 (즉, 50m / min)에서 견고한 Al / Cu 접합을 얻었습니다 [22]. Mai et al. [23]은 다른 Al / Cu 용접을 달성하기 위해 펄스 레이저를 사용했습니다.

그들은 Al / Cu 용접성이 레이저 공정 매개 변수에 크게 의존한다는 것을 밝혔으며 100mm / min 미만의 스캔 속도에서 균열없는 Al / Cu 접합을 달성하는 데 성공했습니다.

본문 내용 생략 : 문서 하단부의 원문보기를 참고하시기 바랍니다.

Figure 1. Schematic diagram of the insulated gate bipolar transistors (IGBT) power module. Red‐dotted box indicated the electrical connections
Figure 1. Schematic diagram of the insulated gate bipolar transistors (IGBT) power module. Red‐dotted box indicated the electrical connections
Figure 2. Experimental setups for the (a) Al/Cu overlap joint and (b) laser welding process.
Figure 2. Experimental setups for the (a) Al/Cu overlap joint and (b) laser welding process.
Figure 3. Schematic diagram of the numerical simulation domain and boundary conditions.
Figure 3. Schematic diagram of the numerical simulation domain and boundary conditions.
Figure 4. Experimental setup for the four‐point electrical resistance measurement.
Figure 4. Experimental setup for the four‐point electrical resistance measurement.
Figure 5. Cross‐sectional OM image of the Al/Cu joints in parallel to the laser welding direction. The laser power and scan speed were set at 2300 W and 20 mm/s, respectively.
Figure 5. Cross‐sectional OM image of the Al/Cu joints in parallel to the laser welding direction. The laser power and scan speed were set at 2300 W and 20 mm/s, respectively.
Figure 6 shows the cross‐sectional SEM images of the Al/Cu joints, and corresponding EPMA element mapping of Al and Cu for the (a) 23/20, (b) 25/28.6, (c) 25/15.4, and (d) 27/20.
Figure 6 shows the cross‐sectional SEM images of the Al/Cu joints, and corresponding EPMA element mapping of Al and Cu for the (a) 23/20,
Figure 6. Cross‐sectional SEM image and elemental distribution mapping of Al and Cu elements for the (a) 23/20, (b) 25/28.6, (c) 25/15.4, and (d) 27/20.
Figure 6. Cross‐sectional SEM image and elemental distribution mapping of Al and Cu elements for the (d) 27/20.
Figure 7. EPMA line scan analysis and identification of the IMCs for the (a) 23/20 and (b) 25/15.4.
Figure 7. EPMA line scan analysis and identification of the IMCs for the (a) 23/20 and (b) 25/15.4.
Figure 8. TEM analysis for the 25/28.6. (a) Indicating the location of TEM analysis in SEM image of the welding zone. (b) TEM bright‐field image and SAED pattern insets, examined at the location (1) in figure (a), confirmed Al‐rich phase (white globular shape) and Al2Cu eutectic phase (gray region), and (c) TEM bright‐field image and SAED pattern inset of Al4Cu9, examined at the location (2) in figure (a).
Figure 8. TEM analysis for the 25/28.6. (a) Indicating the location of TEM analysis in SEM image of the welding zone. (b) TEM bright‐field image and SAED pattern insets, examined at the location (1) in figure (a), confirmed Al‐rich phase (white globular shape) and Al2Cu eutectic phase (gray region), and (c) TEM bright‐field image and SAED pattern inset of Al4Cu9, examined at the location (2) in figure (a).
Figure 9. Temperature profiles and molten pool flow on transverse cross‐section (y–z plane at x = 1.23 cm): (a) Negative surface tension gradient for the 23/20 (Case 1), (b) negative surface tension gradient for the 25/15.4 (Case 2), (c) positive surface tension gradient for the 25/15.4 (Case 3), and (d) without surface tension for the 25/15.4 (Case 4).
Figure 9. Temperature profiles and molten pool flow on transverse cross‐section (y–z plane at x = 1.23 cm): (a) Negative surface tension gradient for the 23/20 (Case 1), (b) negative surface tension gradient for the 25/15.4 (Case 2), (c) positive surface tension gradient for the 25/15.4 (Case 3), and (d) without surface tension for the 25/15.4 (Case 4).
Figure 12. Results of the tensile shear tests for the (a) 23/20: fracture at the Al ribbon and (b) 25/15.4: fracture at the weld
Figure 12. Results of the tensile shear tests for the (a) 23/20: fracture at the Al ribbon and (b) 25/15.4: fracture at the weld
Figure 13. Stress–strain curves obtained by the tensile shear tests.
Figure 13. Stress–strain curves obtained by the tensile shear tests.

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Figure 1 (A) A schematic of ovarian cancer metastases involving tumor cells or clusters (yellow) shedding from a primary site and disseminating along ascitic currents of peritoneal fluid (green arrows) in the abdominal cavity. Ovarian cancer typically disseminates in four common abdomino-pelvic sites: (1) cul-de-sac (an extension of the peritoneal cavity between the rectum and back wall of the uterus); (2) right infracolic space (the apex formed by the termination of the small intestine of the small bowel mesentery at the ileocecal junction); (3) left infracolic space (superior site of the sigmoid colon); (4) Right paracolic gutter (communication between the upper and lower abdomen defined by the ascending colon and peritoneal wall). (B) The schematic of a perfusion model used to study the impact of sustained fluid flow on treatment resistance and molecular features of 3D ovarian cancer nodules (Top left). A side view of the perfusion model and growth of ovarian cancer nodules to a stromal bed (Top right). The photograph of a perfusion model used in the experiments (Bottom left) and depth-informed confocal imaging of ovarian cancer nodules in channels with and without carboplatin treatment (Bottom right). The perfusion model is 24 × 40 mm, with three channels that are 4 × 30 mm each and a height of 254 μm. The inlet and outlet ports of channels are 2.2 mm in diameter and positioned 5 mm from the edge of the chip. (C) A schematic of a 24-well plate model used to study the treatment resistance and molecular features of 3D ovarian cancer nodules under static conditions (without flow) (Top left). A side view of the static models and growth of ovarian cancer nodules on a stromal bed (Top right). Confocal imaging of 3D ovarian cancer nodules in a 24-well plate without and with carboplatin treatment (Bottom). Scale bars: 1 mm.

Flow-induced Shear Stress Confers Resistance to Carboplatin in an Adherent Three-Dimensional Model for Ovarian Cancer: A Role for EGFR-Targeted Photoimmunotherapy Informed by Physical Stress

난소암에 대한 일관된 3차원 모델에서 카보플라틴에 대한 유동에 의한 전단응력변화에 관한 연구

Abstract

A key reason for the persistently grim statistics associated with metastatic ovarian cancer is resistance to conventional agents, including platinum-based chemotherapies. A major source of treatment failure is the high degree of genetic and molecular heterogeneity, which results from significant underlying genomic instability, as well as stromal and physical cues in the microenvironment. Ovarian cancer commonly disseminates via transcoelomic routes to distant sites, which is associated with the frequent production of malignant ascites, as well as the poorest prognosis. In addition to providing a cell and protein-rich environment for cancer growth and progression, ascitic fluid also confers physical stress on tumors. An understudied area in ovarian cancer research is the impact of fluid shear stress on treatment failure. Here, we investigate the effect of fluid shear stress on response to platinum-based chemotherapy and the modulation of molecular pathways associated with aggressive disease in a perfusion model for adherent 3D ovarian cancer nodules. Resistance to carboplatin is observed under flow with a concomitant increase in the expression and activation of the epidermal growth factor receptor (EGFR) as well as downstream signaling members mitogen-activated protein kinase/extracellular signal-regulated kinase (MEK) and extracellular signal-regulated kinase (ERK). The uptake of platinum by the 3D ovarian cancer nodules was significantly higher in flow cultures compared to static cultures. A downregulation of phospho-focal adhesion kinase (p-FAK), vinculin, and phospho-paxillin was observed following carboplatin treatment in both flow and static cultures. Interestingly, low-dose anti-EGFR photoimmunotherapy (PIT), a targeted photochemical modality, was found to be equally effective in ovarian tumors grown under flow and static conditions. These findings highlight the need to further develop PIT-based combinations that target the EGFR, and sensitize ovarian cancers to chemotherapy in the context of flow-induced shear stress.

전이성 난소 암과 관련된 지속적으로 암울한 통계의 주요 이유는 백금 기반 화학 요법을 포함한 기존 약제에 대한 내성 때문입니다. 치료 실패의 주요 원인은 높은 수준의 유전적 및 분자적 이질성이며, 이는 중요한 기본 게놈 불안정성과 미세 환경의 기질 및 물리적 단서로 인해 발생합니다.

난소 암은 흔히 transcoelomic 경로를 통해 먼 부위로 전파되며, 이는 악성 복수의 빈번한 생산과 가장 나쁜 예후와 관련이 있습니다. 암 성장 및 진행을위한 세포 및 단백질이 풍부한 환경을 제공하는 것 외에도 복수 액은 종양에 물리적 스트레스를 부여합니다. 난소 암 연구에서 잘 연구되지 않은 분야는 유체 전단 응력이 치료 실패에 미치는 영향입니다.

여기, 우리는 백금 기반 화학 요법에 대한 반응과 부착 3D 난소 암 결절에 대한 관류 모델에서 공격적인 질병과 관련된 분자 경로의 변조에 대한 유체 전단 응력의 효과를 조사합니다.

카르보플라틴에 대한 내성은 상피 성장 인자 수용체 (EGFR)의 발현 및 활성화의 수반되는 증가 뿐만 아니라 다운 스트림 신호 구성원인 미토겐 활성화 단백질 키나제/세포 외 신호 조절 키나제 (MEK) 및 세포 외 신호 조절과 함께 관찰됩니다. 키나아제 (ERK). 3D 난소 암 결절에 의한 백금 흡수는 정적 배양에 비해 유동 배양에서 상당히 높았습니다.

포스 포-포컬 접착 키나제 (p-FAK), 빈 쿨린 및 포스 포-팍 실린의 하향 조절은 유동 및 정적 배양 모두에서 카보 플 라틴 처리 후 관찰되었습니다. 흥미롭게도, 표적 광 화학적 양식 인 저용량 항 EGFR 광 면역 요법 (PIT)은 유동 및 정적 조건에서 성장한 난소 종양에서 똑같이 효과적인 것으로 밝혀졌습니다.

이러한 발견은 EGFR을 표적으로하는 PIT 기반 조합을 추가로 개발하고 흐름 유도 전단 응력의 맥락에서 화학 요법에 난소 암을 민감하게 할 필요성을 강조합니다.

Keywords: ovarian cancer, epidermal growth factor receptor (EGFR), mitogen-activated protein kinase/extracellular signal-regulated kinase (MEK), extracellular signal-regulated kinase (ERK), chemoresistance, fluid shear stress, ascites, perfusion model, photoimmunotherapy (PIT), photodynamic therapy (PDT), carboplatin

Figure 1 (A) A schematic of ovarian cancer metastases involving tumor cells or clusters (yellow) shedding from a primary site and disseminating along ascitic currents of peritoneal fluid (green arrows) in the abdominal cavity. Ovarian cancer typically disseminates in four common abdomino-pelvic sites: (1) cul-de-sac (an extension of the peritoneal cavity between the rectum and back wall of the uterus); (2) right infracolic space (the apex formed by the termination of the small intestine of the small bowel mesentery at the ileocecal junction); (3) left infracolic space (superior site of the sigmoid colon); (4) Right paracolic gutter (communication between the upper and lower abdomen defined by the ascending colon and peritoneal wall). (B) The schematic of a perfusion model used to study the impact of sustained fluid flow on treatment resistance and molecular features of 3D ovarian cancer nodules (Top left). A side view of the perfusion model and growth of ovarian cancer nodules to a stromal bed (Top right). The photograph of a perfusion model used in the experiments (Bottom left) and depth-informed confocal imaging of ovarian cancer nodules in channels with and without carboplatin treatment (Bottom right). The perfusion model is 24 × 40 mm, with three channels that are 4 × 30 mm each and a height of 254 μm. The inlet and outlet ports of channels are 2.2 mm in diameter and positioned 5 mm from the edge of the chip. (C) A schematic of a 24-well plate model used to study the treatment resistance and molecular features of 3D ovarian cancer nodules under static conditions (without flow) (Top left). A side view of the static models and growth of ovarian cancer nodules on a stromal bed (Top right). Confocal imaging of 3D ovarian cancer nodules in a 24-well plate without and with carboplatin treatment (Bottom). Scale bars: 1 mm.
Figure 1 (A) A schematic of ovarian cancer metastases involving tumor cells or clusters (yellow) shedding from a primary site and disseminating along ascitic currents of peritoneal fluid (green arrows) in the abdominal cavity. Ovarian cancer typically disseminates in four common abdomino-pelvic sites: (1) cul-de-sac (an extension of the peritoneal cavity between the rectum and back wall of the uterus); (2) right infracolic space (the apex formed by the termination of the small intestine of the small bowel mesentery at the ileocecal junction); (3) left infracolic space (superior site of the sigmoid colon); (4) Right paracolic gutter (communication between the upper and lower abdomen defined by the ascending colon and peritoneal wall). (B) The schematic of a perfusion model used to study the impact of sustained fluid flow on treatment resistance and molecular features of 3D ovarian cancer nodules (Top left). A side view of the perfusion model and growth of ovarian cancer nodules to a stromal bed (Top right). The photograph of a perfusion model used in the experiments (Bottom left) and depth-informed confocal imaging of ovarian cancer nodules in channels with and without carboplatin treatment (Bottom right). The perfusion model is 24 × 40 mm, with three channels that are 4 × 30 mm each and a height of 254 μm. The inlet and outlet ports of channels are 2.2 mm in diameter and positioned 5 mm from the edge of the chip. (C) A schematic of a 24-well plate model used to study the treatment resistance and molecular features of 3D ovarian cancer nodules under static conditions (without flow) (Top left). A side view of the static models and growth of ovarian cancer nodules on a stromal bed (Top right). Confocal imaging of 3D ovarian cancer nodules in a 24-well plate without and with carboplatin treatment (Bottom). Scale bars: 1 mm.
Figure 2 (A) Geometry of the micronodule located at the center of the microchannel. The flow velocity is in the X-direction. The nodule is modeled as an ellipse with a semi-minor axis of 40 μm in the Z-direction. The semi-major axis varies from 40-100 μm in the X-direction. The section over which the fluid dynamics are studied is the middle part of the channel with dimensions 4 mm along the Y-axis and 250 μm along the Z-axis. The nodule is located at (0, 20 μm). The black dotted line shows the centerline of the largest nodule. (B) Shear stress distribution over the surface of the solid micro-nodule on the XZ-plane. (C) Shear stress distribution over the surface of the porous micro-nodule on the XZ-plane. (D) Flow flux distribution over the centerline of the porous micro-nodule on the XZ-plane. The flux enters the surface at the left and leaves at the right.
Figure 2 (A) Geometry of the micronodule located at the center of the microchannel. The flow velocity is in the X-direction. The nodule is modeled as an ellipse with a semi-minor axis of 40 μm in the Z-direction. The semi-major axis varies from 40-100 μm in the X-direction. The section over which the fluid dynamics are studied is the middle part of the channel with dimensions 4 mm along the Y-axis and 250 μm along the Z-axis. The nodule is located at (0, 20 μm). The black dotted line shows the centerline of the largest nodule. (B) Shear stress distribution over the surface of the solid micro-nodule on the XZ-plane. (C) Shear stress distribution over the surface of the porous micro-nodule on the XZ-plane. (D) Flow flux distribution over the centerline of the porous micro-nodule on the XZ-plane. The flux enters the surface at the left and leaves at the right.
Figure 3 Cytotoxic response in carboplatin-treated 3D OVCAR-5 cultures under static conditions. (A) Representative confocal images of 3D tumors treated with carboplatin (0-500 μM) for 96 h showing a dose-dependent reduction in viable tumor (calcein signal). (B) Image-based quantification of normalized viable tumor area in 3D OVCAR-5 cultures following treatment with increasing doses of carboplatin. A minimum nodule size cut-off of 2000 µm2 (clusters of ~15–20 cells) was applied to the fluorescence images for quantitative analysis of the normalized viable tumor area. (One-way ANOVA with Dunnett’s post hoc test; n.s., not significant; * p < 0.05; *** p < 0.001; N = 9) (C) Inductively coupled plasma mass spectrometry (ICP-MS)-based quantification of carboplatin uptake in static 3D OVCAR-5 tumors shows a dose-dependent increase in platinum levels, up to 9774 ± 3,052 ng/mg protein at an incubation concentration of 500 μM carboplatin. (One-way ANOVA with Dunn’s multiple comparisons test; n.s., not significant; * p < 0.05; ** p < 0.01; N = 3). Results are expressed as mean ± standard error of mean (SEM). Scale bars: 500 μm.
Figure 3 Cytotoxic response in carboplatin-treated 3D OVCAR-5 cultures under static conditions. (A) Representative confocal images of 3D tumors treated with carboplatin (0-500 μM) for 96 h showing a dose-dependent reduction in viable tumor (calcein signal). (B) Image-based quantification of normalized viable tumor area in 3D OVCAR-5 cultures following treatment with increasing doses of carboplatin. A minimum nodule size cut-off of 2000 µm2 (clusters of ~15–20 cells) was applied to the fluorescence images for quantitative analysis of the normalized viable tumor area. (One-way ANOVA with Dunnett’s post hoc test; n.s., not significant; * p < 0.05; *** p < 0.001; N = 9) (C) Inductively coupled plasma mass spectrometry (ICP-MS)-based quantification of carboplatin uptake in static 3D OVCAR-5 tumors shows a dose-dependent increase in platinum levels, up to 9774 ± 3,052 ng/mg protein at an incubation concentration of 500 μM carboplatin. (One-way ANOVA with Dunn’s multiple comparisons test; n.s., not significant; * p < 0.05; ** p < 0.01; N = 3). Results are expressed as mean ± standard error of mean (SEM). Scale bars: 500 μm.
Figure 4 flow-induced chemo-resistance
Figure 4 flow-induced chemo-resistance
Figure 5 The effects of flow-induced shear stress on 3D ovarian cancer biology. (A) Western blot analysis of OVCAR-5 tumors was performed 7 days after culture under static or flow conditions. A flow-induced increase in EGFR and p-ERK, compared to static cultures, was observed. Conversely, a reduction in p-FAK, p-Paxillin, and Vinculin was observed under flow, relative to static conditions. (B) Western blot analysis of 3D OVCAR-5 tumors was performed 11 days after culture under static or flow conditions, including 4 days of treatment with 500 µM carboplatin, and respective controls. In both static and flow 3D cultures, carboplatin treatment resulted in downregulation of EGFR, FAK, p-Paxillin, Paxillin, and Vinculin. Upregulation of p-ERK was observed after carboplatin treatment in both static and flow 3D cultures. (C) Baseline levels of EGFR activity and expression are maintained by a complex array of factors, including recycling and degradation of the activated receptor complex. Flow-induced shear stress has been shown to cause a posttranslational up-regulation of EGFR expression and activation, likely resulting from increased receptor recycling and decreased EGFR degradation. Activation of EGFR results in ERK phosphorylation to induce gene expression, ultimately leading to cell proliferation, survival, and chemoresistance. FAK and other tyrosine kinases are activated by the engagement of integrins with the ECM. Subsequent phosphorylation of paxillin by FAK not only influences the remodeling of the actin cytoskeleton, but also modulates vinculin activation to regulate mitogen-activated protein kinase (MAPK) cascades, thereby stimulating pro-survival gene expression.
Figure 5 The effects of flow-induced shear stress on 3D ovarian cancer biology. (A) Western blot analysis of OVCAR-5 tumors was performed 7 days after culture under static or flow conditions. A flow-induced increase in EGFR and p-ERK, compared to static cultures, was observed. Conversely, a reduction in p-FAK, p-Paxillin, and Vinculin was observed under flow, relative to static conditions. (B) Western blot analysis of 3D OVCAR-5 tumors was performed 11 days after culture under static or flow conditions, including 4 days of treatment with 500 µM carboplatin, and respective controls. In both static and flow 3D cultures, carboplatin treatment resulted in downregulation of EGFR, FAK, p-Paxillin, Paxillin, and Vinculin. Upregulation of p-ERK was observed after carboplatin treatment in both static and flow 3D cultures. (C) Baseline levels of EGFR activity and expression are maintained by a complex array of factors, including recycling and degradation of the activated receptor complex. Flow-induced shear stress has been shown to cause a posttranslational up-regulation of EGFR expression and activation, likely resulting from increased receptor recycling and decreased EGFR degradation. Activation of EGFR results in ERK phosphorylation to induce gene expression, ultimately leading to cell proliferation, survival, and chemoresistance. FAK and other tyrosine kinases are activated by the engagement of integrins with the ECM. Subsequent phosphorylation of paxillin by FAK not only influences the remodeling of the actin cytoskeleton, but also modulates vinculin activation to regulate mitogen-activated protein kinase (MAPK) cascades, thereby stimulating pro-survival gene expression.
Figure 6 PIT efficacy in 3D tumors. (A) Dose-dependent change in normalized viable tumor area in static 3D cultures treated with PIC (1 μM BPD equivalent) and increasing energy densities (10–50 J/cm2 @ 50 mW/cm2). Significant tumoricidal efficacy is observed in a light-dose-dependent manner, starting at 15 J/cm2. (One-way ANOVA with Dunnett’s post hoc test; n.s., not significant; ** p < 0.01, *** p < 0.001, N = 9) (B) Comparison of cytotoxic response in PIT-treated 3D cultures under static and flow conditions. For quantitative analysis of fluorescence images, a minimum nodule size cut-off of 2000 µm2 (clusters of ~15–20 cells) was used to establish normalized viable tumor area. PIT is equally effective in 3D tumors grown in static cultures (green) and under flow-induced shear stress (blue) (in contrast to flow-induced chemo-resistance shown in Figure 4) (Two-tailed t test; n.s., not significant; N = 9).
Figure 6 PIT efficacy in 3D tumors. (A) Dose-dependent change in normalized viable tumor area in static 3D cultures treated with PIC (1 μM BPD equivalent) and increasing energy densities (10–50 J/cm2 @ 50 mW/cm2). Significant tumoricidal efficacy is observed in a light-dose-dependent manner, starting at 15 J/cm2. (One-way ANOVA with Dunnett’s post hoc test; n.s., not significant; ** p < 0.01, *** p < 0.001, N = 9) (B) Comparison of cytotoxic response in PIT-treated 3D cultures under static and flow conditions. For quantitative analysis of fluorescence images, a minimum nodule size cut-off of 2000 µm2 (clusters of ~15–20 cells) was used to establish normalized viable tumor area. PIT is equally effective in 3D tumors grown in static cultures (green) and under flow-induced shear stress (blue) (in contrast to flow-induced chemo-resistance shown in Figure 4) (Two-tailed t test; n.s., not significant; N = 9).

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Figure 3. (a) Velocity distribution in a section perpendicular to the flow for rectangular (left) and Ushaped (right) cross section channels, and (b) particle location in these cross sections.

Continuous-Flow Separation of Magnetic Particles from Biofluids: How Does the Microdevice Geometry Determine the Separation Performance?

Cristina González Fernández,1 Jenifer Gómez Pastora,2 Arantza Basauri,1 Marcos Fallanza,1 Eugenio Bringas,1 Jeffrey J. Chalmers,2 and Inmaculada Ortiz1,*
Author information Article notes Copyright and License information Disclaimer

생체 유체에서 자성 입자의 연속 흐름 분리 : 마이크로 장치 형상이 분리 성능을 어떻게 결정합니까?

Abstract

The use of functionalized magnetic particles for the detection or separation of multiple chemicals and biomolecules from biofluids continues to attract significant attention. After their incubation with the targeted substances, the beads can be magnetically recovered to perform analysis or diagnostic tests. Particle recovery with permanent magnets in continuous-flow microdevices has gathered great attention in the last decade due to the multiple advantages of microfluidics. As such, great efforts have been made to determine the magnetic and fluidic conditions for achieving complete particle capture; however, less attention has been paid to the effect of the channel geometry on the system performance, although it is key for designing systems that simultaneously provide high particle recovery and flow rates. Herein, we address the optimization of Y-Y-shaped microchannels, where magnetic beads are separated from blood and collected into a buffer stream by applying an external magnetic field. The influence of several geometrical features (namely cross section shape, thickness, length, and volume) on both bead recovery and system throughput is studied. For that purpose, we employ an experimentally validated Computational Fluid Dynamics (CFD) numerical model that considers the dominant forces acting on the beads during separation. Our results indicate that rectangular, long devices display the best performance as they deliver high particle recovery and high throughput. Thus, this methodology could be applied to the rational design of lab-on-a-chip devices for any magnetically driven purification, enrichment or isolation.

생체 유체에서 여러 화학 물질과 생체 분자의 검출 또는 분리를 위한 기능화된 자성 입자의 사용은 계속해서 상당한 관심을 받고 있습니다. 표적 물질과 함께 배양 한 후 비드는 자기적으로 회수되어 분석 또는 진단 테스트를 수행 할 수 있습니다.

연속 흐름 마이크로 장치에서 영구 자석을 사용한 입자 회수는 마이크로 유체의 여러 장점으로 인해 지난 10 년 동안 큰 관심을 모았습니다. 따라서 완전한 입자 포획을 달성하기 위한 자기 및 유체 조건을 결정하기 위해 많은 노력을 기울였습니다.

그러나 높은 입자 회수율과 유속을 동시에 제공하는 시스템을 설계하는데 있어 핵심이기는 하지만 시스템 성능에 대한 채널 형상의 영향에 대해서는 덜 주의를 기울였습니다.

여기에서 우리는 자기 비드가 혈액에서 분리되어 외부 자기장을 적용하여 버퍼 스트림으로 수집되는 Y-Y 모양의 마이크로 채널의 최적화를 다룹니다. 비드 회수 및 시스템 처리량에 대한 여러 기하학적 특징 (즉, 단면 형상, 두께, 길이 및 부피)의 영향을 연구합니다.

이를 위해 분리 중에 비드에 작용하는 지배적인 힘을 고려하는 실험적으로 검증된 CFD (Computational Fluid Dynamics) 수치 모델을 사용합니다.

우리의 결과는 직사각형의 긴 장치가 높은 입자 회수율과 높은 처리량을 제공하기 때문에 최고의 성능을 보여줍니다. 따라서 이 방법론은 자기 구동 정제, 농축 또는 분리를 위한 랩 온어 칩 장치의 합리적인 설계에 적용될 수 있습니다.

Keywords: particle magnetophoresis, CFD, cross section, chip fabrication

Figure 1 (a) Top view of the microfluidic-magnetophoretic device, (b) Schematic representation of the channel cross-sections studied in this work, and (c) the magnet position relative to the channel location (Sepy and Sepz are the magnet separation distances in y and z, respectively).
Figure 1 (a) Top view of the microfluidic-magnetophoretic device, (b) Schematic representation of the channel cross-sections studied in this work, and (c) the magnet position relative to the channel location (Sepy and Sepz are the magnet separation distances in y and z, respectively).
Figure 2. (a) Channel-magnet configuration and (b–d) magnetic force distribution in the channel midplane for 2 mm, 5 mm and 10 mm long rectangular (left) and U-shaped (right) devices.
Figure 2. (a) Channel-magnet configuration and (b–d) magnetic force distribution in the channel midplane for 2 mm, 5 mm and 10 mm long rectangular (left) and U-shaped (right) devices.
Figure 3. (a) Velocity distribution in a section perpendicular to the flow for rectangular (left) and Ushaped (right) cross section channels, and (b) particle location in these cross sections.
Figure 3. (a) Velocity distribution in a section perpendicular to the flow for rectangular (left) and Ushaped (right) cross section channels, and (b) particle location in these cross sections.
Figure 4. Influence of fluid flow rate on particle recovery when the applied magnetic force is (a) different and (b) equal in U-shaped and rectangular cross section microdevices.
Figure 4. Influence of fluid flow rate on particle recovery when the applied magnetic force is (a) different and (b) equal in U-shaped and rectangular cross section microdevices.
Figure 5. Magnetic bead capture as a function of fluid flow rate for all of the studied geometries.
Figure 5. Magnetic bead capture as a function of fluid flow rate for all of the studied geometries.
Figure 6. Influence of (a) magnetic and fluidic forces (J parameter) and (b) channel geometry (θ parameter) on particle recovery. Note that U-2mm does not accurately fit a line.
Figure 6. Influence of (a) magnetic and fluidic forces (J parameter) and (b) channel geometry (θ parameter) on particle recovery. Note that U-2mm does not accurately fit a line.
Figure 7. Dependence of bead capture on the (a) functional channel volume, and (b) particle residence time (tres). Note that in the curve fitting expressions V represents the functional channel volume and that U-2mm does not accurately fit a line.
Figure 7. Dependence of bead capture on the (a) functional channel volume, and (b) particle residence time (tres). Note that in the curve fitting expressions V represents the functional channel volume and that U-2mm does not accurately fit a line.

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Figure 1. Experimental flume used (a) Side view of the flume; (b) Pool detail.

Modelling of Pool-Type Fishways Flows: Efficiency and Scale Effects Assessment

by Ana L. Quaresma *OrcID andAntónio N. PinheiroOrcID
CERIS—Civil Engineering for Research and Innovation for Sustainability, Instituto Superior Técnico (IST), Universidade de Lisboa, 1049-001 Lisboa, Portugal*
Author to whom correspondence should be addressed.
Academic Editor: Bommanna Krishnappan
Water 2021, 13(6), 851; https://doi.org/10.3390/w13060851
Received: 16 January 2021 / Revised: 8 March 2021 / Accepted: 18 March 2021 / Published: 20 March 2021
(This article belongs to the Special Issue Ecohydraulics of Pool-Type Fishways)

Abstract

이 연구에서는 전산 유체 역학 (CFD) 소프트웨어 (FLOW-3D®)를 사용하여 바닥 오리피스가 있는 풀형 어로에서 흐름의 3D 수치 모델링을 수행했습니다. 수치 결과는 음향 도플러 속도계 (ADV) 및 입자 이미지 속도계 (PIV) 측정에서 얻은 실험 데이터와 비교되었습니다.

흐름 깊이, 흐름 패턴, 수속, 난류 운동 에너지, Reynolds 수직 응력 및 바닥 구성 요소에 평행한 Reynolds 전단 응력과 같이 어로 효율에 영향을 미치는 여러 유체 역학적 변수를 정성 및 정량적으로 비교했습니다.

