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Chapitre D'ouvrage Année : 2018

Estimation of Model Error Using Bayesian Model-Scenario Averaging with Maximum a Posterori-Estimates

Résumé

The lack of an universal modelling approach for turbulence in Reynolds-Averaged Navier–Stokes simulations creates the need for quantifying the modelling error without additional validation data. Bayesian Model-Scenario Averaging (BMSA), which exploits the variability on model closure coefficients across several flow scenarios and multiple models, gives a stochastic, a posteriori estimate of a quantity of interest. The full BMSA requires the propagation of the posterior probability distribution of the closure coefficients through a CFD code, which makes the approach infeasible for industrial relevant flow cases. By using maximum a posteriori (MAP) estimates on the posterior distribution, we drastically reduce the computational costs. The approach is applied to turbulent flow in a pipe at Re= 44,000 over 2D periodic hills at Re=5600, and finally over a generic falcon jet test case (Industrial challenge IC-03 of the UMRIDA project).
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Dates et versions

hal-02167214 , version 1 (29-08-2019)

Identifiants

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Martin Schmelzer, Richard P. Dwight, Wouter N. Edeling, Paola Cinnella. Estimation of Model Error Using Bayesian Model-Scenario Averaging with Maximum a Posterori-Estimates. Uncertainty Management for Robust Industrial Design in Aeronautics, Springer International Publishing, pp.53-69, 2018, 978-3-319-77767-2. ⟨10.1007/978-3-319-77767-2_4⟩. ⟨hal-02167214⟩
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