储能科学与技术 ›› 2025, Vol. 14 ›› Issue (4): 1522-1532.doi: 10.19799/j.cnki.2095-4239.2024.1011

• 储能系统与工程 • 上一篇    下一篇

压缩空气储能轴流压缩机叶片应力优化方法

石贵月1,2(), 陶海亮1,2,3,4, 左志涛1,2,3,4(), 李靖鑫1,2,3,4, 陈吉祥1,2, 陈家希1,2, 陈海生1,2,3,4   

  1. 1.中国科学院工程热物理研究所，北京 100190
    2.中国科学院大学，北京 100049
    3.国家能源大规模物理储能技术(毕节)研发中心，贵州 毕节 551712
    4.长时规模储能重点实验室，北京 100191
  • 收稿日期:2024-10-30 修回日期:2025-02-10 出版日期:2025-04-28 发布日期:2025-05-20
  • 通讯作者: 左志涛 E-mail:shiguiyue@iet.cn;zuozhitao@iet.cn
  • 作者简介:石贵月（2001—），女，硕士研究生，研究方向为叶轮机械强度应力，E-mail：shiguiyue@iet.cn；
  • 基金资助:
    国家重点研发计划(2023YFB2406500);国家自然科学基金(52306285);山东能源研究院企业联合基金项目(U202301)

Research on blade stress optimization method of axial flow compressor in compressed air energy storage system

Guiyue SHI1,2(), Hailiang TAO1,2,3,4, Zhitao ZUO1,2,3,4(), Jingxin LI1,2,3,4, Jixiang CHEN1,2, Jiaxi CHEN1,2, Haisheng CHEN1,2,3,4   

  1. 1.Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China
    2.University of Chinese Academy of Sciences, Beijing 100049, China
    3.National Energy Large Scale Physical Energy Storage Technologies R&D Center of Bijie High-Tech Industrial Development Zone, Bijie 551712, Guizhou, China
    4.Key Laboratory of Long-Duration and Large-Scale Energy Storage, Beijing 100191, China
  • Received:2024-10-30 Revised:2025-02-10 Online:2025-04-28 Published:2025-05-20
  • Contact: Zhitao ZUO E-mail:shiguiyue@iet.cn;zuozhitao@iet.cn

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摘要：

压缩空气储能系统被认为是目前最有发展前景的大规模物理储能技术，轴流压缩机作为其核心部件，其长期安全稳定运行是保障先进压缩空气储能系统经济性和安全性的前提。轴流压缩机工作过程中，叶片作为其能量转换的关键部件易受到疲劳破坏，而构造叶片三维造型的方式可以有效调整应力分布特征。为此，对压缩空气储能1.5级轴流压缩机进行结构优化设计，采用拉丁超立方抽样(Latin hypercube sampling，LHS)、径向基函数人工神经网络(radial basis function neural network，RBFNN)建立代理模型、快速非支配排序遗传算法(non-dominated sorting genetic algorithms-II，NSGA-II)捕捉目标值的优化方法，建立了轴流动叶三维弯、掠结构造型参数化一体优化流程。优化结果表明：优化后叶片最大等效应力从376.8 MPa降低到255.9 MPa，应力降低幅度32.1%，优化效果明显，叶片应力分布形态及大小主要取决于离心力，合理的弯掠改型调整了各叶身截面重心位置和离心力弯矩；优化方法对流场分布、动叶表面载荷分布、叶顶间隙的能量耗散未产生明显影响，可实现气动性能和结构应力分布解耦。

关键词: 轴流压缩机, 积叠方式, 参数化建模, 数值模拟

Abstract:

Compressed air energy storage (CAES) systems are recognized as one of the most promising large-scale physical energy storage technologies. At the heart of these systems lies the axial flow compressor, whose safety and operational stability are critical for ensuring the economic viability and safe use of advanced CAES. Within the compressor, the blades play a key role in energy conversion but are vulnerable to fatigue damage during operation. The stacking line of the blade can effectively adjust the stress distribution characteristics. This study focuses on the structural optimization of a 1.5-stage axial flow compressor within a CAES system. Latin hypercube planning (LHS) is employed for parameter selection, a radial basis function neural network (RBFNN) is used to establish the agent model, and the non-dominated sorting genetic algorithm-II (NSGA-II) is applied to capture the target value. Together, these approaches establish an integrated parametric optimization framework for three-dimensional bending and sweeping structure modeling of axial flow blades. The optimization results show that the maximum equivalent stress of the optimized blade decreases from 376.8 MPa to 255.9 MPa, achieving a stress reduction of 32.1%. The blade stress distribution is primarily influenced by centrifugal forces, while appropriate bending and sweeping modifications can effectively adjust the center of gravity and the centrifugal bending moment for each blade section. Notably, the optimization method does not significantly impact the flow field distribution, rotor surface load distribution, and tip clearance energy dissipation and achieve the decoupling of aerodynamic performance and structural stress distribution.

Key words: axial compressor, stacking method, parametric modeling, numerical simulation

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