Recurrent neural network for end-to-end modeling of laminar-turbulent transition

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dc.contributor.authorZafar, Muhammad I.en
dc.contributor.authorChoudhari, Meelan M.en
dc.contributor.authorParedes, Pedroen
dc.contributor.authorXiao, Hengen
dc.date.accessioned2022-06-16T20:44:46Zen
dc.date.available2022-06-16T20:44:46Zen
dc.date.issued2021-06-29en
dc.description.abstractAccurate prediction of laminar-turbulent transition is a critical element of computational fluid dynamics simulations for aerodynamic design across multiple flow regimes.Traditional methods of transition prediction cannot be easily extended to flow configurations where the transition process depends on a large set of parameters. In comparison, neural network methods allow higher dimensional input features to be considered without compromising the efficiency and accuracy of the traditional data-driven models. Neural network methods proposed earlier follow a cumbersome methodology of predicting instability growth rates over a broad range of frequencies, which are then processed to obtain the N-factor envelope, and then, the transition location based on the correlatingN-factor. This paper presents an end-to-end transition model based on a recurrent neural network, which sequentially processes the mean boundary-layer profiles along the surface of the aerodynamic body to directly predict the N-factor envelope and the transition locations over a two-dimensional airfoil. The proposed transition model has been developed and assessed using a large database of 53 airfoils over a wide range of chord Reynolds numbers and angles of attack. The large universe of airfoils encountered in various applications causes additional difficulties. As such, we provide further insights on selecting training datasets from large amounts of available data.Although the proposed model has been analyzed for two-dimensional boundary layers in this paper, it can be easily generalized to other flows due to embedded feature extraction capability of convolutional neural network in the model.en
dc.description.sponsorshipThis research was supported by the Revolutionary Computational AeroSciences discipline of NASA’s Transformational Tools and Technologies Project.en
dc.description.versionPublished versionen
dc.format.extent31 pagesen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1017/dce.2021.11en
dc.identifier.urihttp://hdl.handle.net/10919/110819en
dc.identifier.volume2en
dc.language.isoenen
dc.publisherCambridge University Pressen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectLaminar-turbulent transitionen
dc.subjectscientific machine learningen
dc.subjectrecurrent neural networken
dc.titleRecurrent neural network for end-to-end modeling of laminar-turbulent transitionen
dc.title.serialData-Centric Engineeringen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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