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Model-based safety analysis with time resolution (MBSA-TR) method for complex aerothermal–mechanical systems of aero-engines

Author

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  • Gan, Chenyu
  • Ding, Shuiting
  • Qiu, Tian
  • Liu, Peng
  • Ma, Qinglin

Abstract

Current aero-engine safety assessments mostly rely on experience-based safety analysis methods such as Fault Tree Analysis (FTA) or Failure Modes and Effects Analysis (FMEA). The progressively significant dynamic characteristics of aero-engines during their transition between states render these methods incapable of providing accurate quantitative safety guidance, which is vital for complex aerothermal–mechanical systems. To solve this problem, a Model-Based Safety Analysis with Time Resolution (MBSA-TR) method was established, which included system modeling, fault modeling, and Safety Critical Parameter (SCP) identification. An integrated system model combining the main gas path and air system was built based on dynamics principles, and it was capable of reflecting the dynamic characteristics of aero-engines. The fault model included several time-resolved sub-modules and interacted bidirectionally with the system model at each time step to realize time-sequential fault propagation. SCPs were defined as parametric analysis objectives, and they were identified using an FTA-like identification framework. Finally, a case study of a typical turbofan engine with low-pressure compressor blade fractures as the primary fault was analyzed. The results showed that some hazard risks are incapable of detection without time resolution. Thus, time resolution is indispensable for the safety evaluation of complex aerothermal–mechanical systems of aero-engines.

Suggested Citation

  • Gan, Chenyu & Ding, Shuiting & Qiu, Tian & Liu, Peng & Ma, Qinglin, 2024. "Model-based safety analysis with time resolution (MBSA-TR) method for complex aerothermal–mechanical systems of aero-engines," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:reensy:v:243:y:2024:i:c:s0951832023007780
    DOI: 10.1016/j.ress.2023.109864
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