수치 모델은 복잡한 유동장을 정확하게 재현하여 수치 모델 예측과 분석 된 변수에 대한 실험 데이터 사이에 전반적으로 좋은 일치를 보여줍니다. 분석중인 모든 매개 변수에 대한 수치 모델 검증 수행의 중요성이 강조되었습니다.

또한 프로토 타입 어로의 업 스케일 된 수치 모델을 실행하여 스케일링 효과를 분석했습니다. 스케일 효과의 증거없이 실제 모델과 프로토 타입 치수 모두에 대해 유사한 정확도로 모델을 수행했습니다.

현재 연구는 CFD 모델 (즉, FLOW-3D®)이 새로운 수영장 유형 어로 형상을 위한 적절하고 효율적인 설계 및 분석 도구로 사용될 수 있으며 물리적 모델 테스트를 줄이고 보완 할 수 있다고 결론지었습니다.

In this study, the 3D numerical modelling of flow in a pool-type fishway with bottom orifices was performed using computational fluid dynamics (CFD) software (FLOW-3D®). Numerical results were compared with experimental data obtained from acoustic Doppler velocimetry (ADV) and particle image velocimetry (PIV) measurements. Several hydrodynamic variables that influence fishways efficiencies, such as flow depths, flow patterns, water velocity, turbulent kinetic energy, Reynolds normal stresses, and Reynolds shear stress parallel to the bottom component, were qualitatively and quantitatively compared. The numerical model accurately reproduced the complex flow field, showing an overall good agreement between the numerical model predictions and the experimental data for the analysed variables. The importance of performing a numerical model validation for all the parameters under analyses was highlighted. Additionally, scaling effects were analysed by running an upscaled numerical model of the prototype fishway. The model performed with similar accuracy for both physical model and prototype dimensions with no evidence of scale effects. The present study concludes that CFD models (namely FLOW-3D®) may be used as an adequate and efficient design and analysis tool for new pool-type fishways geometries, reducing and complementing physical model testing.Keywords: pool-type fishways3D numerical modellingLESscale effectsflow patternsCFD model assessment

Introduction

강의 종단 연결성을 복원하는 것은 담수 생태계의 회복에있어 여전히 중요한 문제입니다 [1,2]. 잘 설계되고 건설된 경우 어로는 물고기가 댐과 둑을 지나 계속 이동할 수 있는 경로를 제공합니다.

물고기 통과 효율성에 대한 검토에서 Noonan et al. [3]은 기존의 많은 어로의 설계 특성이 어종의 요구를 적절하게 충족시키지 못했지만, 풀형 어로가 모든 어류 그룹에 대해 가장 높은 효율성을 보여 주었다는 것을 발견했습니다.
여러 어종에 적합한 수영 조건을 제공하는 것은 어항의 흐름과 난류 패턴이 성공에 중요한 역할을 하기 때문에 다소 어려운 일입니다 [2,4,5,6,7,8,9,10,11,12].

물리적 모델링은 풀형 유형 어로의 유체 역학을 연구하기 위한 주요 접근 방식이었습니다 (예 : [13,14,15,16,17,18,19,20,21,22]). 그러나 물리적 실험은 비용과 시간이 많이 소요됩니다. 따라서 컴퓨터 기술의 발전으로 인해 물리적 모델 테스트를 줄이기 위해 복잡한 기하학적 구조를 가진 유압 구조의 흐름 패턴을 분석하는 데 전산 유체 역학 (CFD) 3 차원 (3D) 모델이 점점 더 많이 사용되고 있습니다 [23,24].

따라서 이러한 모델은 어로 유체 역학 연구 및 효율적인 어로 설계에 필수적인 역할을 할 수 있습니다.
어로에 대한 수치 모델링 연구는 주로 수직 슬롯 어로에 초점을 맞추고 있습니다 [12,25,26,27,28,29,30,31,32,33,34,35,36,37]. 수영장의 주요 부분에서 수직 슬롯 어로 흐름은 거의 2 차원 (2D)이고 수직 속도 구성 요소가 수평 요소 [26]보다 훨씬 작기 때문에 이러한 연구의 대부분은 2D 모델을 사용했습니다.

바닥 오리피스가있는 수영장 유형 어로에서는 흐름이 매우 복잡하고 3D이므로 정확한 유동장 특성화를 얻기 위해 3D 모델을 사용해야합니다. 이 어로 구성을 모델링하는 것은 높은 속도 구배, 높은 와도 및 높은 전단 영역을 포함하기 때문에 다소 어렵습니다.

이 연구에서는 FLOW-3D® (Flow Science, Inc., Santa Fe, NM, USA)를 사용하여 바닥 오리피스가 있는 수영장 유형 어로의 3D 수치 시뮬레이션을 수행하여 흐름 깊이, 속도 및 난류 패턴을 예측하는 능력을 평가했습니다. .

최근 몇 년 동안 실내에 가까운 프로토 타입 수영장 형 어로가 사이프 린드 종의 행동과 움직임을 연구하는데 사용되었습니다 [1,7,8,11,38,39,40,41,42,43]. Silva et al. [38]은 노치, 급락 및 스트리밍에 대한 두 가지 다른 유동 체제와 관련하여 조정 가능한 치수를 가진 침수된 오리피스와 표면 노치의 동시 존재에 대한 Iberian barbel Luciobarbus bocagei (Steindachner, 1864)의 반응을 평가했습니다.

이 연구의 결과는 이베리아 바벨이 어로를 협상하기 위해 오리피스 (76 %)를 선호했으며 어로에 들어가는 데 걸리는 시간도 오리피스에 비해 훨씬 적다는 것을 보여주었습니다.

Silva et al. [39] 오프셋 및 직선 오리피스가있는 수영장 유형 어로의 이베리아 바벨에 대한 적합성을 테스트했습니다. 이 연구는 오프셋 구성이 직선 오리피스 레이아웃 (28 %)에 비해 물고기 통과 성공률 (68 %)이 훨씬 더 높음을 발견했습니다. 어로를 성공적으로 협상하는 데 걸리는 시간도 오프셋 구성, 특히 작은 성인의 경우 훨씬 더 낮았습니다.

이 연구에서는 유속과 난류 매개 변수가 물고기 수영 성능에 미치는 영향을 분석했습니다. 수영장의 유동장을 특성화하기 위해 음향 도플러 속도계가 사용되었습니다.

이 연구의 결과에 따르면 레이놀즈 전단 응력은 어로 내 이베리아 미늘의 움직임에 가장 큰 영향을 미치는 매개 변수임이 입증되었습니다. Branco et al. [40] 두 가지 다른 흐름을 가진 오리피스와 노치가 있는 풀형 유형 어로에서 형태 학적 및 생태학적 특성이 다른 두 종, 바닥 지향 이베리아 바벨 Luciobarbus bocagei 및 물기둥 수영 자 Iberian chub Squalius pyrenaicus의 거동과 성능을 평가했습니다.

풀의 유체 역학을 특성화하기 위해 음향 도플러 속도계가 사용되었습니다. 결과는 두 종 모두 흐름 흐름이있는 노치를 선호했으며 이 흐름 체제로 상류로 이동하는데 더 성공적이었습니다.
이 연구에서는 이 시설의 1 : 2.5 스케일 어로 모델을 사용하여 Silva et al.에 의해 테스트된 바닥 오리피스 구성이 있는 풀형 유형 어로의 속도와 난류를 측정했습니다.

[7,38] 효과가 입증된 바벨 사용. 2D 입자 이미지 속도계 (PIV) 시스템 및 음향 도플러 속도계 (ADV)를 사용하여 순간 속도의 광범위한 측정을 수행하고, 후 처리하고, 수치 모델 정확도를 평가하는 데 사용했습니다.

Haque et al. [44] 대부분의 경우 수치 모델의 검증에 사용할 수있는 실험 데이터 세트에 높은 측정 오류가 있고 / 또는 측정 메시가 너무 거칠어 서 이들의 예측 기능을 올바르게 평가할 수없는 문제를 언급했습니다.

모델. Blocken과 Gualtieri [23]는 검증 및 검증 연구가 필수적이며 CFD 연구를 검증하기위한 데이터를 제공하기 위해 고품질 실험이 필요하다고 언급합니다.

Fuentes-Pérez et al. [35]는 특히 난류 메트릭에 대한 어로 연구에서 수치 모델 검증 데이터를 찾는 데 어려움을 언급합니다. 두 가지 측정 기술을 사용하고 상당한 양의 실험 데이터를 얻었기 때문에 이 연구에서는 이러한 문제를 극복했습니다.

물리적 모델은 종종 Froude 수 유사성을 기반으로하며, 두 유사성 법칙을 모두 충족하는 데 어려움이있어 무시되는 레이놀즈 수 유사성입니다. 프로토 타입 레이놀즈 수가 일반적으로 훨씬 더 크기 때문에 레이놀즈 수 관련 스케일 효과가 도입될 수 있습니다.

레이놀즈 수 증가는 속도 분포와 경계층 속성에 영향을 미칠 수 있습니다 [45]. 척도 효과를 평가하기 위해 수치 시뮬레이션을 사용할 수 있습니다 [46,47]. 따라서 본 연구에서는 바닥 오리피스 흐름이있는 풀형어도에 대한 스케일 효과를 분석하기 위해 두 가지 크기의 수치 모델을 개발했습니다.

프로토 타입 치수의 대형 모델과 물리적 모델 치수의 스케일 된 소형 모델입니다. .
바닥 오리피스가있는 수영장 형 어로의 유동장은 수직 슬롯 어로 (VSF)의 유동장보다 매우 3 차원 적이며 훨씬 더 복잡합니다. 이는 어로 수치 모델 검증에 대한 이전 연구에서 더 자주 고려 된 설계입니다 [26, 27,28,29,35].

저자가 아는 한, 이것은 바닥 오리피스가있는 풀형 어로에 대한 최초의 CFD 연구이며, 여기에는 실험 속도 데이터와 풀형 어로에 대한 3 차원 수치 모델링 결과 간의 가장 광범위한 비교도 포함됩니다. 두 가지 다른 측정 기술 (PIV 및 ADV)이 사용되어 자세한 비교가 가능하고 이러한 유형의 유동장에 대한 CFD 시뮬레이션 결과에 대한 확신을 제공합니다.

이 연구는 다른 어로 유형의 이전 수치 모델 연구에서 제시되지 않았던 난류 매개 변수를 포함하여 수치 모델 결과와 측정 간의 일치에 대한 통계적 테스트를 통해 정성적 비교 뿐만 아니라 상세한 정량적 비교도 제공합니다. 스케일 효과도 다룹니다.

따라서 이 연구는 전 세계적으로 가장 많이 사용되는 풀 유형 어로의 CFD 모델 검증을 원활하게 할 것이며 [10] 설계자들의 사용을 장려 할 것입니다.
또한 새로운 풀 유형 어로 형상을 위한 설계 도구로 CFD 모델 (즉, FLOW 3D®)을 사용하는 방법에 대해 설명합니다.

Figure 1. Experimental flume used (a) Side view of the flume; (b) Pool detail.
Figure 1. Experimental flume used (a) Side view of the flume; (b) Pool detail.
Figure 2. Three dimensional representations of a pool showing the measurement planes and the acoustic Doppler velocimetry (ADV) measurement grid (a) measurement planes parallel to the flume bottom; (b) vertical measurement planes (ADV measurement grid is only shown in one plane).
Figure 2. Three dimensional representations of a pool showing the measurement planes and the acoustic Doppler velocimetry (ADV) measurement grid (a) measurement planes parallel to the flume bottom; (b) vertical measurement planes (ADV measurement grid is only shown in one plane).
Figure 3. Computational domain, showing Pool 3 mesh block.
Figure 3. Computational domain, showing Pool 3 mesh block.
Figure 4. Streamlines of time-averaged velocities (left: PIV; right: mesh Amodel): (a,b) plane 2 (z = 0.088 m); (c,d) plane 5 (y = 0.20 m).
Figure 4. Streamlines of time-averaged velocities (left: PIV; right: mesh Amodel): (a,b) plane 2 (z = 0.088 m); (c,d) plane 5 (y = 0.20 m).
Figure 5. Longitudinal variation of velocity components: (a,c,e) planes 1 and 6 intersection (y = 0.36 m and z = 0.04 m); (b,d,f) planes 2 and 5 intersection (y = 0.20 m and z = 0.088 m).
Figure 5. Longitudinal variation of velocity components: (a,c,e) planes 1 and 6 intersection (y = 0.36 m and z = 0.04 m); (b,d,f) planes 2 and 5 intersection (y = 0.20 m and z = 0.088 m).
Figure 6. Longitudinal variation of Reynolds normal stress components and Reynolds shear stress parallel to the bottom component: (a,c,e,g) planes 1 and 6 intersection (y = 0.36 m and z = 0.04m); (b,d,f,h) planes 2 and 5 intersection (y = 0.20 m and z = 0.088 m).
Figure 6. Longitudinal variation of Reynolds normal stress components and Reynolds shear stress parallel to the bottom component: (a,c,e,g) planes 1 and 6 intersection (y = 0.36 m and z = 0.04m); (b,d,f,h) planes 2 and 5 intersection (y = 0.20 m and z = 0.088 m).

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Figure 5. Flow pattern of operating condition 1: (a) Physical model flow diagram; (b) Simulation model flow.

Numerical Study of Fluctuating Pressure on Stilling Basin Slab with Sudden Lateral Enlargement and Bottom Drop

급격한 측면 확대 및 바닥 낙하에 따른 정류지(stilling basin) 슬래브의 변동 압력에 대한 수치 연구

by Yangliang Lu,Jinbu Yin *OrcID,Zhou Yang,Kebang Wei andZhiming Liu
College of Water Resources and Architectural Engineering, Northwest A&F University, Weihui Road, Yangling 712100, China*
Author to whom correspondence should be addressed.
Water 2021, 13(2), 238; https://doi.org/10.3390/w13020238
Received: 6 November 2020 / Revised: 7 January 2021 / Accepted: 11 January 2021 / Published: 19 January 2021
(This article belongs to the Special Issue Physical Modelling in Hydraulics Engineering)

Abstract

갑작스런 확장 및 바닥 낙하가 있는 고요한 정류지(stilling basin) 유역은 복잡한 수력 특성, 특히 3D 공간 수력 점프 아래에서 변동하는 압력 분포로 이어집니다.

이 논문은 FLOW-3D 소프트웨어를 기반으로 한 LES (Large Eddy Simulation) 모델과 TruVOF 방법을 사용하여 시간 평균 압력, 변동 압력의 RMS (Root Mean Square), 정물(stilling basin) 조 슬래브의 최대 및 최소 압력을 시뮬레이션했습니다.

실제 모델 결과와 비교하여 시뮬레이션 결과는 LES 모델이 정물 유역의 변동하는 수류 압력을 안정적으로 시뮬레이션 할 수 있음을 보여줍니다. 변동 압력의 RMS의 최대 값은 정수조 전면과 측벽의 연장선 부근에 나타납니다.

이 논문은 변동 압력의 생성 메커니즘과 Navier-Stokes 방정식에서 파생된 Poisson 방정식을 기반으로 영향 요인 (변동 속도, 속도 구배, 변동 와도)의 정량 분석과 특성의 정성 분석을 결합하는 연구 방법을 제공합니다.

변동하는 압력의. 정류 지의 소용돌이 영역과 벽에 부착 된 제트 영역의 변동 압력 분포는 주로 각각 와류 및 변동 유속의 영향을 받으며 충돌 영역의 분포는 변동 속도, 속도 구배 및 변동에 의해 발생합니다.

A stilling basin with sudden enlargement and bottom drop leads to complicated hydraulic characteristics, especially a fluctuating pressure distribution beneath 3D spatial hydraulic jumps. This paper used the large eddy simulation (LES) model and the TruVOF method based on FLOW-3D software to simulate the time-average pressure, root mean square (RMS) of fluctuating pressure, maximum and minimum pressure of a stilling basin slab. Compared with physical model results, the simulation results show that the LES model can simulate the fluctuating water flow pressure in a stilling basin reliably. The maximum value of RMS of fluctuating pressure appears in the vicinity of the front of the stilling basin and the extension line of the side wall. Based on the generating mechanism of fluctuating pressure and the Poisson Equation derived from the Navier–Stokes Equation, this paper provides a research method of combining quantitative analysis of influencing factors (fluctuating velocity, velocity gradient, and fluctuating vorticity) and qualitative analysis of the characteristics of fluctuating pressure. The distribution of fluctuating pressure in the swirling zone of the stilling basin and the wall-attached jet zone is mainly affected by the vortex and fluctuating flow velocity, respectively, and the distribution in the impinging zone is caused by fluctuating velocity, velocity gradient and fluctuating vorticity. 

Keywords: submerged jumpsudden lateral enlargement and bottom droplarge eddy simulationvortexfluctuating pressure

Figure 1. Schematic design of model test: (a) Sectional view; (b) Plan view.
Figure 1. Schematic design of model test: (a) Sectional view; (b) Plan view.
Figure 2. Model layout in laboratory: (a) Discharge chute; (b) The stilling basin.
Figure 2. Model layout in laboratory: (a) Discharge chute; (b) The stilling basin.

Table 1. Operating conditions.

ConditionFlow Discharge
(m3/s)
Inflow Froude NumberInflow Velocity (m/s)Inflow Water Depth (m)
10.9425.2955.6110.114
20.6434.5454.4890.097
30.2324.2273.0180.052
Figure 3. Schematic diagram of fluctuating pressure data-processing process.
Figure 3. Schematic diagram of fluctuating pressure data-processing process.
Figure 4. 3D simulation model: (a) Boundary conditions; (b) Grid mesh.
Figure 4. 3D simulation model: (a) Boundary conditions; (b) Grid mesh.

Table 2. Grid independence test.

GridContaining Block Cell Size (m)Nested Block Cell Size (m)Discharge
(m3/s)
Relative Error (%)
10.0500.0250.9905.10
20.0400.0200.9692.70
30.0300.0150.9561.49
40.0200.0100.9521.06
Figure 5. Flow pattern of operating condition 1: (a) Physical model flow diagram; (b) Simulation model flow.
Figure 5. Flow pattern of operating condition 1: (a) Physical model flow diagram; (b) Simulation model flow.
Figure 6. Numerical simulation of water surface profile and x-z plane flow rate vector.
Figure 6. Numerical simulation of water surface profile and x-z plane flow rate vector.
Figure 7. Comparison of bottom velocity.
Figure 7. Comparison of bottom velocity.
Figure 8. Comparison of pressure at 10 pressure measurement points: (a) Comparison of root mean square (RMS) of fluctuating and time-average pressure; (b) Comparison of maximum and minimum pressure.
Figure 8. Comparison of pressure at 10 pressure measurement points: (a) Comparison of root mean square (RMS) of fluctuating and time-average pressure; (b) Comparison of maximum and minimum pressure.
Figure 9. The distribution diagram of time-average pressure and RMS of fluctuating pressure of bottom of stilling basin under three cases.
Figure 9. The distribution diagram of time-average pressure and RMS of fluctuating pressure of bottom of stilling basin under three cases.
Figure 10. Speed vector in stilling basin at z = 40 cm horizontal plane and bottom plate plane in three cases.
Figure 10. Speed vector in stilling basin at z = 40 cm horizontal plane and bottom plate plane in three cases.
Figure 11. Distribution of fluctuating velocity and vorticity in the horizontal section of the stilling basin slab: (a) Distribution of fluctuating velocity; (b) Distribution of fluctuating vorticity.
Figure 11. Distribution of fluctuating velocity and vorticity in the horizontal section of the stilling basin slab: (a) Distribution of fluctuating velocity; (b) Distribution of fluctuating vorticity.
Figure 12. Distribution of root time-average square fluctuating pressure of x = 50 cm cross-section of bottom plate: (a) Distributions of fluctuating velocity and fluctuating pressure; (b) Distributions of fluctuating vorticity and fluctuating pressure.
Figure 12. Distribution of root time-average square fluctuating pressure of x = 50 cm cross-section of bottom plate: (a) Distributions of fluctuating velocity and fluctuating pressure; (b) Distributions of fluctuating vorticity and fluctuating pressure.
Figure 13. Variance of fluctuating pressure coefficient (Cp′).
Figure 13. Variance of fluctuating pressure coefficient (Cp′).

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Figure 6. Scour depth (in negative value) at different views around pier

Three-dimensional numerical simulation of local scour around circular bridge pier using Flow-3D software

Flow-3D 소프트웨어를 이용한 원형 교각 주변 지역 scour의 3 차원 수치 시뮬레이션

To cite this article: Halah Kais Jalal and Waqed H. Hassan 2020 IOP Conf. Ser.: Mater. Sci. Eng. 745 012150

Halah Kais Jalal1
, Waqed H. Hassan2
1 Graduate student, Civil Engineering Department, University of Kerbala, Kerbala, Iraq.
2 Professor, University of Kerbala, Kerbala, Iraq.
E-mail: halah.q@s.uokerbala.edu.iq, Waaqidh@uokerbala.edu.iq

Abstract

주어진 값의 내부 드리프트를 나타내는 다항식 순서 또는 자체 정의 함수 목록을 제공 할 수 있습니다. 이 드리프트는 kriging 보간 동안 내부적으로 적합합니다. 다음에서는 선형 드리프트가 추가된 인공 데이터를 생성합니다. 그런 다음 결과 샘플은 Universal kriging의 입력으로 사용됩니다. 그런 다음 보간 중에 “선형”드리프트가 추정됩니다. 추정된 평균 / 드리프트에만 액세스하기 위해 호출 루틴에 스위치 only_mean을 제공합니다. 원형 교각 주변의 국부 수색 문제는 Flow-3D 모델을 사용하여 전산 유체 역학 (CFD)에서 국부적 진화를 나타냅니다. 교각 설계에서 중요한 scour 및 scour 구멍의 최대 깊이. 이 연구의 목적은 교각 주변의 수색 깊이를 정확하게 시뮬레이션하고 예측하는 수치 시뮬레이션 모델 Flow-3D의 능력을 검증하는 것입니다. 이 검증은 수치 결과를 Melville 실험실 실험 모델과 비교하여 수행됩니다. 30 분후 수치 결과에서 얻은 원형 부두 주변의 최대 scour 깊이는 3.6cm이고 Melville 모델에서 얻은 scour 깊이는 4cm입니다. 이 결과에 따르면 수치 모델과 실험 모델 간의 오류율 비율은 10 %에 가깝습니다. 결과는 실험 결과와 함께 좋은 검증을 보여주었습니다. 마지막으로 제안 된 Flow-3D 모델은 교각 주변의 수색 깊이를 예측하고 시뮬레이션 하는데 효과적인 도구를 고려하고 잠재적인 결과를 예측하는 경제적인 방법을 고려했습니다.

The problem of local scouring around circular bridge pier has been studied numerically
by Computational Fluid Dynamics (CFD) using Flow-3D model to represent the evolution of local
scour and the maximum depth of the scour hole which is important in the bridge pier design. The
aim of this study is to verify the ability of the numerical simulation model Flow-3D to accurately
simulate and predict the scour depth around the bridge pier. This verification is conducted by
comparison the numerical results with Melville laboratory experimental model. The maximum
scours depth around the circular pier obtained from numerical results after 30 min is 3.6 cm, while
the scouring depth obtained from Melville model is 4 cm. According to these results, the error rate
ratio between the numerical and experimental models is close to 10%. The results showed a good
validation with experimental results. Finally, the proposed Flow-3D model considered an effective
tool in predicting and simulating the scour depth around bridge pier and considered an economic
method to predict potential results.
Keywords: Local scour, Flow-3D, CFD, Verfication

scour은 흐르는 물의 침식 작용으로 정의 할 수 있으며, 이는 가까운 교각 및 교각에서 베드를 제거하고 침식합니다 [1]. 다리의 교각 주변을 scour하는 것은 다리의 실패 원인이 충돌 및 과부하와 함께 엄청난 인명 손실과 경제적 영향으로 이어지는 주요 원인 중 하나로 간주됩니다 [2], 지역 scour 예측, 특히 최대 scour 깊이는 다음과 같습니다.

교량 설계, 유지 보수 및 평가에 필수적입니다. 전 세계의 많은 연구자들은 다양한 관점과 다양한 조건에서 광범위하게 scour 문제를 연구했습니다.

교량 부지에서 만든 scour에는 일반적으로 세 가지 유형이 포함되어 있습니다. 일반 scour, 수축 scour 및 국부 scour [3], 세 가지 scour 유형 중, scour는 다리와 관련된 위험에서 가장 중요한 역할을 하기 때문에, local scour는 이 연구의 중요한 부분으로 간주됩니다.

많은 선행 연구가 경험적 테스트를 사용하여 교량의 국부 scour을 분석하는 기술과 방법론을 목표로 했습니다 [4], [5], [6], [7], [8], [9], [10], [11] . 이러한 경험적 scour 테스트의 대부분은 비용이 많이 들고 노동 집약적이기 때문에 크고 중요한 교량에서 종종 수행됩니다.

그러나 가장 인기 있는 고속도로 교량의 경우 경험적 테스트가 적용되지 않지만 이러한 일반 교량에서 scour이 자주 발생하지만 일부 연구에서는 경제적이고 실용적인 목적으로 교량 scour에 대한 분석 솔루션을 조사했습니다.

지난 몇 년 동안 전산 유체 역학 (CFD를 사용하여 산업 및 환경 응용 분야에서 유체 흐름 동작을 결정하는 데 사용)을 더 많이 사용할 수 있는 컴퓨터 및 소프트웨어의 기능이 증가함에 따라 scour의 3 차원 시뮬레이션 방법이 더욱 널리 보급되었습니다.

FLUENT, CFX, PHOENIX와 같은 CFD 소프트웨어는 실험 설정과 여러면에서 유사하므로 이 수치 시뮬레이션의 원래 개념은 속도계와 같은 확장된 부속품을 사용하여 물리적 모델을 설계하고 구성하는 것입니다. 복잡한 모델 실험실 조건에서 모델링하기 어려운 모델은 수치 시뮬레이션을 사용하여 간단하게 모델링 할 수 있습니다.

좋은 수치 모델은 확실히 모델 테스트를 보완 할 수 있으며 설계 엔지니어가 모델 테스트를 수행 할 수 있는 가장 중요한 사례를 식별하는 데 도움이 될 수 있다는 것이 널리 알려져 있습니다.

복잡한 문제와 대규모 모델 연구를 해결할 수 있는 매력적인 아이디어입니다. 실제 결과를 결정하기 위해 추가 작업자 또는 기존의 대규모 설정이 필요하지 않습니다.

CFD (Computational Fluid Dynamics) 방법은 Navier-Stokes의 이산화 및 해석과 계산 셀의 연속성 방정식을 통해 유동 프로세스 시뮬레이션에 항상 사용됩니다. 현재 연구에서 상용 코드 Flow-3D는 교각 주변의 scour 깊이를 모델링하는 데 사용됩니다.

Flow-3D 모델은 유압 공학 응용을 위한 특수 장치가 있는 CFD 패키지입니다. 수치 기법은 다중 스케일 다중 물리 흐름 문제를 얻기 위해 과도 및 3 차원 솔루션에 대한 유체 운동 방정식을 해결하는 데 사용됩니다.

물리적 옵션과 수치 옵션의 조합을 통해 사용자는 Flow-3D를 광범위한 유체 흐름 및 열 전달 현상에 적용 할 수 있으며 다양한 유압 문제를 해결하는 데 널리 사용됩니다 [12]. Flow-3D에 의한 scour의 수치 시뮬레이션은 많은 연구자들에 의해 제안 되었습니다.

Flow-3D에 의한 Scour의 수치 시뮬레이션은 많은 연구자들에 의해 제안 되었습니다.

예를 들어, [13]은 Scour Hole 내의 원형 브리지 부두의 기초에서 발생하는 흐름을 시뮬레이션하기 위해 Flow-3D를 사용했고, [14]는 조수 아래의 복잡한 브리지 피어에서 국소 스캐닝을 시뮬레이션하기 위해 숫자 모델을 사용했고 [15]는 Flow-3D를 사용했습니다.다양한 조건에서 국부적 골절 깊이의 더미 모양과 [16] CFD 코드를 사용하여 3D 흐름과 다양한 모양의 교량 부두 주위의 국부적 스캐닝을 시뮬레이션했습니다.

이 모든 연구는 맑은 물 조건에서 흐르는 물이 주로 흐름과 강바닥 사이의 대부분의 상호 작용으로 이어진다는 가설을 세웠습니다.

본 논문에서는 [4]의 실험실 모델에 의한 수치 시뮬레이션 검증을 통해 교량 주변의 국부 scour 실험 결과를 CFD 코드 Flow-3D의 수치 시뮬레이션 결과와 비교하여 검증을 목적으로 합니다. 이 검증의 주요 목적은 교량 부두 주변의 scour 깊이를 예측할 때 수치 모델 Flow-3D의 효과를 테스트하는 것입니다.

Figure 1. Plan view of Melville experimental setup [4]
Figure 1. Plan view of Melville experimental setup [4]
Figure 2. Geometry of the numerical model configured by the FLOW-3D
Figure 2. Geometry of the numerical model configured by the FLOW-3D
Figure 3. Effect of Cell Size on Scour Depth
Figure 3. Effect of Cell Size on Scour Depth
Figure 4. Meshing Plane Structure Around a Circular Pier
Figure 4. Meshing Plane Structure Around a Circular Pier
Figure 6. Scour depth (in negative value) at different views around pier
Figure 6. Scour depth (in negative value) at different views around pier
Figure 7. Contour Lines Represented the Depth of Scour Around Circular Bridge Pier for Melville Model
Figure 7. Contour Lines Represented the Depth of Scour Around Circular Bridge Pier for Melville Model
Figure 8. Contour Lines Represented the Depth of Scour Around the bridge Pier for the Numerical model
Figure 8. Contour Lines Represented the Depth of Scour Around the bridge Pier for the Numerical model
Figure 9. Scour depth against time around cylindrical pier.
Figure 9. Scour depth against time around cylindrical pier.
Figure 10. Contour map of flow velocity around a pier at 30 min resulted by Melville [4]
Figure 10. Contour map of flow velocity around a pier at 30 min resulted by Melville [4]
Figure 11. Contour map of flow velocity distribution around a pier at 30 min resulted by numerical simulation.
Figure 11. Contour map of flow velocity distribution around a pier at 30 min resulted by numerical simulation.

Conclusion

이 연구는 교각에서 scour깊이의 발달을 예측하는 데 있어 이 수치 시뮬레이션의 효과를 검증하는 것을 목표로 합니다. 검증은 30 분의 scour 깊이 공식화 후 Flow-3D의 수치 결과를 Melville 실험 모델과 비교하여 결론을 내립니다.

결과의 비교는 최대 수세공 깊이에 대한 오류율이 10 %임을 나타내며,이 관찰은 수치 및 실험 작업 사이에 좋은 검증을 보여 주므로 수치 시뮬레이션은 scour 깊이를 성공적으로 재현합니다.

