| Full text | |
| Author(s): |
Total Authors: 2
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| Affiliation: | [1] Universidade de São Paulo. Escola de Engenharia de São Carlos. Laboratório de Aeroelasticidade, Dinâmica Laboratório de Aeroelasticidade, Dinâmica de Vôo e Controle, LADinC - Brasil
[2] University of Glasgow. Department of Aerospace Engineering - Ucrânia
Total Affiliations: 2
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| Document type: | Journal article |
| Source: | Journal of the Brazilian Society of Mechanical Sciences; v. 24, n. 1, p. 32-39, 2002-03-00. |
| Abstract | |
Non-linear functional representation of the aerodynamic response provides a convenient mathematical model for motion-induced unsteady transonic aerodynamic loads response, that accounts for both complex non-linearities and time-history effects. A recent development, based on functional approximation theory, has established a novel functional form; namely, the multi-layer functional. For a large class of non-linear dynamic systems, such multi-layer functional representations can be realised via finite impulse response (FIR) neural networks. Identification of an appropriate FIR neural network model is facilitated by means of a supervised training process in which a limited sample of system input-output data sets is presented to the temporal neural network. The present work describes a procedure for the systematic identification of parameterised neural network models of motion-induced unsteady transonic aerodynamic loads response. The training process is based on a conventional genetic algorithm to optimise the network architecture, combined with a simplified random search algorithm to update weight and bias values. Application of the scheme to representative transonic aerodynamic loads response data for a bidimensional airfoil executing finite-amplitude motion in transonic flow is used to demonstrate the feasibility of the approach. The approach is shown to furnish a satisfactory generalisation property to different motion histories over a range of Mach numbers in the transonic regime. (AU) | |