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dc.contributor.author
 hal.structure.identifier
CINNELLA, Paola
134975 Laboratoire de Dynamique des Fluides [DynFluid]
dc.date.accessioned2015
dc.date.available2015
dc.date.issued2014
dc.date.submitted2015
dc.identifier.issn0021-9991
dc.identifier.urihttp://hdl.handle.net/10985/10035
dc.description.abstractThe 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.
dc.description.sponsorshipANR UFO
dc.language.isoen
dc.publisherElsevier
dc.rightsPre-print
dc.subjectbayesian model averaging
dc.subjectturbulence modelling
dc.subjectmodel errors
dc.titlePredictive RANS simulations via Bayesian Model-Scenario Averaging
dc.identifier.doi10.1016/j.jcp.2014.06.052
dc.typdocArticle dans une revue avec comité de lecture
dc.localisationCentre de Paris
dc.subject.halMathématique: Probabilités
dc.subject.halSciences de l'ingénieur: Mécanique: Mécanique des fluides
ensam.audienceInternationale
ensam.page65-91
ensam.journalJournal of Computational Physics
ensam.volume275
hal.identifierhal-01200771
hal.version1
hal.statusaccept
dc.identifier.eissn1090-2716


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