Predictive RANS simulations via Bayesian Model-Scenario Averaging
dc.contributor.author
hal.structure.identifier | CINNELLA, Paola
|
dc.date.accessioned | 2015 |
dc.date.available | 2015 |
dc.date.issued | 2014 |
dc.date.submitted | 2015 |
dc.identifier.issn | 0021-9991 |
dc.identifier.uri | http://hdl.handle.net/10985/10035 |
dc.description.abstract | 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. |
dc.description.sponsorship | ANR UFO |
dc.language.iso | en |
dc.publisher | Elsevier |
dc.rights | Pre-print |
dc.subject | bayesian model averaging |
dc.subject | turbulence modelling |
dc.subject | model errors |
dc.title | Predictive RANS simulations via Bayesian Model-Scenario Averaging |
dc.identifier.doi | 10.1016/j.jcp.2014.06.052 |
dc.typdoc | Article dans une revue avec comité de lecture |
dc.localisation | Centre de Paris |
dc.subject.hal | Mathématique: Probabilités |
dc.subject.hal | Sciences de l'ingénieur: Mécanique: Mécanique des fluides |
ensam.audience | Internationale |
ensam.page | 65-91 |
ensam.journal | Journal of Computational Physics |
ensam.volume | 275 |
hal.identifier | hal-01200771 |
hal.version | 1 |
hal.status | accept |
dc.identifier.eissn | 1090-2716 |