We try to find a highly efficient and highly accurate method for optimizing transonic airfoil with fewer design variables and proper design space. Therefore we use the RSM and the genetic algorithm (CA) to do the optimization. Sections 1 through 4 of the full paper explain the airfoil optimization method mentioned in the title, which we believe is effective. Their core consists of; ( 1) we use the high-fidelity N-S equation solutions to construct the quadratic polynomial response surfaces with only eight design variables and reduced computational complexity; (2) with the help of response surface approximations, we use the CA to find the optimal design of an airfoil; (3) we study the design space with design variables given in Tables 2 and 3 and conclude that the smaller design space is likely to obtain the locally optimal solutions with high accuracy and that the larger design space may reduce the accuracy of RSM, thus leading to big differences from the N-S equation solutions; (4) we use the eight design variables and the design space between-0. 004 and 0. 004 for each of the design variables; the single-point and two-point optimal design results, presented in Tables 6 and 8 and Figs. 2, 4 and 5, and their analysis show preliminarily that our transonic airfoil optimization method is indeed accurate and efficient and has less computational complexity. According to sections 1 through 4, our method has the following strong points: (1) our method is both efficient and accurate in optimizing a transonic airfoil; (2) it can be widely applied in engineering practice.%文章针对气动优化设计中高效率和高精度的矛盾,综合响应面方法和遗传算法的优点和不足,采用多项式响应面模型代替原始遗传算法中计算量庞大的目标特性分析模型,建立了多项式响应面模型和遗传算法相结合的翼型优化设计方法,在采用N-S方程进行气动求解达到较高的精度情况下,求解次数显著减少,优化效率大大提高.研究了设计变量对优化设计效率的影响,文中方法在较少设计变量、较少N-S方程求解次数情况下,即可得到满意的优化设计结果.设计区间对响应面模型精度的影响显著,合适的设计区间既可保证模型精度,减少响应面预测结果和CFD计算结果的偏差,又可获得最佳优化结果.文中方法适用于翼型的单设计点和多设计点优化设计问题,具有原理简单、适应面宽、快速易行,且精度高等特点,可广泛应用于工程设计问题中.
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