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

Martin Schmelzer*, Richard P. Dwight, Wouter Edeling, Paola Cinnella

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientificpeer-review

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).

Original languageEnglish
Title of host publicationNotes on Numerical Fluid Mechanics and Multidisciplinary Design
PublisherSpringer
Pages53-69
Number of pages17
Volume140
DOIs
Publication statusPublished - 1 Jan 2019

Publication series

NameNotes on Numerical Fluid Mechanics and Multidisciplinary Design
Volume140
ISSN (Print)1612-2909

Keywords

  • Bayesian calibration
  • Bayesian scenario-model averaging
  • CFD
  • RANS
  • Turbulence modelling
  • UQ

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