Fluid flow around a random distribution of stationary spherical particles is a problem of substantial importance in the study of dispersed multiphase flows. In this paper we present a machine learning methodology using Generative Adversarial Network framework and Convolutional Neural Network architecture to recreate particle-resolved fluid flow around a random distribution of monodispersed particles. The model was applied to various Reynolds number and particle volume fraction combinations spanning over a range of [2.69, 172.96] and [0.11, 0.45] respectively. Test performance of the model for the studied cases is very promising.
DNS of compressible multiphase flows through the Eulerian approach
Scuola Normale Superiore di Pisa（比萨高等师范学院）；Istituto Nazionale di Vulcanologia e Geofisica（意大利火山学与地球物理研究所）；Istituto Nazionale di Geofisica e Vulcanologia（意大利地球物理与火山学研究所）
Discontinuity-aware physics-informed neural network for phase-field method in three-phase flow with phase change
School of Interdisciplinary Science, Beijing Institute of Technology（北京理工大学交叉科学学院）；School of Mechatronical Engineering, Beijing Institute of Technology（北京理工大学机电学院）；IFP Energies nouvelles（法国石油研究院）；School of Aerospace Engineering, Beijing Institute of Technology（北京理工大学航天航空学院）；Beijing Institute of Technology (Zhuhai)（北京理工大学（珠海））
The Twente turbulent Taylor-Couette (T3C) facility: Strongly turbulent (multiphase) flow between two independently rotating cylinders
本文构造了一个新的湍流Taylor-Couette系统，它由两个独立旋转的圆柱体组成。圆柱体之间的间隙高度为0.927 m，内半径为0.200 m，外半径可变（从0.279 m到0.220 m）。内圆柱体和外圆柱体的最大角旋转速率分别为20 Hz和10 Hz，以水为工作流体时，雷诺数高达3.4 x 10^6。利用该泰勒-库埃特系统，参数空间（Re_i，Re_o，η）扩展到（2.0 x 10^6，{ \pm }1.4 x 10^6，0.716-0.909）。该系统配备了气泡喷射器，温度控制，皮肤摩擦阻力传感器，和几个本地传感器研究湍流单相和两相流。内圆筒测力传感器通过扭矩测量检测皮肤摩擦阻力。透明的丙烯酸外筒允许液体流动和分散相（气泡、颗粒、纤维等）的动力学。在间隙内部，使用专门的局部传感器和非侵入式光学成像技术进行研究。该系统允许在高雷诺数制度的泰勒-库埃特流的研究，和背后的皮肤摩擦阻力改变由于气泡注射，聚合物注射，和表面疏水性和粗糙度的机制。
Direct numerical simulation of a compressible multiphase flow through the fast Eulerian approach
Scuola Normale Superiore di Pisa（比萨高等师范学院）；Istituto Nazionale di Vulcanologia e Geofisica（火山学与地球物理学国家研究所）；Istituto Nazionale di Geofisica e Vulcanologia（地球物理学与火山学国家研究所）
Learning Pore-scale Multiphase Flow from 4D Velocimetry
Department of Earth Science and Engineering, Imperial College London（帝国理工学院伦敦地球科学与工程系）；Department of Geology, Ghent University（根特大学地质系）；Department of Energy Science and Engineering, Stanford University（斯坦福大学能源科学与工程系）；Department of Computer Science, Yale University（耶鲁大学计算机科学系）；NVIDIA
PINNs4Drops: Video-conditioned physics-informed neural networks for two-phase flow reconstruction
Department One（第一系）；Department Two（第二系）；Institute of Fluid Mechanics, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院流体力学研究所）；Division of Applied Mathematics, Brown University（布朗大学应用数学系）；Pacific Northwest National Laboratory（太平洋西北国家实验室）
Characterization of partial wetting by CMAS droplets using multiphase many-body dissipative particle dynamics and data-driven discovery based on PINNs
The University of Western Ontario（西安大略大学）；Cold Spring Harbor Laboratory（冷泉港实验室）；Brown University（布朗大学）；University of Maryland（马里兰大学）；Clemson University（克莱姆森大学）；US Army Research Laboratory（美国陆军研究实验室）
Finite volume simulations of particle-laden viscoelastic fluid flows: application to hydraulic fracture processes
精确求解复杂流体的液固两相间的耦合动量传递是水力压裂等多相输送过程中的一个基本问题。具体来说，我们需要表征的归一化平均流体颗粒力$\langle F \rangle$上的分散的固相的体积分数和复杂的流体基质的流变学的依赖性。在这里，我们使用直接数值模拟（DNS）研究蠕变流（$Re\ll 1$）的粘弹性流体通过静态随机阵列的单分散球形颗粒使用有限体积的Navier-Stokes/Cauchy动量求解器。数值研究由$N=150$不同的系统，其中归一化的平均流体-颗粒力$\langle F \rangle$作为分散固相的体积分数$ϕ$ $(0 < ϕ\leq 0.2)$和Weissenberg数$Wi$ $(0 \leq Wi \leq 4)$的函数获得。从这些预测闭合律$\langle F \rangle(Wi,ϕ)$的阻力推导出准线性Oldrophil-B粘弹性流体模型，平均而言，在$5.7\%$的DNS结果。此外，能够耦合欧拉和拉格朗日阶段的流动求解器的开发，它结合了连续相的粘弹性性质和封闭形式的阻力定律。使用该求解器模拟了两个案例研究，以评估新开发的用于处理具有代表水力压裂操作的$O(10^5-10^6)$刚性球体的含颗粒粘弹性流配置的方法的准确性和鲁棒性。
