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Title: Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty

Abstract

Reynolds Averaged Navier Stokes (RANS) models are widely used in industry to predict fluid flows, despite their acknowledged deficiencies. Not only do RANS models often produce inaccurate flow predictions, but there are very limited diagnostics available to assess RANS accuracy for a given flow configuration. If experimental or higher fidelity simulation results are not available for RANS validation, there is no reliable method to evaluate RANS accuracy. This paper explores the potential of utilizing machine learning algorithms to identify regions of high RANS uncertainty. Three different machine learning algorithms were evaluated: support vector machines, Adaboost decision trees, and random forests. The algorithms were trained on a database of canonical flow configurations for which validated direct numerical simulation or large eddy simulation results were available, and were used to classify RANS results on a point-by-point basis as having either high or low uncertainty, based on the breakdown of specific RANS modeling assumptions. Classifiers were developed for three different basic RANS eddy viscosity model assumptions: the isotropy of the eddy viscosity, the linearity of the Boussinesq hypothesis, and the non-negativity of the eddy viscosity. It is shown that these classifiers are able to generalize to flows substantially different from those on whichmore » they were trained. As a result, feature selection techniques, model evaluation, and extrapolation detection are discussed in the context of turbulence modeling applications.« less

Authors:
 [1];  [1]
  1. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1235329
Report Number(s):
SAND-2015-2173J
Journal ID: ISSN 1070-6631; PHFLE6; 579403
Grant/Contract Number:
AC04-94AL85000
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Physics of Fluids (1994)
Additional Journal Information:
Journal Name: Physics of Fluids (1994); Journal Volume: 27; Journal Issue: 8; Journal ID: ISSN 1070-6631
Publisher:
American Institute of Physics
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Reynolds stress modeling; Navier Stokes equations; viscosity; databases; Eddy viscosity closure

Citation Formats

Ling, Julia, and Templeton, Jeremy Alan. Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty. United States: N. p., 2015. Web. doi:10.1063/1.4927765.
Ling, Julia, & Templeton, Jeremy Alan. Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty. United States. doi:10.1063/1.4927765.
Ling, Julia, and Templeton, Jeremy Alan. 2015. "Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty". United States. doi:10.1063/1.4927765. https://www.osti.gov/servlets/purl/1235329.
@article{osti_1235329,
title = {Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty},
author = {Ling, Julia and Templeton, Jeremy Alan},
abstractNote = {Reynolds Averaged Navier Stokes (RANS) models are widely used in industry to predict fluid flows, despite their acknowledged deficiencies. Not only do RANS models often produce inaccurate flow predictions, but there are very limited diagnostics available to assess RANS accuracy for a given flow configuration. If experimental or higher fidelity simulation results are not available for RANS validation, there is no reliable method to evaluate RANS accuracy. This paper explores the potential of utilizing machine learning algorithms to identify regions of high RANS uncertainty. Three different machine learning algorithms were evaluated: support vector machines, Adaboost decision trees, and random forests. The algorithms were trained on a database of canonical flow configurations for which validated direct numerical simulation or large eddy simulation results were available, and were used to classify RANS results on a point-by-point basis as having either high or low uncertainty, based on the breakdown of specific RANS modeling assumptions. Classifiers were developed for three different basic RANS eddy viscosity model assumptions: the isotropy of the eddy viscosity, the linearity of the Boussinesq hypothesis, and the non-negativity of the eddy viscosity. It is shown that these classifiers are able to generalize to flows substantially different from those on which they were trained. As a result, feature selection techniques, model evaluation, and extrapolation detection are discussed in the context of turbulence modeling applications.},
doi = {10.1063/1.4927765},
journal = {Physics of Fluids (1994)},
number = 8,
volume = 27,
place = {United States},
year = 2015,
month = 8
}

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