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Journal of the Brazilian Society of Mechanical Sciences

versão impressa ISSN 0100-7386

Resumo

MARQUES, F. D.  e  ANDERSON, J.. Non-Linear Unsteady Aerodynamic Response Approximation Using Multi-Layer Functionals. J. Braz. Soc. Mech. Sci. [online]. 2002, vol.24, n.1, pp. 32-39. ISSN 0100-7386.  http://dx.doi.org/10.1590/S0100-73862002000100005.

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.

Palavras-chave : nsteady aerodynamics; aeroelasticity; multi-layer functionals; neural networks; genetic algorithms.

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