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<p>During the operation of an aero-engine, its system stability and stability margin reflect the ability of the engine to resist external interference, and they are significant indicators for determining whether the aero-engine is functioning normally. Analyzing the system stability of aero-engines during operation and improving their stability margins are of great importance. Nevertheless, as a large and complex system, an aero-engine has been the focus of most existing studies, which mostly concentrate on individual components such as control systems or aerodynamic components. System stability analysis and stability margin calculation are among the key challenges in aero-engine control. In this study, complex networks are regarded as a crucial approach to address such difficult issues, and a numerical simulation algorithm capable of accurately computing the system stability margin of aero-engines is put forward. Specifically, in this study, the stability margin of the system was calculated using the network models and dynamic models of the system, and the correctness of the algorithm was verified in three typical networks. Subsequently, based on the test-run data of aero-engines, the algorithm was applied to the actual network of aero-engines under twelve different states. The system stability margin of aero-engines was calculated quantitatively, and the effectiveness of the algorithm was verified. Additionally, this study verified through experiments that the proposed algorithm enables accurate calculation of system stability margins of aero-engines. It then analyzed the key factors affecting system stability of aero-engines from a system perspective and proposed a stability improvement scheme. Results demonstrate that from a system perspective, stability is primarily influenced by system noise and node correlation, and enhancing the correlation between Hub nodes and other nodes can effectively improve system stability margins. This novel method can provide some support for the health management of aero-engines and can also be further applied to the real-time condition monitoring or comprehensive assessment of complex systems.</p>

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Numerical simulation algorithm for aero-engine system stability based on complex network dynamics

  • 期刊论文

  • 发表日期：

    2026年2月19日

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  • Zhaoqi Fan,
  • Yuting Wang,
  • Feng Liu,
  • Feng Xi,
  • Dongli Duan,
  • Zhiqiang Cai,
  • Yingfeng Zhang,
  • Shubin Si

摘要

During the operation of an aero-engine, its system stability and stability margin reflect the ability of the engine to resist external interference, and they are significant indicators for determining whether the aero-engine is functioning normally. Analyzing the system stability of aero-engines during operation and improving their stability margins are of great importance. Nevertheless, as a large and complex system, an aero-engine has been the focus of most existing studies, which mostly concentrate on individual components such as control systems or aerodynamic components. System stability analysis and stability margin calculation are among the key challenges in aero-engine control. In this study, complex networks are regarded as a crucial approach to address such difficult issues, and a numerical simulation algorithm capable of accurately computing the system stability margin of aero-engines is put forward. Specifically, in this study, the stability margin of the system was calculated using the network models and dynamic models of the system, and the correctness of the algorithm was verified in three typical networks. Subsequently, based on the test-run data of aero-engines, the algorithm was applied to the actual network of aero-engines under twelve different states. The system stability margin of aero-engines was calculated quantitatively, and the effectiveness of the algorithm was verified. Additionally, this study verified through experiments that the proposed algorithm enables accurate calculation of system stability margins of aero-engines. It then analyzed the key factors affecting system stability of aero-engines from a system perspective and proposed a stability improvement scheme. Results demonstrate that from a system perspective, stability is primarily influenced by system noise and node correlation, and enhancing the correlation between Hub nodes and other nodes can effectively improve system stability margins. This novel method can provide some support for the health management of aero-engines and can also be further applied to the real-time condition monitoring or comprehensive assessment of complex systems.