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Robust Bayesian Calibration of a k-ε Model for Compressible Jet-in-Crossflow Simulations

Journal Article · · AIAA Journal
DOI:https://doi.org/10.2514/1.J057204· OSTI ID:1478069
 [1];  [2];  [2];  [3];  [2]
  1. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  3. Citrine Informatics, Redwood City, CA (United States)
+ Show Author Affiliations

Compressible jet-in-crossflow interactions are difficult to simulate accurately using Reynolds-averaged 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 k-ε model to experimental data had led to a weak hypothesis that inaccurate simulations could be due to inappropriate constants more than model-form inadequacies of RANS. In this work, Bayesian calibration of k-ε 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 model-form errors. An analytical model of jet-in-crossflow interactions has also been developed, and estimates of k-ε constants that are free of any conflation of parametric and RANS’s model-form uncertainties have been obtained. It has been found that the analytical k-ε constants provide mean-flow 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 jet-in-crossflow simulations is mostly due to parametric inadequacies, and our analytical estimates may provide a simple way of obtaining predictive compressible jet-in-crossflow simulations.

Research Organization:
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA), Office of Defense Science (NA-113)
Grant/Contract Number:
AC04-94AL85000
OSTI ID:
1478069
Report Number(s):
SAND--2018-7772J; 665861
Journal Information:
AIAA Journal, Journal Name: AIAA Journal Journal Issue: 12 Vol. 56; ISSN 0001-1452
Publisher:
AIAACopyright Statement
Country of Publication:
United States
Language:
English

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Figures / Tables (15)

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Table 1(p. 29)table Table 1
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