The paper presents a method for reducing the cost of Computational Fluid Dynamics optimization by using a neural network to fill-in the design space. The method trains a network to approximate the aero-or hydrodynamic performance of vehicles with the Cascade Correlation algorithm. This network is coupled with a Genetic algorithm to optimize the hydrodynamic performance of the configuration.
I.U>, which, in CFD, is the aero-or hydrodynamic performance for a given configuration.A general optimization process is illustrated in Fig. 1. An initial set of design variables, which hight represent the configuration designed by experienced engineers, is supplied to the optimizer. Then, for this design, the objective function, f; is evaluated and the constraints, gi, are analyzed to check whether they are violated or not. If the optimum is not reached, these values are fed back to the optimizer that modifies the D.V.'s. The process is repeated until convergence.
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