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 \(Re_H=5600\), and finally over a generic falcon jet test case (Industrial challenge IC-03 of the UMRIDA project).
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- 1.
The MAP estimates are available online at [11].
- 2.
The template-cases for each turbulence model are available on GitHub: https://github.com/shmlzr/UQOpenFOAM.
- 3.
Underlying flow regime 3–30, 2D Periodic Hill Flow: http://qnet-ercoftac.cfms.org.uk.
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Acknowledgements
We thank Dassault Aviation and especially Gilbert Roge for the collaboration on the test case of the generic falcon jet.
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Schmelzer, M., Dwight, R.P., Edeling, W., Cinnella, P. (2019). Estimation of Model Error Using Bayesian Model-Scenario Averaging with Maximum a Posterori-Estimates. In: Hirsch, C., Wunsch, D., Szumbarski, J., Łaniewski-Wołłk, Ł., Pons-Prats, J. (eds) Uncertainty Management for Robust Industrial Design in Aeronautics . Notes on Numerical Fluid Mechanics and Multidisciplinary Design, vol 140. Springer, Cham. https://doi.org/10.1007/978-3-319-77767-2_4
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