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Title: Predictive RANS simulations via Bayesian Model-Scenario Averaging

The turbulence closure model is the dominant source of error in most Reynolds-Averaged Navier–Stokes simulations, yet no reliable estimators for this error component currently exist. Here we develop a stochastic, a posteriori error estimate, calibrated to specific classes of flow. It is based on variability in model closure coefficients across multiple flow scenarios, for multiple closure models. The variability is estimated using Bayesian calibration against experimental data for each scenario, and Bayesian Model-Scenario Averaging (BMSA) is used to collate the resulting posteriors, to obtain a stochastic estimate of a Quantity of Interest (QoI) in an unmeasured (prediction) scenario. The scenario probabilities in BMSA are chosen using a sensor which automatically weights those scenarios in the calibration set which are similar to the prediction scenario. The methodology is applied to the class of turbulent boundary-layers subject to various pressure gradients. For all considered prediction scenarios the standard-deviation of the stochastic estimate is consistent with the measurement ground truth. Furthermore, the mean of the estimate is more consistently accurate than the individual model predictions.
Authors:
 [1] ;  [2] ;  [1] ;  [3]
  1. Arts et Métiers ParisTech, DynFluid laboratory, 151 Boulevard de l'Hospital, 75013 Paris (France)
  2. (Netherlands)
  3. Delft University of Technology, Faculty of Aerospace Engineering, Kluyverweg 2, Delft (Netherlands)
Publication Date:
OSTI Identifier:
22382128
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Computational Physics; Journal Volume: 275; Other Information: Copyright (c) 2014 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; BOUNDARY LAYERS; CALIBRATION; ERRORS; GROUND TRUTH MEASUREMENTS; MATHEMATICAL MODELS; NAVIER-STOKES EQUATIONS; PROBABILITY; REYNOLDS NUMBER; SENSORS; STOCHASTIC PROCESSES; TURBULENCE