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将基因算法与博奕论中的 Nash平衡相结合 ,构建了多目标基因优化算法—— Nash基因算法 (NashGAs) ,并对初始翼型为 NACA0 0 1 2二维翼型进行给定跨音速流动下的形状增升优化。计算中应用 Bézier曲线对翼型形状进行参数化 ,避免了非流线型的不合理形状产生 ;采用有限元非结构网格 AUSM+通量分裂格式Euler方程数值解进行个体适应度值评估 ;应用动态网格技术调整计算网络 ,节约了 CPU机时 ;最后给出了优化结果
Combining genetic algorithms with Nash equilibrium in game theory, a NashGAs (multi-objective gene optimization algorithm) is constructed, and given a transonic flow with initial airfoil NACA0 0 1 2 The shape of the increase optimization. In the calculation, the airfoil shape is parameterized by Bézier curve to avoid the non-streamline irrational shape. The fitness value of the individual is evaluated by using the finite element unstructured grid AUSM + flux splitting scheme Euler equation. The dynamic grid Technology to adjust the computing network, saving CPU time; Finally, the optimization results are given