Neural network-based modelling of subsonic cavity flows

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buir.contributor.authorÖzbay, Hitay
dc.citation.epage117en_US
dc.citation.issueNumber2en_US
dc.citation.spage105en_US
dc.citation.volumeNumber39en_US
dc.contributor.authorEfe, M. Ö.en_US
dc.contributor.authorDebiasi, M.en_US
dc.contributor.authorYan, P.en_US
dc.contributor.authorÖzbay, Hitayen_US
dc.contributor.authorSamimy, M.en_US
dc.date.accessioned2016-02-08T10:10:19Z
dc.date.available2016-02-08T10:10:19Z
dc.date.issued2008en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractA fundamental problem in the applications involved with aerodynamic flows is the difficulty in finding a suitable dynamical model containing the most significant information pertaining to the physical system. Especially in the design of feedback control systems, a representative model is a necessary tool constraining the applicable forms of control laws. This article addresses the modelling problem by the use of feedforward neural networks (NNs). Shallow cavity flows at different Mach numbers are considered, and a single NN admitting the Mach number as one of the external inputs is demonstrated to be capable of predicting the floor pressures. Simulations and real time experiments have been presented to support the learning and generalization claims introduced by NN-based models.en_US
dc.identifier.doi10.1080/00207720701726188en_US
dc.identifier.eissn1464-5319
dc.identifier.issn0020-7721
dc.identifier.urihttp://hdl.handle.net/11693/23208
dc.language.isoEnglishen_US
dc.publisherTaylor & Francisen_US
dc.relation.isversionofhttps://doi.org/10.1080/00207720701726188en_US
dc.source.titleInternational Journal of Systems Scienceen_US
dc.subjectFlow modelingen_US
dc.subjectNeural networksen_US
dc.subjectIdentificationen_US
dc.titleNeural network-based modelling of subsonic cavity flowsen_US
dc.typeArticleen_US

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