The design of turbomachinery cascades is a typical high dimensional and computationally expensive problem, a metamodel-based global optimization and data mining method is proposed to solve it. A modified Efficient Global Optimization (EGO) algorithm named Multi-Point Search based Efficient Global Optimization (MSEGO) is proposed, which is characterized by adding multiple samples at per iteration. By testing on typical mathematical functions, MSEGO outperforms EGO in accuracy and convergence rate. MSEGO is used for the optimization of a turbine vane with non-axisymmetric endwall contouring (NEC), the total pressure coefficient of the optimal vane is increased by 0.499%. Under the same settings, another two optimization processes are conducted by using the EGO and an Adaptive Range Differential Evolution algorithm (ARDE), respectively. The optimal solution of MSEGO is far better than EGO. While achieving similar optimal solutions, the cost of MSEGO is only 3% of ARDE. Further, data mining techniques are used to extract information of design space and analyze the influence of variables on design performance. Through the analysis of variance (ANOVA), the variables of section profile are found to have most significant effects on cascade loss performance. However, the NEC seems not so important through the ANOVA analysis. This is due to the fact the performance difference between different NEC designs is very small in our prescribed space. However, the designs with NEC are always much better than the reference design as shown by parallel axis, i.e., the NEC would significantly influence the cascade performance. Further, it indicates that the ensemble learning by combing results of ANOVA and parallel axis is very useful to gain full knowledge from the design space.
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ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition
June 26–30, 2017
Charlotte, North Carolina, USA
Conference Sponsors:
- International Gas Turbine Institute
ISBN:
978-0-7918-5080-0
PROCEEDINGS PAPER
Design Optimization of a 3D Parameterized Vane Cascade With Non-Axisymmetric Endwall Based on a Modified EGO Algorithm and Data Mining Techniques
Chenxi Li,
Chenxi Li
Xi’an Jiaotong University, Xi’an, China
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Zhendong Guo,
Zhendong Guo
Xi’an Jiaotong University, Xi’an, China
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Liming Song,
Liming Song
Xi’an Jiaotong University, Xi’an, China
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Zhenping Feng
Zhenping Feng
Xi’an Jiaotong University, Xi’an, China
Search for other works by this author on:
Chenxi Li
Xi’an Jiaotong University, Xi’an, China
Zhendong Guo
Xi’an Jiaotong University, Xi’an, China
Liming Song
Xi’an Jiaotong University, Xi’an, China
Jun Li
Xi’an Jiaotong University, Xi’an, China
Zhenping Feng
Xi’an Jiaotong University, Xi’an, China
Paper No:
GT2017-63738, V02CT47A009; 14 pages
Published Online:
August 17, 2017
Citation
Li, C, Guo, Z, Song, L, Li, J, & Feng, Z. "Design Optimization of a 3D Parameterized Vane Cascade With Non-Axisymmetric Endwall Based on a Modified EGO Algorithm and Data Mining Techniques." Proceedings of the ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. Volume 2C: Turbomachinery. Charlotte, North Carolina, USA. June 26–30, 2017. V02CT47A009. ASME. https://doi.org/10.1115/GT2017-63738
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