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 var...

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Bibliographic Details
Published in:Journal of computational physics 2014-10, Vol.275, p.65-91
Main Authors: Edeling, W.N., Cinnella, P., Dwight, R.P.
Format: Article
Language:English
Subjects:
Bayesian analysis
Bayesian model averaging
Bayesian Model-Scenario Averaging
BOUNDARY LAYERS
CALIBRATION
CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS
Closures
Engineering Sciences
Error estimation
ERRORS
Estimates
Fluids mechanics
GROUND TRUTH MEASUREMENTS
MATHEMATICAL MODELS
Mathematics
Mechanics
Model inadequacy
NAVIER-STOKES EQUATIONS
PROBABILITY
RANS models
REYNOLDS NUMBER
SENSORS
STOCHASTIC PROCESSES
Stochasticity
TURBULENCE
Uncertainty quantification
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Summary: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.
ISSN:0021-9991
1090-2716
DOI:10.1016/j.jcp.2014.06.052