时序卷积网络在转子热固耦合应力预测及寿命评估中的应用

李伟业; 冯建欣; 文思果; 张小龙; 袁奇; 李浦

doi:10.7652/xjtuxb202510008

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时序卷积网络在转子热固耦合应力预测及寿命评估中的应用

更新时间：2025-09-28

- 时序卷积网络在转子热固耦合应力预测及寿命评估中的应用
- Application of Temporal Convolutional Network in Rotor Thermo-Mechanical Coupling Stress Prediction and Life Assessment
- 西安交通大学学报 2025年59卷第10期页码：87-95
- 作者机构：
- 作者简介：
  
  作者简介：李伟业（2000—），男，硕士生；
  李浦（通信作者），男，副研究员，博士生导师。
- 基金信息：
  
  国家自然科学基金资助项目。(12372058;12372057)
- DOI：10.7652/xjtuxb202510008
  中图分类号： TK14
- 收稿：2025-04-02，
  
  纸质出版：2025-10-10
- 稿件说明：
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李伟业, 冯建欣, 文思果, 等. 时序卷积网络在转子热固耦合应力预测及寿命评估中的应用[J]. 西安交通大学学报, 2025,59(10):87-95.

LI Weiye, FENG Jianxin, WEN Siguo, et al. Application of Temporal Convolutional Network in Rotor Thermo-Mechanical Coupling Stress Prediction and Life Assessment[J]. Journal of Xi'an Jiaotong University, 2025, 59(10): 87-95.
李伟业, 冯建欣, 文思果, 等. 时序卷积网络在转子热固耦合应力预测及寿命评估中的应用[J]. 西安交通大学学报, 2025,59(10):87-95. DOI： 10.7652/xjtuxb202510008.

LI Weiye, FENG Jianxin, WEN Siguo, et al. Application of Temporal Convolutional Network in Rotor Thermo-Mechanical Coupling Stress Prediction and Life Assessment[J]. Journal of Xi'an Jiaotong University, 2025, 59(10): 87-95. DOI： 10.7652/xjtuxb202510008.

摘要

针对透平机械转子启动过程中，瞬态热应力由于计算成本高而难以实现快速预测的问题，提出了一种基于时序卷积网络（TCN）的压缩机转子表面温度场和应力场预测方法。采用有限元方法计算压缩机转子在冷态启动工况下的温度场、应力场和使用寿命，基于TCN模型开展转子的温度场和应力场预测及寿命评估，并与长短时记忆（LSTM）网络、门控循环单元（GRU）和Transformer 3种神经网络模型的预测结果进行对比。模拟结果表明：冷态启动工况下，TCN模型在预测转子瞬态热应力时的性能表现最优，相较于Transformer、LSTM和GRU模型，其热应力预测决定系数分别提高了0.03%、0.60%、0.36%，综合加载等效应力预测决定系数分别提高了0.10%、0.48%、0.02%；与传统的有限元热固耦合分析方法相比，TCN模型的计算效率显著提高，耗时仅为有限元方法的0.25%。所提方法提升了预测的准确性，可为透平机械转子瞬态热应力的快速预测和寿命评估提供技术支撑。

Abstract

To address the challenge of rapidly predicting transient thermal stress during the startup of turbomachinery rotors

which is difficult due to high computational costs

a compressor rotor surface temperature field and stress field prediction method based on temporal convolutional network (TCN) is proposed. The finite element method is utilized to compute the temperature field

stress field

and service life of the compressor rotor under cold startup conditions. A TCN model is then employed for temperature field and stress field prediction and life assessment of the rotor

and the results are compared with those obtained from three other neural network models: long short-term memory (LSTM)

gated recurrent unit (GRU)

and Transformer. Simulation results demonstrate that under cold startup conditions

the TCN model exhibits optimal performance in predicting transient thermal stress of the rotor. Compared to the Transformer

LSTM

and GRU models

the coefficient of determination for thermal stress predictions improves by 0.03%

0.60%

and 0.36%

respectively

while the coefficient of determination for equivalent stress predictions under combined loading increases by 0.10%

0.48%

and 0.02%.Moreover

the computational efficiency of the TCN model is significantly improved compared to traditional finite element thermo-mechanical coupled analysis

with a computation time only 0.25% that of the finite element method. This proposed method enhances prediction accuracy and provides technical support for the rapid prediction of transient thermal stresses and life assessment of turbomachinery rotors.

关键词

Keywords

references

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