| Citation: | CHENG Muwei, YANG Xiaoguang, FAN Yongsheng, et al. Rapid prediction method for local service temperature and stress on aeroengine turbine disks based on LSTM network[J]. Journal of Aerospace Power, 2025, 40（10）：20240547 doi: 10.13224/j.cnki.jasp.20240547
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A predictive model for temperature and stress at critical locations of turbine disks was developed using engine service data, with its aim to provide essential inputs for service damage assessment and life analysis of hot-section components. The research derived gas path parameters related to turbine disk loading through engine thermodynamic cycle analysis. Based on numerical simulation results, a Long Short-Term Memory (LSTM) neural network model was constructed and trained to predict turbine disk temperature and stress. The generalization of proposed model capability was evaluated through cross-validation and noise injection. Result demonstrated that the established model achieved a normalized mean square error of 5.4×10−4 on the test set. The developed methodology allowed to analyze temperature and stress evolution patterns during typical flight maneuvers, providing crucial inputs for engine mission design and turbine disk service damage assessment.
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