Identification and prediction of unsteady transonic aerodynamic loads by multi-layer functionals

摘要

Nonlinear unsteady aerodynamic effects present major modelling difficulties in the analysis and control of aeroelastic response. A rigorous mathematical framework, that can account for the complex nonlinearities and time-history effects of the unsteady aerodynamic response, is provided by the use of functional representations. A recent development, based on functional approximation theory, has achieved a new functional form, namely, multi-layer functionals. The development of a multi-layer functional for discrete-time, finite memory, causal systems has been shown to be realizable via finite impulse response neural networks. Identification of an appropriate temporal neural network model of e nonlinear transonic aerodynamic response is facilitated via a supervised training process using multiple input-output sets, with data obtained by an Euler CFD code. The training process is based on a genetic algorithm to optimize the network architecture, combined with a random search I algorithm to update weight and bias values. The approach is examined for two different multiple aerodynamic input-output data sets, and in both cases, the prediction properties of the network model establish the multi-layer functional as a suitable representation of unsteady aerodynamic response.