이러한 결과에 따르면 제안된 수치 모델 Flow-3D는 교각 주변의 scour 깊이와 유동장을 시뮬레이션하고 예측하는데 효과적인 도구로 간주되었습니다.

References
[1] Breusers Nicollet and Shen 1977 Local scour around cylindrical piers Journal of Hydraulic
Research, IAHR,15 (3): 211-252.
[2] Shepherd R. and Frost J D 1995 Failures in civil engineering: Structural, foundation and
geoenvironmental case studies Journal of Hydraulic Engineering, Puolisher ASCE.
[3] Cheremisinoff N P and Cheng S L 1987 Hydraulic mechanics 2 Civil Engineering Practice,
Technomic Published Company, Lancaster, Pennsylvania, U.S.A. 780 p.
[4] Melville B W 1975 Local scour at bridge sites University of Auckland, New Zealand, phd. Thesis,
Dept. of Civil eng., Rep. No. 117.
[5] Abdul-Nour M 1990 Scouring depth around multiple M.Sc. Thesis , Department of Irrigation and
Drainage , University of Baghdad.
[6] Hosny M M 1995 Experimental study of local scour around circular bridge piers in cohesive soils
Colorado State University, Fort Collins.
[7] Ansari S A Kothyari U C and Ranga Raju K G 2002 Influence of cohesion on scour around bridge
piers Journal of Hydraulic Research, IAHR, pp. 40(6): 717-729.
[8] Khsaf S I 2010 A study of scour around Al-Kufa bridge piers Kufa Engineering
Journal.Vol.1No.1,2010, University of Kufa / College Engineering / Civil Department.
[9] Hassan W H Jassem M H and Mohammed S S 2018 A GA-HP Model for the Optimal Design of
Sewer Networks Water Resour. Manag., vol. 32, no. 3, pp. 865–879.
[10] Hassan W H 2017 Application of a genetic algorithm for the optimization of a cutoff wall under
hydraulic structures J. Appl. Water Eng. Res., vol. 5, no. 1, pp. 22–30, Jan.
[11] Ataie-Ashtiani B 2013 Flow field around single and tandem piers Flow Turbulence and Combustion
Journal of Hydraulic Engineering,volume 9429.
[12] Flow -3D manual 2014 Flow-3D user manual version 11, Flow Science Santa Fe, NM.
[13] Richardson J E and Panchang V G 1998 Three-Dimensional Simulation of Scour Inducing Flow at
Bridge Piers Journal of Hydraulic Engineering, 124(5), pp. 530–540. doi: 10.1061/(asce)0733-
9429(1998)124:5(530).
[14] Vasquez J and Walsh B 2009 CFD simulation of local scour in complex piers under tidal flow
Proceedings of the thirty-third IAHR Congress: Water Engineering for a Sustainable Environment,
(604), pp. 913–920.
[15] W H H and Halah k Jalal 2019 Effect of Bridge Pier Shape on Depth of Scour Iop, Conf. Ser.,(under
puplication).
[16] Obeid Z H 2016 3D numerical simulation of local scouring and velocity distributions around bridge
piers with different shapes A Peer Reviewed International Journal of Asian Academic Research
Associates, 20(16), p. 2801. doi: 10.1186/1757-7241-20-67.
[17] Drikakis D 2003 Advances in turbulent flow computations using high-resolution methods Progress
in Aerospace Sciences, 39(6–7), pp. 405–424. doi: 10.1016/S03760421(03)00075-7.
[18] Yakhot and Orszag 1986 Renormalization Group Analysis of Turbulence, Basic Theory Journal of
Scientific Computing, pp. 3–51. 1, pp. 3–51.
[19] Mastbergen D R and Van Den Berg J H 2003 Breaching in fine sands and the generation of
sustained turbidity currents in submarine canyons Sedimentology, 50(4), pp. 625–637. doi:
10.1046/j.1365-3091.2003.00554.x.
[20] Soulsby R L and Whitehouse R J S W 1997 Threshold of sediment motion in Coastal Environments
Proc. Combined Australian Coastal Engineering and Port Conference, EA, pp. 149-154.
[21] Meyer-Peter E and Müller R 1948 Formulas for bed-load transport Proceedings of the 2nd Meeting
of the International Association for Hydraulic Structures Research, 39– 64.
[22] Wei G Brethour J Grünzner M and Burnham J 2014 Sedimentation Scour Model Flow Science
Report 03-14.

FLOW-3D (x) Workflow

Optimization of a Tilt Pour Casting

경동 주조 최적화

최적화 목표

연소 엔진 피스톤의 경동 주조를 최적화하여 공기 혼입을 최소화합니다.

엔지니어링 과제

이 최적화의 목적은 경동 주조 중에 공기 혼입 및 난류의 양을 최소화하는 것입니다. 이 목표는 주물 채우기 모션의 프로필을 수정하여 달성됩니다. 공기 혼입과 난류를 최소화하면 주조에 결함이 발생할 가능성이 줄어 듭니다. 또한 충전 매개 변수를 최적화하면 비용 증가 없이 품질을 높일 수 있습니다.

최적화 전 틸트 타설 주조

최적화 솔루션

사용자가 경동 주조 시뮬레이션의 여러 반복을 실행할 수 있는 워크 플로우를 생성합니다. FLOW-3D (x) 는 노드를 사용하여 최적화를위한 자동화 된 워크 플로를 구성합니다. 세 가지 프로세스 변수 (회전 시작, 회전 지속 시간 및 체적 유량)는 변수 입력으로 사용되며 시뮬레이션이 반복 될 때마다 달라집니다.

FLOW-3D (x) 워크 플로우

Excel 스프레드 시트 노드는 금형 회전의 시작 및 지속 시간과 충전 프로파일의 체적 유량에 대한 테이블을 정의하는 데 사용됩니다. 계산기 노드는 프로파일 설명을 레이들 동작을 규정하는 movin.inp 파일로 변환합니다. 다음으로 FLOW-3D 노드는 시뮬레이션을 실행하는 데 사용됩니다. 각 시뮬레이션의 출력은 후 처리 노드에 의해 결과에서 추출된 총 충전 비율과 동반 공기량 비율입니다. 채우기 비율은 시뮬레이션의 동적 종료 조건으로 사용되어 금형이 완전히 채워지도록 합니다. 최적화 연구에 허용되는 예산 또는 시뮬레이션 수는 30 개로 설정됩니다. 단일 시뮬레이션 실행은 약 15 분입니다.

최적화 결과

사용 FLOW-3D (X) 의 데이터를 분석 도구를 결과 Pareto Front 그래픽 표현이 혼입된 공기의 최소량과 높은 충전 분율 최적 충전 프로파일에 있는 시뮬레이션 대응을 보여준다. 시뮬레이션 및 반복 설계 기능은 모두 FLOW-3D (x)에 의해 자율적으로 생성됩니다 . 또한 각 개별 시뮬레이션의 이미지와 비디오를 출력하도록 설정할 수 있습니다.

다음은 원래의 주입 속도와 주입 시간 (왼쪽)과 오른쪽의 최적화 된 값을 비교 한 것입니다. 주입 속도가 약간 증가하고 주입이 약간 더 일찍 완료됩니다.

원래 주입 속도
최적화 된 주입 속도

다음은 원래 금형 회전 속도 및 기간 (왼쪽)과 오른쪽의 최적화 된 값을 비교한 것입니다. 회전 속도가 증가하고 회전 시간이 원본보다 짧다는 것을 알 수 있습니다.

원래 금형 회전율

FLOW-3D (X)에 대한 자세한 내용은  기술 문의 담당자에게 문의 바랍니다.

FLOW-3D (x)

FLOW-3D (x)

Achieve Better CFD Workflows with FLOW-3D (x)

FLOW-3D(x) 는 자동화, 최적화 및 배치 처리를 CFD 워크 플로에 연결하여 CFD를 수행하는 방식을 크게 변화시킵니다. FLOW-3D(x)를 사용하면 자동화 및 최적화 워크 플로우를 그래픽적이고 직관적으로 구축 할 수 있을 뿐만 아니라 Solidworks, Rhino 및 Excel과 같은 외부 프로그램을 연결하여 시뮬레이션에 정보를 동적으로 제공할 수 있습니다. 

설계 매개 변수 공간을 실행하거나 실험 설계에 관심이 있거나 최상의 성능을 위해 형상 부품을 최적화 하는 경우 FLOW-3D(x)를 사용하면 배치 워크 플로우를 구성하고 고급 매개 변수 형상 연구를 수행하며 자동화 및 최적화를 결합하여 신속하게 설계 목표를 충족하고 최적의 해결 방안에 도달할 수 있습니다.

FLOW-3D (x) Case Studies

  


FLOW-3D (x) Features

OPTIMIZATION

  • 최적의 설계 매개 변수를 식별하여 제품 성능을 향상시킵니다.

WORKFLOW AUTOMATION

  • 일반적인 시뮬레이션 작업 자동화 : 사전 정의된 매개 변수 세트를 실행하고 시뮬레이션 결과를 추출하고 그래픽 출력을 생성합니다.

SIMULATION CALIBRATION

  • 원하는 결과를 얻는데 필요한 시뮬레이션 매개 변수를 식별합니다.

PARAMETER SENSITIVITY

  • 입력 매개 변수에 대한 시뮬레이션의 민감도를 결정합니다.

PYTHON INTEROPERABILITY

  • Python 스크립트를 실행하여 POST 처리 및 입력 사용자 지정을 제공합니다.

EXPERIMENTAL/LAB RESULTS

  • 기존 실험실 데이터에 대한 반응 표면을 만듭니다.

CAD PLUGINS

  • FLOW-3D (x) 내에서 직접 매개 변수화 된 CAD 모델과 상호 작용 합니다.
  • Solidworks, Rhino/Grasshopper, PTC Creo, NX, Spaceclaim, Catia 및 Autodesk Inventor.

DISTRIBUTED SOLVING

  • 최대의 효율성을 위해 원격 Windows 및 Linux 워크스테이션에서 시뮬레이션을 실행할 수 있습니다.


MICROSOFT EXCEL PLUGIN

  • Excel의 강력한 기능을 활용할 수 있습니다.
Mixing Tank with FLOW-3D

CFD Stirs Up Mixing 일반

CFD (전산 유체 역학) 전문가가 필요하고 때로는 실행하는데 몇 주가 걸리는 믹싱 시뮬레이션의 시대는 오래 전입니다. 컴퓨팅 및 관련 기술의 엄청난 도약에 힘 입어 Ansys, Comsol 및 Flow Science와 같은 회사는 엔지니어의 데스크톱에 사용하기 쉬운 믹싱 시뮬레이션을 제공하고 있습니다.

“병렬화 및 고성능 컴퓨팅의 발전과 템플릿화는 비전문 화학 엔지니어에게 정확한 CFD 시뮬레이션을 제공했습니다.”라고 펜실베이니아  피츠버그에있는 Ansys Inc.의 수석 제품 마케팅 관리자인 Bill Kulp는 말합니다 .

흐름 개선을위한 실용적인 지침이 필요하십니까? 다운로드 화학 처리의 eHandbook을 지금 흐름 도전 싸우는 방법!

예를 들어, 회사는 휴스턴에있는 Nalco Champion과 함께 프로젝트를 시작했습니다. 이 프로젝트는 시뮬레이션 전문가가 아닌 화학 엔지니어에게 Ansys Fluent 및 ACT (분석 제어 기술) 템플릿 기반 시뮬레이션 앱에 대한 액세스 권한을 부여합니다. 새로운 화학 물질을위한 프로세스를 빠르고 효율적으로 확장합니다.

Giving Mixing Its Due

“화학 산업은 CFD와 같은 계산 도구를 사용하여 많은 것을 얻을 수 있지만 혼합 프로세스는 단순하다고 가정하기 때문에 간과되는 경우가 있습니다. 그러나 최신 수치 기법을 사용하여 우수한 성능을 달성하는 흥미로운 방법이 많이 있습니다.”라고 Flow Science Inc. , Santa Fe, NM의 CFD 엔지니어인 Ioannis Karampelas는 말합니다 .

이러한 많은 기술이 회사의 Flow-3D Multiphysics 모델링 소프트웨어 패키지와 전용 포스트 프로세서 시각화 도구 인 FlowSight에 포함되어 있습니다.

“모든 상업용 CFD 패키지는 어떤 형태의 시각화 도구와 번들로 제공되지만 FlowSight는 매우 강력하고 사용하기 쉽고 이해하기 쉽게 설계되었습니다. 예를 들어, 프로세스를 재 설계하려는 엔지니어는 다양한 설계 변경의 효과를 평가하기 위해 매우 직관적인 시각화 도구가 필요합니다.”라고 그는 설명합니다.

이 접근 방식은 실험 측정을 얻기 어려운 공정 (예 : 쉽게 측정 할 수없는 매개 변수 및 독성 물질의 존재로 인해 본질적으로 위험한 공정)을 더 잘 이해하고 최적화하는데 특히 효과적입니다.

동일한 접근 방식은 또한 믹서 관련 장비 공급 업체가 고객 요구에 맞게 제품을보다 정확하게 개발하고 맞춤화하는 데 도움이되었습니다. “이는 불필요한 프로토 타이핑 비용이나 잠재적 인 과도한 엔지니어링을 방지합니다. 두 가지 모두 일부 공급 업체의 문제였습니다.”라고 Karampelas는 말합니다.

CFD 기술 자체는 계속해서 발전하고 있습니다. 예를 들어, 수치 알고리즘의 관점에서 볼 때 구형 입자의 상호 작용이 열 전달을 적절하게 모델링하는 데 중요한 다양한 문제에 대해 이산 요소 모델링을 쉽게 적용 할 수있는 반면, LES 난류 모델은 난류 흐름 패턴을 정확하게 시뮬레이션하는 데 이상적입니다.

컴퓨팅 리소스에 대한 비용과 수요에도 불구하고 Karampelas는 난류 모델의 전체 제품군을 제공 할 수있는 것이 중요하다고 생각합니다. 특히 LES는 이미 대부분의 학계와 일부 산업 (예 : 전력 공학)에서 선택하는 방법이기 때문입니다. .

그럼에도 불구하고 CFD의 사용이 제한적이거나 비실용적 일 수있는 경우는 확실히 있습니다. 여기에는 나노 입자에서 벌크 유체 증발을 모델링하는 것과 같이 관심의 규모가 다른 규모에 따라 달라질 수있는 문제와 중요한 물리적 현상이 아직 알려지지 않았거나 제대로 이해되지 않았거나 아마도 매우 복잡한 문제 (예 : 모델링)가 포함됩니다. 음 펨바 효과”라고 Karampelas는 경고합니다.

반면에 더욱 강력한 하드웨어와 업데이트 된 수치 알고리즘의 출현은 CFD 소프트웨어를 사용하여 과다한 설계 및 최적화 문제를 해결하기위한 최적의 접근 방식이 될 것이라고 그는 믿습니다.

“복잡한 열교환 시스템 및 새로운 혼합 기술과 같이 점점 더 복잡한 공정을 모델링 할 수있는 능력은 가까운 장래에 가능할 수있는 일을 간단히 보여줍니다. 수치적 방법 사용의 주요 이점은 설계자가 상상력에 의해서만 제한되어 소규모 믹서에서 대규모 반응기 및 증류 컬럼에 이르기까지 다양한 화학 플랜트 공정을 최적화 할 수있는 길을 열어 준다는 것입니다. 실험적 또는 경험적 접근 방식은 항상 관련성이 있지만 CFD가 미래의 엔지니어를위한 선택 도구가 될 것이라고 확신합니다.”라고 그는 결론을 내립니다.


Ottewell2
Seán Ottewell은 Chemical Processing의 편집장입니다. sottewell@putman.net으로 이메일을 보낼 수 있습니다 .

기사 원문 : https://www.chemicalprocessing.com/articles/2017/cfd-stirs-up-mixing/

Evaluation of the Wind Effects on the Iron-Ore Stock Pile

Energy

Energy

전 세계 에너지 부문의 엔지니어는 전산 유체 역학(CFD)을 통해 해결책을 찾기 위해 광범위한 프로세스에서 매일 복잡한 설계 문제에 직면합니다. 특히 자유 표면 흐름과 관련이 높은 이러한 문제의 대부분은 FLOW-3D가 매우 정확한 분석을 제공하여 문제 해결에 적합합니다.

  • 공해에서 컨테이너 내부의 연료 또는화물 슬로싱 / Fuel or cargo sloshing inside containers on the high seas
  • 해양 플랫폼에 대한 파도 효과 / Wave effects on offshore platforms
  • 6 자유도 모션을 받는 분리 장치의 성능 최적화 / Performance optimization for separation devices undergoing 6 DOF motion
  • 파동 에너지 포착 장치 / Design of devices to capture energy from waves

Energy Case Studies

천연자원이 계속 감소함에 따라, 대체 자원과 방법을 탐구하고 가능한 한 효과적으로 현재 공급량을 사용하고 있습니다. 엔지니어는 사고를 예방하고 채굴 및 기타 에너지 수확 기법으로 인한 환경적 영향을 평가하기 위해 FLOW-3D를 사용합니다.

Tailing Breach Simulation – CFD Analysis with FLOW-3D

점성이 높은 유체, 비 뉴턴 흐름, 슬러리 또는 심지어 세분화 된 흐름의 형태를 취할 수있는 많은 채광 응용 프로그램의 잔여 물인 테일링은 악명 높은 시뮬레이션 전제를 제공합니다. FLOW-3D  는 비 뉴턴 유체, 슬러리 및 입상 흐름에 대한 특수 모델을 포함하여 이러한 분석을 수행하는 데 필요한 모든 도구를 제공합니다. FLOW-3D 의 자유 표면 유동 모델링 기능 과 결합되어  이러한 어렵고 환경 적으로 민감한 문제에 대한 탁월한 모델링 솔루션을 제공합니다.

관련 응용 분야에는 바람 강제 분석에 따른 광석 비축 더미 먼지 드리프트가 포함되며, 여기서 FLOW-3D 의 드리프트 플럭스 모델을 통해 엔지니어는 광석 침착 및 유입 패턴과 개선 솔루션의 효과를 연구 할 수 있습니다.

액화와 기계적 방해가 물과 같은 뉴턴 흐름과는 대조적으로 입자 흐름의 매우 독특한 속성 인 결국 저절로 멈추는 위반의 동적 특징의 일부라는 점에 유의하십시오.

오일 및 가스 분리기

FLOW-3D  는 기름과 물과 같은 혼합 불가능한 유체를 모델링 할 수 있으며 개방 된 환경 (주변 공기)과 관련된 구성 요소 간의 뚜렷한 인터페이스를 정확하게 추적 할 수 있습니다. 유체는 전체 도메인에 영향을 미치는 역학으로 인해 자유롭게 혼합 될 수 있습니다. 시간이 지남에 따라 유체는 연속 상과 분산 상 간의 드리프트 관계에 따라 다시 분리됩니다. 중력 분리기의 성능은 CFD 모델링을 통해 향상 될 수 있습니다.

  • 기체 및 액체 흐름의 균일성을 개선하고 파도에 의한 슬로싱으로 인한 오일과 물의 혼합을 방지하기 위해 용기 입구 구성을 개발합니다.
  • 유압 효율 및 분리 성능에 대한 내부 장비의 영향을 결정합니다.
  • 작동 조건 변화의 영향 측정
  • 소규모 현상 (다상 흐름, 방울, 입자, 기포)을 정확하게 모델링

생산 파이프 | Production Pipes

생산에 사용되는 공정 파이프의 청소 과정에서 유체가 위로 흘러도 고밀도 입자가 침전될 수 있습니다. 침전 입자를 포착하도록 장치를 설계 할 수 있습니다. 파이프 중앙에 있는 “버킷”이 그러한 잠재적 장치중 하나 입니다. 흐름 변위로 인해 버킷 외부의 상류 속도는 고밀도 입자에 대한 침전 속도보다 높으며 버킷 내부에 모여 있습니다. 표시된 디자인에서 버킷 주변의 상향 유체 속도는 입자 안정화 속도보다 높습니다. 이로 인해 입자가 버킷과 파이프 벽 사이의 틈새를 통해 빠져 나갈 수 없습니다. 따라서 시뮬레이션된 입자는 버킷을 통과하여 아래에 정착하지 않습니다.

파동 에너지 장치 모델링 | Modeling Wave Energy Devices

포인트 흡수 장치 | Point Absorber Devices

이 시뮬레이션은 상단에 부력이있는 구형 구조가있는 점 흡수 장치를 보여 주며, 들어오는 파도의 볏과 골과 함께 위아래로 이동합니다. FLOW-3D 의 움직이는 물체 모델은 x 또는 y 방향으로의 움직임을 제한하면서 z 방향으로 결합 된 움직임을 허용하는 데 사용됩니다. 진폭 5m, 파장 100m의 스톡 스파를 사용했다. RNG 모델은 파도가 점 흡수 장치와 상호 작용할 때 발생하는 난류를 포착하는 데 사용되었습니다. 예상대로 많은 난류 운동 에너지가 장치 근처에서 생성됩니다. 플롯은 난류로 인해 장치 근처의 복잡한 속도 장의 진화로 인해 질량 중심의 불규칙한 순환 운동을 보여줍니다.

다중 플랩, 하단 경첩 파동 에너지 변환기 | Multi-Flap, Bottom-Hinged Wave Energy Converter

진동하는 플랩은 바다의 파도에서 에너지를 추출하여 기계 에너지로 변환합니다. Arm은 물결에 반응하여 피벗된 조인트에 장착된 진자로 진동합니다. 플랩을 배열로 구성하여 다중 플랩 파동 에너지 변환기를 만들 수 있습니다. 아래 상단에 표시된 CFD 시뮬레이션에서 3 개의 플랩 배열이 시뮬레이션됩니다. 모든 플랩은 바닥에 경첩이 달려 있으며 폭 15m x 높이 10m x 두께 2m입니다. 어레이는 30m 깊이에서 10 초의 주파수로 4m 진폭파에서 작동합니다. 시뮬레이션은 중앙 평면을 따라 복잡한 속도 등 가면을 보여줍니다. 이는 한 플랩이 어레이 내의 다른 플랩에 미치는 영향을 연구하는 데 중요합니다. 3 개의 플랩이 유사한 동적 동작으로 시작하는 동안 플랩의 상호 작용 효과는 곧 동작을 위상에서 벗어납니다. 유사한 플랩 에너지 변환기가 오른쪽 하단에 표시됩니다. 이 시뮬레이션에서 플랩은 가장 낮은 지점에서 물에 완전히 잠 깁니다. 이러한 에너지 변환기를 Surface Piercing 플랩 에너지 변환기라고합니다. 이 두 시뮬레이션 예제는 모두 미네르바 역학 .

진동 수주 | Oscillating Water Column

진동하는 수주는 부분적으로 잠긴 중공 구조입니다. 그것은 물의 기둥 위에 공기 기둥을 둘러싸고 수면 아래의 바다로 열려 있습니다. 파도는 물 기둥을 상승 및 하강시키고, 차례로 공기 기둥을 압축 및 감압합니다. 이 갇힌 공기는 일반적으로 기류의 방향에 관계없이 회전 할 수 있는 터빈을 통해 대기로 흐르게 됩니다. 터빈의 회전은 전기를 생성하는 데 사용됩니다.

아래의 CFD 시뮬레이션은 진동하는 수주를 보여줍니다. FLOW-3D에서 포착한 물리학을 강조하기 위해 중공 구조에서 물기둥이 상승 및 하강하는 부분만 모델링  합니다. 시뮬레이션은 다른 파형 생성 선택을 제외하고 유사한 결과를 전달합니다. 아래의 시뮬레이션은 웨이브 유형 경계 조건을 사용하는 반면 그 아래의 시뮬레이션은  움직이는 물체 모델  을 사용하여 실험실에서 수행한 것처럼 차례로 웨이브를 생성하는 움직이는 플런저를 생성합니다. 각 시뮬레이션에 대해 속이 빈 구조의 압력 플롯이 표시됩니다. 결국 그 압력에 기초하여 터빈이 회전 운동으로 설정되기 때문에 챔버에서 얼마나 많은 압력이 생성되는지 아는 것이 중요합니다.

사례 연구

eadership-in-energy-and-environmental-design

Architects Achieve LEED Certification in Sustainable Buildings

LEED (Leadership in Energy and Environmental Design)는 제 3자가 친환경 건축물 인증을 제공하는 자발적 인증 시스템입니다.

FLOW-3D는 보고타(콜롬비아)의 사무실 건물에서 “IEQ-Credit2–환기 증가”라는 신뢰를 얻는 데 큰 도움을 주었습니다. 이러한 인정을 받기 위해서는 실외 공기가 ASHRAE의 표준 비율인 30%를 초과한다는 것을 증명해야만 합니다. 이 건물에서 실외 공기는 태양 광선에 의해, 가열되는 지붕 위의 2개의 유리 굴뚝에 의해 발생되는 온도 차이에 의해 발생하는 열 부력의 영향으로 제공됩니다. 이것은 바람이 불지 않는 조건에서 이루어져야 합니다.

Comparing HVAC System Designs

최근 프로젝트에서 Tecsult의 HVAC(난방, 냉방 및 환기)시스템 엔지니어는 강력한 에어컨 시스템의 두 가지 다른 구성을 고려해야 했고 노동자들에게 어떤 것이 가장 쾌적함을 제공하는지 보여주기를 원했습니다. FLOW-3D는 대체 설계를 시뮬레이션하고 비교하는 데 사용되었습니다.

이 발전소는 대형(길이 90m, 너비 33m, 높이 26m)건물로 변압기, 전력선, 조명 등 열 발생 장비를 갖추고 있습니다. 에어컨 시스템의 목적은 건물 내 최대 온도를 35ºC로 제한하는 것입니다. 디퓨저가 하부 레벨에 위치하고 천장 근처의 환기구가 있기 때문에 천장 근처에서 최대 공기 온도가 발생하고 바닥 레벨은 반드시 몇도 더 낮습니다.

Modeling velocity of debris types

Debris Transport in a Nuclear Reactor Containment Building

이 기사는 FLOW-3D가 원자력 시설에서 봉쇄 시설의 성능을 모델링하는데 사용된 방법을 설명하며, Alion Science and Technology의 Tim Sande & Joe Tezak이 기고 한 바 있습니다.

가압수형 원자로 원자력 발전소에서 원자로 노심을 통해 순환되는 물은 약 2,080 psi 및 585°F의 압력과 온도로 유지되는 1차 배관 시스템에 밀폐됩니다. 수압이 높기 때문에 배관이 파손되면 격납건물 내에 여러 가지 이물질 유형이 생성될 수 있습니다. 이는 절연재가 장비와 균열 주변 영역의 배관에서 떨어져 나가기 때문에 발생합니다. 생성될 수 있는 다양한 유형의 이물질의 일반적인 예가 나와 있습니다(오른쪽).

Evaluation of the Wind Effects on the Iron-Ore Stock Pile

Evaluation of the Wind Effects on the Iron-Ore Stock Pile

바람이 개방형 골재 저장소에 미치는 영향은 전 세계적으로 환경 문제가 되고 있습니다. 2.7km철골 저장소 부지에서 이런 문제가 관찰되었습니다. 이 시설은 철도 운송차량를 통해 광석을 공급받는데, 이 운송차량은 자동 덤프에 의해 비워집니다. 그런 다음 이 광석은 일련의 컨베이어와 이송 지점을 통과하여 저장 장소중 하나로 운송됩니다. 비산먼지 배출은 풍력이 비축된량에 미치는 영향의 결과로 관찰된 결과입니다.

관련 기술자료


Figure 1. Photorealistic view of an inclined axis TAST (photo A. Stergiopoulou).

그리스 수로의 작은 수력 전위를 활용하는 관형 아르키메데스 스크류 터빈의 CFD 시뮬레이션

CFD Simulations of Tubular Archimedean Screw Turbines Harnessing the Small Hydropotential of Greek Watercourses Alkistis Stergiopoulou1, Vassilios Stergiopoulos21Institut für Wasserwirtschaft, ...
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Fig. 1. Nysted Offshore Wind Farm

FLOW-3D 모형을 이용한 해상풍력기초 세굴현상 분석

박영진1, 김태원2*1 서일대학교 토목공학과, 2 (주)지티이 Analysis of Scour Phenomenon around Offshore Wind Foundation using Flow-3D Mode Abstract 국내․외에서 다양한 ...
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Figure 2.1. Test Setup.The test setup consists of a clear plastic scale model tank attached to a rigid aluminum frame by three multi-axis load cells driven by a position-controlled servo hydraulic system.(Data acquisition cabling removed for clarity).

Coupled Simulation of Vehicle Dynamics and Tank Slosh. Phase 1 Report. Testing and Validation of Tank Slosh Analysis

Prepared byGlenn R. WendelSteven T. GreenRussell C. Burkey Abstract: 차량 동력학의 컴퓨터 시뮬레이션은 차량 설계에서 귀중한 도구가 되었다. 그러나 그들은 ...
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Figure 6. Scour depth (in negative value) at different views around pier

Three-dimensional numerical simulation of local scour around circular bridge pier using Flow-3D software

Flow-3D 소프트웨어를 이용한 원형 교각 주변 지역 scour의 3 차원 수치 시뮬레이션 To cite this article: Halah Kais Jalal and ...
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Abb. 3 Detail des Rechens am Vorversuch zum Seilrechen – Blick in Fließrichtung

Implementation of an angled trash rack in the 3D-numerical simulation with FLOW-3D

Abstract Sebastian Krzyzagorski · Roman Gabl · Jakob Seibl · Heidi Böttcher · Markus AuflegerOnline publiziert: 17. Februar 2016© Die ...
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Figure 1: Die configuration for a multi-attribute composite die for high die life and self-lubricating surface

Innovative Die Material and Lubrication Strategies for Clean and Energy Conserving Forging Technologies

청정 및 에너지 절약 단조 기술을 위한 혁신적인 다이 재료 및 윤활 전략 이 최종 기술 보고서에는 수상 번호 DE-FC07-01ID14206에 ...
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(a) Moving Reference Frame

Study on Swirl and Cross Flow of 3D-Printed Rotational Mixing Vane in 2×3 Subchannel

A thesis/dissertationsubmitted to the Graduate School of UNISTin partial fulfillment of therequirements for the degree ofMaster of ScienceHaneol Park07/09/2019Approved by_________________________AdvisorIn ...
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Figure 8 Evaluation test of thermal sprayed coatings

Development of Advanced Materials and Manufacturing Technologies for High-efficiency Gas Turbines

고효율 가스 터빈용 신소재 및 제조 기술 개발 Mitsubishi Heavy Industries Technical Review Vol. 52 No. 4 (December 2015) 가스 ...
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Figure 2. Diagram. Schematic design of a living snow fence. Source: Wyatt et al., 2012b

Design of Living Barriers to Reduce the Impacts of Snowdrifts on Illinois Freeways

눈사태가 일리노이 고속도로에 미치는 영향을 줄이기 위한 생활장벽 설계 John Petrie, et al. (2020) 일리노이 교통 센터 시리즈 번호 20-019, 연구 ...
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aerospace-sloshing-simulation

Aerospace Sloshing Dynamics

Sloshing Dynamics 우주선의 연료 탱크에서 추진체의 움직임에 대한 지식은 작동 및 성능의 다양한 측면을 이해하는 데 필수적입니다. 추진체 운동은 액체 ...
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Micro/Bio/Nano Fluidics

Micro/Bio/Nano Fluidics

기계적, 유체적, 광학적 및 전자적 기능을 매우 작은 패키지에 통합한 현대적인 마이크로 유체 장치는 비용, 규모 및 대규모 시스템에 직접 통합하는 능력 면에서 기존 장치에 비해 중요한 장점을 가지고 있다. 3D모델링 및 시각화는 풍부한 기능을 제공하는 효율적인 도구이다. Ivy분석을 통해 연구 시간, 설계 및 생산 비용을 크게 절감할 수 있습니다. 마이크로, 바이오 및 나노 유체 역학은 FLOW-3D의 자유 표면 및 다중 유체 모델링 기능으로 쉽고 정확하게 시뮬레이션할 수 있습니다. 이 섹션의 시뮬레이션을 통해 보다 잘 이해할 수 있는 다양한 애플리케이션과 프로세스를 살펴보시기 바랍니다.

