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Random Vibration Fatigue Life Analysis of Aeroengine Bolts Using the Frequency-Domain Method Cover

Random Vibration Fatigue Life Analysis of Aeroengine Bolts Using the Frequency-Domain Method

Open Access
|Oct 2025

Abstract

Bolt connections are widely used in aircraft engines due to their advantages of high stiffness, low weight, and ease of assembly and disassembly. However, they are subjected to complex stress states in service, including high preloads, combustion-induced forces, and random vibrations, which may lead to fatigue failure. Since the fatigue performance of bolts directly affects the reliability of engines and their fuel accessories, accurate fatigue life estimation is essential for safe design. This study proposes a frequency-domain method for evaluating the random vibration fatigue life of bolts in aeroengine fuel accessories. A detailed finite element model of the bolted connection was established, with excitation boundary conditions defined by the assembly configuration. Modal, frequency response, and random response analyses were performed to obtain the stress power spectral density (PSD) of the bolts. The Dirlik method, combined with the material’s S–N curve, was then applied to estimate fatigue life under broadband and narrowband vibration excitation. Results show that bolts in the Y-direction experience the highest RMS stresses, leading to the shortest fatigue life – approximately 4.44 hours under critical loading conditions – which does not meet design requirements. The proposed method enables rapid evaluation of bolt fatigue life under random vibration environments, providing a practical tool to support bolt selection and design optimization in aeroengine applications.

DOI: https://doi.org/10.2478/fas-2024-0010 | Journal eISSN: 2300-7591 | Journal ISSN: 2081-7738
Language: English
Page range: 147 - 160
Published on: Oct 15, 2025
Published by: ŁUKASIEWICZ RESEARCH NETWORK – INSTITUTE OF AVIATION
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2025 Lilin Tian, Jun Wang, Chaofeng Zhang, Yuchao Guo, Liang Chang, published by ŁUKASIEWICZ RESEARCH NETWORK – INSTITUTE OF AVIATION
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.