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Fatigue Life Prediction Study for Vane Thermal Barrier Coatings Based on an Axisymmetric Model and Genetic Algorithm

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Abstract

Thermal barrier coatings (TBCs) are widely used on turbine guide vanes (TGVs) in aero engines. The construction of a reasonable TBC fatigue life prediction model is of great significance to the development of aero engines. A 2D axisymmetric finite element model (FEM) is established, based on experimental data from a tube with a TBC. Then, a reasonable TBC life fatigue prediction model is established by combining the Manson–Coffin equation, linear cumulative damage theory, and growth characteristics of the oxide layer. The fitting problem is transformed into an optimization problem in the process of establishing the TBC fatigue life prediction model, and the coefficients of the model are solved by a genetic algorithm (GA). Finally, a strain analysis FEM for TGVs with a TBC is established, based on the master–slave model method, and TGVs coating fatigue life is predicted by a fatigue life prediction model. The results show that the maximum fatigue life prediction error for tubes with a TBC is 104.7%, which is 114.4% lower than that obtained in previous studies, and most of the coating fatigue life prediction values are distributed within 50% confidence bounds. The coating fatigue life of the TGV on the trailing edge is 1948 cycles, which is a reasonable result. The efforts of this study provide a framework to predict the coating fatigue life of aero engine hot components.

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Authors and Affiliations

  1. Liaoning Key Laboratory of Advanced Measurement and Test Technology for Aviation Propulsion System, Shenyang Aerospace University, Shenyang, 110136, China

    Peng Guan, Jia-Ning He, Jia-Rui Zhang, Yan-Ting Ai & Yu-Dong Yao

  2. School of Power and Energy, Northwestern Polytechnical University, Xi’an, 710072, China

    Tian-Nan Bao

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  1. Peng Guan
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Guan, P., He, JN., Zhang, JR. et al. Fatigue Life Prediction Study for Vane Thermal Barrier Coatings Based on an Axisymmetric Model and Genetic Algorithm. J Therm Spray Tech 31, 2327–2341 (2022). https://doi.org/10.1007/s11666-022-01453-6

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