典型文献
A gradient-based method assisted by surrogate model for robust optimization of turbomachinery blades
文献摘要:
The design optimization taking into account the impact of uncertainties favors improv-ing the robustness of the design.A Surrogate-Assisted Gradient-Based(SAGB)method for the robust aerodynamic design optimization of turbomachinery blades considering large-scale uncer-tainty is introduced,verified and validated in the study.The gradient-based method is employed due to its high optimization efficiency and any one surrogate model with sufficient response accu-racy can be employed to quantify the nonlinear performance changes.The gradients of objective performance function to the design parameters are calculated first for all the training samples,from which the gradients of cost function can be fast determined.To reveal the high efficiency and high accuracy of SAGB on gradient calculation,the number of flow computations needed is evaluated and compared with three other methods.Through the aerodynamic design optimization of a tran-sonic turbine cascade minimizing total pressure loss at the outlet,the SAGB-based gradients of the base and optimized blades are compared with those obtained by the Monte Carlo-assisted finite dif-ference method.Moreover,the results of both the robust and deterministic aerodynamic design optimizations are presented and compared to demonstrate the practicability of SAGB on improving the aerodynamic robustness of turbomachinery blades.
文献关键词:
中图分类号:
作者姓名:
Jiaqi LUO;Zeshuai CHEN;Yao ZHENG
作者机构:
School of Aeronautics and Astronautics,Zhejiang University,Hangzhou 310027,China
文献出处:
引用格式:
[1]Jiaqi LUO;Zeshuai CHEN;Yao ZHENG-.A gradient-based method assisted by surrogate model for robust optimization of turbomachinery blades)[J].中国航空学报(英文版),2022(10):1-7
A类:
turbomachinery,SAGB
B类:
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AB值:
0.490802
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