Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7124
Title: Neural network-based modelling of subsonic cavity flows
Authors: Efe, Mehmet Önder
Debias, Marco
Yan, Peng
Özbay, Hitay
Samimy, Mohammad
Keywords: flow modeling
neural networks
identification
Issue Date: 2008
Publisher: Taylor & Francis Ltd
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.
URI: https://doi.org/10.1080/00207720701726188
https://hdl.handle.net/20.500.11851/7124
ISSN: 0020-7721
1464-5319
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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