FLOW-3D는 시각적 관찰과 양호한 정량적 추세 예측을 바탕으로 우수한 정성적 합의를 제공했습니다. 마찬가지로 중요한 것은 소프트웨어가 설계 민감도를 정확하게 예측한다는 점이다. 그 결과, FLOW-3D는 Kodak의 고급 연구 개발 작업을 지원하는 데 유용한 통찰력을 제공했습니다.

FLOW-3D는 시각적 관찰과 양호한 정량적 추세 예측을 바탕으로 우수한 정성적 합의를 제공했습니다. 마찬가지로 중요한 것은 소프트웨어가 설계 민감도를 정확하게 예측한다는 점이다. 그 결과, FLOW-3D는 Kodak의 고급 연구 개발 작업을 지원하는 데 유용한 통찰력을 제공했습니다.

Christopher Delametter, Senior Research Scientist, Eastman Kodak Company

Acoustophoresis
Acoustophoresis
Microfluidics palette
Cell Behavior
Microfluidics particle sorting using hydrodynamics
Continuous Flow Microfluidics
Digital microfluidics
Digital Microfluidics
Droplet based microfluidics
Droplet Based Microfluidics
Optofluidics
Optofluidics
Phase change
Phase Change

Customer Case Studies

육안으로 볼 수 있는 것보다 더 작은 도전은 FLOW-3D를 사용하여 미세 유체 소자 응용 프로그램을 모델링하는 고객들이 매일 직면하는 과제입니다. FLOW-3D를 통해 이러한 엔지니어와 과학자들은 실험실에서 복제할 수 없는 것을 모델링하고, 생명을 구하는 의료 기기를 검증하고, 잉크젯 형성을 연구하며, 경우에 따라 육안 모델을 제작할 수 있습니다. 때로는 가장 작은 문제가 가장 큰 문제이기도 하지만, FLOW-3D가 도움이 될 수 있습니다.

CFD analysis of stem cell culture
Advances in Nanotechnology
Computational analysis drop formation low viscosity
Computational Analysis of Drop Formation and Detachment
Inkjet formations simulations
Inkjet Printhead Performance
Thermal bubble model
Kodak Develops New Printhead Design in 1/3rd the Time
Photonic switching platform
Microscopic Bubbles Switch Fiber-Optic Circuits
Blood volumetric fraction
Optimization of Magnetic Blood Cleansing Microdevices

관련 기술자료

Fig.1 Schematic diagram of the novel cytometric device

Fabrication and Experimental Investigation of a Novel 3D Hydrodynamic Focusing Micro Cytometric Device

Yongquan Wang*a , Jingyuan Wangb, Hualing Chenc School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, 710049, P. R. Chinaa ...
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Figure 1. (a) Top view of the microfluidic-magnetophoretic device, (b) Schematic representation of the channel cross-sections studied in this work, and (c) the magnet position relative to the channel location (Sepy and Sepz are the magnet separation distances in y and z, respectively).

Continuous-Flow Separation of Magnetic Particles from Biofluids: How Does the Microdevice Geometry Determine the Separation Performance?

by  Cristina González Fernández1, Jenifer Gómez Pastora2, Arantza Basauri1, Marcos Fallanza1, Eugenio Bringas1, Jeffrey J. Chalmers2 and Inmaculada Ortiz1,* 1Department of Chemical and Biomolecular Engineering, ETSIIT, ...
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Fluid velocity magnitude including velocity vectors and blood volumetric fraction contours for scenario 3: (a,b) Magnet distance d = 0; (c,d) Magnet distance d = 1 mm.

Numerical Analysis of Bead Magnetophoresis from Flowing Blood in a Continuous-Flow Microchannel: Implications to the Bead-Fluid Interactions

Jenifer Gómez-Pastora,  Ioannis H. Karampelas,  Eugenio Bringas,  Edward P. Furlani &  Inmaculada Ortiz  Scientific Reports volume 9, Article number: 7265 (2019) Cite this article Abstract 이 연구에서는 ...
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Fig. 12. Comparison of simulation results with experimental data for a flow rate of water = Ql=15 ml/hr and a flow rate of air = Qg =3 ml/hr.

Simulation of Droplet Dynamics and Mixing in Microfluidic Devices using a VOF-Based Method

A. Chandorkar Published 2009 Abstract This paper demonstrates that the Volume of Fluid (TruVOF) method in FLOW-3D (a general purpose CFD ...
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Figure 1. Cross-sectional dimensions of a V-groove channel

Modeling Open Surface Microfluidics

개방형 표면 미세 유체 모델링 Open surface microfluidic systems are becoming increasingly popular in the fields of biology, biotechnology, medicine, ...
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Fig.4 Schematic of a package structure

Three-Dimensional Flow Analysis of a Thermosetting Compound during Mold Filling

Junichi Saeki and Tsutomu KonoProduction Engineering Research Laboratory, Hitachi Ltd.292, Y shida-cho, Totsuka-ku, Yokohama, 244-0817 Japan Abstract Thermosetting molding compounds ...
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The Simulation of Droplet Impact on the Super-Hydrophobic Surface with Micro-Pillar Arrays Fabricated by Laser Irradiation and Silanization Processes

The simulation of droplet impact on the super-hydrophobic surface with micro-pillar arrays fabricated by laser irradiation and silanization processes

레이저 조사 및 silanization 공정으로 제작된 micro-pillar arrays를 사용하여 초 소수성 표면에 대한 액적 영향 시뮬레이션 ZhenyanXiaa YangZhaoa ZhenYangabc ChengjuanYangab ...
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A new dynamic masking technique for time resolved PIV analysis

A new dynamic masking technique for time resolved PIV analysis

시간 분해 PIV 분석을위한 새로운 동적 마스킹 기술 물체 가시성을 허용하기 위해 형광 코팅과 결합 된 새로운 프리웨어 레이 캐스팅 ...
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On the role of the powder stream on the heat and fluid flow conditions during Directed Energy Deposition of maraging steel-Fig3

On the role of the powder stream on the heat and fluid flow conditions during Directed Energy Deposition of maraging steel—Multiphysics modeling and experimental validation

MohamadBayataVenkata K.NadimpalliaFrancesco G.BiondaniaSinaJafarzadehbJesperThorborgaNiels S.TiedjeaGiulianoBissaccoaDavid B.PedersenaJesper H.Hattela a Department of Mechanical Engineering, Technical University of Denmark, Building 425, Lyngby, Denmark b Department ...
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Fig. 2: Scheme of the LED photo-crosslinking and 3D-printing section of the microfluidic/3D-printing device. The droplet train is transferred from the chip microchannel into a microtubing in a straight section with nearly identical inner channel and inner microtubing diameter. Further downstream, the microtubing passes an LED-section for fast photo cross-linking to generate the microgels. This section is contained in an aluminum encasing to avoid premature crosslinking of polymer precursor in upstream channel sections by stray light. Subsequently, the microtubing is integrated into a 3D-printhead, where the microgels are jammed into a filament that is directly 3D-printed into the scaffold.

On-chip fabrication and in-flow 3D-printing of cellladen microgel constructs: From chip to scaffold materials in one integral process

cellladen 마이크로 겔 구조의 온칩 제작 및 인플 로우 3D 프린팅 : 하나의 통합 프로세스에서 칩에서 스캐폴드 재료까지 Benjamin Reineke ...
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FLOW-3D CAST Bibliography

FLOW-3D CAST bibliography

아래는 FSI의 금속 주조 참고 문헌에 수록된 기술 논문 모음입니다. 이 모든 논문에는 FLOW-3D CAST 해석 결과가 수록되어 있습니다. FLOW-3D CAST를 사용하여 금속 주조 산업의 응용 프로그램을 성공적으로 시뮬레이션하는 방법에 대해 자세히 알아보십시오.

Below is a collection of technical papers in our Metal Casting Bibliography. All of these papers feature FLOW-3D CAST results. Learn more about how FLOW-3D CAST can be used to successfully simulate applications for the Metal Casting Industry.

33-20     Eric Riedel, Martin Liepe Stefan Scharf, Simulation of ultrasonic induced cavitation and acoustic streaming in liquid and solidifying aluminum, Metals, 10.4; 476, 2020. doi.org/10.3390/met10040476

20-20   Wu Yue, Li Zhuo and Lu Rong, Simulation and visual tester verification of solid propellant slurry vacuum plate casting, Propellants, Explosives, Pyrotechnics, 2020. doi.org/10.1002/prep.201900411

17-20   C.A. Jones, M.R. Jolly, A.E.W. Jarfors and M. Irwin, An experimental characterization of thermophysical properties of a porous ceramic shell used in the investment casting process, Supplimental Proceedings, pp. 1095-1105, TMS 2020 149th Annual Meeting and Exhibition, San Diego, CA, February 23-27, 2020. doi.org/10.1007/978-3-030-36296-6_102

12-20   Franz Josef Feikus, Paul Bernsteiner, Ricardo Fernández Gutiérrez and Michal Luszczak , Further development of electric motor housings, MTZ Worldwide, 81, pp. 38-43, 2020. doi.org/10.1007/s38313-019-0176-z

09-20   Mingfan Qi, Yonglin Kang, Yuzhao Xu, Zhumabieke Wulabieke and Jingyuan Li, A novel rheological high pressure die-casting process for preparing large thin-walled Al–Si–Fe–Mg–Sr alloy with high heat conductivity, high plasticity and medium strength, Materials Science and Engineering: A, 776, art. no. 139040, 2020. doi.org/10.1016/j.msea.2020.139040

07-20   Stefan Heugenhauser, Erhard Kaschnitz and Peter Schumacher, Development of an aluminum compound casting process – Experiments and numerical simulations, Journal of Materials Processing Technology, 279, art. no. 116578, 2020. doi.org/10.1016/j.jmatprotec.2019.116578

05-20   Michail Papanikolaou, Emanuele Pagone, Mark Jolly and Konstantinos Salonitis, Numerical simulation and evaluation of Campbell running and gating systems, Metals, 10.1, art. no. 68, 2020. doi.org/10.3390/met10010068

102-19   Ferencz Peti and Gabriela Strnad, The effect of squeeze pin dimension and operational parameters on material homogeneity of aluminium high pressure die cast parts, Acta Marisiensis. Seria Technologica, 16.2, 2019. doi.org/0.2478/amset-2019-0010

94-19   E. Riedel, I. Horn, N. Stein, H. Stein, R. Bahr, and S. Scharf, Ultrasonic treatment: a clean technology that supports sustainability incasting processes, Procedia, 26th CIRP Life Cycle Engineering (LCE) Conference, Indianapolis, Indiana, USA, May 7-9, 2019. 

93-19   Adrian V. Catalina, Liping Xue, Charles A. Monroe, Robin D. Foley, and John A. Griffin, Modeling and Simulation of Microstructure and Mechanical Properties of AlSi- and AlCu-based Alloys, Transactions, 123rd Metalcasting Congress, Atlanta, GA, USA, April 27-30, 2019. 

84-19   Arun Prabhakar, Michail Papanikolaou, Konstantinos Salonitis, and Mark Jolly, Sand casting of sheet lead: numerical simulation of metal flow and solidification, The International Journal of Advanced Manufacturing Technology, pp. 1-13, 2019. doi.org/10.1007/s00170-019-04522-3

72-19   Santosh Reddy Sama, Eric Macdonald, Robert Voigt, and Guha Manogharan, Measurement of metal velocity in sand casting during mold filling, Metals, 9:1079, 2019. doi.org/10.3390/met9101079

71-19   Sebastian Findeisen, Robin Van Der Auwera, Michael Heuser, and Franz-Josef Wöstmann, Gießtechnische Fertigung von E-Motorengehäusen mit interner Kühling (Casting production of electric motor housings with internal cooling), Geisserei, 106, pp. 72-78, 2019 (in German).

58-19     Von Malte Leonhard, Matthias Todte, and Jörg Schäffer, Realistic simulation of the combustion of exothermic feeders, Casting, No. 2, pp. 28-32, 2019. In English and German.

52-19     S. Lakkum and P. Kowitwarangkul, Numerical investigations on the effect of gas flow rate in the gas stirred ladle with dual plugs, International Conference on Materials Research and Innovation (ICMARI), Bangkok, Thailand, December 17-21, 2018. IOP Conference Series: Materials Science and Engineering, Vol. 526, 2019. doi.org/10.1088/1757-899X/526/1/012028

47-19     Bing Zhou, Shuai Lu, Kaile Xu, Chun Xu, and Zhanyong Wang, Microstructure and simulation of semisolid aluminum alloy castings in the process of stirring integrated transfer-heat (SIT) with water cooling, International Journal of Metalcasting, Online edition, pp. 1-13, 2019. doi.org/10.1007/s40962-019-00357-6

31-19     Zihao Yuan, Zhipeng Guo, and S.M. Xiong, Skin layer of A380 aluminium alloy die castings and its blistering during solution treatment, Journal of Materials Science & Technology, Vol. 35, No. 9, pp. 1906-1916, 2019. doi.org/10.1016/j.jmst.2019.05.011

25-19     Stefano Mascetti, Raul Pirovano, and Giulio Timelli, Interazione metallo liquido/stampo: Il fenomeno della metallizzazione, La Metallurgia Italiana, No. 4, pp. 44-50, 2019. In Italian.

20-19     Fu-Yuan Hsu, Campbellology for runner system design, Shape Casting: The Minerals, Metals & Materials Series, pp. 187-199, 2019. doi.org/10.1007/978-3-030-06034-3_19

19-19     Chengcheng Lyu, Michail Papanikolaou, and Mark Jolly, Numerical process modelling and simulation of Campbell running systems designs, Shape Casting: The Minerals, Metals & Materials Series, pp. 53-64, 2019. doi.org/10.1007/978-3-030-06034-3_5

18-19     Adrian V. Catalina, Liping Xue, and Charles Monroe, A solidification model with application to AlSi-based alloys, Shape Casting: The Minerals, Metals & Materials Series, pp. 201-213, 2019. doi.org/10.1007/978-3-030-06034-3_20

17-19     Fu-Yuan Hsu and Yu-Hung Chen, The validation of feeder modeling for ductile iron castings, Shape Casting: The Minerals, Metals & Materials Series, pp. 227-238, 2019. doi.org/10.1007/978-3-030-06034-3_22

04-19   Santosh Reddy Sama, Tony Badamo, Paul Lynch and Guha Manogharan, Novel sprue designs in metal casting via 3D sand-printing, Additive Manufacturing, Vol. 25, pp. 563-578, 2019. doi.org/10.1016/j.addma.2018.12.009

02-19   Jingying Sun, Qichi Le, Li Fu, Jing Bai, Johannes Tretter, Klaus Herbold and Hongwei Huo, Gas entrainment behavior of aluminum alloy engine crankcases during the low-pressure-die-casting-process, Journal of Materials Processing Technology, Vol. 266, pp. 274-282, 2019. doi.org/10.1016/j.jmatprotec.2018.11.016

92-18   Fast, Flexible… More Versatile, Foundry Management Technology, March, 2018. 

82-18   Xu Zhao, Ping Wang, Tao Li, Bo-yu Zhang, Peng Wang, Guan-zhou Wang and Shi-qi Lu, Gating system optimization of high pressure die casting thin-wall AlSi10MnMg longitudinal loadbearing beam based on numerical simulation, China Foundry, Vol. 15, no. 6, pp. 436-442, 2018. doi: 10.1007/s41230-018-8052-z

80-18   Michail Papanikolaou, Emanuele Pagone, Konstantinos Salonitis, Mark Jolly and Charalampos Makatsoris, A computational framework towards energy efficient casting processes, Sustainable Design and Manufacturing 2018: Proceedings of the 5th International Conference on Sustainable Design and Manufacturing (KES-SDM-18), Gold Coast, Australia, June 24-26 2018, SIST 130, pp. 263-276, 2019. doi.org/10.1007/978-3-030-04290-5_27

64-18   Vasilios Fourlakidis, Ilia Belov and Attila Diószegi, Strength prediction for pearlitic lamellar graphite iron: Model validation, Metals, Vol. 8, No. 9, 2018. doi.org/10.3390/met8090684

51-18   Xue-feng Zhu, Bao-yi Yu, Li Zheng, Bo-ning Yu, Qiang Li, Shu-ning Lü and Hao Zhang, Influence of pouring methods on filling process, microstructure and mechanical properties of AZ91 Mg alloy pipe by horizontal centrifugal casting, China Foundry, vol. 15, no. 3, pp.196-202, 2018. doi.org/10.1007/s41230-018-7256-6

47-18   Santosh Reddy Sama, Jiayi Wang and Guha Manogharan, Non-conventional mold design for metal casting using 3D sand-printing, Journal of Manufacturing Processes, vol. 34-B, pp. 765-775, 2018. doi.org/10.1016/j.jmapro.2018.03.049

42-18   M. Koru and O. Serçe, The Effects of Thermal and Dynamical Parameters and Vacuum Application on Porosity in High-Pressure Die Casting of A383 Al-Alloy, International Journal of Metalcasting, pp. 1-17, 2018. doi.org/10.1007/s40962-018-0214-7

41-18   Abhilash Viswanath, S. Savithri, U.T.S. Pillai, Similitude analysis on flow characteristics of water, A356 and AM50 alloys during LPC process, Journal of Materials Processing Technology, vol. 257, pp. 270-277, 2018. doi.org/10.1016/j.jmatprotec.2018.02.031

29-18   Seyboldt, Christoph and Liewald, Mathias, Investigation on thixojoining to produce hybrid components with intermetallic phase, AIP Conference Proceedings, vol. 1960, no. 1, 2018. doi.org/10.1063/1.5034992

28-18   Laura Schomer, Mathias Liewald and Kim Rouven Riedmüller, Simulation of the infiltration process of a ceramic open-pore body with a metal alloy in semi-solid state to design the manufacturing of interpenetrating phase composites, AIP Conference Proceedings, vol. 1960, no. 1, 2018. doi.org/10.1063/1.5034991

41-17   Y. N. Wu et al., Numerical Simulation on Filling Optimization of Copper Rotor for High Efficient Electric Motors in Die Casting Process, Materials Science Forum, Vol. 898, pp. 1163-1170, 2017.

12-17   A.M.  Zarubin and O.A. Zarubina, Controlling the flow rate of melt in gravity die casting of aluminum alloys, Liteynoe Proizvodstvo (Casting Manufacturing), pp 16-20, 6, 2017. In Russian.

10-17   A.Y. Korotchenko, Y.V. Golenkov, M.V. Tverskoy and D.E. Khilkov, Simulation of the Flow of Metal Mixtures in the Mold, Liteynoe Proizvodstvo (Casting Manufacturing), pp 18-22, 5, 2017. In Russian.

08-17   Morteza Morakabian Esfahani, Esmaeil Hajjari, Ali Farzadi and Seyed Reza Alavi Zaree, Prediction of the contact time through modeling of heat transfer and fluid flow in compound casting process of Al/Mg light metals, Journal of Materials Research, © Materials Research Society 2017

04-17   Huihui Liu, Xiongwei He and Peng Guo, Numerical simulation on semi-solid die-casting of magnesium matrix composite based on orthogonal experiment, AIP Conference Proceedings 1829, 020037 (2017); doi.org/10.1063/1.4979769.

100-16  Robert Watson, New numerical techniques to quantify and predict the effect of entrainment defects, applied to high pressure die casting, PhD Thesis: University of Birmingham, 2016.

88-16   M.C. Carter, T. Kauffung, L. Weyenberg and C. Peters, Low Pressure Die Casting Simulation Discovery through Short Shot, Cast Expo & Metal Casting Congress, April 16-19, 2016, Minneapolis, MN, Copyright 2016 American Foundry Society.

61-16   M. Koru and O. Serçe, Experimental and numerical determination of casting mold interfacial heat transfer coefficient in the high pressure die casting of a 360 aluminum alloy, ACTA PHYSICA POLONICA A, Vol. 129 (2016)

59-16   R. Pirovano and S. Mascetti, Tracking of collapsed bubbles during a filling simulation, La Metallurgia Italiana – n. 6 2016

43-16   Kevin Lee, Understanding shell cracking during de-wax process in investment casting, Ph.D Thesis: University of Birmingham, School of Engineering, Department of Chemical Engineering, 2016.

35-16   Konstantinos Salonitis, Mark Jolly, Binxu Zeng, and Hamid Mehrabi, Improvements in energy consumption and environmental impact by novel single shot melting process for casting, Journal of Cleaner Production, doi.org/10.1016/j.jclepro.2016.06.165, Open Access funded by Engineering and Physical Sciences Research Council, June 29, 2016

20-16   Fu-Yuan Hsu, Bifilm Defect Formation in Hydraulic Jump of Liquid Aluminum, Metallurgical and Materials Transactions B, 2016, Band: 47, Heft 3, 1634-1648.

15-16   Mingfan Qia, Yonglin Kanga, Bing Zhoua, Wanneng Liaoa, Guoming Zhua, Yangde Lib,and Weirong Li, A forced convection stirring process for Rheo-HPDC aluminum and magnesium alloys, Journal of Materials Processing Technology 234 (2016) 353–367

112-15   José Miguel Gonçalves Ledo Belo da Costa, Optimization of filling systems for low pressure by FLOW-3D, Dissertação de mestrado integrado em Engenharia Mecânica, 2015.

89-15   B.W. Zhu, L.X. Li, X. Liu, L.Q. Zhang and R. Xu, Effect of Viscosity Measurement Method to Simulate High Pressure Die Casting of Thin-Wall AlSi10MnMg Alloy Castings, Journal of Materials Engineering and Performance, Published online, November 2015, doi.org/10.1007/s11665-015-1783-8, © ASM International.

88-15   Peng Zhang, Zhenming Li, Baoliang Liu, Wenjiang Ding and Liming Peng, Improved tensile properties of a new aluminum alloy for high pressure die casting, Materials Science & Engineering A651(2016)376–390, Available online, November 2015.

83-15   Zu-Qi Hu, Xin-Jian Zhang and Shu-Sen Wu, Microstructure, Mechanical Properties and Die-Filling Behavior of High-Performance Die-Cast Al–Mg–Si–Mn Alloy, Acta Metall. Sin. (Engl. Lett.), doi.org/10.1007/s40195-015-0332-7, © The Chinese Society for Metals and Springer-Verlag Berlin Heidelberg 2015.

82-15   J. Müller, L. Xue, M.C. Carter, C. Thoma, M. Fehlbier and M. Todte, A Die Spray Cooling Model for Thermal Die Cycling Simulations, 2015 Die Casting Congress & Exposition, Indianapolis, IN, October 2015

81-15   M. T. Murray, L.F. Hansen, L. Chilcott, E. Li and A.M. Murray, Case Studies in the Use of Simulation- Improved Yield and Reduced Time to Market, 2015 Die Casting Congress & Exposition, Indianapolis, IN, October 2015

80-15   R. Bhola, S. Chandra and D. Souders, Predicting Castability of Thin-Walled Parts for the HPDC Process Using Simulations, 2015 Die Casting Congress & Exposition, Indianapolis, IN, October 2015

76-15   Prosenjit Das, Sudip K. Samanta, Shashank Tiwari and Pradip Dutta, Die Filling Behaviour of Semi Solid A356 Al Alloy Slurry During Rheo Pressure Die Casting, Transactions of the Indian Institute of Metals, pp 1-6, October 2015

74-15   Murat KORU and Orhan SERÇE, Yüksek Basınçlı Döküm Prosesinde Enjeksiyon Parametrelerine Bağlı Olarak Döküm Simülasyon, Cumhuriyet University Faculty of Science, Science Journal (CSJ), Vol. 36, No: 5 (2015) ISSN: 1300-1949, May 2015

69-15   A. Viswanath, S. Sivaraman, U. T. S. Pillai, Computer Simulation of Low Pressure Casting Process Using FLOW-3D, Materials Science Forum, Vols. 830-831, pp. 45-48, September 2015

68-15   J. Aneesh Kumar, K. Krishnakumar and S. Savithri, Computer Simulation of Centrifugal Casting Process Using FLOW-3D, Materials Science Forum, Vols. 830-831, pp. 53-56, September 2015

59-15   F. Hosseini Yekta and S. A. Sadough Vanini, Simulation of the flow of semi-solid steel alloy using an enhanced model, Metals and Materials International, August 2015.

44-15   Ulrich E. Klotz, Tiziana Heiss and Dario Tiberto, Platinum investment casting material properties, casting simulation and optimum process parameters, Jewelry Technology Forum 2015

41-15   M. Barkhudarov and R. Pirovano, Minimizing Air Entrainment in High Pressure Die Casting Shot Sleeves, GIFA 2015, Düsseldorf, Germany

40-15   M. Todte, A. Fent, and H. Lang, Simulation in support of the development of innovative processes in the casting industry, GIFA 2015, Düsseldorf, Germany

19-15   Bruce Morey, Virtual casting improves powertrain design, Automotive Engineering, SAE International, March 2015.

15-15   K.S. Oh, J.D. Lee, S.J. Kim and J.Y. Choi, Development of a large ingot continuous caster, Metall. Res. Technol. 112, 203 (2015) © EDP Sciences, 2015, doi.org/10.1051/metal/2015006, www.metallurgical-research.org

14-15   Tiziana Heiss, Ulrich E. Klotz and Dario Tiberto, Platinum Investment Casting, Part I: Simulation and Experimental Study of the Casting Process, Johnson Matthey Technol. Rev., 2015, 59, (2), 95, doi.org/10.1595/205651315×687399

138-14 Christopher Thoma, Wolfram Volk, Ruben Heid, Klaus Dilger, Gregor Banner and Harald Eibisch, Simulation-based prediction of the fracture elongation as a failure criterion for thin-walled high-pressure die casting components, International Journal of Metalcasting, Vol. 8, No. 4, pp. 47-54, 2014. doi.org/10.1007/BF03355594

107-14  Mehran Seyed Ahmadi, Dissolution of Si in Molten Al with Gas Injection, ProQuest Dissertations And Theses; Thesis (Ph.D.), University of Toronto (Canada), 2014; Publication Number: AAT 3637106; ISBN: 9781321195231; Source: Dissertation Abstracts International, Volume: 76-02(E), Section: B.; 191 p.

99-14   R. Bhola and S. Chandra, Predicting Castability for Thin-Walled HPDC Parts, Foundry Management Technology, December 2014

92-14   Warren Bishenden and Changhua Huang, Venting design and process optimization of die casting process for structural components; Part II: Venting design and process optimization, Die Casting Engineer, November 2014

90-14   Ken’ichi Kanazawa, Ken’ichi Yano, Jun’ichi Ogura, and Yasunori Nemoto, Optimum Runner Design for Die-Casting using CFD Simulations and Verification with Water-Model Experiments, Proceedings of the ASME 2014 International Mechanical Engineering Congress and Exposition, IMECE2014, November 14-20, 2014, Montreal, Quebec, Canada, IMECE2014-37419

89-14   P. Kapranos, C. Carney, A. Pola, and M. Jolly, Advanced Casting Methodologies: Investment Casting, Centrifugal Casting, Squeeze Casting, Metal Spinning, and Batch Casting, In Comprehensive Materials Processing; McGeough, J., Ed.; 2014, Elsevier Ltd., 2014; Vol. 5, pp 39–67.

77-14   Andrei Y. Korotchenko, Development of Scientific and Technological Approaches to Casting Net-Shaped Castings in Sand Molds Free of Shrinkage Defects and Hot Tears, Post-doctoral thesis: Russian State Technological University, 2014. In Russian.

69-14   L. Xue, M.C. Carter, A.V. Catalina, Z. Lin, C. Li, and C. Qiu, Predicting, Preventing Core Gas Defects in Steel Castings, Modern Casting, September 2014

68-14   L. Xue, M.C. Carter, A.V. Catalina, Z. Lin, C. Li, and C. Qiu, Numerical Simulation of Core Gas Defects in Steel Castings, Copyright 2014 American Foundry Society, 118th Metalcasting Congress, April 8 – 11, 2014, Schaumburg, IL

51-14   Jesus M. Blanco, Primitivo Carranza, Rafael Pintos, Pedro Arriaga, and Lakhdar Remaki, Identification of Defects Originated during the Filling of Cast Pieces through Particles Modelling, 11th World Congress on Computational Mechanics (WCCM XI), 5th European Conference on Computational Mechanics (ECCM V), 6th European Conference on Computational Fluid Dynamics (ECFD VI), E. Oñate, J. Oliver and A. Huerta (Eds)

47-14   B. Vijaya Ramnatha, C.Elanchezhiana, Vishal Chandrasekhar, A. Arun Kumarb, S. Mohamed Asif, G. Riyaz Mohamed, D. Vinodh Raj , C .Suresh Kumar, Analysis and Optimization of Gating System for Commutator End Bracket, Procedia Materials Science 6 ( 2014 ) 1312 – 1328, 3rd International Conference on Materials Processing and Characterisation (ICMPC 2014)

42-14  Bing Zhou, Yong-lin Kang, Guo-ming Zhu, Jun-zhen Gao, Ming-fan Qi, and Huan-huan Zhang, Forced convection rheoforming process for preparation of 7075 aluminum alloy semisolid slurry and its numerical simulation, Trans. Nonferrous Met. Soc. China 24(2014) 1109−1116

37-14    A. Karwinski, W. Lesniewski, P. Wieliczko, and M. Malysza, Casting of Titanium Alloys in Centrifugal Induction Furnaces, Archives of Metallurgy and Materials, Volume 59, Issue 1, doi.org/10.2478/amm-2014-0068, 2014.

