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dc.contributor.authorWu, Jinlongen_US
dc.date.accessioned2018-09-26T08:00:24Z
dc.date.available2018-09-26T08:00:24Z
dc.date.issued2018-09-25
dc.identifier.othervt_gsexam:17277en_US
dc.identifier.urihttp://hdl.handle.net/10919/85129
dc.description.abstractReynolds-Averaged Navier-Stokes (RANS) simulations are widely used for engineering design and analysis involving turbulent flows. In RANS simulations, the Reynolds stress needs closure models and the existing models have large model-form uncertainties. Therefore, the RANS simulations are known to be unreliable in many flows of engineering relevance, including flows with three-dimensional structures, swirl, pressure gradients, or curvature. This lack of accuracy in complex flows has diminished the utility of RANS simulations as a predictive tool for engineering design, analysis, optimization, and reliability assessments. Recently, data-driven methods have emerged as a promising alternative to develop the model of Reynolds stress for RANS simulations. In this dissertation I explore two physics-informed, data-driven frameworks to improve RANS modeled Reynolds stresses. First, a Bayesian inference framework is proposed to quantify and reduce the model-form uncertainty of RANS modeled Reynolds stress by leveraging online sparse measurement data with empirical prior knowledge. Second, a machine-learning-assisted framework is proposed to utilize offline high-fidelity simulation databases. Numerical results show that the data-driven RANS models have better prediction of Reynolds stress and other quantities of interest for several canonical flows. Two metrics are also presented for an a priori assessment of the prediction confidence for the machine-learning-assisted RANS model. The proposed data-driven methods are also applicable to the computational study of other physical systems whose governing equations have some unresolved physics to be modeled.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis item is protected by copyright and/or related rights. Some uses of this item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectTurbulence modelingen_US
dc.subjectRANSen_US
dc.subjectModel-form uncertaintyen_US
dc.subjectData-drivenen_US
dc.subjectUncertainty quantificationen_US
dc.subjectBayesian Inferenceen_US
dc.subjectMachine learningen_US
dc.titlePredictive Turbulence Modeling with Bayesian Inference and Physics-Informed Machine Learningen_US
dc.typeDissertationen_US
dc.contributor.departmentAerospace and Ocean Engineeringen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineAerospace Engineeringen_US
dc.contributor.committeechairXiao, Hengen_US
dc.contributor.committeechairPaterson, Eric G.en_US
dc.contributor.committeememberRoy, Christopher Johnen_US
dc.contributor.committeememberLowe, Kevin T.en_US


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