Multi-objective aero acoustic optimization of rear end in a simplified car model by using hybrid Robust Parameter Design, Artificial Neural Networks and Genetic Algorithm methods

• Performing the numerical drag and noise analysis for a simplified car model.
• Identification of aerodynamic noise sources for considered car model.
• Reduction of computational complexity using Taguchi and ANN methods.
• Multi objective optimization by considering aerodynamic and acoustic as objectives.

In this paper, optimization of rear end of a simplified car model is performed considering aerodynamic and acoustic objectives. Slant angle, rear box angle, boat tail angle, and rear box length are considered as main variables of the rear end. For numerical simulation of flow around the model and studying aerodynamic noise, realizable turbulent model and broad band noise model are used, respectively. Simulation results are validated by the experimental results reported in the literature. To reduce number of simulations to reach optimum values of parameters, Taguchi method has been used. The results of Taguchi are in good agreement with simulation results. Then, the results of Taguchi have been used to obtain a relation between parameters and objectives employing Artificial Neural Networks. Optimization of the model has been conducted by the Neural Network and Multi Objective Genetic Algorithm methods. Finally, flow around the optimized model has been studied by numerical simulation and results have been reported.