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

• Published in: AIChE journal Vol. 69; no. 11
• Main Authors: Zhu, Li‐Tao, Wachs, Anthony
• Format: Journal Article
• Language: English
• Published: New York American Institute of Chemical Engineers 01.11.2023
• Subjects: Fluid flow; Hydrodynamics; Interpolation; Reynolds number; Simulation; Statistical analysis; Torque
• ISSN: 0001-1541; 1547-5905
• DOI: 10.1002/aic.18209

Abstract 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 ( Φ ) and Reynolds number (Re). To overcome this limitation, we propose an interpolated MPP (iMPP) method, involving interpolation in the Φ 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.