Interpolation of probability-driven model to predict hydrodynamic forces and torques in particle-laden flows

The development of hydrodynamic force/torque closure models with physical fidelity is crucial for ensuring reliable Euler-Lagrange simulations in particle-laden flows. Our previous work (Seyed-Ahmadi and Wachs. J Fluid Mech. 2020;900:A21) proposed a microstructure-informed probability-driven point-particle (MPP) method to construct a data-driven particle-position-dependent closure model, incorporating the effect of surrounding particle positions on forces/torques. However, the MPP model is not pluggable in Euler-Lagrange simulations due to the computation of constant coefficients through linear regression and reliance on statistical arguments to obtain the probability map for a pair of values of solid volume fraction (f) and Reynolds number (Re). To overcome this limitation, we propose an interpolated MPP (iMPP) method, involving interpolation in the f and Re spaces. Our results demonstrate that the iMPP method can capture over 70% of the total fluctuations in hydrodynamic forces/torques in approximately 97.8% of the tested cases. This advancement contributes to a more versatile closure model suitable for integration into E-L simulations.

Automated summary

Researchers propose a microstructure-informed probability-driven point-particle (MPP) method to enhance the reliability of Euler-Lagrange simulations in particle-laden flows. However, the MPP model cannot be directly used due to computation limitations. To overcome this, an interpolated MPP (iMPP) method is introduced, which shows promising results in capturing hydrodynamic forces/torques fluctuations in a wide range of cases. This advancement contributes to the development of a more versatile closure model suitable for integration into Euler-Lagrange simulations.