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      Neural network-based modelling of subsonic cavity flows

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      Author(s)
      Efe, M. Ö.
      Debiasi, M.
      Yan, P.
      Özbay, Hitay
      Samimy, M.
      Date
      2008
      Source Title
      International Journal of Systems Science
      Print ISSN
      0020-7721
      Electronic ISSN
      1464-5319
      Publisher
      Taylor & Francis
      Volume
      39
      Issue
      2
      Pages
      105 - 117
      Language
      English
      Type
      Article
      Item Usage Stats
      128
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      123
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      Abstract
      A 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.
      Keywords
      Flow modeling
      Neural networks
      Identification
      Permalink
      http://hdl.handle.net/11693/23208
      Published Version (Please cite this version)
      https://doi.org/10.1080/00207720701726188
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      • Department of Electrical and Electronics Engineering 3702
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