TY - CHAP
T1 - Estimation of model error using bayesian model-scenario averaging with Maximum a Posterori-estimates
AU - Schmelzer, Martin
AU - Dwight, Richard P.
AU - Edeling, Wouter
AU - Cinnella, Paola
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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).
AB - 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).
KW - Bayesian calibration
KW - Bayesian scenario-model averaging
KW - CFD
KW - RANS
KW - Turbulence modelling
KW - UQ
UR - http://www.scopus.com/inward/record.url?scp=85051109102&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-77767-2_4
DO - 10.1007/978-3-319-77767-2_4
M3 - Chapter
AN - SCOPUS:85051109102
VL - 140
T3 - Notes on Numerical Fluid Mechanics and Multidisciplinary Design
SP - 53
EP - 69
BT - Notes on Numerical Fluid Mechanics and Multidisciplinary Design
PB - Springer Science+Business Media
ER -