Estimation of kε parameters using surrogate models and jetincrossflow data
We demonstrate a Bayesian method that can be used to calibrate computationally expensive 3D RANS (Reynolds Av eraged Navier Stokes) models with complex response surfaces. Such calibrations, conditioned on experimental data, can yield turbulence model parameters as probability density functions (PDF), concisely capturing the uncertainty in the parameter estimates. Methods such as Markov chain Monte Carlo (MCMC) estimate the PDF by sampling, with each sample requiring a run of the RANS model. Consequently a quickrunning surrogate is used instead to the RANS simulator. The surrogate can be very difficult to design if the model's response i.e., the dependence of the calibration variable (the observable) on the parameter being estimated is complex. We show how the training data used to construct the surrogate can be employed to isolate a promising and physically realistic part of the parameter space, within which the response is wellbehaved and easily modeled. We design a classifier, based on treed linear models, to model the "wellbehaved region". This classifier serves as a prior in a Bayesian calibration study aimed at estimating 3 k  ε parameters ( C _{μ}, C _{ε2} , C _{ε1} ) from experimental data of a transonic jetincrossflow interaction. The robustness of themore »
 Authors:

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 Sandia National Lab. (SNLCA), Livermore, CA (United States)
 Publication Date:
 OSTI Identifier:
 1170402
 Report Number(s):
 SAND20150707
563548
 DOE Contract Number:
 AC0494AL85000
 Resource Type:
 Technical Report
 Research Org:
 Sandia National Lab. (SNLCA), Livermore, CA (United States); Sandia National Lab. (SNLNM), Albuquerque, NM (United States)
 Sponsoring Org:
 USDOE National Nuclear Security Administration (NNSA)
 Country of Publication:
 United States
 Language:
 English
 Subject:
 97 MATHEMATICS AND COMPUTING
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