Aerodynamic shape optimization using efficient evolutionary algorithms and unstructured CFD solver
Aerodynamic shape optimization using efficient evolutionary algorithms and unstructured CFD solver
复制标题
使用高效进化算法和非结构化 CFD 求解器进行空气动力学形状优化
DOI：
10.1016/j.compfluid.2011.02.010
复制
发表时间：
2011
影响因子：
2.8
通讯作者：
A. Shahrokhi
中科院分区：
文献类型：
--
作者：
A. Jahangirian;A. Shahrokhi
提出了一种有效的跨音速翼型外形优化的进化算法。为了提高优化遗传算法（GA）的效率和收敛速度，人们采用了多种技术。采用一种新的翼型形状参数化方法，该方法能够在粘性流条件下产生更有效的形状。为了提高遗传算法的鲁棒性和收敛速度，提出了一种实数编码的种群离散遗传算法。利用多层感知器神经网络（NN）来减少目标函数评估的巨大计算成本。通过使用训练数据的动态再训练和正态分布来确定NN的设计空间的良好训练部分，从而获得NN性能的进一步改善。采用上述技术，优化算法的总计算时间比传统的遗传算法减少了60%。
An efficient evolutionary algorithm is presented for shape optimization of transonic airfoils. Several techniques have been used to improve the efficiency and convergence rate of the optimization Genetic Algorithm (GA). A new airfoil shape parameterization method is used which is capable of producing more efficient shapes at viscous flow conditions. A Real-Coded Population Dispersion (PD) Genetic Algorithm is developed in order to increase the robustness and convergence rate of the Genetic Algorithm. A Multi-Layer Perceptron Neural Network (NN) is utilized to reduce the huge computational cost of the objective function evaluation. Further improvement in the performance of NN is obtained by using dynamic retraining and normal distribution of the training data to determine well trained parts of the design space to NN. Using the above techniques, the total computational time of optimization algorithm is reduced up to 60% compared with the conventional GA.