Please use this identifier to cite or link to this item: https://doi.org/10.1080/00207720701726188
Title: Neural network-based modelling of subsonic cavity flows
Authors: Efe, M.Ö.
Debiasi, M. 
Yan, P.
Özbay, H.
Samimy, M.
Keywords: Flow modeling
Identification
Neural networks
Issue Date: Feb-2008
Citation: Efe, M.Ö., Debiasi, M., Yan, P., Özbay, H., Samimy, M. (2008-02). Neural network-based modelling of subsonic cavity flows. International Journal of Systems Science 39 (2) : 105-117. ScholarBank@NUS Repository. https://doi.org/10.1080/00207720701726188
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.
Source Title: International Journal of Systems Science
URI: http://scholarbank.nus.edu.sg/handle/10635/115827
ISSN: 00207721
DOI: 10.1080/00207720701726188
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