A hybrid point-particle force model that combines physical and data-driven approaches

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

doi:10.1016/j.jcp.2019.01.053

Title: A hybrid point-particle force model that combines physical and data-driven approaches

Abstract

Here this study improves upon the physics-based pairwise interaction extended point-particle (PIEP) model. The PIEP model leverages our physical understanding to predict fluid mediated interactions between solid particles. By considering the relative location of neighboring particles, the PIEP model is able to provide better predictions than existing drag models. While the current physical PIEP model is a powerful tool, its assumptions lead to increased error in flows with higher particle volume fractions. To reduce this error, a regression algorithm makes direct use of the results of direct numerical simulations (DNS) of an array of monodisperse solid particles subjected to uniform ambient flow at varying Reynolds numbers. The resulting statistical model and the physical PIEP model are superimposed to construct a hybrid, physics-based data-driven PIEP model. It must be noted that the performance of a pure data-driven approach without the model-form provided by the physical PIEP model is substantially inferior. The hybrid model's predictive capabilities are analyzed using additional DNS data that was not part of training the data-driven model. In every case tested, the hybrid models resulting from the regression were capable of (1) improving upon the physical PIEP model's prediction and (2) recovering underlying relevant physics from the DNSmore »« less

Authors:

^[1];

^[1]; Akiki, G. ^[2]

University of Florida, Gainesville, FL (United States)
Notre Dame University – Louaize, Zouk Mosbeh (Lebanon)

Publication Date:: Fri Feb 22 00:00:00 EST 2019

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

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA); US Department of the Navy, Office of Naval Research (ONR); National Science Foundation (NSF)

OSTI Identifier:: 1614516

Alternate Identifier(s):: OSTI ID: 1547727

Grant/Contract Number:: NA0002378; DGE-1315138; DGE-1842473; N00014-16-1-2617

Resource Type:: Accepted Manuscript

Journal Name:: Journal of Computational Physics

Additional Journal Information:: Journal Volume: 385; Journal Issue: C; Journal ID: ISSN 0021-9991

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; Euler-Lagrange method; point-particle model; drag law; nonlinear regression; pairwise interaction

Citation Formats


                    Moore, W. C., Balachandar, S., and Akiki, G. A hybrid point-particle force model that combines physical and data-driven approaches.  United States: N. p., 2019. 
Web.  doi:10.1016/j.jcp.2019.01.053.

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                    Moore, W. C., Balachandar, S., & Akiki, G. A hybrid point-particle force model that combines physical and data-driven approaches.  United States.  https://doi.org/10.1016/j.jcp.2019.01.053

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                    Moore, W. C., Balachandar, S., and Akiki, G. Fri .  
"A hybrid point-particle force model that combines physical and data-driven approaches".  United States.  https://doi.org/10.1016/j.jcp.2019.01.053.  https://www.osti.gov/servlets/purl/1614516.

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@article{osti_1614516,

  title        = {A hybrid point-particle force model that combines physical and data-driven approaches},

  author       = {Moore, W. C. and Balachandar, S. and Akiki, G.},

  abstractNote = {Here this study improves upon the physics-based pairwise interaction extended point-particle (PIEP) model. The PIEP model leverages our physical understanding to predict fluid mediated interactions between solid particles. By considering the relative location of neighboring particles, the PIEP model is able to provide better predictions than existing drag models. While the current physical PIEP model is a powerful tool, its assumptions lead to increased error in flows with higher particle volume fractions. To reduce this error, a regression algorithm makes direct use of the results of direct numerical simulations (DNS) of an array of monodisperse solid particles subjected to uniform ambient flow at varying Reynolds numbers. The resulting statistical model and the physical PIEP model are superimposed to construct a hybrid, physics-based data-driven PIEP model. It must be noted that the performance of a pure data-driven approach without the model-form provided by the physical PIEP model is substantially inferior. The hybrid model's predictive capabilities are analyzed using additional DNS data that was not part of training the data-driven model. In every case tested, the hybrid models resulting from the regression were capable of (1) improving upon the physical PIEP model's prediction and (2) recovering underlying relevant physics from the DNS data. As the particle volume fraction increases, the physical PIEP model loses the ability to approximate the forces experienced by the particles, but the statistical model continues to produce accurate approximations.},

  doi          = {10.1016/j.jcp.2019.01.053},

  journal      = {Journal of Computational Physics},

  number       = C,

  volume       = 385,

  place        = {United States},

  year         = {Fri Feb 22 00:00:00 EST 2019},

  month        = {Fri Feb 22 00:00:00 EST 2019}

}

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Works referenced in this record:

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Evaluation of kriging based surrogate models constructed from mesoscale computations of shock interaction with particles
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Drag law for monodisperse gas–solid systems using particle-resolved direct numerical simulation of flow past fixed assemblies of spheres
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Pairwise-interaction extended point-particle model for particle-laden flows
journal, December 2017

Akiki, G.; Moore, W. C.; Balachandar, S.
Journal of Computational Physics, Vol. 351
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Correction scheme for point-particle models applied to a nonlinear drag law in simulations of particle-fluid interaction
journal, April 2018

