A numerical model for translational and rotational momentum transfer of soft deformable micro particles in dilute two-phase flows

该项目的主要目标是将上一个项目中开发的数值模型（稀分散两相流中使用拉格朗日-欧拉方法的刚性微颗粒）扩展到软变形微颗粒的平移和旋转动量传递的数值模型。第一部分致力于开发一个直接数值模拟（DNS）工具，该工具能够局部计算有限尺寸的软可变形微颗粒上的流诱导牵引力以及由此产生的力和扭矩以及颗粒变形。在此基础上，建立了基于柔性可变形点-粒子法的全局流场CFD求解器与拉格朗日粒子跟踪的耦合关系。这种组合是通过基于DNS工具的在线方法以及基于机器学习（ML）代理模型的离线方法实现的。在机器学习框架中，针对不同的颗粒形状、方向和流动条件，建立了一个大型的流诱导力和扭矩DNS数据库，使训练好的机器学习模型能够应用于具有数十万个软可变形微颗粒的计算挑战性的流动情况。最后，将该模型应用于具有分布式透镜状囊泡的稀多相流实例。

The main objective of the project is to extend the numerical model developed in the previous project (rigid micro particles in dilute dispersed two-phase flow using the Lagrange-Euler approach) to a numerical model for translational and rotational momentum transfer of soft deformable micro particles. The first part is devoted to the development of a direct numerical simulation (DNS) tool capable of locally computing the flow-induced tractions and the resulting force and torque on a finite-sized soft deformable micro particle along with the particle deformation. Furthermore, the coupling between a CFD solver for the global-scale flow and Lagrangian particle tracking based on the novel soft deformable point-particle approach is established. This combination is achieved by an online approach based on the DNS tool, as well as by an offline approach based on a machine learned (ML) surrogate model. In the ML framework, a large DNS database of flow-induced forces and torques is built for different particle shapes, orientations, and flow conditions, allowing the trained ML model to be applied in computationally challenging flow cases with hundreds of thousands of soft deformable micro particles. Finally, the derived model is applied to a technologically relevant example of dilute multiphase flow with distributed lenticular vesicles.