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

作     者：Beigmoradi, Sajjad Hajabdollahi, Hassan Ramezani, Asghar 

作者机构：SAIPA Automot Ind Res & Innovat Ctr Tehran Iran Vali e Asr Univ Rafsanjan Dept Mech Engn Rafsanjan Iran Zamyad Co Tehran Iran 

出 版 物：《COMPUTERS & FLUIDS》 (Comput. Fluids)

年 卷 期：2014年第90卷

页      面：123-132页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0801[工学-力学（可授工学、理学学位）] 

主　　题：Computational Fluid Dynamics Drag coefficient Aerodynamic noise Taguchi method Neural Networks Multi-Objective Genetic Algorithm 

摘      要：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. (C) 2013 Elsevier Ltd. All rights reserved.