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Performance Degradation Evaluation of Low Bypass Ratio Turbofan Engine Based on Flight Data

Author

Listed:
  • Haiqin Qin

    (Department of Mechanical Engineering, Qingdao Campus, Naval Aviation University, Qingdao 266041, China)

  • Jie Zhao

    (Department of Mechanical Engineering, Qingdao Campus, Naval Aviation University, Qingdao 266041, China)

  • Likun Ren

    (Department of Mechanical Engineering, Qingdao Campus, Naval Aviation University, Qingdao 266041, China)

  • Bianjiang Li

    (Department of Mechanical Engineering, Qingdao Campus, Naval Aviation University, Qingdao 266041, China)

  • Zhengguang Li

    (Department of Mechanical Engineering, Qingdao Campus, Naval Aviation University, Qingdao 266041, China)

Abstract

A low bypass ratio turbofan engine operates in a hostile environment, resulting in performance degradation. This seriously affects the security and reliability of the engine. Therefore, a performance degradation evaluation method for engines based on flight data is proposed. The method expands the equation system to solve the underdetermined problem caused by the lack of engine sensors based on multiple operating point analysis. The improved evolution algorithm is employed to solve the equation system, which relieves the problem of insufficient precision. The engine performance degradation dataset is established based on the engine performance calculation model to verify the reliability of the degradation evaluation method. The results show that the method is applicable to the dataset. Finally, the method is applied to the actual flight data to study the law of the performance degradation of the researched engine, which indicates that the engine’s fan efficiency and high-pressure compressor flow capacity have an apparent downward trend over time.

Suggested Citation

  • Haiqin Qin & Jie Zhao & Likun Ren & Bianjiang Li & Zhengguang Li, 2022. "Performance Degradation Evaluation of Low Bypass Ratio Turbofan Engine Based on Flight Data," Sustainability, MDPI, vol. 14(13), pages 1-12, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:8052-:d:853782
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    References listed on IDEAS

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    1. Sampath, Suresh & Ogaji, Stephen & Singh, Riti & Probert, Douglas, 2002. "Engine-fault diagnostics:an optimisation procedure," Applied Energy, Elsevier, vol. 73(1), pages 47-70, September.
    2. Tahan, Mohammadreza & Tsoutsanis, Elias & Muhammad, Masdi & Abdul Karim, Z.A., 2017. "Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review," Applied Energy, Elsevier, vol. 198(C), pages 122-144.
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    Cited by:

    1. Wenxiang Zhou & Sangwei Lu & Wenjie Kai & Jichang Wu & Chenyang Zhang & Feng Lu, 2023. "A Novel Adaptive Generation Method for Initial Guess Values of Component-Level Aero-Engine Start-Up Models," Sustainability, MDPI, vol. 15(4), pages 1-25, February.

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