Estimation of k-e parameters using surrogate models and jet-in-crossflow 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 quick-running 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 well-behaved and easily modeled. We design a classifier, based on treed linear models, to model the "well-behaved region". This classifier serves as a prior in a Bayesian calibration study aimed at estimating 3 k [?] e parameters ( C u , C e 2 , C e 1 ) from experimental data of a transonic jet-in-crossflow interaction. Themore »
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- OSTI Identifier:
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- DOE Contract Number:
- Resource Type:
- Technical Report
- Research Org:
- Sandia National Laboratories (SNL-CA), Livermore, CA (United States); Sandia National Laboratories, Albuquerque, NM
- Sponsoring Org:
- USDOE National Nuclear Security Administration (NNSA)
- Country of Publication:
- United States
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