26-14    Bing Zhou, Yonglin Kang, Mingfan Qi, Huanhuan Zhang and Guoming ZhuR-HPDC Process with Forced Convection Mixing Device for Automotive Part of A380 Aluminum Alloy, Materials 2014, 7, 3084-3105; doi.org/10.3390/ma7043084

20-14  Johannes Hartmann, Tobias Fiegl, Carolin Körner, Aluminum integral foams with tailored density profile by adapted blowing agents, Applied Physics A, doi.org/10.1007/s00339-014-8377-4, March 2014.

19-14    A.Y. Korotchenko, N.A. Nikiforova, E.D. Demjanov, N.C. Larichev, The Influence of the Filling Conditions on the Service Properties of the Part Side Frame, Russian Foundryman, 1 (January), pp 40-43, 2014. In Russian.

11-14 B. Fuchs and C. Körner, Mesh resolution consideration for the viability prediction of lost salt cores in the high pressure die casting process, Progress in Computational Fluid Dynamics, Vol. 14, No. 1, 2014, Copyright © 2014 Inderscience Enterprises Ltd.

08-14 FY Hsu, SW Wang, and HJ Lin, The External and Internal Shrinkages in Aluminum Gravity Castings, Shape Casting: 5th International Symposium 2014. Available online at Google Books

103-13  B. Fuchs, H. Eibisch and C. Körner, Core Viability Simulation for Salt Core Technology in High-Pressure Die Casting, International Journal of Metalcasting, July 2013, Volume 7, Issue 3, pp 39–45

94-13    Randall S. Fielding, J. Crapps, C. Unal, and J.R.Kennedy, Metallic Fuel Casting Development and Parameter Optimization Simulations, International Conference on Fast reators and Related Fuel Cycles (FR13), 4-7 March 2013, Paris France

90-13  A. Karwińskia, M. Małyszaa, A. Tchórza, A. Gila, B. Lipowska, Integration of Computer Tomography and Simulation Analysis in Evaluation of Quality of Ceramic-Carbon Bonded Foam Filter, Archives of Foundry Engineering, doi.org/10.2478/afe-2013-0084, Published quarterly as the organ of the Foundry Commission of the Polish Academy of Sciences, ISSN, (2299-2944), Volume 13, Issue 4/2013

88-13  Litie and Metallurgia (Casting and Metallurgy), 3 (72), 2013, N.V.Sletova, I.N.Volnov, S.P.Zadrutsky, V.A.Chaikin, Modeling of the Process of Removing Non-metallic Inclusions in Aluminum Alloys Using the FLOW-3D program, pp 138-140. In Russian.

85-13    Michał Szucki,Tomasz Goraj, Janusz Lelito, Józef S. Suchy, Numerical Analysis of Solid Particles Flow in Liquid Metal, XXXVII International Scientific Conference Foundryman’ Day 2013, Krakow, 28-29 November 2013

84-13  Körner, C., Schwankl, M., Himmler, D., Aluminum-Aluminum compound castings by electroless deposited zinc layers, Journal of Materials Processing Technology (2014), doi.org/10.1016/j.jmatprotec.2013.12.01483-13.

77-13  Antonio Armillotta & Raffaello Baraggi & Simone Fasoli, SLM tooling for die casting with conformal cooling channels, The International Journal of Advanced Manufacturing Technology, doi.org/10.1007/s00170-013-5523-7, December 2013.

64-13   Johannes Hartmann, Christina Blümel, Stefan Ernst, Tobias Fiegl, Karl-Ernst Wirth, Carolin Körner, Aluminum integral foam castings with microcellular cores by nano-functionalization, J Mater Sci, doi.org/10.1007/s10853-013-7668-z, September 2013.

46-13  Nicholas P. Orenstein, 3D Flow and Temperature Analysis of Filling a Plutonium Mold, LA-UR-13-25537, Approved for public release; distribution is unlimited. Los Alamos Annual Student Symposium 2013, 2013-07-24 (Rev.1)

42-13   Yang Yue, William D. Griffiths, and Nick R. Green, Modelling of the Effects of Entrainment Defects on Mechanical Properties in a Cast Al-Si-Mg Alloy, Materials Science Forum, 765, 225, 2013.

39-13  J. Crapps, D.S. DeCroix, J.D Galloway, D.A. Korzekwa, R. Aikin, R. Fielding, R. Kennedy, C. Unal, Separate effects identification via casting process modeling for experimental measurement of U-Pu-Zr alloys, Journal of Nuclear Materials, 15 July 2013.

35-13   A. Pari, Real Life Problem Solving through Simulations in the Die Casting Industry – Case Studies, © Die Casting Engineer, July 2013.

34-13  Martin Lagler, Use of Simulation to Predict the Viability of Salt Cores in the HPDC Process – Shot Curve as a Decisive Criterion, © Die Casting Engineer, July 2013.

24-13    I.N.Volnov, Optimizatsia Liteynoi Tekhnologii, (Casting Technology Optimization), Liteyshik Rossii (Russian Foundryman), 3, 2013, 27-29. In Russian

23-13  M.R. Barkhudarov, I.N. Volnov, Minimizatsia Zakhvata Vozdukha v Kamere Pressovania pri Litie pod Davleniem, (Minimization of Air Entrainment in the Shot Sleeve During High Pressure Die Casting), Liteyshik Rossii (Russian Foundryman), 3, 2013, 30-34. In Russian

09-13  M.C. Carter and L. Xue, Simulating the Parameters that Affect Core Gas Defects in Metal Castings, Copyright 2012 American Foundry Society, Presented at the 2013 CastExpo, St. Louis, Missouri, April 2013

08-13  C. Reilly, N.R. Green, M.R. Jolly, J.-C. Gebelin, The Modelling Of Oxide Film Entrainment In Casting Systems Using Computational Modelling, Applied Mathematical Modelling, http://dx.doi.org/10.1016/j.apm.2013.03.061, April 2013.

03-13  Alexandre Reikher and Krishna M. Pillai, A fast simulation of transient metal flow and solidification in a narrow channel. Part II. Model validation and parametric study, Int. J. Heat Mass Transfer (2013), http://dx.doi.org/10.1016/j.ijheatmasstransfer.2012.12.061.

02-13  Alexandre Reikher and Krishna M. Pillai, A fast simulation of transient metal flow and solidification in a narrow channel. Part I: Model development using lubrication approximation, Int. J. Heat Mass Transfer (2013), http://dx.doi.org/10.1016/j.ijheatmasstransfer.2012.12.060.

116-12  Jufu Jianga, Ying Wang, Gang Chena, Jun Liua, Yuanfa Li and Shoujing Luo, “Comparison of mechanical properties and microstructure of AZ91D alloy motorcycle wheels formed by die casting and double control forming, Materials & Design, Volume 40, September 2012, Pages 541-549.

107-12  F.K. Arslan, A.H. Hatman, S.Ö. Ertürk, E. Güner, B. Güner, An Evaluation for Fundamentals of Die Casting Materials Selection and Design, IMMC’16 International Metallurgy & Materials Congress, Istanbul, Turkey, 2012.

103-12 WU Shu-sen, ZHONG Gu, AN Ping, WAN Li, H. NAKAE, Microstructural characteristics of Al−20Si−2Cu−0.4Mg−1Ni alloy formed by rheo-squeeze casting after ultrasonic vibration treatment, Transactions of Nonferrous Metals Society of China, 22 (2012) 2863-2870, November 2012. Full paper available online.

109-12 Alexandre Reikher, Numerical Analysis of Die-Casting Process in Thin Cavities Using Lubrication Approximation, Ph.D. Thesis: The University of Wisconsin Milwaukee, Engineering Department (2012) Theses and Dissertations. Paper 65.

97-12 Hong Zhou and Li Heng Luo, Filling Pattern of Step Gating System in Lost Foam Casting Process and its Application, Advanced Materials Research, Volumes 602-604, Progress in Materials and Processes, 1916-1921, December 2012.

93-12  Liangchi Zhang, Chunliang Zhang, Jeng-Haur Horng and Zichen Chen, Functions of Step Gating System in the Lost Foam Casting Process, Advanced Materials Research, 591-593, 940, DOI: 10.4028/www.scientific.net/AMR.591-593.940, November 2012.

91-12  Hong Yan, Jian Bin Zhu, Ping Shan, Numerical Simulation on Rheo-Diecasting of Magnesium Matrix Composites, 10.4028/www.scientific.net/SSP.192-193.287, Solid State Phenomena, 192-193, 287.

89-12  Alexandre Reikher and Krishna M. Pillai, A Fast Numerical Simulation for Modeling Simultaneous Metal Flow and Solidification in Thin Cavities Using the Lubrication Approximation, Numerical Heat Transfer, Part A: Applications: An International Journal of Computation and Methodology, 63:2, 75-100, November 2012.

82-12  Jufu Jiang, Gang Chen, Ying Wang, Zhiming Du, Weiwei Shan, and Yuanfa Li, Microstructure and mechanical properties of thin-wall and high-rib parts of AM60B Mg alloy formed by double control forming and die casting under the optimal conditions, Journal of Alloys and Compounds, http://dx.doi.org/10.1016/j.jallcom.2012.10.086, October 2012.

78-12   A. Pari, Real Life Problem Solving through Simulations in the Die Casting Industry – Case Studies, 2012 Die Casting Congress & Exposition, © NADCA, October 8-10, 2012, Indianapolis, IN.

77-12  Y. Wang, K. Kabiri-Bamoradian and R.A. Miller, Rheological behavior models of metal matrix alloys in semi-solid casting process, 2012 Die Casting Congress & Exposition, © NADCA, October 8-10, 2012, Indianapolis, IN.

76-12  A. Reikher and H. Gerber, Analysis of Solidification Parameters During the Die Cast Process, 2012 Die Casting Congress & Exposition, © NADCA, October 8-10, 2012, Indianapolis, IN.

75-12 R.A. Miller, Y. Wang and K. Kabiri-Bamoradian, Estimating Cavity Fill Time, 2012 Die Casting Congress & Exposition, © NADCA, October 8-10, 2012Indianapolis, IN.

65-12  X.H. Yang, T.J. Lu, T. Kim, Influence of non-conducting pore inclusions on phase change behavior of porous media with constant heat flux boundaryInternational Journal of Thermal Sciences, Available online 10 October 2012. Available online at SciVerse.

55-12  Hejun Li, Pengyun Wang, Lehua Qi, Hansong Zuo, Songyi Zhong, Xianghui Hou, 3D numerical simulation of successive deposition of uniform molten Al droplets on a moving substrate and experimental validation, Computational Materials Science, Volume 65, December 2012, Pages 291–301.

52-12 Hongbing Ji, Yixin Chen and Shengzhou Chen, Numerical Simulation of Inner-Outer Couple Cooling Slab Continuous Casting in the Filling Process, Advanced Materials Research (Volumes 557-559), Advanced Materials and Processes II, pp. 2257-2260, July 2012.

47-12    Petri Väyrynen, Lauri Holappa, and Seppo Louhenkilpi, Simulation of Melting of Alloying Materials in Steel Ladle, SCANMET IV – 4th International Conference on Process Development in Iron and Steelmaking, Lulea, Sweden, June 10-13, 2012.

46-12  Bin Zhang and Dave Salee, Metal Flow and Heat Transfer in Billet DC Casting Using Wagstaff® Optifill™ Metal Distribution Systems, 5th International Metal Quality Workshop, United Arab Emirates Dubai, March 18-22, 2012.

45-12 D.R. Gunasegaram, M. Givord, R.G. O’Donnell and B.R. Finnin, Improvements engineered in UTS and elongation of aluminum alloy high pressure die castings through the alteration of runner geometry and plunger velocity, Materials Science & Engineering.

44-12    Antoni Drys and Stefano Mascetti, Aluminum Casting Simulations, Desktop Engineering, September 2012

42-12   Huizhen Duan, Jiangnan Shen and Yanping Li, Comparative analysis of HPDC process of an auto part with ProCAST and FLOW-3D, Applied Mechanics and Materials Vols. 184-185 (2012) pp 90-94, Online available since 2012/Jun/14 at www.scientific.net, © (2012) Trans Tech Publications, Switzerland, doi:10.4028/www.scientific.net/AMM.184-185.90.

41-12    Deniece R. Korzekwa, Cameron M. Knapp, David A. Korzekwa, and John W. Gibbs, Co-Design – Fabrication of Unalloyed Plutonium, LA-UR-12-23441, MDI Summer Research Group Workshop Advanced Manufacturing, 2012-07-25/2012-07-26 (Los Alamos, New Mexico, United States)

29-12  Dario Tiberto and Ulrich E. Klotz, Computer simulation applied to jewellery casting: challenges, results and future possibilities, IOP Conf. Ser.: Mater. Sci. Eng.33 012008. Full paper available at IOP.

28-12  Y Yue and N R Green, Modelling of different entrainment mechanisms and their influences on the mechanical reliability of Al-Si castings, 2012 IOP Conf. Ser.: Mater. Sci. Eng. 33,012072.Full paper available at IOP.

27-12  E Kaschnitz, Numerical simulation of centrifugal casting of pipes, 2012 IOP Conf. Ser.: Mater. Sci. Eng. 33 012031, Issue 1. Full paper available at IOP.

15-12  C. Reilly, N.R Green, M.R. Jolly, The Present State Of Modeling Entrainment Defects In The Shape Casting Process, Applied Mathematical Modelling, Available online 27 April 2012, ISSN 0307-904X, 10.1016/j.apm.2012.04.032.

12-12   Andrei Starobin, Tony Hirt, Hubert Lang, and Matthias Todte, Core drying simulation and validation, International Foundry Research, GIESSEREIFORSCHUNG 64 (2012) No. 1, ISSN 0046-5933, pp 2-5

10-12  H. Vladimir Martínez and Marco F. Valencia (2012). Semisolid Processing of Al/β-SiC Composites by Mechanical Stirring Casting and High Pressure Die Casting, Recent Researches in Metallurgical Engineering – From Extraction to Forming, Dr Mohammad Nusheh (Ed.), ISBN: 978-953-51-0356-1, InTech

07-12     Amir H. G. Isfahani and James M. Brethour, Simulating Thermal Stresses and Cooling Deformations, Die Casting Engineer, March 2012

06-12   Shuisheng Xie, Youfeng He and Xujun Mi, Study on Semi-solid Magnesium Alloys Slurry Preparation and Continuous Roll-casting Process, Magnesium Alloys – Design, Processing and Properties, ISBN: 978-953-307-520-4, InTech.

04-12 J. Spangenberg, N. Roussel, J.H. Hattel, H. Stang, J. Skocek, M.R. Geiker, Flow induced particle migration in fresh concrete: Theoretical frame, numerical simulations and experimental results on model fluids, Cement and Concrete Research, http://dx.doi.org/10.1016/j.cemconres.2012.01.007, February 2012.

01-12   Lee, B., Baek, U., and Han, J., Optimization of Gating System Design for Die Casting of Thin Magnesium Alloy-Based Multi-Cavity LCD Housings, Journal of Materials Engineering and Performance, Springer New York, Issn: 1059-9495, 10.1007/s11665-011-0111-1, Volume 1 / 1992 – Volume 21 / 2012. Available online at Springer Link.

104-11  Fu-Yuan Hsu and Huey Jiuan Lin, Foam Filters Used in Gravity Casting, Metall and Materi Trans B (2011) 42: 1110. doi:10.1007/s11663-011-9548-8.

99-11    Eduardo Trejo, Centrifugal Casting of an Aluminium Alloy, thesis: Doctor of Philosophy, Metallurgy and Materials School of Engineering University of Birmingham, October 2011. Full paper available upon request.

93-11  Olga Kononova, Andrejs Krasnikovs ,Videvuds Lapsa,Jurijs Kalinka and Angelina Galushchak, Internal Structure Formation in High Strength Fiber Concrete during Casting, World Academy of Science, Engineering and Technology 59 2011

76-11  J. Hartmann, A. Trepper, and C. Körner, Aluminum Integral Foams with Near-Microcellular Structure, Advanced Engineering Materials 2011, Volume 13 (2011) No. 11, © Wiley-VCH

71-11  Fu-Yuan Hsu and Yao-Ming Yang Confluence Weld in an Aluminum Gravity Casting, Journal of Materials Processing Technology, Available online 23 November 2011, ISSN 0924-0136, 10.1016/j.jmatprotec.2011.11.006.

65-11     V.A. Chaikin, A.V. Chaikin, I.N.Volnov, A Study of the Process of Late Modification Using Simulation, in Zagotovitelnye Proizvodstva v Mashinostroenii, 10, 2011, 8-12. In Russian.

54-11  Ngadia Taha Niane and Jean-Pierre Michalet, Validation of Foundry Process for Aluminum Parts with FLOW-3D Software, Proceedings of the 2011 International Symposium on Liquid Metal Processing and Casting, 2011.

51-11    A. Reikher and H. Gerber, Calculation of the Die Cast parameters of the Thin Wall Aluminum Cast Part, 2011 Die Casting Congress & Tabletop, Columbus, OH, September 19-21, 2011

50-11   Y. Wang, K. Kabiri-Bamoradian, and R.A. Miller, Runner design optimization based on CFD simulation for a die with multiple cavities, 2011 Die Casting Congress & Tabletop, Columbus, OH, September 19-21, 2011

48-11 A. Karwiński, W. Leśniewski, S. Pysz, P. Wieliczko, The technology of precision casting of titanium alloys by centrifugal process, Archives of Foundry Engineering, ISSN: 1897-3310), Volume 11, Issue 3/2011, 73-80, 2011.

46-11  Daniel Einsiedler, Entwicklung einer Simulationsmethodik zur Simulation von Strömungs- und Trocknungsvorgängen bei Kernfertigungsprozessen mittels CFD (Development of a simulation methodology for simulating flow and drying operations in core production processes using CFD), MSc thesis at Technical University of Aalen in Germany (Hochschule Aalen), 2011.

44-11  Bin Zhang and Craig Shaber, Aluminum Ingot Thermal Stress Development Modeling of the Wagstaff® EpsilonTM Rolling Ingot DC Casting System during the Start-up Phase, Materials Science Forum Vol. 693 (2011) pp 196-207, © 2011 Trans Tech Publications, July, 2011.

43-11 Vu Nguyen, Patrick Rohan, John Grandfield, Alex Levin, Kevin Naidoo, Kurt Oswald, Guillaume Girard, Ben Harker, and Joe Rea, Implementation of CASTfill low-dross pouring system for ingot casting, Materials Science Forum Vol. 693 (2011) pp 227-234, © 2011 Trans Tech Publications, July, 2011.

40-11  A. Starobin, D. Goettsch, M. Walker, D. Burch, Gas Pressure in Aluminum Block Water Jacket Cores, © 2011 American Foundry Society, International Journal of Metalcasting/Summer 2011

37-11 Ferencz Peti, Lucian Grama, Analyze of the Possible Causes of Porosity Type Defects in Aluminum High Pressure Diecast Parts, Scientific Bulletin of the Petru Maior University of Targu Mures, Vol. 8 (XXV) no. 1, 2011, ISSN 1841-9267

31-11  Johannes Hartmann, André Trepper, Carolin Körner, Aluminum Integral Foams with Near-Microcellular Structure, Advanced Engineering Materials, 13: n/a. doi: 10.1002/adem.201100035, June 2011.

27-11  A. Pari, Optimization of HPDC Process using Flow Simulation Case Studies, Die Casting Engineer, July 2011

26-11    A. Reikher, H. Gerber, Calculation of the Die Cast Parameters of the Thin Wall Aluminum Die Casting Part, Die Casting Engineer, July 2011

21-11 Thang Nguyen, Vu Nguyen, Morris Murray, Gary Savage, John Carrig, Modelling Die Filling in Ultra-Thin Aluminium Castings, Materials Science Forum (Volume 690), Light Metals Technology V, pp 107-111, 10.4028/www.scientific.net/MSF.690.107, June 2011.

19-11 Jon Spangenberg, Cem Celal Tutum, Jesper Henri Hattel, Nicolas Roussel, Metter Rica Geiker, Optimization of Casting Process Parameters for Homogeneous Aggregate Distribution in Self-Compacting Concrete: A Feasibility Study, © IEEE Congress on Evolutionary Computation, 2011, New Orleans, USA

16-11  A. Starobin, C.W. Hirt, H. Lang, and M. Todte, Core Drying Simulation and Validations, AFS Proceedings 2011, © American Foundry Society, Presented at the 115th Metalcasting Congress, Schaumburg, Illinois, April 2011.

15-11  J. J. Hernández-Ortega, R. Zamora, J. López, and F. Faura, Numerical Analysis of Air Pressure Effects on the Flow Pattern during the Filling of a Vertical Die Cavity, AIP Conf. Proc., Volume 1353, pp. 1238-1243, The 14th International Esaform Conference on Material Forming: Esaform 2011; doi:10.1063/1.3589686, May 2011. Available online.

10-11 Abbas A. Khalaf and Sumanth Shankar, Favorable Environment for Nondentric Morphology in Controlled Diffusion Solidification, DOI: 10.1007/s11661-011-0641-z, © The Minerals, Metals & Materials Society and ASM International 2011, Metallurgical and Materials Transactions A, March 11, 2011.

08-11 Hai Peng Li, Chun Yong Liang, Li Hui Wang, Hong Shui Wang, Numerical Simulation of Casting Process for Gray Iron Butterfly Valve, Advanced Materials Research, 189-193, 260, February 2011.

04-11  C.W. Hirt, Predicting Core Shooting, Drying and Defect Development, Foundry Management & Technology, January 2011.

76-10  Zhizhong Sun, Henry Hu, Alfred Yu, Numerical Simulation and Experimental Study of Squeeze Casting Magnesium Alloy AM50, Magnesium Technology 2010, 2010 TMS Annual Meeting & ExhibitionFebruary 14-18, 2010, Seattle, WA.

68-10  A. Reikher, H. Gerber, K.M. Pillai, T.-C. Jen, Natural Convection—An Overlooked Phenomenon of the Solidification Process, Die Casting Engineer, January 2010

54-10    Andrea Bernardoni, Andrea Borsi, Stefano Mascetti, Alessandro Incognito and Matteo Corrado, Fonderia Leonardo aveva ragione! L’enorme cavallo dedicato a Francesco Sforza era materialmente realizzabile, A&C – Analisis e Calcolo, Giugno 2010. In  Italian.

48-10  J. J. Hernández-Ortega, R. Zamora, J. Palacios, J. López and F. Faura, An Experimental and Numerical Study of Flow Patterns and Air Entrapment Phenomena During the Filling of a Vertical Die Cavity, J. Manuf. Sci. Eng., October 2010, Volume 132, Issue 5, 05101, doi:10.1115/1.4002535.

47-10  A.V. Chaikin, I.N. Volnov, and V.A. Chaikin, Development of Dispersible Mixed Inoculant Compositions Using the FLOW-3D Program, Liteinoe Proizvodstvo, October, 2010, in Russian.

42-10  H. Lakshmi, M.C. Vinay Kumar, Raghunath, P. Kumar, V. Ramanarayanan, K.S.S. Murthy, P. Dutta, Induction reheating of A356.2 aluminum alloy and thixocasting as automobile component, Transactions of Nonferrous Metals Society of China 20(20101) s961-s967.

41-10  Pamela J. Waterman, Understanding Core-Gas Defects, Desktop Engineering, October 2010. Available online at Desktop Engineering. Also published in the Foundry Trade Journal, November 2010.

39-10  Liu Zheng, Jia Yingying, Mao Pingli, Li Yang, Wang Feng, Wang Hong, Zhou Le, Visualization of Die Casting Magnesium Alloy Steering Bracket, Special Casting & Nonferrous Alloys, ISSN: 1001-2249, CN: 42-1148/TG, 2010-04. In Chinese.

37-10  Morris Murray, Lars Feldager Hansen, and Carl Reinhardt, I Have Defects – Now What, Die Casting Engineer, September 2010

36-10  Stefano Mascetti, Using Flow Analysis Software to Optimize Piston Velocity for an HPDC Process, Die Casting Engineer, September 2010. Also available in Italian: Ottimizzare la velocita del pistone in pressofusione.  A & C, Analisi e Calcolo, Anno XII, n. 42, Gennaio 2011, ISSN 1128-3874.

32-10  Guan Hai Yan, Sheng Dun Zhao, Zheng Hui Sha, Parameters Optimization of Semisolid Diecasting Process for Air-Conditioner’s Triple Valve in HPb59-1 Alloy, Advanced Materials Research (Volumes 129 – 131), Vol. Material and Manufacturing Technology, pp. 936-941, DOI: 10.4028/www.scientific.net/AMR.129-131.936, August 2010.

29-10 Zheng Peng, Xu Jun, Zhang Zhifeng, Bai Yuelong, and Shi Likai, Numerical Simulation of Filling of Rheo-diecasting A357 Aluminum Alloy, Special Casting & Nonferrous Alloys, DOI: CNKI:SUN:TZZZ.0.2010-01-024, 2010.

27-10 For an Aerospace Diecasting, Littler Uses Simulation to Reveal Defects, and Win a New Order, Foundry Management & Technology, July 2010

23-10 Michael R. Barkhudarov, Minimizing Air Entrainment, The Canadian Die Caster, June 2010

15-10 David H. Kirkwood, Michel Suery, Plato Kapranos, Helen V. Atkinson, and Kenneth P. Young, Semi-solid Processing of Alloys, 2010, XII, 172 p. 103 illus., 19 in color., Hardcover ISBN: 978-3-642-00705-7.

09-10  Shannon Wetzel, Fullfilling Da Vinci’s Dream, Modern Casting, April 2010.

08-10 B.I. Semenov, K.M. Kushtarov, Semi-solid Manufacturing of Castings, New Industrial Technologies, Publication of Moscow State Technical University n.a. N.E. Bauman, 2009 (in Russian)

07-10 Carl Reilly, Development Of Quantitative Casting Quality Assessment Criteria Using Process Modelling, thesis: The University of Birmingham, March 2010 (Available upon request)

06-10 A. Pari, Optimization of HPDC Process using Flow Simulation – Case Studies, CastExpo ’10, NADCA, Orlando, Florida, March 2010

05-10 M.C. Carter, S. Palit, and M. Littler, Characterizing Flow Losses Occurring in Air Vents and Ejector Pins in High Pressure Die Castings, CastExpo ’10, NADCA, Orlando, Florida, March 2010

04-10 Pamela Waterman, Simulating Porosity Factors, Foundry Management Technology, March 2010, Article available at Foundry Management Technology

03-10 C. Reilly, M.R. Jolly, N.R. Green, JC Gebelin, Assessment of Casting Filling by Modeling Surface Entrainment Events Using CFD, 2010 TMS Annual Meeting & Exhibition (Jim Evans Honorary Symposium), Seattle, Washington, USA, February 14-18, 2010

02-10 P. Väyrynen, S. Wang, J. Laine and S.Louhenkilpi, Control of Fluid Flow, Heat Transfer and Inclusions in Continuous Casting – CFD and Neural Network Studies, 2010 TMS Annual Meeting & Exhibition (Jim Evans Honorary Symposium), Seattle, Washington, USA, February 14-18, 2010

60-09   Somlak Wannarumon, and Marco Actis Grande, Comparisons of Computer Fluid Dynamic Software Programs applied to Jewelry Investment Casting Process, World Academy of Science, Engineering and Technology 55 2009.

59-09   Marco Actis Grande and Somlak Wannarumon, Numerical Simulation of Investment Casting of Gold Jewelry: Experiments and Validations, World Academy of Science, Engineering and Technology, Vol:3 2009-07-24

56-09  Jozef Kasala, Ondrej Híreš, Rudolf Pernis, Start-up Phase Modeling of Semi Continuous Casting Process of Brass Billets, Metal 2009, 19.-21.5.2009

51-09  In-Ting Hong, Huan-Chien Tung, Chun-Hao Chiu and Hung-Shang Huang, Effect of Casting Parameters on Microstructure and Casting Quality of Si-Al Alloy for Vacuum Sputtering, China Steel Technical Report, No. 22, pp. 33-40, 2009.

42-09  P. Väyrynen, S. Wang, S. Louhenkilpi and L. Holappa, Modeling and Removal of Inclusions in Continuous Casting, Materials Science & Technology 2009 Conference & Exhibition, Pittsburgh, Pennsylvania, USA, October 25-29, 2009

41-09 O.Smirnov, P.Väyrynen, A.Kravchenko and S.Louhenkilpi, Modern Methods of Modeling Fluid Flow and Inclusions Motion in Tundish Bath – General View, Proceedings of Steelsim 2009 – 3rd International Conference on Simulation and Modelling of Metallurgical Processes in Steelmaking, Leoben, Austria, September 8-10, 2009

21-09 A. Pari, Case Studies – Optimization of HPDC Process Using Flow Simulation, Die Casting Engineer, July 2009

20-09 M. Sirvio, M. Wos, Casting directly from a computer model by using advanced simulation software, FLOW-3D Cast, Archives of Foundry Engineering Volume 9, Issue 1/2009, 79-82

19-09 Andrei Starobin, C.W. Hirt, D. Goettsch, A Model for Binder Gas Generation and Transport in Sand Cores and Molds, Modeling of Casting, Welding, and Solidification Processes XII, TMS (The Minerals, Metals & Minerals Society), June 2009

11-09 Michael Barkhudarov, Minimizing Air Entrainment in a Shot Sleeve during Slow-Shot Stage, Die Casting Engineer (The North American Die Casting Association ISSN 0012-253X), May 2009

10-09 A. Reikher, H. Gerber, Application of One-Dimensional Numerical Simulation to Optimize Process Parameters of a Thin-Wall Casting in High Pressure Die Casting, Die Casting Engineer (The North American Die Casting Association ISSN 0012-253X), May 2009

7-09 Andrei Starobin, Simulation of Core Gas Evolution and Flow, presented at the North American Die Casting Association – 113th Metalcasting Congress, April 7-10, 2009, Las Vegas, Nevada, USA

6-09 A.Pari, Optimization of HPDC PROCESS: Case Studies, North American Die Casting Association – 113th Metalcasting Congress, April 7-10, 2009, Las Vegas, Nevada, USA

2-09 C. Reilly, N.R. Green and M.R. Jolly, Oxide Entrainment Structures in Horizontal Running Systems, TMS 2009, San Francisco, California, February 2009

30-08 I.N.Volnov, Computer Modeling of Casting of Pipe Fittings, © 2008, Pipe Fittings, 5 (38), 2008. Russian version

28-08 A.V.Chaikin, I.N.Volnov, V.A.Chaikin, Y.A.Ukhanov, N.R.Petrov, Analysis of the Efficiency of Alloy Modifiers Using Statistics and Modeling, © 2008, Liteyshik Rossii (Russian Foundryman), October, 2008

27-08 P. Scarber, Jr., H. Littleton, Simulating Macro-Porosity in Aluminum Lost Foam Castings, American Foundry Society, © 2008, AFS Lost Foam Conference, Asheville, North Carolina, October, 2008

25-08 FMT Staff, Forecasting Core Gas Pressures with Computer Simulation, Foundry Management and Technology, October 28, 2008 © 2008 Penton Media, Inc. Online article

24-08 Core and Mold Gas Evolution, Foundry Management and Technology, January 24, 2008 (excerpted from the FM&T May 2007 issue) © 2008 Penton Media, Inc.

