Abstract
An aerodynamic shape optimization tool for complex industrial flows is developed, based on an hybrid process. The optimization method couples a stochastic genetic algorithm and a deterministic BFGS hill-climbing method. For each evaluation required by the optimizer, the Navier–Stokes equations with the k– ϵ turbulence model are solved with a commercial CFD code on an unstructured mesh surrounding the shape to optimize. After various validation test cases, the method is successfully applied to optimize the rear of a simplified car shape in order to bring acceleration in the computational time of the minimization of the drag coefficient.
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