Using statistical learning to close two-fluid multiphase flow equations for a simple bubbly system

Ma, Ming; Lu, Jiacai; Tryggvason, Gretar

doi:10.1063/1.4930004

Title: Using statistical learning to close two-fluid multiphase flow equations for a simple bubbly system

Abstract

Direct numerical simulations of bubbly multiphase flows are utilized to find closure terms for a simple model of the average flow, using Neural Networks (NNs). The flow considered consists of several nearly spherical bubbles rising in a periodic domain where the initial vertical velocity and the average bubble density are homogeneous in two directions but non-uniform in one of the horizontal directions. After an initial transient motion the average void fraction and vertical velocity become approximately uniform. The NN is trained on a dataset from one simulation and then used to simulate the evolution of other initial conditions. As a whole, the resulting model predicts the evolution of the various initial conditions reasonably well.

Authors:

^[1];

^[1]; Tryggvason, Gretar ^[1]

Univ. of Notre Dame, IN (United States)

Publication Date:: Tue Sep 15 00:00:00 EDT 2015

Research Org.:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)

Sponsoring Org.:: USDOE Office of Science (SC); National Science Foundation (NSF)

OSTI Identifier:: 1565344

Grant/Contract Number:: AC05-00OR22725; CBET-1335913

Resource Type:: Accepted Manuscript

Journal Name:: Physics of Fluids

Additional Journal Information:: Journal Volume: 27; Journal Issue: 9; Journal ID: ISSN 1070-6631

Publisher:: American Institute of Physics (AIP)

Country of Publication:: United States

Language:: English

Subject:: 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS

Citation Formats


                    Ma, Ming, Lu, Jiacai, and Tryggvason, Gretar. Using statistical learning to close two-fluid multiphase flow equations for a simple bubbly system.  United States: N. p., 2015. 
Web.  doi:10.1063/1.4930004.

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                    Ma, Ming, Lu, Jiacai, & Tryggvason, Gretar. Using statistical learning to close two-fluid multiphase flow equations for a simple bubbly system.  United States.  https://doi.org/10.1063/1.4930004

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                    Ma, Ming, Lu, Jiacai, and Tryggvason, Gretar. Tue .  
"Using statistical learning to close two-fluid multiphase flow equations for a simple bubbly system".  United States.  https://doi.org/10.1063/1.4930004.  https://www.osti.gov/servlets/purl/1565344.

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@article{osti_1565344,

  title        = {Using statistical learning to close two-fluid multiphase flow equations for a simple bubbly system},

  author       = {Ma, Ming and Lu, Jiacai and Tryggvason, Gretar},

  abstractNote = {Direct numerical simulations of bubbly multiphase flows are utilized to find closure terms for a simple model of the average flow, using Neural Networks (NNs). The flow considered consists of several nearly spherical bubbles rising in a periodic domain where the initial vertical velocity and the average bubble density are homogeneous in two directions but non-uniform in one of the horizontal directions. After an initial transient motion the average void fraction and vertical velocity become approximately uniform. The NN is trained on a dataset from one simulation and then used to simulate the evolution of other initial conditions. As a whole, the resulting model predicts the evolution of the various initial conditions reasonably well.},

  doi          = {10.1063/1.4930004},

  journal      = {Physics of Fluids},

  number       = 9,

  volume       = 27,

  place        = {United States},

  year         = {Tue Sep 15 00:00:00 EDT 2015},

  month        = {Tue Sep 15 00:00:00 EDT 2015}

}

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Title: Using statistical learning to close two-fluid multiphase flow equations for a simple bubbly system

Abstract

Citation Formats

DNS of the interaction between a deformable buoyant bubble and a spatially decaying turbulence: A priori tests for LES two-phase flow modelling journal, August 2008

Neural network based multi-criterion optimization image reconstruction technique for imaging two- and three-phase flow systems using electrical capacitance tomography journal, November 2001

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Jump conditions for filtered quantities at an under-resolved discontinuous interface. Part 2: A priori tests journal, December 2009

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Numerical procedure for multiscale bone adaptation prediction based on neural networks and finite element simulation journal, July 2011

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Study of in situ calibration performance of co-located multi-sensor hot-film and sonic anemometers using a ‘virtual probe’ algorithm journal, May 2014

Upward vertical two-phase flow local flow regime identification using neural network techniques journal, January 2008

Numerical calculation of multiphase fluid flow journal, January 1975

Analysis of multiphase flows using dual-energy gamma densitometry and neural networks journal, April 1993

Effect of bubble size in turbulent bubbly downflow in a vertical channel journal, June 2007

Measuring local droplet parameters using single optical fiber probe journal, March 2019

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The effect of fluid shear on oscillating bubbly flows journal, April 2019

DNS of the interaction between a deformable buoyant bubble and a spatially decaying turbulence: A priori tests for LES two-phase flow modelling
journal, August 2008

Neural network based multi-criterion optimization image reconstruction technique for imaging two- and three-phase flow systems using electrical capacitance tomography
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Flow regime identification methodology with neural networks and two-phase flow models
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A direct numerical simulation study of the buoyant rise of bubbles at O(100) Reynolds number
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Application of neural networks to turbulence control for drag reduction
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Coupling of scales in a multiscale simulation using neural networks
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Numerical simulation of behavior of gas bubbles using a 3-D front-tracking method
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Optimal estimation for large-eddy simulation of turbulence and application to the analysis of subgrid models
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The simulation and interpretation of free turbulence with a cognitive neural system
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Mathematical Modeling of Two-Phase Flow
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Neural network modelling for mean velocity and turbulence intensities of steep channel flows
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Numerical study of turbulent bubbly downflows in a vertical channel
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