| Citation: | Guo Yifan, Gao Zhiyuan, Geng Mingze, et al. Reduced-order modeling method for low-cycle fatigue life prediction of gas turbine rotor blades[J]. Journal of Aerospace Power, 2026, 41（X）：20250453 doi: 10.13224/j.cnki.jasp.20250453
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Multi-physics numerical simulations of gas turbine rotor blades are computationally intensive, making it difficult to be directly applied for online condition monitoring and life prediction. In order to address this challenge, a reduce-order modeling method for multi-physics-based low-cycle fatigue life prediction of gas turbine rotor blades was developed, aiming to improve both computational efficiency and prediction accuracy. Based on the multi-physics results of fluid-thermal-solid coupled numerical simulations of turbine blades under representative operating conditions, a reduced-order model was constructed using proper orthogonal decomposition combined with data-driven regression techniques, enabling rapid and accurate prediction of temperature, stress, and strain fields. On this basis, the Manson-Coffin and Smith-Watson-Topper methods were employed for efficient evaluation of the blade’s low-cycle fatigue life. Results showed that the average relative error of the constructed reduced-order model was 0.11% for the temperature field, 1.01% for the stress field, and 0.75% for the strain field. The prediction speed was only 0.005 s for the temperature field, 0.03 s for the stress field, and 0.31 s for the strain field. The average relative error of the low-cycle fatigue life prediction was less than 3.5%, providing important theoretical and methodological support for online condition monitoring and life assessment of gas turbine rotor blades.
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