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Drag correlation for dilute and moderately dense fluid-particle systems using the lattice Boltzmann method
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Works referencing / citing this record:

Towards Particle-Resolved Accuracy in Euler-Lagrange Simulations of Multiphase Flow Using Machine Learning and Pairwise Interaction Extended Point-particle (PIEP) Approximation
text, January 2020

Balachandar, S.; Moore, W. C.; Akiki, G.
arXiv
DOI: 10.48550/arxiv.2005.09569

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Similar Records

Title: A hybrid point-particle force model that combines physical and data-driven approaches

Abstract

Citation Formats

Flow past a sphere with an oscillation in the free-stream velocity and unsteady drag at finite Reynolds number journal, April 1992

Force variation within arrays of monodisperse spherical particles journal, August 2016

Evaluation of kriging based surrogate models constructed from mesoscale computations of shock interaction with particles journal, May 2017

Drag law for monodisperse gas–solid systems using particle-resolved direct numerical simulation of flow past fixed assemblies of spheres journal, November 2011

A new drag correlation from fully resolved simulations of flow past monodisperse static arrays of spheres journal, October 2014

Drag force of intermediate Reynolds number flow past mono- and bidisperse arrays of spheres journal, January 2007

Immersed boundary method with non-uniform distribution of Lagrangian markers for a non-uniform Eulerian mesh journal, February 2016

Self-induced velocity correction for improved drag estimation in Euler–Lagrange point-particle simulations journal, January 2019

Role of pseudo-turbulent stresses in shocked particle clouds and construction of surrogate models for closure journal, February 2018

Using statistical learning to close two-fluid multiphase flow equations for bubbly flows in vertical channels journal, October 2016

Pairwise-interaction extended point-particle model for particle-laden flows journal, December 2017

Correction scheme for point-particle models applied to a nonlinear drag law in simulations of particle-fluid interaction journal, April 2018

Drag correlation for dilute and moderately dense fluid-particle systems using the lattice Boltzmann method journal, January 2015

Multi-scale modeling of shock interaction with a cloud of particles using an artificial neural network for model representation journal, January 2012

Pairwise interaction extended point-particle model for a random array of monodisperse spheres journal, January 2017

Direct numerical simulation of finite sized particles settling for high Reynolds number and dilute suspension journal, December 2014

Equation of motion for a small rigid sphere in a nonuniform flow journal, January 1983

DNS–Assisted Modeling of Bubbly Flows in Vertical Channels journal, November 2016

Turbulent Dispersed Multiphase Flow journal, January 2010

The Motion of High-Reynolds-Number Bubbles in Inhomogeneous Flows journal, January 2000

Towards Particle-Resolved Accuracy in Euler-Lagrange Simulations of Multiphase Flow Using Machine Learning and Pairwise Interaction Extended Point-particle (PIEP) Approximation text, January 2020

Flow past a sphere with an oscillation in the free-stream velocity and unsteady drag at finite Reynolds number
journal, April 1992

Force variation within arrays of monodisperse spherical particles
journal, August 2016

Evaluation of kriging based surrogate models constructed from mesoscale computations of shock interaction with particles
journal, May 2017

Drag law for monodisperse gas–solid systems using particle-resolved direct numerical simulation of flow past fixed assemblies of spheres
journal, November 2011

A new drag correlation from fully resolved simulations of flow past monodisperse static arrays of spheres
journal, October 2014

Drag force of intermediate Reynolds number flow past mono- and bidisperse arrays of spheres
journal, January 2007

Immersed boundary method with non-uniform distribution of Lagrangian markers for a non-uniform Eulerian mesh
journal, February 2016

Self-induced velocity correction for improved drag estimation in Euler–Lagrange point-particle simulations
journal, January 2019

Role of pseudo-turbulent stresses in shocked particle clouds and construction of surrogate models for closure
journal, February 2018

Using statistical learning to close two-fluid multiphase flow equations for bubbly flows in vertical channels
journal, October 2016

Pairwise-interaction extended point-particle model for particle-laden flows
journal, December 2017

Correction scheme for point-particle models applied to a nonlinear drag law in simulations of particle-fluid interaction
journal, April 2018

Drag correlation for dilute and moderately dense fluid-particle systems using the lattice Boltzmann method
journal, January 2015

Multi-scale modeling of shock interaction with a cloud of particles using an artificial neural network for model representation
journal, January 2012

Pairwise interaction extended point-particle model for a random array of monodisperse spheres
journal, January 2017

Direct numerical simulation of finite sized particles settling for high Reynolds number and dilute suspension
journal, December 2014

Equation of motion for a small rigid sphere in a nonuniform flow
journal, January 1983

DNS–Assisted Modeling of Bubbly Flows in Vertical Channels
journal, November 2016

Turbulent Dispersed Multiphase Flow
journal, January 2010

The Motion of High-Reynolds-Number Bubbles in Inhomogeneous Flows
journal, January 2000

Towards Particle-Resolved Accuracy in Euler-Lagrange Simulations of Multiphase Flow Using Machine Learning and Pairwise Interaction Extended Point-particle (PIEP) Approximation
text, January 2020