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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:
ORCiD logo [1]; ORCiD logo [1];  [1]
  1. Univ. of Notre Dame, IN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (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.
Ma, Ming, Lu, Jiacai, & Tryggvason, Gretar. Using statistical learning to close two-fluid multiphase flow equations for a simple bubbly system. United States. doi:10.1063/1.4930004.
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. doi:10.1063/1.4930004. https://www.osti.gov/servlets/purl/1565344.
@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 = {2015},
month = {9}
}

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Cited by: 32 works
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