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dc.contributor.author
 hal.structure.identifier
MERLE, Xavier
134975 Laboratoire de Dynamique des Fluides [DynFluid]
dc.contributor.author
 hal.structure.identifier
CINNELLA, Paola
134975 Laboratoire de Dynamique des Fluides [DynFluid]
dc.date.accessioned2015
dc.date.available2016
dc.date.issued2015
dc.date.submitted2015
dc.identifier.issn0951-8320
dc.identifier.urihttp://hdl.handle.net/10985/10073
dc.description.abstractA Bayesian inference methodology is developed for calibrating complex equations of state used in numerical fluid flow solvers. Precisely, the input parameters of three equations of state commonly used for modeling the thermodynamic behavior of so-called dense gas flows, – i.e. flows of gases characterized by high molecular weights and complex molecules, working in thermodynamic conditions close to the liquid-vapor saturation curve–, are calibrated by means of Bayesian inference from reference aerodynamic data for a dense gas flow over a wing section. Flow thermodynamic conditions are such that the gas thermodynamic behavior strongly deviates from that of a perfect gas. In the aim of assessing the proposed methodology, synthetic calibration data –specifically, wall pressure data– are generated by running the numerical solver with a more complex and accurate thermodynamic model. The statistical model used to build the likelihood func-tion includes a model-form inadequacy term, accounting for the gap between the model output associated to the best-fit parameters, and the true phenomenon. Results show that, for all of the relatively simple models under investigation, calibrations lead to infor-mative posterior probability density distributions of the input parameters and improve the predictive distribution significantly. Nevertheless, calibrated parameters strongly differ from their expected physical values. The relationship between this behavior and model-form inadequacy is discussed.
dc.description.sponsorshipANR UFO
dc.language.isoen
dc.publisherElsevier
dc.rightsPost-print
dc.titleBayesian quantification of thermodynamic uncertainties in dense gas flows
ensam.embargo.terms1 Year
dc.identifier.doi10.1016/j.ress.2014.08.006
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.page305-323
ensam.journalReliability Engineering and System Safety
ensam.volume134
hal.statusunsent
dc.identifier.eissn1879-0836


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