Robust Bayesian Calibration of a kε Model for Compressible JetinCrossflow Simulations
Abstract
Compressible jetincrossflow interactions are difficult to simulate accurately using Reynoldsaveraged Navier–Stokes (RANS) models. This could be due to simplifications inherent in RANS or the use of inappropriate RANS constants estimated by fitting to experiments of simple or canonical flows. Our previous work on Bayesian calibration of a kε model to experimental data had led to a weak hypothesis that inaccurate simulations could be due to inappropriate constants more than modelform inadequacies of RANS. In this work, Bayesian calibration of kε constants to a set of experiments that span a range of Mach numbers and jet strengths has been performed. The variation of the calibrated constants has been checked to assess the degree to which parametric estimates compensate for RANS’s modelform errors. An analytical model of jetincrossflow interactions has also been developed, and estimates of kε constants that are free of any conflation of parametric and RANS’s modelform uncertainties have been obtained. It has been found that the analytical kε constants provide meanflow predictions that are similar to those provided by the calibrated constants. Further, both of them provide predictions that are far closer to experimental measurements than those computed using “nominal” values of these constants simply obtained from the literature.more »
 Authors:

 Sandia National Lab. (SNLCA), Livermore, CA (United States)
 Sandia National Lab. (SNLNM), Albuquerque, NM (United States)
 Citrine Informatics, Redwood City, CA (United States)
 Publication Date:
 Research Org.:
 Sandia National Lab. (SNLCA), Livermore, CA (United States)
 Sponsoring Org.:
 USDOE National Nuclear Security Administration (NNSA), Office of Defense Science (NA113)
 OSTI Identifier:
 1478069
 Report Number(s):
 SAND20187772J
Journal ID: ISSN 00011452; 665861
 Grant/Contract Number:
 AC0494AL85000
 Resource Type:
 Accepted Manuscript
 Journal Name:
 AIAA Journal
 Additional Journal Information:
 Journal Volume: 56; Journal Issue: 12; Journal ID: ISSN 00011452
 Publisher:
 AIAA
 Country of Publication:
 United States
 Language:
 English
 Subject:
 42 ENGINEERING
Citation Formats
Ray, Jaideep, Dechant, Lawrence, Lefantzi, Sophia, Ling, Julia, and Arunajatesan, Srinivasan. Robust Bayesian Calibration of a kε Model for Compressible JetinCrossflow Simulations. United States: N. p., 2018.
Web. doi:10.2514/1.J057204.
Ray, Jaideep, Dechant, Lawrence, Lefantzi, Sophia, Ling, Julia, & Arunajatesan, Srinivasan. Robust Bayesian Calibration of a kε Model for Compressible JetinCrossflow Simulations. United States. doi:10.2514/1.J057204.
Ray, Jaideep, Dechant, Lawrence, Lefantzi, Sophia, Ling, Julia, and Arunajatesan, Srinivasan. Fri .
"Robust Bayesian Calibration of a kε Model for Compressible JetinCrossflow Simulations". United States. doi:10.2514/1.J057204. https://www.osti.gov/servlets/purl/1478069.
@article{osti_1478069,
title = {Robust Bayesian Calibration of a kε Model for Compressible JetinCrossflow Simulations},
author = {Ray, Jaideep and Dechant, Lawrence and Lefantzi, Sophia and Ling, Julia and Arunajatesan, Srinivasan},
abstractNote = {Compressible jetincrossflow interactions are difficult to simulate accurately using Reynoldsaveraged Navier–Stokes (RANS) models. This could be due to simplifications inherent in RANS or the use of inappropriate RANS constants estimated by fitting to experiments of simple or canonical flows. Our previous work on Bayesian calibration of a kε model to experimental data had led to a weak hypothesis that inaccurate simulations could be due to inappropriate constants more than modelform inadequacies of RANS. In this work, Bayesian calibration of kε constants to a set of experiments that span a range of Mach numbers and jet strengths has been performed. The variation of the calibrated constants has been checked to assess the degree to which parametric estimates compensate for RANS’s modelform errors. An analytical model of jetincrossflow interactions has also been developed, and estimates of kε constants that are free of any conflation of parametric and RANS’s modelform uncertainties have been obtained. It has been found that the analytical kε constants provide meanflow predictions that are similar to those provided by the calibrated constants. Further, both of them provide predictions that are far closer to experimental measurements than those computed using “nominal” values of these constants simply obtained from the literature. It can be concluded that the lack of predictive skill of RANS jetincrossflow simulations is mostly due to parametric inadequacies, and our analytical estimates may provide a simple way of obtaining predictive compressible jetincrossflow simulations.},
doi = {10.2514/1.J057204},
journal = {AIAA Journal},
number = 12,
volume = 56,
place = {United States},
year = {2018},
month = {9}
}
Web of Science
Figures / Tables:
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