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Microstructure-informed probability-driven point-particle model for hydrodynamic forces and torques in particle-laden flows

Published online by Cambridge University Press:  12 August 2020

Arman Seyed-Ahmadi Open the ORCID record for Arman Seyed-Ahmadi [Opens in a new window]  and
Anthony Wachs Open the ORCID record for Anthony Wachs [Opens in a new window]
Arman Seyed-Ahmadi
Affiliation:
Department of Chemical & Biological Engineering, The University of British Columbia, 2360 East Mall, Vancouver, BCV6T 1Z3, Canada
Anthony Wachs*
Affiliation:
Department of Chemical & Biological Engineering, The University of British Columbia, 2360 East Mall, Vancouver, BCV6T 1Z3, Canada Department of Mathematics, The University of British Columbia, 1984 Mathematics Road, Vancouver, BCV6T 1Z2, Canada
*
†Email address for correspondence: wachs@mail.ubc.ca
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Abstract

We present a novel deterministic model that is capable of predicting particle-to-particle force and torque fluctuations in a fixed bed of randomly distributed monodisperse spheres. First, we generate our dataset by performing particle-resolved direct numerical simulations (PR-DNS) of arrays of stationary spheres in moderately inertial regimes with a Reynolds number range of $2 \leq \textit {Re} \leq 150$ and a solid volume fraction range of $0.1 \leq \phi \leq 0.4$. The key idea exploited by our model is that, while the arrangement of neighbours around each particle is uniform and random, conditioning forces or torques exerted on a reference sphere to specific ranges of values results in the emergence of significantly non-uniform distributions of neighbouring particles. Based on probabilistic arguments, we take advantage of the statistical information extracted from PR-DNS to construct force/torque-conditioned probability distribution maps, which are ultimately used as basis functions for regression. Given the locations of surrounding particles as input to the model, our results demonstrate that the present probability-driven framework is capable of predicting up to 85 % of the actual observed force and torque variation in the best cases. Since the precise location of each particle is known in an Eulerian–Lagrangian (EL) simulation, our model would be able to estimate the unresolved subgrid force and torque fluctuations reasonably well, and thereby considerably enhance the fidelity of EL simulations via improved interphase coupling.

JFM classification

Multiphase and Particle-laden Flows: Particle/fluid flow Mathematical Foundations: Computational methods
Type
JFM Papers
Information
Journal of Fluid Mechanics , Volume 900 , 10 October 2020 , A21
Copyright
© The Author(s), 2020. Published by Cambridge University Press

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References

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