A finite element / neural network framework for modeling suspensions of non-spherical particles. Concepts and medical applications

TL;DR

This paper introduces a combined finite element and neural network framework to accurately predict hydrodynamic forces on non-spherical particles in fluids, with applications in medical suspensions like blood flow.

Contribution

It develops a novel method that uses numerical simulations to train neural networks for rapid force estimation on complex-shaped particles in fluid flows.

Findings

Validated the neural network model against numerical simulations.

Demonstrated applicability to diverse particle shapes.

Efficiently predicts forces in high-particle-number suspensions.

Abstract

An accurate prediction of the translational and rotational motion of particles suspended in a fluid is only possible if a complete set of correlations for the force coefficients of fluid-particle interaction is known. The present study is thus devoted to the derivation and validation of a new framework to determine the drag, lift, rotational and pitching torque coefficients for different non-spherical particles in a fluid flow. The motivation for the study arises from medical applications, where particles may have an arbitrary and complex shape. Here, it is usually not possible to derive accurate analytical models for predicting the different hydrodynamic forces. However, considering for example the various components of blood, their shape takes an important role in controlling several body functions such as control of blood viscosity or coagulation. Therefore, the presented model is designed to be applicable to a broad range of shapes. Another important feature of the suspensions occurring in medical and biological applications is the high number of particles. The modelling approach we propose can be efficiently used for simulations of solid-liquid suspensions with numerous particles. Based on resolved numerical simulations of prototypical particles we generate data to train a neural network which allows us to quickly estimate the hydrodynamic forces experienced by a specific particle immersed in a fluid.