摘要
To weaken the influence of profile error on compressor aerodynamic performance, especially on pressure ratio and efficiency, a robust design method considering profile error is built to improve the robustness of aerodynamic performance of the blade. The characteristics of profile error are random and small-scaled, which means that to evaluate the influence of profile error on blade aerodynamic performance is a time-intensive and high-precision work. For this reason, non-intrusive polynomial chaos(NIPC) and Kriging surrogate model are introduced in this robust design method to improve the efficiency of uncertainty quantification(UQ) and ensure the evaluate accuracy. The profile error satisfies the Gaussian distribution, and NIPC is carried out to do uncertainty quantification since it has advantages in prediction accuracy and efficiency to get statistical behavior of random profile error. In the integrand points of NIPC, several surrogate models are established based on Latin hypercube sampling(LHS)+ Kriging, which further reduces the costs of optimization design by replacing calling computational fluid dynamic(CFD) repeatedly. The results show that this robust design method can significantly improve the performance robustness in shorter time(40 times faster) without losing accuracy, which is meaningful in engineering application to reduce manufacturing cost in the premise of ensuring the aerodynamic performance. Mechanism analysis of the robustness improvement samples carried out in current work can help find out the key parameter dominating the robustness under the disturbance of profile error, which is meaningful to further improvement of compressor robustness.
To weaken the influence of profile error on compressor aerodynamic performance, especially on pressure ratio and efficiency, a robust design method considering profile error is built to improve the robustness of aerodynamic performance of the blade. The characteristics of profile error are random and small-scaled, which means that to evaluate the influence of profile error on blade aerodynamic performance is a time-intensive and high-precision work. For this reason, non-intrusive polynomial chaos(NIPC) and Kriging surrogate model are introduced in this robust design method to improve the efficiency of uncertainty quantification(UQ) and ensure the evaluate accuracy. The profile error satisfies the Gaussian distribution, and NIPC is carried out to do uncertainty quantification since it has advantages in prediction accuracy and efficiency to get statistical behavior of random profile error. In the integrand points of NIPC, several surrogate models are established based on Latin hypercube sampling(LHS) + Kriging, which further reduces the costs of optimization design by replacing calling computational fluid dynamic(CFD) repeatedly. The results show that this robust design method can significantly improve the performance robustness in shorter time(40 times faster) without losing accuracy, which is meaningful in engineering application to reduce manufacturing cost in the premise of ensuring the aerodynamic performance. Mechanism analysis of the robustness improvement samples carried out in current work can help find out the key parameter dominating the robustness under the disturbance of profile error, which is meaningful to further improvement of compressor robustness.
基金
support of the National Natural Science Foundation of China (NSFC) under the Grant No. 51790512
the Overseas Expertise Introduction Project for Discipline Innovation (111 Project) under Grant No. B17037
Industry-University-Research Cooperation Project of Aero Engine Corporation of China (AECC) under Grant No. HFZL2018CXY011-1 and MIIT
