The turbine disc is a critical component of an aeroengine, where the mortise experiences severe stress concentration due to the notch effect, making it a vulnerable position for disc fatigue failure. In this study, the simulation specimens were designed based on the mortise structure. Fatigue tests were conducted to explore the failure mechanism under fatigue loading. Elastic-plastic finite element analyses were performed subsequently to investigate the distribution of local mean stress and stress gradient near the notch. Considering the influence of local mean stress and stress gradient, a novel fatigue life prediction method was proposed based on the theory of critical distance (TCD). This method overcomes the disadvantages of the TCD in which the critical distance cannot be determined beforehand and the requirement of a complex iterative process when predicting fatigue life. By using this method, the predicted fatigue life of the simulation specimens under different loads is within ± 2 error factor.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Aeroengine Disc Simulation Specimen Fatigue Life Prediction Using Modified Critical Distance Theory


    Contributors:
    Sun, Qian-Yang (author) / Li, Yan-Jie (author) / Zhang, Da-Hai (author) / Lu, Fang-Zhou (author) / Xu, Pei-Fei (author) / Zhang, Pei-Wei (author) / Fei, Qing-Guo (author)

    Published in:

    AIAA Journal ; 63 , 4 ; 1512-1522


    Publication date :

    2025-04-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Modal Analysis of Aeroengine Turbine Blade-Disc System

    Zhao, Wei-Qiang / Liu, Yong-Xian / Lu, Mo-Wu et al. | Tema Archive | 2012


    Critical Distance for Fatigue Life Prediction in Aerospace Materials

    Yamashita, Y. / Ueda, Y. / Kuroki, H. | British Library Conference Proceedings | 2011


    Aeroengine Remaining Life Prediction Using Feature Selection and Improved SE Blocks

    Hairui Wang / Shijie Xu / Guifu Zhu et al. | DOAJ | 2024

    Free access