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An accurate prediction of the translational and rotational motion ofparticles suspended in a fluid is only possible if a complete set ofcorrelations for the force coefficients of fluid-particle interaction is known.The present study is thus devoted to the derivation and validation of a newframework to determine the drag, lift, rotational and pitching torquecoefficients for different non-spherical particles in a fluid flow. Themotivation for the study arises from medical applications, where particles mayhave an arbitrary and complex shape. Here, it is usually not possible to deriveaccurate analytical models for predicting the different hydrodynamic forces.However, considering for example the various components of blood, their shapetakes an important role in controlling several body functions such as controlof blood viscosity or coagulation. Therefore, the presented model is designedto be applicable to a broad range of shapes. Another important feature of thesuspensions occurring in medical and biological applications is the high numberof particles. The modelling approach we propose can be efficiently used forsimulations of solid-liquid suspensions with numerous particles. Based onresolved numerical simulations of prototypical particles we generate data totrain a neural network which allows us to quickly estimate the hydrodynamicforces experienced by a specific particle immersed in a fluid.

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