A continuous-curvature parameterization method was coupled with the genetic algorithm and a RANS flow solver to optimize the cascade of VKI and Aachen turbine blades. The main advantage of the method is to generate blades with a continuous curvature distribution, which results in a smooth distribution of pressure and velocity on the blade surface. The geometry of the blade cascade was parameterized by 33 variables, and two objective functions were considered for the optimization. The first cost function was to reduce the total pressure loss with the constraints of mass flow rate, blade loading, and outlet flow angle. At the second cost function, the constraint of constant cross-sectional area was added to the previous constraints to preserve the structural strength of the turbine blade. The total pressure loss for the VKI blade decreased by 14.7 % and 10.6 % for the first and second objective functions, respectively. The total pressure loss for the Aachen blade was also reduced by 9.5 % and 7.5 % for the first and second objective functions, respectively. Due to the efficient geometry parameterization, the proposed method quickly converged to a high-efficiency blade at the early generations. The proposed method can be developed for optimizing the different blades of turbine, compressor, and airfoil types.

连续曲率参数化方法与遗传算法和 RANS 流求解器相结合，以优化 VKI 和亚琛涡轮叶片的级联。该方法的主要优点是产生具有连续曲率分布的叶片，从而使叶片表面的压力和速度分布平稳。叶栅的几何形状由 33 个变量参数化，优化时考虑了两个目标函数。第一个成本函数是在质量流量、叶片负载和出口流动角的约束下减少总压力损失。在第二个成本函数中，恒定横截面积的约束被添加到之前的约束中，以保持涡轮叶片的结构强度。VKI 叶片的总压力损失减少了 14。第一个和第二个目标函数分别为 7% 和 10.6%。对于第一个和第二个目标函数，亚琛叶片的总压力损失也分别减少了 9.5% 和 7.5%。由于有效的几何参数化，所提出的方法在早期快速收敛到高效叶片。可以开发所提出的方法来优化涡轮、压气机和翼型类型的不同叶片。