22-08 Mark Littler, Simulation Eliminates Die Casting Scrap, Modern Casting/September 2008

21-08 X. Chen, D. Penumadu, Permeability Measurement and Numerical Modeling for Refractory Porous Materials, AFS Transactions © 2008 American Foundry Society, CastExpo ’08, Atlanta, Georgia, May 2008

20-08 Rolf Krack, Using Solidification Simulations for Optimising Die Cooling Systems, FTJ July/August 2008

19-08 Mark Littler, Simulation Software Eliminates Die Casting Scrap, ECS Casting Innovations, July/August 2008

13-08 T. Yoshimura, K. Yano, T. Fukui, S. Yamamoto, S. Nishido, M. Watanabe and Y. Nemoto, Optimum Design of Die Casting Plunger Tip Considering Air Entrainment, Proceedings of 10th Asian Foundry Congress (AFC10), Nagoya, Japan, May 2008

08-08 Stephen Instone, Andreas Buchholz and Gerd-Ulrich Gruen, Inclusion Transport Phenomena in Casting Furnaces, Light Metals 2008, TMS (The Minerals, Metals & Materials Society), 2008

07-08 P. Scarber, Jr., H. Littleton, Simulating Macro-Porosity in Aluminum Lost Foam Casting, AFS Transactions 2008 © American Foundry Society, CastExpo ’08, Atlanta, Georgia, May 2008

06-08 A. Reikher, H. Gerber and A. Starobin, Multi-Stage Plunger Deceleration System, CastExpo ’08, NADCA, Atlanta, Georgia, May 2008

05-08 Amol Palekar, Andrei Starobin, Alexander Reikher, Die-casting end-of-fill and drop forge viscometer flow transients examined with a coupled-motion numerical model, 68th World Foundry Congress, Chennai, India, February 2008

03-08 Petri J. Väyrynen, Sami K. Vapalahti and Seppo J. Louhenkilpi, On Validation of Mathematical Fluid Flow Models for Simulation of Tundish Water Models and Industrial Examples, AISTech 2008, May 2008

53-07   A. Kermanpur, Sh. Mahmoudi and A. Hajipour, Three-dimensional Numerical Simulation of Metal Flow and Solidification in the Multi-cavity Casting Moulds of Automotive Components, International Journal of Iron & Steel Society of Iran, Article 2, Volume 4, Issue 1, Summer and Autumn 2007, pages 8-15.

36-07 Duque Mesa A. F., Herrera J., Cruz L.J., Fernández G.P. y Martínez H.V., Caracterización Defectológica de Piezas Fundida por Lost Foam Casting Mediante Simulación Numérica, 8° Congreso Iberoamericano de Ingenieria Mecanica, Cusco, Peru, 23 al 25 de Octubre de 2007 (in Spanish)

27-07 A.Y. Korotchenko, A.M. Zarubin, I.A.Korotchenko, Modeling of High Pressure Die Casting Filling, Russian Foundryman, December 2007, pp 15-19. (in Russian)

26-07 I.N. Volnov, Modeling of Casting Processes with Variable Geometry, Russian Foundryman, November 2007, pp 27-30. (in Russian)

16-07 P. Väyrynen, S. Vapalahti, S. Louhenkilpi, L. Chatburn, M. Clark, T. Wagner, Tundish Flow Model Tuning and Validation – Steady State and Transient Casting Situations, STEELSIM 2007, Graz/Seggau, Austria, September 12-14 2007

11-07 Marco Actis Grande, Computer Simulation of the Investment Casting Process – Widening of the Filling Step, Santa Fe Symposium on Jewelry Manufacturing Technology, May 2007

09-07 Alexandre Reikher and Michael Barkhudarov, Casting: An Analytical Approach, Springer, 1st edition, August 2007, Hardcover ISBN: 978-1-84628-849-4. U.S. Order FormEurope Order Form.

07-07 I.N. Volnov, Casting Modeling Systems – Current State, Problems and Perspectives, (in Russian), Liteyshik Rossii (Russian Foundryman), June 2007

05-07 A.N. Turchin, D.G. Eskin, and L. Katgerman, Solidification under Forced-Flow Conditions in a Shallow Cavity, DOI: 10.1007/s1161-007-9183-9, © The Minerals, Metals & Materials Society and ASM International 2007

04-07 A.N. Turchin, M. Zuijderwijk, J. Pool, D.G. Eskin, and L. Katgerman, Feathery grain growth during solidification under forced flow conditions, © Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved. DOI: 10.1016/j.actamat.2007.02.030, April 2007

03-07 S. Kuyucak, Sponsored Research – Clean Steel Casting Production—Evaluation of Laboratory Castings, Transactions of the American Foundry Society, Volume 115, 111th Metalcasting Congress, May 2007

02-07 Fu-Yuan Hsu, Mark R. Jolly and John Campbell, The Design of L-Shaped Runners for Gravity Casting, Shape Casting: 2nd International Symposium, Edited by Paul N. Crepeau, Murat Tiryakioðlu and John Campbell, TMS (The Minerals, Metals & Materials Society), Orlando, FL, Feb 2007

30-06 X.J. Liu, S.H. Bhavnani, R.A. Overfelt, Simulation of EPS foam decomposition in the lost foam casting process, Journal of Materials Processing Technology 182 (2007) 333–342, © 2006 Elsevier B.V. All rights reserved.

25-06 Michael Barkhudarov and Gengsheng Wei, Modeling Casting on the Move, Modern Casting, August 2006; Modeling of Casting Processes with Variable Geometry, Russian Foundryman, December 2007, pp 10-15. (in Russian)

24-06 P. Scarber, Jr. and C.E. Bates, Simulation of Core Gas Production During Mold Fill, © 2006 American Foundry Society

7-06 M.Y.Smirnov, Y.V.Golenkov, Manufacturing of Cast Iron Bath Tubs Castings using Vacuum-Process in Russia, Russia’s Foundryman, July 2006. In Russian.

6-06 M. Barkhudarov, and G. Wei, Modeling of the Coupled Motion of Rigid Bodies in Liquid Metal, Modeling of Casting, Welding and Advanced Solidification Processes – XI, May 28 – June 2, 2006, Opio, France, eds. Ch.-A. Gandin and M. Bellet, pp 71-78, 2006.

2-06 J.-C. Gebelin, M.R. Jolly and F.-Y. Hsu, ‘Designing-in’ Controlled Filling Using Numerical Simulation for Gravity Sand Casting of Aluminium Alloys, Int. J. Cast Met. Res., 2006, Vol.19 No.1

1-06 Michael Barkhudarov, Using Simulation to Control Microporosity Reduces Die Iterations, Die Casting Engineer, January 2006, pp. 52-54

30-05 H. Xue, K. Kabiri-Bamoradian, R.A. Miller, Modeling Dynamic Cavity Pressure and Impact Spike in Die Casting, Cast Expo ’05, April 16-19, 2005

22-05 Blas Melissari & Stavros A. Argyropoulous, Measurement of Magnitude and Direction of Velocity in High-Temperature Liquid Metals; Part I, Mathematical Modeling, Metallurgical and Materials Transactions B, Volume 36B, October 2005, pp. 691-700

21-05 M.R. Jolly, State of the Art Review of Use of Modeling Software for Casting, TMS Annual Meeting, Shape Casting: The John Campbell Symposium, Eds, M. Tiryakioglu & P.N Crepeau, TMS, Warrendale, PA, ISBN 0-87339-583-2, Feb 2005, pp 337-346

20-05 J-C Gebelin, M.R. Jolly & F-Y Hsu, ‘Designing-in’ Controlled Filling Using Numerical Simulation for Gravity Sand Casting of Aluminium Alloys, TMS Annual Meeting, Shape Casting: The John Campbell Symposium, Eds, M. Tiryakioglu & P.N Crepeau, TMS, Warrendale, PA, ISBN 0-87339-583-2, Feb 2005, pp 355-364

19-05 F-Y Hsu, M.R. Jolly & J Campbell, Vortex Gate Design for Gravity Castings, TMS Annual Meeting, Shape Casting: The John Campbell Symposium, Eds, M. Tiryakioglu & P.N Crepeau, TMS, Warrendale, PA, ISBN 0-87339-583-2, Feb 2005, pp 73-82

18-05 M.R. Jolly, Modelling the Investment Casting Process: Problems and Successes, Japanese Foundry Society, JFS, Tokyo, Sept. 2005

13-05 Xiaogang Yang, Xiaobing Huang, Xiaojun Dai, John Campbell and Joe Tatler, Numerical Modelling of the Entrainment of Oxide Film Defects in Filling of Aluminium Alloy Castings, International Journal of Cast Metals Research, 17 (6), 2004, 321-331

10-05 Carlos Evaristo Esparza, Martha P. Guerro-Mata, Roger Z. Ríos-Mercado, Optimal Design of Gating Systems by Gradient Search Methods, Computational Materials Science, October 2005

6-05 Birgit Hummler-Schaufler, Fritz Hirning, Jurgen Schaufler, A World First for Hatz Diesel and Schaufler Tooling, Die Casting Engineer, May 2005, pp. 18-21

4-05 Rolf Krack, The W35 Topic—A World First, Die Casting World, March 2005, pp. 16-17

3-05 Joerg Frei, Casting Simulations Speed Up Development, Die Casting World, March 2005, p. 14

2-05 David Goettsch and Michael Barkhudarov, Analysis and Optimization of the Transient Stage of Stopper-Rod Pour, Shape Casting: The John Campbell Symposium, The Minerals, Metals & Materials Society, 2005

36-04  Ik Min Park, Il Dong Choi, Yong Ho Park, Development of Light-Weight Al Scroll Compressor for Car Air Conditioner, Materials Science Forum, Designing, Processing and Properties of Advanced Engineering Materials, 449-452, 149, March 2004.

32-04 D.H. Kirkwood and P.J Ward, Numerical Modelling of Semi-Solid Flow under Processing Conditions, steel research int. 75 (2004), No. 8/9

30-04 Haijing Mao, A Numerical Study of Externally Solidified Products in the Cold Chamber Die Casting Process, thesis: The Ohio State University, 2004 (Available upon request)

28-04 Z. Cao, Z. Yang, and X.L. Chen, Three-Dimensional Simulation of Transient GMA Weld Pool with Free Surface, Supplement to the Welding Journal, June 2004.

23-04 State of the Art Use of Computational Modelling in the Foundry Industry, 3rd International Conference Computational Modelling of Materials III, Sicily, Italy, June 2004, Advances in Science and Technology,  Eds P. Vincenzini & A Lami, Techna Group Srl, Italy, ISBN: 88-86538-46-4, Part B, pp 479-490

22-04 Jerry Fireman, Computer Simulation Helps Reduce Scrap, Die Casting Engineer, May 2004, pp. 46-49

21-04 Joerg Frei, Simulation—A Safe and Quick Way to Good Components, Aluminium World, Volume 3, Issue 2, pp. 42-43

20-04 J.-C. Gebelin, M.R. Jolly, A. M. Cendrowicz, J. Cirre and S. Blackburn, Simulation of Die Filling for the Wax Injection Process – Part II Numerical Simulation, Metallurgical and Materials Transactions, Volume 35B, August 2004

14-04 Sayavur I. Bakhtiyarov, Charles H. Sherwin, and Ruel A. Overfelt, Hot Distortion Studies In Phenolic Urethane Cold Box System, American Foundry Society, 108th Casting Congress, June 12-15, 2004, Rosemont, IL, USA

13-04 Sayavur I. Bakhtiyarov and Ruel A. Overfelt, First V-Process Casting of Magnesium, American Foundry Society, 108th Casting Congress, June 12-15, 2004, Rosemont, IL, USA

5-04 C. Schlumpberger & B. Hummler-Schaufler, Produktentwicklung auf hohem Niveau (Product Development on a High Level), Druckguss Praxis, January 2004, pp 39-42 (in German).

3-04 Charles Bates, Dealing with Defects, Foundry Management and Technology, February 2004, pp 23-25

1-04 Laihua Wang, Thang Nguyen, Gary Savage and Cameron Davidson, Thermal and Flow Modeling of Ladling and Injection in High Pressure Die Casting Process, International Journal of Cast Metals Research, vol. 16 No 4 2003, pp 409-417

2-03 J-C Gebelin, AM Cendrowicz, MR Jolly, Modeling of the Wax Injection Process for the Investment Casting Process – Prediction of Defects, presented at the Third International Conference on Computational Fluid Dynamics in the Minerals and Process Industries, December 10-12, 2003, Melbourne, Australia, pp. 415-420

29-03 C. W. Hirt, Modeling Shrinkage Induced Micro-porosity, Flow Science Technical Note (FSI-03-TN66)

28-03 Thixoforming at the University of Sheffield, Diecasting World, September 2003, pp 11-12

26-03 William Walkington, Gas Porosity-A Guide to Correcting the Problems, NADCA Publication: 516

22-03 G F Yao, C W Hirt, and M Barkhudarov, Development of a Numerical Approach for Simulation of Sand Blowing and Core Formation, in Modeling of Casting, Welding, and Advanced Solidification Process-X”, Ed. By Stefanescu et al pp. 633-639, 2003

21-03 E F Brush Jr, S P Midson, W G Walkington, D T Peters, J G Cowie, Porosity Control in Copper Rotor Die Castings, NADCA Indianapolis Convention Center, Indianapolis, IN September 15-18, 2003, T03-046

12-03 J-C Gebelin & M.R. Jolly, Modeling Filters in Light Alloy Casting Processes,  Trans AFS, 2002, 110, pp. 109-120

11-03 M.R. Jolly, Casting Simulation – How Well Do Reality and Virtual Casting Match – A State of the Art Review, Intl. J. Cast Metals Research, 2002, 14, pp. 303-313

10-03 Gebelin., J-C and Jolly, M.R., Modeling of the Investment Casting Process, Journal of  Materials Processing Tech., Vol. 135/2-3, pp. 291 – 300

9-03 Cox, M, Harding, R.A. and Campbell, J., Optimised Running System Design for Bottom Filled Aluminium Alloy 2L99 Investment Castings, J. Mat. Sci. Tech., May 2003, Vol. 19, pp. 613-625

8-03 Von Alexander Schrey and Regina Reek, Numerische Simulation der Kernherstellung, (Numerical Simulation of Core Blowing), Giesserei, June 2003, pp. 64-68 (in German)

7-03 J. Zuidema Jr., L Katgerman, Cyclone separation of particles in aluminum DC Casting, Proceedings from the Tenth International Conference on Modeling of Casting, Welding and Advanced Solidification Processes, Destin, FL, May 2003, pp. 607-614

6-03 Jean-Christophe Gebelin and Mark Jolly, Numerical Modeling of Metal Flow Through Filters, Proceedings from the Tenth International Conference on Modeling of Casting, Welding and Advanced Solidification Processes, Destin, FL, May 2003, pp. 431-438

5-03 N.W. Lai, W.D. Griffiths and J. Campbell, Modelling of the Potential for Oxide Film Entrainment in Light Metal Alloy Castings, Proceedings from the Tenth International Conference on Modeling of Casting, Welding and Advanced Solidification Processes, Destin, FL, May 2003, pp. 415-422

21-02 Boris Lukezic, Case History: Process Modeling Solves Die Design Problems, Modern Casting, February 2003, P 59

20-02 C.W. Hirt and M.R. Barkhudarov, Predicting Defects in Lost Foam Castings, Modern Casting, December 2002, pp 31-33

19-02 Mark Jolly, Mike Cox, Ric Harding, Bill Griffiths and John Campbell, Quiescent Filling Applied to Investment Castings, Modern Casting, December 2002 pp. 36-38

18-02 Simulation Helps Overcome Challenges of Thin Wall Magnesium Diecasting, Foundry Management and Technology, October 2002, pp 13-15

17-02 G Messmer, Simulation of a Thixoforging Process of Aluminum Alloys with FLOW-3D, Institute for Metal Forming Technology, University of Stuttgart

16-02 Barkhudarov, Michael, Computer Simulation of Lost Foam Process, Casting Simulation Background and Examples from Europe and the USA, World Foundrymen Organization, 2002, pp 319-324

15-02 Barkhudarov, Michael, Computer Simulation of Inclusion Tracking, Casting Simulation Background and Examples from Europe and the USA, World Foundrymen Organization, 2002, pp 341-346

14-02 Barkhudarov, Michael, Advanced Simulation of the Flow and Heat Transfer of an Alternator Housing, Casting Simulation Background and Examples from Europe and the USA, World Foundrymen Organization, 2002, pp 219-228

8-02 Sayavur I. Bakhtiyarov, and Ruel A. Overfelt, Experimental and Numerical Study of Bonded Sand-Air Two-Phase Flow in PUA Process, Auburn University, 2002 American Foundry Society, AFS Transactions 02-091, Kansas City, MO

7-02 A Habibollah Zadeh, and J Campbell, Metal Flow Through a Filter System, University of Birmingham, 2002 American Foundry Society, AFS Transactions 02-020, Kansas City, MO

6-02 Phil Ward, and Helen Atkinson, Final Report for EPSRC Project: Modeling of Thixotropic Flow of Metal Alloys into a Die, GR/M17334/01, March 2002, University of Sheffield

5-02 S. I. Bakhtiyarov and R. A. Overfelt, Numerical and Experimental Study of Aluminum Casting in Vacuum-sealed Step Molding, Auburn University, 2002 American Foundry Society, AFS Transactions 02-050, Kansas City, MO

4-02 J. C. Gebelin and M. R. Jolly, Modelling Filters in Light Alloy Casting Processes, University of Birmingham, 2002 American Foundry Society AFS Transactions 02-079, Kansas City, MO

3-02 Mark Jolly, Mike Cox, Jean-Christophe Gebelin, Sam Jones, and Alex Cendrowicz, Fundamentals of Investment Casting (FOCAST), Modelling the Investment Casting Process, Some preliminary results from the UK Research Programme, IRC in Materials, University of Birmingham, UK, AFS2001

49-01   Hua Bai and Brian G. Thomas, Bubble formation during horizontal gas injection into downward-flowing liquid, Metallurgical and Materials Transactions B, Vol. 32, No. 6, pp. 1143-1159, 2001. doi.org/10.1007/s11663-001-0102-y

45-01 Jan Zuidema; Laurens Katgerman; Ivo J. Opstelten;Jan M. Rabenberg, Secondary Cooling in DC Casting: Modelling and Experimental Results, TMS 2001, New Orleans, Louisianna, February 11-15, 2001

43-01 James Andrew Yurko, Fluid Flow Behavior of Semi-Solid Aluminum at High Shear Rates,Ph.D. thesis; Massachusetts Institute of Technology, June 2001. Abstract only; full thesis available at http://dspace.mit.edu/handle/1721.1/8451 (for a fee).

33-01 Juang, S.H., CAE Application on Design of Die Casting Dies, 2001 Conference on CAE Technology and Application, Hsin-Chu, Taiwan, November 2001, (article in Chinese with English-language abstract)

32-01 Juang, S.H. and C. M. Wang, Effect of Feeding Geometry on Flow Characteristics of Magnesium Die Casting by Numerical Analysis, The Preceedings of 6th FADMA Conference, Taipei, Taiwan, July 2001, Chinese language with English abstract

26-01 C. W. Hirt., Predicting Defects in Lost Foam Castings, December 13, 2001

21-01 P. Scarber Jr., Using Liquid Free Surface Areas as a Predictor of Reoxidation Tendency in Metal Alloy Castings, presented at the Steel Founders’ Society of American, Technical and Operating Conference, October 2001

20-01 P. Scarber Jr., J. Griffin, and C. E. Bates, The Effect of Gating and Pouring Practice on Reoxidation of Steel Castings, presented at the Steel Founders’ Society of American, Technical and Operating Conference, October 2001

19-01 L. Wang, T. Nguyen, M. Murray, Simulation of Flow Pattern and Temperature Profile in the Shot Sleeve of a High Pressure Die Casting Process, CSIRO Manufacturing Science and Technology, Melbourne, Victoria, Australia, Presented by North American Die Casting Association, Oct 29-Nov 1, 2001, Cincinnati, To1-014

18-01 Rajiv Shivpuri, Venkatesh Sankararaman, Kaustubh Kulkarni, An Approach at Optimizing the Ingate Design for Reducing Filling and Shrinkage Defects, The Ohio State University, Columbus, OH, Presented by North American Die Casting Association, Oct 29-Nov 1, 2001, Cincinnati, TO1-052

5-01 Michael Barkhudarov, Simulation Helps Overcome Challenges of Thin Wall Magnesium Diecasting, Diecasting World, March 2001, pp. 5-6

2-01 J. Grindling, Customized CFD Codes to Simulate Casting of Thermosets in Full 3D, Electrical Manufacturing and Coil Winding 2000 Conference, October 31-November 2, 20

20-00 Richard Schuhmann, John Carrig, Thang Nguyen, Arne Dahle, Comparison of Water Analogue Modelling and Numerical Simulation Using Real-Time X-Ray Flow Data in Gravity Die Casting, Australian Die Casting Association Die Casting 2000 Conference, September 3-6, 2000, Melbourne, Victoria, Australia

15-00 M. Sirvio, Vainola, J. Vartianinen, M. Vuorinen, J. Orkas, and S. Devenyi, Fluid Flow Analysis for Designing Gating of Aluminum Castings, Proc. NADCA Conf., Rosemont, IL, Nov 6-8, 1999

14-00 X. Yang, M. Jolly, and J. Campbell, Reduction of Surface Turbulence during Filling of Sand Castings Using a Vortex-flow Runner, Conference for Modeling of Casting, Welding, and Advanced Solidification Processes IX, Aachen, Germany, August 2000

13-00 H. S. H. Lo and J. Campbell, The Modeling of Ceramic Foam Filters, Conference for Modeling of Casting, Welding, and Advanced Solidification Processes IX, Aachen, Germany, August 2000

12-00 M. R. Jolly, H. S. H. Lo, M. Turan and J. Campbell, Use of Simulation Tools in the Practical Development of a Method for Manufacture of Cast Iron Camshafts,” Conference for Modeling of Casting, Welding, and Advanced Solidification Processes IX, Aachen, Germany, August, 2000

14-99 J Koke, and M Modigell, Time-Dependent Rheological Properties of Semi-solid Metal Alloys, Institute of Chemical Engineering, Aachen University of Technology, Mechanics of Time-Dependent Materials 3: 15-30, 1999

12-99 Grun, Gerd-Ulrich, Schneider, Wolfgang, Ray, Steven, Marthinusen, Jan-Olaf, Recent Improvements in Ceramic Foam Filter Design by Coupled Heat and Fluid Flow Modeling, Proc TMS Annual Meeting, 1999, pp. 1041-1047

10-99 Bongcheol Park and Jerald R. Brevick, Computer Flow Modeling of Cavity Pre-fill Effects in High Pressure Die Casting, NADCA Proceedings, Cleveland T99-011, November, 1999

8-99 Brad Guthrie, Simulation Reduces Aluminum Die Casting Cost by Reducing Volume, Die Casting Engineer Magazine, September/October 1999, pp. 78-81

7-99 Fred L. Church, Virtual Reality Predicts Cast Metal Flow, Modern Metals, September, 1999, pp. 67F-J

19-98 Grun, Gerd-Ulrich, & Schneider, Wolfgang, Numerical Modeling of Fluid Flow Phenomena in the Launder-integrated Tool Within Casting Unit Development, Proc TMS Annual Meeting, 1998, pp. 1175-1182

18-98 X. Yang & J. Campbell, Liquid Metal Flow in a Pouring Basin, Int. J. Cast Metals Res, 1998, 10, pp. 239-253

15-98 R. Van Tol, Mould Filling of Horizontal Thin-Wall Castings, Delft University Press, The Netherlands, 1998

14-98 J. Daughtery and K. A. Williams, Thermal Modeling of Mold Material Candidates for Copper Pressure Die Casting of the Induction Motor Rotor Structure, Proc. Int’l Workshop on Permanent Mold Casting of Copper-Based Alloys, Ottawa, Ontario, Canada, Oct. 15-16, 1998

10-98 C. W. Hirt, and M.R. Barkhudarov, Lost Foam Casting Simulation with Defect Prediction, Flow Science Inc, presented at Modeling of Casting, Welding and Advanced Solidification Processes VIII Conference, June 7-12, 1998, Catamaran Hotel, San Diego, California

9-98 M. R. Barkhudarov and C. W. Hirt, Tracking Defects, Flow Science Inc, presented at the 1st International Aluminum Casting Technology Symposium, 12-14 October 1998, Rosemont, IL

5-98 J. Righi, Computer Simulation Helps Eliminate Porosity, Die Casting Management Magazine, pp. 36-38, January 1998

3-98 P. Kapranos, M. R. Barkhudarov, D. H. Kirkwood, Modeling of Structural Breakdown during Rapid Compression of Semi-Solid Alloy Slugs, Dept. Engineering Materials, The University of Sheffield, Sheffield S1 3JD, U.K. and Flow Science Inc, USA, Presented at the 5th International Conference Semi-Solid Processing of Alloys and Composites, Colorado School of Mines, Golden, CO, 23-25 June 1998

1-98 U. Jerichow, T. Altan, and P. R. Sahm, Semi Solid Metal Forming of Aluminum Alloys-The Effect of Process Variables Upon Material Flow, Cavity Fill and Mechanical Properties, The Ohio State University, Columbus, OH, published in Die Casting Engineer, p. 26, Jan/Feb 1998

8-97 Michael Barkhudarov, High Pressure Die Casting Simulation Using FLOW-3D, Die Casting Engineer, 1997

15-97 M. R. Barkhudarov, Advanced Simulation of the Flow and Heat Transfer Process in Simultaneous Engineering, Flow Science report, presented at the Casting 1997 – International ADI and Simulation Conference, Helsinki, Finland, May 28-30, 1997

14-97 M. Ranganathan and R. Shivpuri, Reducing Scrap and Increasing Die Life in Low Pressure Die Casting through Flow Simulation and Accelerated Testing, Dept. Welding and Systems Engineering, Ohio State University, Columbus, OH, presented at 19th International Die Casting Congress & Exposition, November 3-6, 1997

13-97 J. Koke, Modellierung und Simulation der Fließeigenschaften teilerstarrter Metallegierungen, Livt Information, Institut für Verfahrenstechnik, RWTH Aachen, October 1997

10-97 J. P. Greene and J. O. Wilkes, Numerical Analysis of Injection Molding of Glass Fiber Reinforced Thermoplastics – Part 2 Fiber Orientation, Body-in-White Center, General Motors Corp. and Dept. Chemical Engineering, University of Michigan, Polymer Engineering and Science, Vol. 37, No. 6, June 1997

9-97 J. P. Greene and J. O. Wilkes, Numerical Analysis of Injection Molding of Glass Fiber Reinforced Thermoplastics. Part 1 – Injection Pressures and Flow, Manufacturing Center, General Motors Corp. and Dept. Chemical Engineering, University of Michigan, Polymer Engineering and Science, Vol. 37, No. 3, March 1997

8-97 H. Grazzini and D. Nesa, Thermophysical Properties, Casting Simulation and Experiments for a Stainless Steel, AT Systemes (Renault) report, presented at the Solidification Processing ’97 Conference, July 7-10, 1997, Sheffield, U.K.

7-97 R. Van Tol, L. Katgerman and H. E. A. Van den Akker, Horizontal Mould Filling of a Thin Wall Aluminum Casting, Laboratory of Materials report, Delft University, presented at the Solidification Processing ’97 Conference, July 7-10, 1997, Sheffield, U.K.

6-97 M. R. Barkhudarov, Is Fluid Flow Important for Predicting Solidification, Flow Science report, presented at the Solidification Processing ’97 Conference, July 7-10, 1997, Sheffield, U.K.

22-96 Grun, Gerd-Ulrich & Schneider, Wolfgang, 3-D Modeling of the Start-up Phase of DC Casting of Sheet Ingots, Proc TMS Annual Meeting, 1996, pp. 971-981

9-96 M. R. Barkhudarov and C. W. Hirt, Thixotropic Flow Effects under Conditions of Strong Shear, Flow Science report FSI96-00-2, to be presented at the “Materials Week ’96” TMS Conference, Cincinnati, OH, 7-10 October 1996

4-96 C. W. Hirt, A Computational Model for the Lost Foam Process, Flow Science final report, February 1996 (FSI-96-57-R2)

3-96 M. R. Barkhudarov, C. L. Bronisz, C. W. Hirt, Three-Dimensional Thixotropic Flow Model, Flow Science report, FSI-96-00-1, published in the proceedings of (pp. 110- 114) and presented at the 4th International Conference on Semi-Solid Processing of Alloys and Composites, The University of Sheffield, 19-21 June 1996

1-96 M. R. Barkhudarov, J. Beech, K. Chang, and S. B. Chin, Numerical Simulation of Metal/Mould Interfacial Heat Transfer in Casting, Dept. Mech. & Process Engineering, Dept. Engineering Materials, University of Sheffield and Flow Science Inc, 9th Int. Symposium on Transport Phenomena in Thermal-Fluid Engineering, June 25-28, 1996, Singapore

11-95 Barkhudarov, M. R., Hirt, C.W., Casting Simulation Mold Filling and Solidification-Benchmark Calculations Using FLOW-3D, Modeling of Casting, Welding, and Advanced Solidification Processes VII, pp 935-946

10-95 Grun, Gerd-Ulrich, & Schneider, Wolfgang, Optimal Design of a Distribution Pan for Level Pour Casting, Proc TMS Annual Meeting, 1995, pp. 1061-1070

9-95 E. Masuda, I. Itoh, K. Haraguchi, Application of Mold Filling Simulation to Die Casting Processes, Honda Engineering Co., Ltd., Tochigi, Japan, presented at the Modelling of Casting, Welding and Advanced Solidification Processes VII, The Minerals, Metals & Materials Society, 1995

6-95 K. Venkatesan, Experimental and Numerical Investigation of the Effect of Process Parameters on the Erosive Wear of Die Casting Dies, presented for Ph.D. degree at Ohio State University, 1995

5-95 J. Righi, A. F. LaCamera, S. A. Jones, W. G. Truckner, T. N. Rouns, Integration of Experience and Simulation Based Understanding in the Die Design Process, Alcoa Technical Center, Alcoa Center, PA 15069, presented by the North American Die Casting Association, 1995

2-95 K. Venkatesan and R. Shivpuri, Numerical Simulation and Comparison with Water Modeling Studies of the Inertia Dominated Cavity Filling in Die Casting, NUMIFORM, 1995

1-95 K. Venkatesan and R. Shivpuri, Numerical Investigation of the Effect of Gate Velocity and Gate Size on the Quality of Die Casting Parts, NAMRC, 1995.

