Estimation of model error using bayesian model-scenario averaging with Maximum a Posterori-estimates

Book chapter (2019)

Authors

M. Schmelzer Aerodynamics - Aerospace Engineering
R.P. Dwight Aerodynamics - Aerospace Engineering
W.N. Edeling Aerodynamics - Aerospace Engineering , Stanford University
P. Cinnella Arts et Métiers ParisTech
Research Group
Aerodynamics (Aerospace Engineering) (TU Delft)
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Published Date

01-01-2019

Language

English

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Faculty

Aerospace Engineering

Department

Flow Physics and Technology

Research Group

Aerodynamics

Abstract

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 ReH= 5600, and finally over a generic falcon jet test case (Industrial challenge IC-03 of the UMRIDA project).

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