Kε Turbulence Model Parameter Estimates Using an Approximate Selfsimilar JetinCrossflow Solution
Abstract
The kε turbulence model has been described as perhaps “the most widely used complete turbulence model.” This family of heuristic Reynolds Averaged NavierStokes (RANS) turbulence closures is supported by a suite of model parameters that have been estimated by demanding the satisfaction of wellestablished canonical flows such as homogeneous shear flow, loglaw behavior, etc. While this procedure does yield a set of socalled nominal parameters, it is abundantly clear that they do not provide a universally satisfactory turbulence model that is capable of simulating complex flows. Recent work on the Bayesian calibration of the kε model using jetincrossflow wind tunnel data has yielded parameter estimates that are far more predictive than nominal parameter values. In this paper, we develop a selfsimilar asymptotic solution for axisymmetric jetincrossflow interactions and derive analytical estimates of the parameters that were inferred using Bayesian calibration. The selfsimilar method utilizes a near field approach to estimate the turbulence model parameters while retaining the classical farfield scaling to model flow field quantities. Our parameter values are seen to be far more predictive than the nominal values, as checked using RANS simulations and experimental measurements. They are also closer to the Bayesian estimates than the nominal parameters. Amore »
 Authors:

 Sandia National Lab. (SNLNM), Albuquerque, NM (United States)
 Sandia National Lab. (SNLCA), Livermore, CA (United States)
 Publication Date:
 Research Org.:
 Sandia National Lab. (SNLNM), Albuquerque, NM (United States); Sandia National Lab. (SNLCA), Livermore, CA (United States)
 Sponsoring Org.:
 USDOE National Nuclear Security Administration (NNSA)
 OSTI Identifier:
 1399490
 Report Number(s):
 SAND20171388J
651099
 Grant/Contract Number:
 AC0494AL85000
 Resource Type:
 Accepted Manuscript
 Journal Name:
 AIAA Journal
 Additional Journal Information:
 Conference: 8. AIAA Theoretical Fluid Mechanics Conference, Denver, CO (United States), 59 Jun 2017
 Country of Publication:
 United States
 Language:
 English
 Subject:
 42 ENGINEERING; 97 MATHEMATICS AND COMPUTING
Citation Formats
DeChant, Lawrence, Ray, Jaideep, Lefantzi, Sophia, Ling, Julia, and Arunajatesan, Srinivasan. Kε Turbulence Model Parameter Estimates Using an Approximate Selfsimilar JetinCrossflow Solution. United States: N. p., 2017.
Web. https://doi.org/10.2514/6.20174167.
DeChant, Lawrence, Ray, Jaideep, Lefantzi, Sophia, Ling, Julia, & Arunajatesan, Srinivasan. Kε Turbulence Model Parameter Estimates Using an Approximate Selfsimilar JetinCrossflow Solution. United States. https://doi.org/10.2514/6.20174167
DeChant, Lawrence, Ray, Jaideep, Lefantzi, Sophia, Ling, Julia, and Arunajatesan, Srinivasan. Fri .
"Kε Turbulence Model Parameter Estimates Using an Approximate Selfsimilar JetinCrossflow Solution". United States. https://doi.org/10.2514/6.20174167. https://www.osti.gov/servlets/purl/1399490.
@article{osti_1399490,
title = {Kε Turbulence Model Parameter Estimates Using an Approximate Selfsimilar JetinCrossflow Solution},
author = {DeChant, Lawrence and Ray, Jaideep and Lefantzi, Sophia and Ling, Julia and Arunajatesan, Srinivasan},
abstractNote = {The kε turbulence model has been described as perhaps “the most widely used complete turbulence model.” This family of heuristic Reynolds Averaged NavierStokes (RANS) turbulence closures is supported by a suite of model parameters that have been estimated by demanding the satisfaction of wellestablished canonical flows such as homogeneous shear flow, loglaw behavior, etc. While this procedure does yield a set of socalled nominal parameters, it is abundantly clear that they do not provide a universally satisfactory turbulence model that is capable of simulating complex flows. Recent work on the Bayesian calibration of the kε model using jetincrossflow wind tunnel data has yielded parameter estimates that are far more predictive than nominal parameter values. In this paper, we develop a selfsimilar asymptotic solution for axisymmetric jetincrossflow interactions and derive analytical estimates of the parameters that were inferred using Bayesian calibration. The selfsimilar method utilizes a near field approach to estimate the turbulence model parameters while retaining the classical farfield scaling to model flow field quantities. Our parameter values are seen to be far more predictive than the nominal values, as checked using RANS simulations and experimental measurements. They are also closer to the Bayesian estimates than the nominal parameters. A traditional simplified jet trajectory model is explicitly related to the turbulence model parameters and is shown to yield good agreement with measurement when utilizing the analytical derived turbulence model coefficients. Finally, the close agreement between the turbulence model coefficients obtained via Bayesian calibration and the analytically estimated coefficients derived in this paper is consistent with the contention that the Bayesian calibration approach is firmly rooted in the underlying physical description.},
doi = {10.2514/6.20174167},
journal = {AIAA Journal},
number = ,
volume = ,
place = {United States},
year = {2017},
month = {6}
}
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Works referencing / citing this record:
Adaptive wavelet compression of large additive manufacturing experimental and simulation datasets
journal, July 2018
 Salloum, Maher; Johnson, Kyle L.; Bishop, Joseph E.
 Computational Mechanics, Vol. 63, Issue 3