Modeling of subsonic cavity flows by neural networks

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buir.contributor.authorÖzbay, Hitay
dc.citation.epage565en_US
dc.citation.spage560en_US
dc.contributor.authorEfe, M.Ö.en_US
dc.contributor.authorDebiasi, M.en_US
dc.contributor.authorÖzbay, Hitayen_US
dc.contributor.authorSamimy, M.en_US
dc.date.accessioned2016-02-08T11:53:22Z
dc.date.available2016-02-08T11:53:22Z
dc.date.issued2004-06en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 3-5 June 2004
dc.descriptionConference name: Proceedings of the IEEE International Conference on Mechatronics, 2004. ICM '04.
dc.description.abstractInfluencing the behavior of a flow field is a core issue as its improvement can yield significant increase of the efficiency and performance of fluidic systems. On the other hand, the tools of classical control systems theory are not directly applicable to processes displaying spatial continuity as in fluid flows. The cavity flow is a good example of this and a recent research focus in aerospace science is its modeling and control. The objective is to develop a finite dimensional representative model for the system with appropriately defined inputs and outputs. Towards the goal of reconstructing the pressure fluctuations measured at the cavity floor, this paper demonstrates that given some history of inputs and outputs, a neural network based feedforward model can be developed such that the response of the neural network matches the measured response. The advantages of using such a model are the representational simplicity of the model, structural flexibility to enable controller design and the ability to store information in an interconnected structure.en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T11:53:22Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2004en
dc.identifier.doi10.1109/ICMECH.2004.1364500
dc.identifier.urihttp://hdl.handle.net/11693/27437
dc.language.isoEnglishen_US
dc.publisherIEEE
dc.relation.isversionofhttps://doi.org/10.1109/ICMECH.2004.1364500
dc.source.titleProceedings of the IEEE International Conference on Mechatronics 2004, ICM'04en_US
dc.subjectAerodynamicsen_US
dc.subjectElectric potentialen_US
dc.subjectFluidicsen_US
dc.subjectNeural networksen_US
dc.subjectPressure effectsen_US
dc.subjectProblem solvingen_US
dc.subjectScatteringen_US
dc.subjectTransfer functionsen_US
dc.subjectAcoustic scatteringen_US
dc.subjectCavity flowen_US
dc.subjectFlow physicsen_US
dc.subjectMaterial fatigueen_US
dc.subjectSubsonic flowen_US
dc.titleModeling of subsonic cavity flows by neural networksen_US
dc.typeConference Paperen_US

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