Design Optimization and Analysis of Exit Rotor with Diffuser Passage based on Neural Network Surrogate Model and Entropy Generation Method

Jin, Yun1,2,3,4; Geng, Shaojuan1,2,3,4; Liu, Shuaipeng1,2,3,4; Ni, Ming5; Zhang, Hongwu1,2,3,4

2023

10.1007/s11630-023-1743-9

Journal of Thermal Science   影响因子和分区

1003-2169

1993-033X

In this paper, a diffuser passage compressor design is introduced via optimization to improve the aerodynamic performance of the exit rotor in a multistage axial compressor. An in-house design optimization platform, based on genetic algorithm and back propagation neural network surrogate model, is constructed to perform the optimization. The optimization parameters include diffusion angle of meridian passage, diffusion length of meridian passage, change of blade camber angle and blade number. The impacts of these design parameters on efficiency and stability improvement are analyzed based on the optimization database. Two optimized diffuser passage compressor designs are selected from the optimization solution set by comprehensively considering efficiency and stability of the rotor, and the influencing mechanisms on efficiency and stability are further studied. The simulation results show that the application of diffuser passage compressor design can improve the load coefficient by 12.1% and efficiency by 1.28% at the design mass flow rate condition, and the stall margin can be improved by 12.5%. According to the local entropy generation model analysis, despite the upper and lower endwall loss of the diffuser passage rotor are increased, the profile loss is reduced compared with the original rotor. The efficiency of the diffuser passage rotor can be influenced by both loss and load. At the near stall condition, decreasing flow blockage at blade root region can improve the stall margin of the diffuser passage rotor.

exit rotor diffuser passage neural network surrogate model entropy generation rate flow blockage

SCI ; EI

英语

其他

National Science and Technology Major Project[2017-II-0006-0020]

Thermodynamics ; Engineering

Thermodynamics ; Engineering, Mechanical

WOS:000919031700004

SPRINGER

20230413430066

Compressors ; Entropy ; Genetic algorithms ; Neural networks ; Torsional stress

Compressors:618.1 ; Thermodynamics:641.1 ; Production Engineering:913.1

Web of Science

1.Chinese Acad Sci, Inst Engn Thermophys, Adv Gas Turbine Lab, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Innovat Acad Light duty Gas Turbine, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Inst Engn Thermophys, Key Lab Adv Energy & Power, Beijing 100190, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
5.Southern Univ Sci & Technol, Dept Mech & Aerosp Engn, Shenzhen 518055, Peoples R China

Jin, Yun,Geng, Shaojuan,Liu, Shuaipeng,et al. Design Optimization and Analysis of Exit Rotor with Diffuser Passage based on Neural Network Surrogate Model and Entropy Generation Method[J]. Journal of Thermal Science,2023,32(2):739-752.

Jin, Yun,Geng, Shaojuan,Liu, Shuaipeng,Ni, Ming,&Zhang, Hongwu.(2023).Design Optimization and Analysis of Exit Rotor with Diffuser Passage based on Neural Network Surrogate Model and Entropy Generation Method.Journal of Thermal Science,32(2),739-752.

Jin, Yun,et al."Design Optimization and Analysis of Exit Rotor with Diffuser Passage based on Neural Network Surrogate Model and Entropy Generation Method".Journal of Thermal Science 32.2(2023):739-752.