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Bayesian Parameter Estimation of a κ-ε Model for Accurate Jet-in-Crossflow Simulations.

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Bibliographic Details
Title:	Bayesian Parameter Estimation of a κ-ε Model for Accurate Jet-in-Crossflow Simulations.
Authors:	Ray, Jaideep¹, Lefantzi, Sophia¹, Arunajatesan, Srinivasan², Dechant, Lawrence²
Source:	AIAA Journal. Aug2016, Vol. 54 Issue 8, p2432-2448. 17p.
Abstract:	Reynolds-averaged Navier-Stokes models are not very accurate for high-Reynolds-number compressible jet-in-crossflow interactions. The inaccuracy arises from the use of inappropriate model parameters and model-form errors in the Reynolds-averaged Navier-Stokes model. In this work, the hypothesis is pursued that Reynolds-averaged Navier-Stokes predictions can be significantly improved by using parameters inferred from experimental measurements of a supersonic jet interacting with a transonic crossflow. A Bayesian inverse problem is formulated to estimate three Reynolds-averaged Navier-Stokes parameters (C956,Cε2,Cε1), and a Markov chain Monte Carlo method is used to develop a probability density function for them. The cost of the Markov chain Monte Carlo is addressed by developing statistical surrogates for the Reynolds-averaged Navier-Stokes model. It is found that only a subset of the (C956,Cε2,Cε1) space RR supports realistic flow simulations. RR is used as a prior belief when formulating the inverse problem. It is enforced with a classifier in the current Markov chain Monte Carlo solution. It is found that the calibrated parameters improve predictions of the entire flowfield substantially when compared to the nominal/literature values of (C956,Cε2,Cε1); furthermore, this improvement is seen to hold for interactions at other Mach numbers and jet strengths for which the experimental data are available to provide a comparison. The residual error is quantifies, which is an approximation of the model-form error; it is most easily measured in terms of turbulent stresses. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	Reynolds-averaged Navier-Stokes models are not very accurate for high-Reynolds-number compressible jet-in-crossflow interactions. The inaccuracy arises from the use of inappropriate model parameters and model-form errors in the Reynolds-averaged Navier-Stokes model. In this work, the hypothesis is pursued that Reynolds-averaged Navier-Stokes predictions can be significantly improved by using parameters inferred from experimental measurements of a supersonic jet interacting with a transonic crossflow. A Bayesian inverse problem is formulated to estimate three Reynolds-averaged Navier-Stokes parameters (C956,Cε2,Cε1), and a Markov chain Monte Carlo method is used to develop a probability density function for them. The cost of the Markov chain Monte Carlo is addressed by developing statistical surrogates for the Reynolds-averaged Navier-Stokes model. It is found that only a subset of the (C956,Cε2,Cε1) space RR supports realistic flow simulations. RR is used as a prior belief when formulating the inverse problem. It is enforced with a classifier in the current Markov chain Monte Carlo solution. It is found that the calibrated parameters improve predictions of the entire flowfield substantially when compared to the nominal/literature values of (C956,Cε2,Cε1); furthermore, this improvement is seen to hold for interactions at other Mach numbers and jet strengths for which the experimental data are available to provide a comparison. The residual error is quantifies, which is an approximation of the model-form error; it is most easily measured in terms of turbulent stresses. [ABSTRACT FROM AUTHOR]
ISSN:	00011452
DOI:	10.2514/1.J054758