气动

Performance-Driven Multi-Objective Optimization Method for DLR Transonic Tandem Cascade Shape Design

  • LI Kunhang ,
  • MENG Fanjie ,
  • WANG Kaibin ,
  • GUO Penghua ,
  • LI Jingyin
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  • School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China

网络出版日期: 2023-11-28

基金资助

This work was financially supported by the National Science and Technology Major Project (2017-II-0007-0021).

版权

Science Press, Institute of Engineering Thermophysics, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2022

Performance-Driven Multi-Objective Optimization Method for DLR Transonic Tandem Cascade Shape Design

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  • School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Online published: 2023-11-28

Supported by

This work was financially supported by the National Science and Technology Major Project (2017-II-0007-0021).

Copyright

Science Press, Institute of Engineering Thermophysics, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2022

摘要

跨音速串列叶栅能够有效提升跨音速压缩机的工作载荷,这一特点满足了当代轴流压气机对大载荷和高压比的日益增长的急切需求。本文提出了一种跨音速串列叶栅优化迭代设计方法,并很好地应用在了设计来流马赫数为1.051的德国宇航局串列叶栅(该叶栅属于单个前排叶片和双后排叶片的重载跨音速叶栅)的气动优化设计工作中。本文采用了19个参数对跨音速串列叶栅进行了参数化表示,采用非支配遗传算法来驱动整个气动优化体系,优化过程中的新样本通过数值计算的得到其气动表现后不断补充到优化种群中。针对不同叶片造型会造成在跨音速叶栅的优化中来流马赫数明显变化的特点,本文提出了一种出口背压自动迭代修正的边界校准方法,保证了不同叶片造型下的跨音速串列叶栅的优化设计任务均保持在恒定的马赫数下进行。优化结果显示,串列叶栅的后排亚音速叶栅在优化后叶片挠度变化明显,串列叶栅的前排跨音速叶片的吸力面曲率降低和最大厚度位置的后移能够降低跨音速叶栅通道中的激波强度。跨音速串列叶栅的帕雷托优化设计结果证实了本文提出的优化方法的可靠性,某典型的优化设计结果证实:在近堵塞攻角、设计攻角和近失速攻角下,优化后的跨音速串列叶栅较原模型相比其叶型的总压损失系数分别降低了15.6%,20.9%和19.9%,且静压压比分别提升了1.3%,1.8%和1.7%。

本文引用格式

LI Kunhang , MENG Fanjie , WANG Kaibin , GUO Penghua , LI Jingyin . Performance-Driven Multi-Objective Optimization Method for DLR Transonic Tandem Cascade Shape Design[J]. 热科学学报, 2023 , 32(1) : 297 -309 . DOI: 10.1007/s11630-022-1707-5

Abstract

Transonic tandem cascades can effectively increase the working load, and this feature conforms with the requirement of the large loads and pressure ratios of modern axial compressors. This paper presents an optimization strategy for a German Aerospace Center (DLR) transonic tandem cascade, with one front blade and two rear blades, at the inlet Mach number of 1.051. The tandem cascade profile was parameterized using 19 control parameters. Non-dominated sorting Genetic algorithm (NSGA-II) was used to drive the optimization evolution, with the computational fluid dynamics (CFD)-based cascade performances correction added for each generation. Inside the automatic optimization system, a pressure boundary condition iterative algorithm was developed for simulating the cascade performance with a constant supersonic inlet Mach number. The optimization results of the cascade showed that the deflection of the subsonic blade changed evidently. The shock wave intensity of the first blade row was weakened because of the reduced curvatures of the optimized pressure and suction sides of the front blade part and the downstream moved maximum thickness position. The total pressure losses decreased by 15.6%, 20.9% and 19.9% with a corresponding increase in cascade static pressure ratio by 1.3%, 1.8% and 1.7%, for the three cascade shapes in the Pareto solution sets under the near choke, the design and near stall conditions, respectively.

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