【摘要】 本文采用了改进的嵌套式稀疏网格随机配置(Modified Nested Sparse-grid Stochastic Collocation, MNSSC)方法研究了涡轮叶片安装角的随机误差对叶栅气动性能的影响。结果表明，MNSSC法解决了维数诅咒问题，降低了高维样本数量的同时避免了样本的浪费，MNSSC法可以快速有效地模拟多个不确定量在内流流场中的传播；安装角的不确定性对吸力边的影响较为明显，而其对于叶片压力边的影响较小，且其在吸力边轮廓处的40%Cax处的影响最为显著；安装角的不确定性对流场的影响沿A向传播，且其主要影响分离区等速度梯度大的区域。更多还原

【Abstract】 In this paper, based on the Modified Nested Sparse-grid Stochastic Collocation(MNSSC)method, the influence of the random error of the turbine blade stagger angle on the aerodynamic performance of the cascade is studied. The results show that the MNSSC method overcomes the curse of dimension, reduces the number of high-dimensional samples and avoids the waste of samples.The MNSSC method can quickly and effectively simulate the propagation of multiple uncertainties in the internal flow field; The influence of uncertainties on the suction side is significantly greater than that on the pressure side, and it has the greatest influence on the suction side of 40%Cax; the uncertainties of the stagger angle affect the flow field along the A direction, and its mainly affects areas with large velocity gradients such as separation regions.更多还原