15-94 D. Liang, Y. Bayraktar, S. A. Moir, M. Barkhudarov, and H. Jones, Primary Silicon Segregation During Isothermal Holding of Hypereutectic AI-18.3%Si Alloy in the Freezing Range, Dept. of Engr. Materials, U. of Sheffield, Metals and Materials, February 1994

13-94 Deniece Korzekwa and Paul Dunn, A Combined Experimental and Modeling Approach to Uranium Casting, Materials Division, Los Alamos National Laboratory, presented at the Symposium on Liquid Metal Processing and Casting, El Dorado Hotel, Santa Fe, New Mexico, 1994

12-94 R. van Tol, H. E. A. van den Akker and L. Katgerman, CFD Study of the Mould Filling of a Horizontal Thin Wall Aluminum Casting, Delft University of Technology, Delft, The Netherlands, HTD-Vol. 284/AMD-Vol. 182, Transport Phenomena in Solidification, ASME 1994

11-94 M. R. Barkhudarov and K. A. Williams, Simulation of ‘Surface Turbulence’ Fluid Phenomena During the Mold Filling Phase of Gravity Castings, Flow Science Technical Note #41, November 1994 (FSI-94-TN41)

10-94 M. R. Barkhudarov and S. B. Chin, Stability of a Numerical Algorithm for Gas Bubble Modelling, University of Sheffield, Sheffield, U.K., International Journal for Numerical Methods in Fluids, Vol. 19, 415-437 (1994)

16-93 K. Venkatesan and R. Shivpuri, Numerical Simulation of Die Cavity Filling in Die Castings and an Evaluation of Process Parameters on Die Wear, Dept. of Industrial Systems Engineering, Presented by: N.A. Die Casting Association, Cleveland, Ohio, October 18-21, 1993

15-93 K. Venkatesen and R. Shivpuri, Numerical Modeling of Filling and Solidification for Improved Quality of Die Casting: A Literature Survey (Chapters II and III), Engineering Research Center for Net Shape Manufacturing, Report C-93-07, August 1993, Ohio State University

1-93 P-E Persson, Computer Simulation of the Solidification of a Hub Carrier for the Volvo 800 Series, AB Volvo Technological Development, Metals Laboratory, Technical Report No. LM 500014E, Jan. 1993

13-92 D. R. Korzekwa, M. A. K. Lewis, Experimentation and Simulation of Gravity Fed Lead Castings, in proceedings of a TMS Symposium on Concurrent Engineering Approach to Materials Processing, S. N. Dwivedi, A. J. Paul and F. R. Dax, eds., TMS-AIME Warrendale, p. 155 (1992)

12-92 M. A. K. Lewis, Near-Net-Shaiconpe Casting Simulation and Experimentation, MST 1992 Review, Los Alamos National Laboratory

2-92 M. R. Barkhudarov, H. You, J. Beech, S. B. Chin, D. H. Kirkwood, Validation and Development of FLOW-3D for Casting, School of Materials, University of Sheffield, Sheffield, UK, presented at the TMS/AIME Annual Meeting, San Diego, CA, March 3, 1992

1-92 D. R. Korzekwa and L. A. Jacobson, Los Alamos National Laboratory and C.W. Hirt, Flow Science Inc, Modeling Planar Flow Casting with FLOW-3D, presented at the TMS/AIME Annual Meeting, San Diego, CA, March 3, 1992

12-91 R. Shivpuri, M. Kuthirakulathu, and M. Mittal, Nonisothermal 3-D Finite Difference Simulation of Cavity Filling during the Die Casting Process, Dept. Industrial and Systems Engineering, Ohio State University, presented at the 1991 Winter Annual ASME Meeting, Atlanta, GA, Dec. 1-6, 1991

3-91 C. W. Hirt, FLOW-3D Study of the Importance of Fluid Momentum in Mold Filling, presented at the 18th Annual Automotive Materials Symposium, Michigan State University, Lansing, MI, May 1-2, 1991 (FSI-91-00-2)

11-90 N. Saluja, O.J. Ilegbusi, and J. Szekely, On the Calculation of the Electromagnetic Force Field in the Circular Stirring of Metallic Melts, accepted in J. Appl. Physics, 1990

10-90 N. Saluja, O. J. Ilegbusi, and J. Szekely, On the Calculation of the Electromagnetic Force Field in the Circular Stirring of Metallic Molds in Continuous Castings, presented at the 6th Iron and Steel Congress of the Iron and Steel Institute of Japan, Nagoya, Japan, October 1990

9-90 N. Saluja, O. J. Ilegbusi, and J. Szekely, Fluid Flow in Phenomena in the Electromagnetic Stirring of Continuous Casting Systems, Part I. The Behavior of a Cylindrically Shaped, Laboratory Scale Installation, accepted for publication in Steel Research, 1990

8-89 C. W. Hirt, Gravity-Fed Casting, Flow Science Technical Note #20, July 1989 (FSI-89-TN20)

6-89 E. W. M. Hansen and F. Syvertsen, Numerical Simulation of Flow Behaviour in Moldfilling for Casting Analysis, SINTEF-Foundation for Scientific and Industrial Research at the Norwegian Institute of Technology, Trondheim, Norway, Report No. STS20 A89001, June 1989

1-88 C. W. Hirt and R. P. Harper, Modeling Tests for Casting Processes, Flow Science report, Jan. 1988 (FSI-88-38-01)

2-87 C. W. Hirt, Addition of a Solidification/Melting Model to FLOW-3D, Flow Science report, April 1987 (FSI-87-33-1)

Lost Foam Casting Workspace, 소실모형주조

Lost Foam Casting Workspace Highlights, 소실모형주조

  • 최첨단 Foam 잔여물 추적
  • 진보된 Foam 증발 및 금속 유동 모델링
  • 응고, 다공성 및 표면 결함 분석

Workspace Overview

Lost Foam Casting Workspace(소실모형주조) 는 Lost Foam Casting에 필요한 충진, 응고 및 냉각 하위 프로세스를 시뮬레이션하는 모든 도구를 제공합니다. 각 하위 프로세스는 해석 엔지니어가 사용하기 쉬운 인터페이스를 제공하도록 맞춤화된 템플릿 디자인을 기반으로합니다.

Lost Foam Casting 의 결함은 충진 프로파일에서 추적할 수 있기 때문에  FLOW-3D  CAST 의 용탕유동 및 소실모형(foam)의 연소 시뮬레이션의 탁월한 정확도는 고품질의 Lost Foam Casting 주물을 생산하는 데 귀중한 통찰력을 제공합니다. 기포. 잔류물 형성과 같은 주입 결함은 최종 주조에서 정확하게 추적되고 처리됩니다.

Lost Foam Casting Workspace | FLOW-3D CAST
Lost Foam Residue Tracking – Filling Simulation | FLOW-3D CAST
Lost Foam Impeller Tree – Filling Simulation | FLOW-3D CAST
Lost Foam Residue Simulation | FLOW-3D CAST

PROCESSES MODELED

  • Filling
  • Solidification
  • Cooling

FLEXIBLE MESHING

  • Structured meshing for fast, easy generation
  • Multi-block meshing for localized accuracy control
  • Foam-conforming meshes for memory optimization

MOLD MODELING

  • Ceramic filters
  • Inserts – standard and porous
  • Air vents
  • Chills
  • Insulating and exothermic sleeves
  • Moving ladles and stoppers

ADVANCED SOLIDIFICATION

  • Chemistry-based solidification
  • Dimensionless Niyama criteria
  • Cooling rates, SDAS, grain size mechanical properties

FILLING ACCURACY

  • Foam/melt interface tracking
  • Gas/bubble entrapment
  • Automatic melt flow drag calculation in filters

DEFECT PREDICTION

  • Foam residue defect tracking
  • Cold shuts
  • Porosity prediction
  • Shrinkage
  • Hot spots

DYNAMIC SIMULATION CONTROL

  • Probe-controlled pouring control

COMPLETE ANALYSIS PACKAGE

  • Animations with multi-viewports – 3D, 2D, history plots, volume rendering
  • Porosity analysis tool
  • Side-by-side simulation results comparison
  • Sensors for measuring melt temperature, solid fraction
  • Particle tracers
  • Batch post-processing
  • Report generation

자기 혈액 정화 마이크로 장치의 최적화

Optimization of Magnetic Blood Cleansing Microdevices

자기 혈액 정화 마이크로 장치의 최적화

이 기사는 스페인 칸타 브리아 대학 (University of Cantabria) 화학과 및 버팔로 (뉴욕), 미국 뉴욕 주립 대학 생화학공학과의 enifer Gómez-Pastora, Eugenio Bringas, Inmaculada Ortiza 및 Edward P. Furlanib에 의해 기고되었습니다.

Separation of toxins with magnetic particles. Why is it so important?

자성 입자와 독소의 분리. 왜 그렇게 중요한가?

자성 입자의 사용은 최근 독성 물질의 혈류에서 다른 독소가 체외로 포획되는 해독 (disoxification) 과정으로 확대되었습니다. 생체 유체의 해독은 많은 수의 임상 상태에서 가장 생각할 수있는 치료법이며, 일부는 패혈증과 같은 높은 사망률과 관련이 있습니다. 이것은 혈류를 통해 퍼지면서 신체의 방어력을 압도하는 미생물 감염에 의한 치명적인 질병입니다. 이는 미국 내에서만 연간 1800 만 명의 사람들에게 고통을주고 매년 20 만 명이 넘는 사망을 초래하는 병원 중환자 실에서의 주요 사망 원인을 나타냅니다. 정확한 치료를 시행하기 전에 사망률이 매 시간마다 9 % 나 증가한다는 것을 볼수 있습니다. 따라서 최첨단 병원 중환자 실에서도 독소를 신속하게 제거하는 것이 가장 중요합니다.

우리는 현재 치료법의 한계가 독소 격리 제로서 자성 비드를 사용하는 것과 같은 새로운 전략의 개발을 필요로한다는 것을 발견했습니다. 입자의 자기 적 특성으로 인해 병원체의 포획이 완료되면 영구 자석에 의해 생성 된 외부 자기장을 사용하여 환자의 혈액과의 분리가 연속적으로 수행 될 수 있습니다. 지난 10 년 동안 개발 된 다중 자기 마이크로 세퍼레이터로부터 우리는 2 상 연속 흐름 시스템의 사용을 제안했습니다. 이러한 시스템은 흐름 제한 및 생체 유체의 임의의 분해 (즉, 포획 영역 내의 세포의 비특이적 포획)가 회피되어 시간 경과에 따른 시스템의 효능 및 용량을 유지하기 때문에 최선의 대안일 것입니다 [1]. 그러나 이러한 프로세스의 최적화는 덜 연구되었고 합리적 설계는 종종 수학적 설명과 관련된 복잡성으로 인해 부족합니다. 따라서 우리는 체외 해독 과정의 설계를 최적화하기 위해 FLOW-3D로 다중 위상 시스템 내부의 흐르는 혈류로부터 자기 구슬의 분리를 모델링했습니다. 그림1에 나타난 제안된 분리기 디자인에서, 비드는 상부 입구를 통해 연속적으로 주입되고, 자기 구배의 적용에 의해 편향되고 유동 버퍼 스트림으로 수집됩니다. 유체 위상의 혼합을 피하면서 효율적인 분리를 달성하기 위해, 자력 및 유체력을 신중하게 연구하고 최적화했습니다. 구슬이 편향 될 때 입자 – 유체 상호 작용에 대한 상세한 연구도 제공됩니다.

그림 1. 제안 된 microfluidic bioseparator의 도식 다이어그램 ([2]에서 채택).

Modeling approach with FLOW-3D

첫번째로 보여지는 생체 분리기 내부의 자기 영동 입자 수송을 예측하기위한 모델은 CFD 기반의 오일러 – 라그랑지안 (Eulerian-Lagrangian) 접근법으로 구성됩니다. Navier-Stokes 방정식을 풀어서 예측 한 유체 이동은 오일러 접근법을 사용하여 계산되지만, 우리는 비드 역학을 모델링하기 위해 라그랑지안 프레임 워크를 사용했습니다. 라그랑지안 (Lagrangian) 접근법에 따르면 입자는 개별 단위로 모델링되었으며 각각의 궤도는 고전적인 뉴턴 역학을 적용하여 추정되었습니다. 분리 동안 입자에 작용하는 힘은 다르지만 영구 자석에 의해 생성 된 자기 구배 하에서 비드 궤적을 예측하기위한 지배적 인 자력 및 유동력만 고려했습니다. 유체의 동일한 유입 속도를 유지하면서 채널의 하부 벽과 자석의 상단 사이의 거리를 변화시킴으로써 다른 입자 궤적 및 따라서 분리 효능을 얻었습니다 (버퍼에 대해 0.035mS-1, m • s-1). 우리가 개발 한 모델링 노력에 대한 자세한 내용은 출판 된 연구 [1, 2]에서 찾을 수 있습니다.

그림 2. 자석과 마이크로 채널 사이의 거리 “d”를 변화시킴으로써 제공되는 서로 다른 자기장 하에서의 입자 궤적 (빨간색 선) ([2]에서 채택). 윤곽 플롯은 채널에서 예상되는 평균 유체 속도 크기를 나타냅니다.

Particle magnetophoresis results

입자 자기 영동 결과

자석의 위치를 ​​변화시킴으로써, 우리는 가변 자장 구배가 발생하고, 따라서 상이한 분리 효율이 얻어짐을 입증했습니다. 그림 2는 자석과 채널 사이의 거리 d가 다른 입자의 궤도를 보여줍니다. 0 ~ 1mm 사이의 거리에서 모든 입자는 입구에서 원래 위치와 별개로 분리됩니다. 더 큰 거리의 경우, 낮은 자기력으로 인해 분리가 불완전합니다. 완전한 입자 분리를 위해서는 중 ~ 고 자력이 필요합니다. 그러나, 우리는 높은 자력이 유체 패턴의 섭동과 유체 계면의 파손으로 이끄는 입자의 극도의 가속으로 인해 해독 목적에 바람직하지 않음을 입증했습니다 (그림 3 참조). 따라서 중간 자력이 나타나게됩니다. 완전한 비드 분리가 혈액의 완전성을 유지하면서 달성 될 수 있기 때문에 이러한 종류의 시스템에 가장 적합할 수 있습니다.

그림 3. 입자가 a) d = 0 mm 및 b) d = 1.15 mm에 대해 상간 경계면을 횡단 할 때의 속도 벡터. c) d = 0 mm 및 b) d = 1.15 mm ([2]에서 채택)에 대한 그 당시의 혈액 체적 분율.

Conclusions

본 연구에서는 다중 위상 연속 흐름 마이크로 디바이스에서 혈액으로부터 자기 비드 분리 과정을 예측하고 최적화 하기위한 새로운 FLOW-3D 모델을 소개했습니다. 이 모델은 입자에 작용하는 우세한 힘을 고려하고 개별 입자의 궤도, 분리에 필요한 시간 및 혈액 / 버퍼 동시 흐름의 섭동을 포함하여 분리 과정의 중요한 세부 사항을 연구하는데 사용될 수 있습니다 . 이 연구의 핵심 요소는 유체 장에서 입자 – 유체 상호 작용의 영향을 고려하면서 장치에서 동시에 흐르는 두 유체 간의 상호 작용을 연구 한 것입니다. 솔루션이 채널의 길이를 따라 독립적으로 흐르고 각각의 출구에서 분리되어 가능한 혈액 손실이나 용해를 피하기 때문에 이러한 문제는 매우 중요합니다. 여기에 이어지는 방법론은 핵심 작동 변수 및 매개 변수를 고려하여 입자 분리를 예측하는 데 사용할 수 있으므로 합리적인 설계 지침을 제공합니다. 일반적으로 혈액 해독 과정뿐만 아니라 미세 유체 장치 내부에 여러 개의 구속 된 액체 상을 포함하는 다른 연구를위한 파라 메트릭 분석 및 최적화에도 적용됩니다. 우리의 미래 연구는 새로운 혈액 해독 과정을 설계하기 위해 전혈을 사용하는 과정의 실험적 분석과 자성 분리 단계의 독소 제거와 통합에 초점을 맞출 것입니다.

References

[1] Gómez-Pastora et al., Separation and Purification Technology2017, 172, 16–31.

[2] Gómez-Pastora et al., Journal of Physical Chemistry C2017, 121, 7466−7477.

Investigation of Mould Leakages in a Gravity Casting

Investigation of Mould Leakages in a Gravity Casting

 

This article was contributed by Gabriele Taricco of CM Taricco and Stefano Mascetti of XC Engineering.

Metal leakages in the original gravity casting mould

몰드 설계는 유체 역학과 금속 응고 패턴뿐만 아니라 주형 자체에서 발생할 수 있는 문제와 응력에 대한 반응을 고려해야 하는 매우 복잡한 작업입니다. 이탈리아에 본사를 둔 주형 제작 업체인 CMTaricco 사는 최근에 새로운 주형 중 하나의 하부에서 금속 누출 문제에 직면했습니다. 주형 누출의 원인은 처음에는 분명하지 않았으며 몇 번의 공정 주기 후에만 나타났습니다. 제작 일정에 차질이 생기고 부품 주조 비용이 급격히 증가하기 때문에 문제가 중요한 것은 분명했습니다.

Investigation of an idea

공정 자체는 주입과 오버플로우 설계인 중력 주조 방식이었기 때문에 유체 역학 부분에서는 문제가 발생할 수 없었습니다. GabrieleTaricco (CMTaricco의 소유주)의 가설은 금속 누출이 주형의 열 손실의 설계 불량에서 기인하여 균일하지 않은 분포를 초래한다는 것이었습니다. 변형률과 그에 따라 주형 바닥에서 크고 원하지 않는 변형이 순환하면서 금속이 유출될 수 있는 중요한 영역의 개방까지 주기적으로 시행되었습니다. 이를 확인하고 문제에 대한 신속한 해결책을 찾기 위해 FLOW-3D시뮬레이션을 실행하여 주형이 가열될 때 발생하는 현상을 정확하게 파악했습니다.

Schematic of a critical area where metal was flowing out of the mould

 

A careful setup, to achieve a fast resolution of the issue

문제의 원인은 신속하게 파악할 수 있어야 했기 때문에 최신 Flow-3D기능을 모두 활용하여 정확한 설정이 필요했습니다. 특히, 채택된 meshing기법은 전통적인 설정과 거의 동일한 정확도를 유지하면서 전산 셀의 수를 크게 줄이는데 매우 도움이 되었습니다. 빠른 시뮬레이션으로 주형 세척에 사용된 첫 번째 방법은 주형 내부의 얇은 캐비티를 직교 축과 정렬하기 위해 주형을 수직 축 주위로 회전시키는 것이었습니다.

Rotating the mould around the vertical axis in order to align the inner thin cavity of the mould

 

두 번째 트릭은 내부 공동 (얇은 벽)에 new conformal mesh기능을 사용하는 한편 전체 도메인에 대해 기존의 더 큰 메쉬 블록을 유지하는 것 이었습니다. The conformal mesh는 open volume과 일치하고, 작은 간극을 갖는 cavity로 제한됩니다.

A global view of the mould with cores and its alignment with the mesh blocks

 

마지막으로, 외부 공간을 주형에 제한하기 위해(현재 구두 상자 모양이 되고, 20도 회전하며, 모델 축과 정렬상태) 일부’ 도메인제거’ 요소가 사용되었습니다.즉, FLOW-3D의 내부 솔리드 모델을 통해 직접 연결됩니다

Domain removing components (yellow) were used to limit the space externally to the mould.

 

나머지 설정은 소프트웨어의 권장 기본값 대부분을 이용하여 기존 체계를 따랐습니다. 이러한 기능과 FLOW-3D의 새로운 하위 도메인 분해 기능 덕분에 설계된 9 000 000 셀을 유체 하위 도메인의 경우에만 1 840 000 셀로, 고체 서브 도메인의 경우 2 430 000 셀로 줄이는 것이 가능했습니다.

 

The analysis

주입 시뮬레이션 후, 양호한 주입 패턴을 보장하기 위해 시뮬레이션의 초점이 열 다이 사이클링 분석으로 리디렉션 되었습니다. 이 경우 설정은 일반 데스크 톱 컴퓨터에서 10개의 생산 사이클을 재현하는 데 1시간이면 간단하고 빠릅니다(i7930 K, 상업적 가치 1500달러). 그 결과 CM의 초기 가설이 확인되었습니다. FlowSight를 사용하여 단일 이미지에서 여러 시점과 횡단면에서 온도 필드를 관찰한 결과 온도가 d라는 것이 분명했습니다. 주형의 침입은 예상되는 변형과 금속 누출을 쉽게 유발할 수 있습니다.

Simulation of the mould’s temperature during the die cyclings

 

Further analysis with the Fluid-Structure Interaction module

 

일단 문제가 확인되고 기술 요원이 향상된 금형 설계를 시작하면 CM Taricco는 다이 상의 응력 및 변형에 대한 FEM 해석을 실행하는 최종 확인을 원했습니다. 이 분석을 수행하기 위해 XC Engineering Srl은 CM이 계산을 설정하고 수행하는 것을 도왔습니다. 분석의 결과는 정확히 CM이 생각하고 있는 것을 보여주었습니다. FLOW-3D는 붓기가 거의 걸리지 않은 금형에서 발견 된 실제 변형과 동일한 위치와 크기를 극도의 정확도로 재현 할 수 있었습니다. 이것은 CM에 대한 좋은 소식이었으며, 실제 주조 조건을 기반으로 실제 금형 변형을 예측하기 위해 설계 단계에서 FSI 모듈을 사용하는 추가 권장 사항을 시행했습니다.

Deformation of the mould during the die cyclings, simulated using the Fluid Structure Interaction model. Deformations are amplified x20.

 

Conclusion

해석결과, CM직원은 CFD솔루션의 온도영역에 대한 모든정보를 사용하여 최적화된 새로운 주형을 설계할 수 있었습니다. 새로운 주형은 열 에너지를 보다 효율적인 방법으로 방출할 수 있었으며 주조물은 수 십번의 공정 주기 후에도 금속 누출의 영향을 받지 않았습니다.

The cast part after mould optimization. No critical leak defects are present.

 

High Pressure Die Casting

Microfluidics Bibliography

Microfluidics Bibliography

다음은 Microfluidics Bibliography의 기술 문서 모음입니다.
이 모든 논문은 FLOW-3D  결과를 특징으로  합니다. 미세 유체 공정 및 장치 를 성공적으로 시뮬레이션하기 위해 FLOW-3D 를 사용 하는 방법에 대해 자세히 알아보십시오  .

2021년 8월 26일 Update

45-21   Navid Tonekaboni, Mahdi Feizbahr, Nima Tonekaboni, Guang-Jun Jiang, Hong-Xia Chen, Optimization of solar CCHP systems with collector enhanced by porous media and nanofluid, Mathematical Problems in Engineering, 2021; 9984940, 2021. doi.org/10.1155/2021/9984840

40-21   B. Hayes, G.L. Whiting, R. MacCurdy, Modeling of contactless bubble–bubble interactions in microchannels with integrated inertial pumps, Physics of Fluids, 33.4; 042002, 2021. doi.org/10.1063/5.0041924

Below is a collection of technical papers in our Microfluidics Bibliography. All of these papers feature FLOW-3D results. Learn more about how FLOW-3D can be used to successfully simulate microfluidic processes and devices.

14-21   Jian-Chiun Liou, Chih-Wei Peng, Philippe Basset, Zhen-Xi Chen, DNA printing integrated multiplexer driver microelectronic mechanical system head (IDMH) and microfluidic flow estimation, Micromachines, 12.1; 25, 2021. doi.org/10.3390/mi12010025

08-20   Li Yong-Qiang, Dong Jun-Yan and Rui Wei, Numerical simulation for capillary driven flow in capsule-type vane tank with clearances under microgravity, Microgravity Science and Technology, 2020. doi.org/10.1007/s12217-019-09773-z

89-19   Tim Dreckmann, Julien Boeuf, Imke-Sonja Ludwig, Jorg Lumkemann, and Jorg Huwyler, Low volume aseptic filling: impact of pump systems on shear stress, European Journal of Pharmeceutics and Biopharmeceutics, in press, 2019. doi:10.1016/j.ejpb.2019.12.006

88-19   V. Amiri Roodan, J. Gomez-Pastora, C. Gonzalez-Fernandez, I.H. Karampelas, E. Bringas, E.P. Furlani, and I. Ortiz, CFD analysis of the generation and manipulation of ferrofluid droplets, TechConnect Briefs, pp. 182-185, 2019. TechConnect World Innovation Conference & Expo, Boston, Massachussetts, USA, June 17-19, 2019.

55-19     Julio Aleman, Sunil K. George, Samuel Herberg, Mahesh Devarasetty, Christopher D. Porada, Aleksander Skardal, and Graça Almeida‐Porada, Deconstructed microfluidic bone marrow on‐a‐chip to study normal and malignant hemopoietic cell–niche interactions, Small, 2019. doi: 10.1002/smll.201902971

37-19     Feng Lin Ng, Miniaturized 3D fibrous scaffold on stereolithography-printed microfluidic perfusion culture, Doctoral Thesis, Nanyang Technological University, Singapore, 2019.

32-19     Jenifer Gómez-Pastora, Ioannis H. Karampelas, Eugenio Bringas, Edward P. Furlani, and Inmaculada Ortiz, Numerical analysis of bead magnetophoresis from flowing blood in a continuous-flow microchannel: Implications to the bead-fluid interactions, Nature: Scientific Reports, Vol. 9, No. 7265, 2019. doi: 10.1038/s41598-019-43827-x

01-19  Jelena Dinic and Vivek Sharma, Computational analysis of self-similar capillary-driven thinning and pinch-off dynamics during dripping using the volume-of-fluid method, Physics of Fluids, Vol. 31, 2019. doi: 10.1063/1.5061715

75-18   Tobias Ladner, Sebastian Odenwald, Kevin Kerls, Gerald Zieres, Adeline Boillon and Julien Bœuf, CFD supported investigation of shear induced by bottom-mounted magnetic stirrer in monoclonal antibody formulation, Pharmaceutical Research, Vol. 35, 2018. doi: 10.1007/s11095-018-2492-4

53-18   Venoos Amiri Roodan, Jenifer Gómez-Pastora, Aditi Verma, Eugenio Bringas, Inmaculada Ortiz and Edward P. Furlani, Computational analysis of magnetic droplet generation and manipulation in microfluidic devices, Proceedings of the 5th International Conference of Fluid Flow, Heat and Mass Transfer, Niagara Falls, Canada, June 7 – 9, 2018; Paper no. 154, 2018.  doi: 10.11159/ffhmt18.154

35-18   Jenifer Gómez-Pastora, Cristina González Fernández, Marcos Fallanza, Eugenio Bringas and Inmaculada Ortiz, Flow patterns and mass transfer performance of miscible liquid-liquid flows in various microchannels: Numerical and experimental studies, Chemical Engineering Journal, vol. 344, pp. 487-497, 2018. doi: 10.1016/j.cej.2018.03.110

16-18   P. Schneider, V. Sukhotskiy, T. Siskar, L. Christie and I.H. Karampelas, Additive Manufacturing of Microfluidic Components via Wax Extrusion, Biotech, Biomaterials and Biomedical TechConnect Briefs, vol. 3, pp. 162 – 165, 2018.

15-18   J. Gómez-Pastora, I.H. Karampelas, A.Q. Alorabi, M.D. Tarn, E. Bringas, A. Iles, V.N. Paunov, N. Pamme, E.P. Furlani, I. Ortiz, CFD analysis and experimental validation of magnetic droplet generation and deflection across multilaminar flow streams, Biotech, Biomaterials and Biomedical TechConnect Briefs, vol. 3, pp. 182-185, 2018.

14-18   J. Gómez-Pastora, C. González-Fernández, I.H. Karampelas, E. Bringas, E.P. Furlani, and I. Ortiz, Design of Magnetic Blood Cleansing Microdevices through Experimentally Validated CFD Modeling, Biotech, Biomaterials and Biomedical TechConnect Briefs, vol. 3, pp. 170-173, 2018.

10-18   A. Gupta, I.H. Karampelas, J. Kitting, Numerical modeling of the formation of dynamically configurable L2 lens in a microchannel, Biotech, Biomaterials and Biomedical TechConnect Briefs, Vol. 3, pp. 186 – 189, 2018.

17-17   I.H. Karampelas, J. Gómez-Pastora, M.J. Cowan, E. Bringas, I. Ortiz and E.P. Furlani, Numerical Analysis of Acoustophoretic Discrete Particle Focusing in Microchannels, Biotech, Biomaterials and Biomedical TechConnect Briefs 2017, Vol. 3

16-17   J. Gómez-Pastora, I.H. Karampelas, E. Bringas, E.P. Furlani and I. Ortiz, CFD analysis of particle magnetophoresis in multiphase continuous-flow bioseparators, Biotech, Biomaterials and Biomedical TechConnect Briefs 2017, Vol. 3

15-17   I.H. Karampelas, S. Vader, Z. Vader, V. Sukhotskiy, A. Verma, G. Garg, M. Tong and E.P. Furlani, Drop-on-Demand 3D Metal Printing, Informatics, Electronics and Microsystems TechConnect Briefs 2017, Vol. 4

102-16   J. Brindha, RA.G. Privita Edwina, P.K. Rajesh and P.Rani, “Influence of rheological properties of protein bio-inks on printability: A simulation and validation study,” Materials Today: Proceedings, vol. 3, no.10, pp. 3285-3295, 2016. doi: 10.1016/j.matpr.2016.10.010

99-16   Ioannis H. Karampelas, Kai Liu, Fatema Alali, and Edward P. Furlani, Plasmonic Nanoframes for Photothermal Energy Conversion, J. Phys. Chem. C, 2016, 120 (13), pp 7256–7264

98-16   Jelena Dinic and Vivek Sharma, Drop formation, pinch-off dynamics and liquid transfer of simple and complex fluidshttp://meetings.aps.org/link/BAPS.2016.MAR.B53.12, APS March Meeting 2016, Volume 61, Number 2, March 14–18, 2016, Baltimore, Maryland

67-16  Vahid Bazargan and Boris Stoeber, Effect of substrate conductivity on the evaporation of small sessile droplets, PHYSICAL REVIEW E 94, 033103 (2016), doi: 10.1103/PhysRevE.94.033103

57-16   Ioannis Karampelas, Computational analysis of pulsed-laser plasmon-enhanced photothermal energy conversion and nanobubble generation in the nanoscale, PhD Dissertation: Department of Chemical and Biological Engineering, University at Buffalo, State University of New York, July 2016

44-16   Takeshi Sawada et al., Prognostic impact of circulating tumor cell detected using a novel fluidic cell microarray chip system in patients with breast cancer, EBioMedicine, Available online 27 July 2016, doi: 10.1016/j.ebiom.2016.07.027.

