| Journal: | Journal of the Brazilian Society of Mechanical Sciences |
| Database: | PERIÓDICA |
| System number: | 000312129 |
| ISSN: | 0100-7386 |
| Authors: | Marques, F.D1 Anderson, J2 |
| Institutions: | 1Universidade de Sao Paulo, Escola de Engenharia de Sao Carlos, Sao Carlos, Sao Paulo. Brasil 2University of Glasgow, Department of Aerospace Engineering, Glasgow, Lanark. Reino Unido |
| Year: | 2002 |
| Season: | Mar |
| Volumen: | 24 |
| Number: | 1 |
| Pages: | 32-39 |
| Country: | Brasil |
| Language: | Inglés |
| Document type: | Artículo |
| Approach: | Experimental |
| English 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 |
| Disciplines: | Ingeniería |
| Keyword: | Ingeniería mecánica, Aerodinámica, Inestabilidad, Aeroelasticidad, Redes neuronales, Algoritmos genéticos |
| Keyword: | Engineering, Mechanical engineering, Aerodynamics, Instability, Aeroelasticity, Neural networks, Genetic algorithms |
| Full text: | Texto completo (Ver HTML) |
