Kabacaoğlu Gökberk, Biros George
Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas 78712, USA.
The Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA.
Phys Rev E. 2019 Jun;99(6-1):063313. doi: 10.1103/PhysRevE.99.063313.
Particulate Stokesian flows describe the hydrodynamics of rigid or deformable particles in Stokes flows. Due to highly nonlinear fluid-structure interaction dynamics, moving interfaces, and multiple scales, numerical simulations of such flows are challenging and expensive. Here, we propose a generic machine-learning-augmented reduced model for these flows. Our model replaces expensive parts of a numerical scheme with regression functions. Given the physical parameters of the particle, our model generalizes to arbitrary geometries and boundary conditions without the need to retrain the regression functions. It is approximately an order of magnitude faster than a state-of-the-art numerical scheme using the same number of degrees of freedom and can reproduce several features of the flow accurately. We illustrate the performance of our model on integral equation formulation of vesicle suspensions in two dimensions.
颗粒斯托克斯流描述了斯托克斯流中刚性或可变形颗粒的流体动力学。由于高度非线性的流固相互作用动力学、移动界面和多尺度问题，此类流动的数值模拟具有挑战性且成本高昂。在此，我们针对这些流动提出了一种通用的机器学习增强简化模型。我们的模型用回归函数替代了数值方案中成本高昂的部分。给定颗粒的物理参数，我们的模型无需重新训练回归函数即可推广到任意几何形状和边界条件。在使用相同自由度数量的情况下，它比最先进的数值方案快大约一个数量级，并且能够准确再现流动的若干特征。我们在二维囊泡悬浮液的积分方程公式中展示了我们模型的性能。
