基于分层策略的翼型多参量气动优化设计研究

Research on Multi-parameters Aerodynamic Shape Optimization Design of Airfoil Based on Hierarchical Starategy

与基于梯度的优化方法相比,遗传算法因其极强的鲁棒性、随机搜索及优化结果全局性等特点在工程优化中得到越来越广泛的应用。为提高优化设计的效率,改进了传统的遗传算法,采用并行分层策略基因遗传算法开展了翼型多参量气动优化设计研究,包括翼型和多段翼型的基因编码、外形参数化,以及动网格技术。结果表明,并行分层策略在得到较优气动优化结果的同时,极大地缩短了优化时间,提高了计算效率,具有广阔的工程应用前景。

Compared with the optimization method based on the gradient theory, genetic algorithm （GA） is more and more widely applied in the field of engineer optimization due to its robustness, randomicity and global optimal performance. In order to improve the efficiency of the optimization design, the traditional GA for multi- parameters aerodynamic shape optimum design of airfoil and multi- element airfoil is improved by using hierarchical parallelization strategy. Genetic coding and shape parameterization are investigated in this paper in combination with mesh moving technical. The results show that hierarchical starat- egy that can dramatically shorten the time for optimization and improve the computational efficiency is efficient and reasonable. So, this method has wide prospects for engineering applications.