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oai:dspace.mit.edu:1721.1/1528292023-11-03T04:00:22Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023

Lozano-Durán, Adrián Ling, Yuenong Massachusetts Institute of Technology. Department of Aeronautics and Astronautics 2023-11-02T20:19:29Z 2023-11-02T20:19:29Z 2023-09 2023-09-20T15:14:40.931Z https://hdl.handle.net/1721.1/152829 A unified subgrid-scale (SGS) and wall model—the building-block flow model (BFM) — for wall modeled large-eddy simulation (WMLES) is proposed by devising the flow as a collection of building blocks that enables the prediction of the eddy viscosity. The core assumption of the model is that simple canonical flows contain the essential physics to provide accurate predictions of the SGS tensor in more complex flows. The model is constructed to predict zero-pressure-gradient wall-bounded turbulence, adverse/ favorable pressure gradient effects, separation and laminar flow. The approach is implemented using a Bayesian classifier, which identifies the contribution of each building block in the flow, and a neural-network-based predictor, which estimates the eddy viscosity based on the building-block units. The training data are directly obtained from wall-modeled LES with an exact SGS/wall model for the mean quantities to guarantee consistency with the numerical discretization. The model is validated in canonical flows, the NASA High-Lift Common Research Model and a Gaussian bump and shown to improve the predictions with respect to current modeling approaches. The modular extensibility of the BFM paradigm will allow for future improvements by incorporating additional physics. S.M. Massachusetts Institute of Technology In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ Wall-modeled Large-eddy Simulation Based on Building-block Flows Thesis application/pdf Master Master of Science in Aeronautics and Astronautics Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="20ab4313-9c14-498a-897f-dd2201a450bf"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>Wall-modeled Large-eddy Simulation Based on Building-block Flows</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2023-09</PublicationDate> <Authors> <Author> <DisplayName>Ling, Yuenong</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>https://rightsstatements.org/page/InC-EDU/1.0/</License> <Abstract>A unified subgrid-scale (SGS) and wall model—the building-block flow model (BFM) — for wall modeled large-eddy simulation (WMLES) is proposed by devising the flow as a collection of building blocks that enables the prediction of the eddy viscosity. The core assumption of the model is that simple canonical flows contain the essential physics to provide accurate predictions of the SGS tensor in more complex flows. The model is constructed to predict zero-pressure-gradient wall-bounded turbulence, adverse/ favorable pressure gradient effects, separation and laminar flow. The approach is implemented using a Bayesian classifier, which identifies the contribution of each building block in the flow, and a neural-network-based predictor, which estimates the eddy viscosity based on the building-block units. The training data are directly obtained from wall-modeled LES with an exact SGS/wall model for the mean quantities to guarantee consistency with the numerical discretization. The model is validated in canonical flows, the NASA High-Lift Common Research Model and a Gaussian bump and shown to improve the predictions with respect to current modeling approaches. The modular extensibility of the BFM paradigm will allow for future improvements by incorporating additional physics.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>