39-16   Chien-Hsun Wang, Ho-Lin Tsai, Yu-Che Wu and Weng-Sing Hwang, Investigation of molten metal droplet deposition and solidification for 3D printing techniques, IOP Publishing, J. Micromech. Microeng. 26 (2016) 095012 (14pp), doi: 10.1088/0960-1317/26/9/095012, July 8, 2016

30-16   Ioannis H. Karampelas, Kai Liu and Edward P. Furlani, Plasmonic Nanocages as Photothermal Transducers for Nanobubble Cancer Therapy, Nanotech 2016 Conference & Expo, May 22-25, Washington, DC.

29-16   Scott Vader, Zachary Vader, Ioannis H. Karampelas and Edward P. Furlani, Advances in Magnetohydrodynamic Liquid Metal Jet Printing, Nanotech 2016 Conference & Expo, May 22-25, Washington, DC.

02-16  Stephen D. Hoath (Editor), Fundamentals of Inkjet Printing: The Science of Inkjet and Droplets, ISBN: 978-3-527-33785-9, 472 pages, February 2016 (see chapters 2 and 3 for FLOW-3D results)

125-15   J. Berthier, K.A. Brakke, E.P. Furlani, I.H. Karampelas, V. Poher, D. Gosselin, M. Cubinzolles and P. Pouteau, Whole blood spontaneous capillary flow in narrow V-groove microchannels, Sensors and Actuators B: Chemical, 206, pp. 258-267, 2015.

86-15   Yousub Lee and Dave F. Farson, Simulation of transport phenomena and melt pool shape for multiple layer additive manufacturing, J. Laser Appl. 28, 012006 (2016). doi: 10.2351/1.4935711, published online 2015.

77-15   Ho-Lin Tsai, Weng-Sing Hwang, Jhih-Kai Wang, Wen-Chih Peng and Shin-Hau Chen, Fabrication of Microdots Using Piezoelectric Dispensing Technique for Viscous Fluids, Materials 2015, 8(10), 7006-7016. doi: 10.3390/ma8105355

63-15   Scott Vader, Zachary Vader, Ioannis H. Karampelas and Edward P. Furlani, Magnetohydrodynamic Liquid Metal Jet Printing, TechConnect World Innovation Conference & Expo, Washington, D.C., June 14-17, 2015

46-15   Adwaith Gupta, 3D Printing Multi-Material, Single Printhead Simulation, Advanced Qualification of Additive Manufacturing Materials Workshop, July 20 – 21, 2015, Santa Fe, NM

28-15   Yongqiang Li, Mingzhu Hu, Ling Liu, Yin-Yin Su, Li Duan, and Qi Kang, Study of Capillary Driven Flow in an Interior Corner of Rounded Wall Under MicrogravityMicrogravity Science and Technology, June 2015

20-15   Pamela J. Waterman, Diversity in Medical Simulation Applications, Desktop Engineering, May 2015, pp 22-26,

16-15   Saurabh Singh, Ann Junghans, Erik Watkins, Yash Kapoor, Ryan Toomey, and Jaroslaw Majewski, Effects of Fluid Shear Stress on Polyelectrolyte Multilayers by Neutron Scattering Studies, © 2015 American Chemical Society, DOI: 10.1021/acs.langmuir.5b00037, Langmuir 2015, 31, 2870−2878, February 17, 2015

11-15   Cheng-Han Wu and Weng-Sing Hwang, The effect of process condition of the ink-jet printing process on the molten metallic droplet formation through the analysis of fluid propagation direction, Canadian Journal of Physics, 2015. doi: 10.1139/cjp-2014-0259

03-15 Hanchul Cho, Sivasubramanian Somu, Jin Young Lee, Hobin Jeong and Ahmed Busnaina, High-Rate Nanoscale Offset Printing Process Using Directed Assembly and Transfer of Nanomaterials, Adv. Materials, doi: 10.1002/adma.201404769, February 2015

122-14  Albert Chi, Sebastian Curi, Kevin Clayton, David Luciano, Kameron Klauber, Alfredo Alexander-Katz, Sebastián D’hers and Noel M Elman, Rapid Reconstitution Packages (RRPs) implemented by integration of computational fluid dynamics (CFD) and 3D printed microfluidics, Research Gate, doi: 10.1007/s13346-014-0198-7, July 2014

113-14 Cihan Yilmaz, Arif E. Cetin, Georgia Goutzamanidis, Jun Huang, Sivasubramanian Somu, Hatice Altug, Dongguang Wei and Ahmed Busnaina, Three-Dimensional Crystalline and Homogeneous Metallic Nanostructures Using Directed Assembly of Nanoparticles, 10.1021/nn500084g, © 2014 American Chemical Society, April 2014

110-14 Koushik Ponnuru, Jincheng Wu, Preeti Ashok, Emmanuel S. Tzanakakis and Edward P. Furlani, Analysis of Stem Cell Culture Performance in a Microcarrier Bioreactor System, Nanotech, Washington, D.C., June 15-18, 2014

109-14   Ioannis H. Karampelas, Young Hwa Kim and Edward P. Furlani, Numerical Analysis of Laser Induced Photothermal Effects using Colloidal Plasmonic Nanostructures, Nanotech, Washington, D.C., June 15-18, 2014

108-14   Chenxu Liu, Xiaozheng Xue and Edward P. Furlani, Numerical Analysis of Fully-Coupled Particle-Fluid Transport and Free-Flow Magnetophoretic Sorting in Microfluidic Systems, Nanotech, Washington, D.C., June 15-18, 2014

95-14   Cheng-Han Wu, Weng-Sing Hwang, The effect of the echo-time of a bipolar pulse waveform on molten metallic droplet formation by squeeze mode piezoelectric inkjet printing, Accepted November 2014, Microelectronics Reliability (2014) , © 2014 Elsevier Ltd. All rights reserved.

85-14   Sudhir Srivastava, Lattice Boltzmann method for contact line dynamics, ISBN: 978-90-386-3608-5, Copyright © 2014 S. Srivastava

61-14   Chenxu Liu, A Computational Model for Predicting Fully-Coupled Particle-Fluid Dynamics and Self-Assembly for Magnetic Particle Applications, Master’s Thesis: State University of New York at Buffalo, 2014, 75 pages; 1561583, http://gradworks.umi.com/15/61/1561583.html

41-14 Albert Chi, Sebastian Curi, Kevin Clayton, David Luciano, Kameron Klauber, Alfredo Alexander-Katz, Sebastian D’hers, and Noel M. Elman, Rapid Reconstitution Packages (RRPs) implemented by integration of computational fluid dynamics (CFD) and 3D printed microfluidics, Drug Deliv. and Transl. Res., DOI 10.1007/s13346-014-0198-7, # Controlled Release Society 2014. Available for purchase online at SpringerLink.

21-14  Suk-Hee Park, Ung Hyun Koh, Mina Kim, Dong-Yol Yang, Kahp-Yang Suh and Jennifer Hyunjong Shin, Hierarchical multilayer assembly of an ordered nanofibrous scaffold via thermal fusion bonding, Biofabrication 6 (2014) 024107 (10pp), doi:10.1088/1758-5082/6/2/024107, IOP Publishing, 2014. Available for purchase online at IOP.

17-14   Vahid Bazargan, Effect of substrate cooling and droplet shape and composition on the droplet evaporation and the deposition of particles, Ph.D. Thesis: Department of Mechanical Engineering, The University of British Columbia, March 2014, © Vahid Bazargan, 2014

73-13  Oliver G. Harlen, J. Rafael Castrejón-Pita, and Arturo Castrejon-Pita, Asymmetric Detachment from Angled Nozzles Plates in Drop-on Demand Inkjet Printing, NIP & Digital Fabrication Conference, 2013 International Conference on Digital Printing Technologies. Pages 253-549, pp. 277-280(4)

63-13  Fatema Alali, Ioannis H. Karampelas, Young Hwa Kim, and Edward P. Furlani, Photonic and Thermofluidic Analysis of Colloidal Plasmonic Nanorings and Nanotori for Pulsed-Laser Photothermal ApplicationsJ. Phys. Chem. C, Article ASAP, DOI: 10.1021/jp406986y, Copyright © 2013 American Chemical Society, September 2013.

25-13  Sudhir Srivastava, Theo Driessen, Roger Jeurissen, Herma Wijshoff, and Federico Toschi, Lattice Boltzmann Method to Study the Contraction of a Viscous Ligament, International Journal of Modern Physics © World Scientific Publishing Company, May 2013.

11-13  Li-Chieh Hsu, Yong-Jhih Chen, Jia-Huang Liou, Numerical Investigation in the Factors on the Pool Boiling, Applied Mechanics and Materials Vol. 311 (2013) pp 456-461, © (2013) Trans Tech Publications, Switzerland, doi:10.4028/www.scientific.net/AMM.311.456. Available for purchase online at Scientific.Net.

10-13 Pamela J. Waterman, CFD: Shaping the Medical World, Desktop Engineering, April 2013. Full article available online at Desktop Engineering.

90-12 Charles R. Ortloff and Martin Vogel, Spray Cooling Heat Transfer- Test and CFD Analysis, Electronics Cooling, June 2012. Available online at Electronics Cooling.

79-12    Daniel Parsaoran Siregar, Numerical simulation of evaporation and absorption of inkjet printed droplets, Ph.D. Thesis: Technische Universiteit Eindhoven, September 18, 2012, Copyright 2012 by D.P. Siregar, ISBN: 978-90-386-3190-5.

71-12   Jong-hyeon Chang, Kyu-Dong Jung, Eunsung Lee, Minseog Choi, Seungwan Lee, and Woonbae Kim, Varifocal liquid lens based on microelectrofluidic technology, Optics Letters, Vol. 37, Issue 21, pp. 4377-4379 (2012) http://dx.doi.org/10.1364/OL.37.004377

70-12   Jong-hyeon Chang, Kyu-Dong Jung, Eunsung Lee, Minseog Choi, and Seunwan Lee, Microelectrofluidic Iris for Variable ApertureProc. SPIE 8252, MOEMS and Miniaturized Systems XI, 82520O (February 9, 2012); doi:10.1117/12.906587

69-12   Jong-hyeon Chang, Eunsung Lee, Kyu-Dong Jung, Seungwan Lee, Minseog Choi, and  Woonbae Kim, Microelectrofluidic Lens for Variable CurvatureProc. SPIE 8486, Current Developments in Lens Design and Optical Engineering XIII, 84860X (October 11, 2012); doi:10.1117/12.925852.

61-12  Biddut Bhattacharjee, Study of Droplet Splitting in an Electrowetting Based Digital Microfluidic System, Thesis: Doctor of Philosophy in the College of Graduate Studies (Applied Sciences), The University of British Columbia, September 2012, © Biddut Bhattacharjee.

55-12 Hejun Li, Pengyun Wang, Lehua Qi, Hansong Zuo, Songyi Zhong, Xianghui Hou, 3D numerical simulation of successive deposition of uniform molten Al droplets on a moving substrate and experimental validation, Computational Materials Science, Volume 65, December 2012, Pages 291–301. Available for purchase online at SciVerse.

54-12   Edward P. Furlani, Anthony Nunez, Gianmarco Vizzeri, Modeling Fluid Structure-Interactions for Biomechanical Analysis of the Human Eye, Nanotech Conference & Expo, June 18-21, 2012, Santa Clara, CA.

53-12   Xinyun Wu, Richard D. Oleschuk and Natalie M. Cann, Characterization of microstructured fibre emitters in pursuit of improved nano electrospray ionization performance, The Royal Society of Chemistry 2012, http://pubs.rsc.org, DOI: 10.1039/c2an35249d, May 2012

25-12    Edward P. Furlani, Ioannis H. Karampelas and Qian Xie, Analysis of Pulsed Laser Plasmon-assisted Photothermal Heating and Bubble Generation at the Nanoscale, Lab on a Chip, 10.1039/C2LC40495H, Received 01 May 2012, Accepted 07 Jun 2012. First published on the web 13 Jun 2012.

22-12  R.A. Sultanov, D. Guster, Numerical Modeling and Simulations of Pulsatile Human Blood Flow in Different 3D-Geometries, Book chapter #21 in Fluid Dynamics, Computational Modeling and Applications (2012), ISBN: 978-953-51-0052-2, p. 475 [18 pages]. Available online at INTECH.

21-12  Guo-Wei Huang, Tzu-Yi Hung, and Chin-Tai Chen, Design, Simulation, and Verification of Fluidic Light-Guide Chips with Various Geometries of Micro Polymer Channels, NEMS 2012, Kyoto, Japan, March 5-8, 2012. Available for purchase online at IEEE.

103-11   Suk-Hee Park, Development of Three-Dimensional Scaffolds containing Electrospun Nanofibers and their Applications to Tissue Regeneration, Ph.D. Thesis: School of Mechanical, Aersospace and Systems Engineering, Division of Mechanical Engineering, KAIST, 2011.

81-11   Xinyun Wu, Modeling and Characterization of Microfabricated Emitters-In Pursuit of Improved ESI-MS Performance, thesis: Department of Chemistry, Queen’s University, December 2011, Copyright © Xinyun Wu, 2011

79-11  Cong Lu, A Cell Preparation Stage for Automatic Cell Injection, thesis: Graduate Department of Mechanical and Industrial Engineering, University of Toronto, Copyright © Cong Lu, 2011

77-11 Ge Bai, W. Thomas Leach, Computational fluid dynamics (CFD) insights into agitation stress methods in biopharmaceutical development, International Journal of Pharmaceutics, Available online 8 December 2011, ISSN 0378-5173, 10.1016/j.ijpharm.2011.11.044. Available online at SciVerse.

72-11  M.R. Barkhudarov, C.W. Hirt, D. Milano, and G. Wei, Comments on a Comparison of CFD Software for Microfluidic Applications, Flow Science Technical Note #93, FSI-11-TN93, December 2011

45-11  Chang-Wei Kang, Jiak Kwang Tan, Lunsheng Pan, Cheng Yee Low and Ahmed Jaffar, Numerical and experimental investigations of splat geometric characteristics during oblique impact of plasma spraying, Applied Surface Science, In Press, Corrected Proof, Available online 20 July 2011, ISSN 0169-4332, DOI: 10.1016/j.apsusc.2011.06.081. Available to purchase online at SciVers

33-11  Edward P. Furlani, Mark T. Swihart, Natalia Litchinitser, Christopher N. Delametter and Melissa Carter, Modeling Nanoscale Plasmon-assisted Bubble Nucleation and Applications, Nanotech Conference and Expo 2011, Boston, MA, June 13-16, 2011

32-11  Lu, Cong and Mills, James K., Three cell separation design for realizing automatic cell injection, Complex Medical Engineering (CME), 2011 IEEE/ICME, pp: 599 – 603, Harbin, China, 10.1109/ICCME.2011.5876811, June 2011. Available online at IEEEXplore.

25-11 Issam M. Bahadur, James K. Mills, Fluidic vacuum-based biological cell holding device with piezoelectrically induced vibration, Complex Medical Engineering (CME), 2011 IEEE/ICME International Conference on, 22-25 May 2011, pp: 85 – 90, Harbin, China. Available online at: IEEE Xplore.

14-11  Edward P. Furlani, Roshni Biswas, Alexander N. Cartwright and Natalia M. Litchinitser, Antiresonant guiding optofluidic biosensor, doi:10.1016/j.optcom.2011.04.014, Optics Communication, April 2011

05-11 Hyeju Eom and Keun Park, Integrated numerical analysis to evaluate replication characteristics of micro channels in a locally heated mold by selective induction, International Journal of Precision Engineering and Manufacturing, Volume 12, Number 1, 53-60, DOI: 10.1007/s12541-011-0007-x, 2011. Available online at: SpringerLink.

70-10  I.N. Volnov, V.S. Nagornyi, Modeling Processes for Generation of Streams of Monodispersed Fluid Droplets in Electro-inkjet Applications, Science and Technology News, St. Petersburg State Polytechnic University, 4, pp 294-300, 2010. In Russian.

62-10  F. Mobadersani, M. Eskandarzade, S. Azizi and S. Abbasnezhad, Effect of Ambient Pressure on Bubble Growth in Micro-Channel and Its Pumping Effect, ESDA2010-24436, pp. 577-584, doi:10.1115/ESDA2010-24436, ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis (ESDA2010), Istanbul, Turkey, July 12–14, 2010. Available online at the ASME Digital Library.

58-10 Tsung-Yi Ho, Jun Zeng, and Chakrabarty, K, Digital microfluidic biochips: A vision for functional diversity and more than moore, Computer-Aided Design (ICCAD), 2010 IEEE/ACM International Conference on, DOI: 10.1109/ICCAD.2010.5654199, © IEEE, November 2010. Available online at IEEE Explore.

51-10  Regina Bleul, Marion Ritzi-Lehnert, Julian Höth, Nico Scharpfenecker, Ines Frese, Dominik Düchs, Sabine Brunklaus, Thomas E. Hansen-Hagge, Franz-Josef Meyer-Almes, Klaus S. Drese, Compact, cost-efficient microfluidics-based stopped-flow device, Anal Bioanal Chem, DOI 10.1007/s00216-010-4446-5, Available online at Springer, November 2010

22-10    Krishendu Chakrabarty, Richard B. Fair and Jun Zeng, Design Tools for Digital Microfluidic Biochips Toward Functional Diversification and More than Moore, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 29, No. 7, July 2010

14-10 E. P. Furlani and M. S. Hanchak, Nonlinear analysis of the deformation and breakup of viscous microjets using the method of lines, International Journal for Numerical Methods in Fluids (2010), © 2010 John Wiley & Sons, Ltd., Published online in Wiley InterScience. DOI: 10.1002/fld.2205

55-09 R.A. Sultanov, and D. Guster, Computer simulations of  pulsatile human blood flow through 3D models of the human aortic arch, vessels of simple geometry and a bifurcated artery, Proceedings of the 31st Annual International Conference of the IEEE EMBS (Engineering in Medicine and Biology Society), Minneapolis, September 2-6, 2009, p.p. 4704-4710.

30-09 Anurag Chandorkar and Shayan Palit, Simulation of Droplet Dynamics and Mixing in Microfluidic Devices using a VOF-Based Method, Sensors & Transducers journal, ISSN 1726-5479 © 2009 by IFSA, Vol.7, Special Issue “MEMS: From Micro Devices to Wireless Systems,” October 2009, pp. 136-149.

13-09 E.P. Furlani, M.C. Carter, Analysis of an Electrostatically Actuated MEMS Drop Ejector, Presented at Nanotech Conference & Expo 2009, Houston, Texas, USA, May 3-7, 2009

12-09 A. Chandorkar, S. Palit, Simulation of Droplet-Based Microfluidics Devices Using a Volume-of-Fluid Approach, Presented at Nanotech Conference & Expo 2009, Houston, Texas, USA, May 3-7, 2009

3-09 Christopher N. Delametter, FLOW-3D Speeds MEMS Inkjet Development, Desktop Engineering, January 2009

42-08  Tien-Li Chang, Jung-Chang Wang, Chun-Chi Chen, Ya-Wei Lee, Ta-Hsin Chou, A non-fluorine mold release agent for Ni stamp in nanoimprint process, Microelectronic Engineering 85 (2008) 1608–1612

26-08 Pamela J. Waterman, First-Pass CFD Analyses – Part 2, Desktop Engineering, November 2008

09-08 M. Ren and H. Wijshoff, Thermal effect on the penetration of an ink droplet onto a porous medium, Proc. Eurotherm2008 MNH, 1 (2008)

04-08 Delametter, Christopher N., MEMS development in less than half the time, Small Times, Online Edition, May 2008

02-08 Renat A. Sultanov, Dennis Guster, Brent Engelbrekt and Richard Blankenbecler, 3D Computer Simulations of Pulsatile Human Blood Flows in Vessels and in the Aortic Arch – Investigation of Non-Newtonian Characteristics of Human Blood, The Journal of Computational Physics, arXiv:0802.2362v1 [physics.comp-ph], February 2008

01-08 Herman Wijshoff, thesis: University of Twente, Structure- and fluid dynamics in piezo inkjet printheads, ISBN 978-90-365-2582-4, Venlo, The Netherlands January 2008.

30-07 A. K. Sen, J. Darabi, and D. R. Knapp, Simulation and parametric study of a novel multi-spray emitter for ESI–MS applications, Microfluidics and Nanofluidics, Volume 3, Number 3, June 2007, pp. 283-298(16)

28-07 Dan Soltman and Vivek Subramanian, Inkjet-Printed Line Morphologies and Temperature Control of the Coffee Ring Effect, Langmuir; 2008; ASAP Web Release Date: 16-Jan-2008; (Research Article) DOI: 10.1021/la7026847

23-07 A K Sen and J Darabi, Droplet ejection performance of a monolithic thermal inkjet print head, Journal of Micromechanical and Microengineering,vol.17, pp.1420-1427 (2007) doi:10.1088/0960-1317/17/8/002; Abstract only.

18-07 Herman Wisjhoff, Better Printheads Via Simulation, Desktop Engineering, October 2007, Vol. 13, Issue 2

17-07 Jos de Jong, Ph.D. Thesis: University of Twente, Air entrapment in piezo inkjet printing, ISBN 978-90-365-2483-4, April 2007

15-07 Krishnendu Chakrabarty and Jun Zeng, (Ed.), Design Automation Methods and Tools for Microfluidics-Based Biochips, Springer, September 2006.

14-07 Fei Su and Jun Zeng, Computer-aided design and test for digital microfluidics, IEEE Design & Test of Computers, 24(1), 2007, 60-70.

13-07 Jun Zeng, Modeling and simulation of electrified droplets and its application to computer-aided design of digital microfluidics, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 25(2), 2006, 224-233.

12-07 Krishnendu Chakrabarty and Jun Zeng, (2005), Automated top-down design for microfluidic biochips, ACM Journal on Emerging Technologies in Computing Systems, 1(3), 2005, 186–223.

01-07 Wijshoff, Herman, Drop formation mechanisms in piezo-acoustic inkjet, NSTI-Nanotech 2007, ISBN 1420061844 Vol. 3, 2007)

23-06 John J. Uebbing, Stephan Hengstler, Dale Schroeder, Shalini Venkatesh, and Rick Haven, Heat and Fluid Flow in an Optical Switch Bubble, Journal of Microelectromechanical Systems, Vol. 15, No. 6, December 2006

21-06 Wijshoff, Herman, Manipulating Drop Formation in Piezo Acoustic Inkjet, Proc. IS&T’s NIP22, 79 (2006)

20-06 J. de Jong, H. Reinten, M. van den Berg, H. Wijshoff, M. Versluis, G. de Bruin, A. Prosperetti and D. Lohse, Air entrapment in piezo-driven inkjet printheads, J. Acoust. Soc. Am. 120(3), 1257 (2006)

11-06 A. K. Sen, J. Darabi, D. R. Knapp and J. Liu, Modeling and Characterization of a Carbon Fiber Emitter for Electrospray Ionization, 1 MEMS and Microsystems Laboratory, Department of Mechanical Engineering, University of South Carolina, 300 Main Street, Columbia, SC 29208, USA, 2 Department of Pharmacology, Medical University of South Carolina, Charleston, SC

5-06 E. P. Furlani, B. G. Price, G. Hawkins, and A. G. Lopez, Thermally Induced Marangoni Instability of Liquid Microjets with Application to Continuous Inkjet Printing, Proceedings of NSTI Nanotech Conference 2006, Vol. 2, pp 534-537.

28-05 O B Fawehinmi, P H Gaskell, P K Jimack, N Kapur, and H M Thompson, A combined experimental and computational fluid dynamics analysis of the dynamics of drop formation, May 2005. DOI: 10.1243/095440605X31788

5-05 E. P. Furlani, Thermal Modulation and Instability of Newtonian Liquid Microjets, presented at Nanotech 2005, Anaheim, CA, May 8-12, 2005.

1-05 C.W. Hirt, Electro-Hydrodynamics of Semi-Conductive Fluids: With Application to Electro-Spraying, Flow Science Technical Note #70, FSI-05-TN70

19-04 G. F. Yao, Modeling of Electroosmosis Without Resolving Physics Inside a Electric Double Layer, Flow Science Technical Note (FSI-04-TN69)

12-04 Jun Zeng and Tom Korsmeyer, Principles of Droplet Electrohydrodynamics for Lab-on-a-Chip, Lab. Chip. Journal, 2004, 4(4), 265-277

9-04 Constantine N. Anagnostopoulos, James M. Chwalek, Christopher N. Delametter, Gilbert A. Hawkins, David L. Jeanmaire, John A. Lebens, Ali Lopez, and David P. Trauernicht, Micro-Jet Nozzle Array for Precise Droplet Metering and Steering Having Increased Droplet Deflection, Proceedings of the 12th International Conference on Solid State Sensors, Actuators and Microsystems, sponsored by IEEE, Boston, June 8-12, 2003, pp. 368-71

8-04 Christopher N. Delametter, David P. Trauernicht, James M. Chwalek, Novel Microfluidic Jet Deflection – Significant Modeling Challenge with Great Application Potential, Technical Proceedings of the 2002 International Conference on Modeling and Simulation of Microsystems sponsored by NSTI, San Juan, Puerto Rico, April 21-25, 2002, pp. 44-47

6-04 D. Vadillo*, G. Desie**, A Soucemarianadin*, Spreading Behavior of Single and Multiple Drops, *Laboratoire des Ecoulements Geophysiques et Industriels (LEGI), and **AGFA-Gevaert Group N.V., XXI ICTAM, 15-21 August 2004, Warsaw, Poland

2-04 Herman Wijshoff, Free Surface Flow and Acousto-Elastic Interaction in Piezo Inkjet, Nanotech 2004, sponsored by the Nano Science & Technology Institute, Boston, MA, March 2004

30-03 D Souders, I Khan and GF Yao, Alessandro Incognito, and Matteo Corrado, A Numerical Model for Simulation of Combined Electroosmotic and Pressure Driven Flow in Microdevices, 7th International Symposium on Fluid Control, Measurement and Visualization

27-03 Jun Zeng, Daniel Sobek and Tom Korsmeyer, Electro-Hydrodynamic Modeling of Electrospray Ionization – CAD for a µFluidic Device-Mass Spectrometer Interface, Agilent Technologies Inc, paper presented at Transducers 2003, June 03 Boston (note: Reference #10 is to FLOW-3D)

17-03 John Uebbing, Switching Fiber-optic Circuits with Microscopic Bubbles, Sensors Magazine, May 2003, Vol 20, No 5, p 36-42

16-03 CFD Speeds Development of MEMS-based Printing Technology, MicroNano Magazine, June 2003, Vol 8, No 6, p 16

3-03 Simulation Speeds Design of Microfluidic Medical Devices, R&D Magazine, March 2003, pp 18-19

1-03 Simulations Help Microscopic Bubbles Switch Fiber-Optic Circuits, Agilent Technologies, Fiberoptic Product News, January 2003, pp 22-23

27-02 Feng, James Q., A General Fluid Dynamic Analysis of Drop Ejection in Drop-on-Demand Ink Jet Devices, Journal of Imaging Science and Technology®, Volume 46, Number 5, September/October 2002

1-02 Feixia Pan, Joel Kubby, and Jingkuang Chen, Numerical Simulation of Fluid Structure Interaction in a MEMS Diaphragm Drop Ejector, Xerox Wilson Research Center, Institute of Physics Publishing, Journal of Micromechanics and Microengineering, 12 (2002), PII: SO960-1317(02)27439-2, pp. 70-76

48-01   Rainer Gruber, Radial Mass Transfer Enhancement in Bubble-Train Flow, PhD thesis in Engineering Sciences, Rheinisch- Westf alischen Technische Hochschule Aachen, December 2001.

34-01 Furlani, E.P., Delametter, C.N., Chwalek, J.M., and Trauernicht, D., Surface Tension Induced Instability of Viscous Liquid Jets, Fourth International Conference on Modeling and Simulation of Microsystems, April 2001

12-01 C. N. Delametter, Eastman Kodak Company, Micro Resolution, Mechanical Engineering, Col 123/No 7, July 2001, pp 70-72

11-01 C. N. Delametter, Eastman Kodak Company, Surface Tension Induced Instability of Viscous Liquid Jets, Technical Proceeding of the Fourth International Conference on Modeling and Simulation of Microsystems, April 2001

9-01 Aman Khan, Unipath Limited Research and Development, Effects of Reynolds Number on Surface Rolling in Small Drops, PVP-Col 431, Emerging Technologies for Fluids, Structures and Fluids, Structures and Fluid Structure Interaction — 2001

2-00 Narayan V. Deshpande, Significance of Inertance and Resistance in Fluidics of Thermal Ink-Jet Transducers, Journal of Imaging Science and Technology, Volume 40, Number 5, Sept./Oct. 1996, pp.457-461

4-98 D. Deitz, Connecting the Dots with CFD, Mechanical Engineering Magazine, pp. 90-91, March 1998

14-94 M. P. O’Hare, N. V. Deshpande, and D. J. Drake, Drop Generation Processes in TIJ Printheads, Xerox Corporation, Adv. Imaging Business Unit, IS&T’s Tenth International Congress on Advances in Non-Impact Printing, Tech. 1994

14-92 Asai, A.,Three-Dimensional Calculation of Bubble Growth and Drop Ejection in a Bubble Jet Printer, Journal of Fluids Engineering Vol. 114 December 1992:638-641

General Applications Bibliography

다음은 일반 응용 분야의 기술 문서 모음입니다.
이 모든 논문은 FLOW-3D  결과를 포함하고 있습니다.