Nonlinear Manifold Learning and Model Reduction for Transonic Flows

dc.cclicenceCC-BY-NCen
dc.contributor.authorZheng, Boda
dc.contributor.authorYao, Weigang
dc.contributor.authorXu, Min
dc.date.acceptance2023-08-14
dc.date.accessioned2023-09-13T10:08:16Z
dc.date.available2023-09-13T10:08:16Z
dc.date.issued2023-09-12
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractIt is aspirational to construct a nonlinear reduced-order model (ROM) with the ability to predict computational fluid dynamics (CFD) solutions accurately and efficiently. One major challenge is that the nonlinearity cannot be captured adequately by interpolation algorithm in low-dimensional space. To preserve the nonlinearity of CFD solutions for transonic flows, a new ROM is presented by integrating manifold learning into a constrained optimization, whereby a neighborhood preserving mapping is constructed by locally linear embedding (LLE) algorithm. Reconstruction errors are minimized in LLE by solving a least square problem subject to weight constraints. A loss function is proposed in the constrained optimization to preserve the geometric properties between high-dimensional space and low-dimensional manifolds. The proposed ROM is validated to predict nonlinear transonic flows over RAE 2822 airfoil and undeflected NASA Common Research Model with aspect ratio 9, in which nonlinearities are induced by shock waves. All results confirm that the ROM replicates CFD solutions accurately at fraction of the cost of CFD calculation or the full-order modeling.en
dc.funderNo external funderen
dc.identifier.citationZheng, B., Yao, W. and Xu, M. (2023) Nonlinear Manifold Learning and Model Reduction for Transonic Flows. AIAA Journal,en
dc.identifier.doihttps://doi.org/10.2514/1.J062894
dc.identifier.urihttps://hdl.handle.net/2086/23205
dc.language.isoen_USen
dc.peerreviewedYesen
dc.publisherAIAAen
dc.researchinstituteInstitute of Engineering Sciences (IES)en
dc.subjectMachine learningen
dc.subjectAerodynamicsen
dc.subjectTransonic flowsen
dc.titleNonlinear Manifold Learning and Model Reduction for Transonic Flowsen
dc.typeArticleen
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