Toward particle-resolved accuracy in Euler–Lagrange simulations of multiphase flow using machine learning and pairwise interaction extended point-particle (PIEP) approximation

Balachandar, S.; Moore, W. C.; Akiki, G.; Liu, K.

doi:10.1007/s00162-020-00538-8

Toward particle-resolved accuracy in Euler–Lagrange simulations of multiphase flow using machine learning and pairwise interaction extended point-particle (PIEP) approximation

Journal Article · Tue Jun 30 00:00:00 EDT 2020 · Theoretical and Computational Fluid Dynamics

DOI:https://doi.org/10.1007/s00162-020-00538-8· OSTI ID:1801095

^[1]; Moore, W. C. ^[2]; Akiki, G. ^[3]; Liu, K. ^[2]

Univ. of Florida, Gainesville, FL (United States); OSTI
Univ. of Florida, Gainesville, FL (United States)
Univ. of Florida, Gainesville, FL (United States); Notre Dame Univ. - Louaize, Zouk Mosbeh (Lebanon)

This study presents two different machine learning approaches for the modeling of hydrodynamic force on particles in a particle-laden multiphase flow. Results from particle-resolved direct numerical simulations (PR-DNS) of flow over a random array of stationary particles for eight combinations of particle Reynolds number (Re) and volume fraction (φ) are used in the development of the models. The first approach follows a two-step process. In the first flow prediction step, the perturbation flow due to a particle is obtained as an axisymmetric superposable wake using linear regression. In the second force prediction step, the force on a particle is evaluated in terms of the perturbation flow induced by all its neighbors using the generalized Faxén form of the force expression. In the second approach, the force data on all the particles from the PR-DNS simulations are used to develop an artificial neural network (ANN) model for direct prediction of force on a particle. Due to the unavoidable limitation on the number of fully resolved particles in the PR-DNS simulations, direct force prediction with the ANN model tends to over-fit the data and performs poorly in the prediction of test data. In contrast, due to the millions of grid points used in the PR-DNS simulations, accurate flow prediction is possible, which then allows accurate prediction of particle force. This hybridization of multiphase physics and machine learning is particularly important, since it blends the strength of each, and the resulting pairwise interaction extended point-particle model cannot be developed by either physics or machine learning alone.

View Accepted Manuscript (DOE)

Research Organization:: Univ. of Florida, Gainesville, FL (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0002378

OSTI ID:: 1801095

Journal Information:: Theoretical and Computational Fluid Dynamics, Journal Name: Theoretical and Computational Fluid Dynamics Journal Issue: 4 Vol. 34; ISSN 0935-4964

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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Toward particle-resolved accuracy in Euler–Lagrange simulations of multiphase flow using machine learning and pairwise interaction extended point-particle (PIEP) approximation

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