Estimation of Model Error Using Bayesian Model-Scenario Averaging with Maximum a Posterori-Estimates
| Autor: | Martin Schmelzer, Paola Cinnella, Wouter N. Edeling, Richard P. Dwight |
|---|---|
| Přispěvatelé: | Delft University of Technology (TU Delft), Stanford University, Laboratoire de Dynamique des Fluides (DynFluid), Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM) |
| Rok vydání: | 2018 |
| Předmět: |
UQ
Turbulence Modelling Computer science RANS Bayesian probability Posterior probability Bayesian inference Bayesian 01 natural sciences Bayesian Calibration 010305 fluids & plasmas Physics::Fluid Dynamics [SPI]Engineering Sciences [physics] 010104 statistics & probability Flow (mathematics) 0103 physical sciences Maximum a posteriori estimation Applied mathematics A priori and a posteriori Errors-in-variables models 0101 mathematics CFD Reynolds-averaged Navier–Stokes equations |
| Zdroj: | Uncertainty Management for Robust Industrial Design in Aeronautics ISBN: 9783319777665 Uncertainty Management for Robust Industrial Design in Aeronautics 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⟩ |
| DOI: | 10.1007/978-3-319-77767-2_4 |
| Popis: | International audience; 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). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|