Modeling strategies for aerodynamic interaction in dense particulate distributions

Gas-particle multiphase systems in several scenarios have dense particulate distributions. In these regimes, the gas flow modification by near neighbor particles alters the drag and lift forces experienced by the particulate distribution. The average drag force experienced by these distributions is provided using empirical relations modeled based on experimental and numerical investigations. The variability within the particle force distribution is not understood clearly or modeled to the best of our knowledge. In this work, we will use the direct simulation Monte Carlo (DSMC) method to obtain particle resolved force distributions. Neural network architecture informed by the high-fidelity results will be used to develop a model for the particle force distribution.

Particular emphasis is to incorporate a Knudsen number correction for dense particulate flows in dilute gas regimes.