  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.
