重型燃气轮机动叶片蠕变寿命预测的轻量化建模方法

耿明泽; 郭怡凡; 姜孝谟; 章旋; 闫斌斌; 周家付; 陈丁聚

doi:10.19805/j.cnki.jcspe.2026.240692

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重型燃气轮机动叶片蠕变寿命预测的轻量化建模方法

动力设备与系统 | 更新时间：2026-10-10

- 重型燃气轮机动叶片蠕变寿命预测的轻量化建模方法
- Lightweight Modeling Method for Creep Life Prediction of Heavy-duty Gas Turbine Rotating Blades
- 动力工程学报 2026年46卷第2期页码：45-53
- 作者机构：
  
  1. 大连理工大学能源与动力工程系,辽宁,大连,116024
  2. 中国联合重型燃气轮机技术有限公司,北京,100016
- 作者简介：
  
  耿明泽(2001—),男,山东济宁人,硕士研究生,研究方向为基于各种降阶模型的燃气轮机叶片智慧运维与寿命预测技术研究
  姜孝谟(通信作者),男,教授,博士,E-mail:xiaomojiang2019@dlut.edu.cn
- 基金信息：
  
  国家重点项目两机专项资助项目(J040);沈阳市揭榜挂帅资助项目;大连市领军人才基金资助项目;国家自然科学基金面上资助项目(52375231)
- DOI：10.19805/j.cnki.jcspe.2026.240692
  中图分类号：
- 网络首发：2026-02-10，
  
  纸质出版：2026-02-10
- 稿件说明：
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耿明泽,郭怡凡,姜孝谟,章旋,闫斌斌,周家付,陈丁聚. 重型燃气轮机动叶片蠕变寿命预测的轻量化建模方法动力工程学报, 2026, 46(2): 45-53 https://doi.

org/10.19805/j.cnki.jcspe.2026.240692
耿明泽,郭怡凡,姜孝谟,章旋,闫斌斌,周家付,陈丁聚. 重型燃气轮机动叶片蠕变寿命预测的轻量化建模方法动力工程学报, 2026, 46(2): 45-53 https://doi. DOI： 10.19805/j.cnki.jcspe.2026.240692.

org/10.19805/j.cnki.jcspe.2026.240692 DOI：

摘要

针对重型燃气轮机动叶片的可靠性运维需求

通过降阶处理数值仿真结果

结合本征正交分解降阶技术和数据驱动的机器学习回归拟合方法

发展了一种轻量化建模方法

从而实现利用拉森-米勒法对蠕变寿命的快速预测。结果表明:所提出的降阶模型在5个危险节点下对叶片蠕变寿命的预测精度误差最大不超过5%

同时降阶方法对温度场和应力场的计算效率分别是传统仿真方法的9.00×10

倍和1.50×10

倍。相关研究结果可以为重型燃气轮机动叶片的在线运维监测提供重要的理论和方法支持。

Abstract

Based on the reliability operation and maintenance requirements for rotating blades of heavy-duty gas turbines

a lightweight modeling method was developed by reducing the order of numerical simulation results

and combining proper orthogonal decomposition (POD) reduced-order technique and a data-driven machine learning regression fitting approach

so as to realize the rapid prediction of creep life using Larson-Miller method. Results indicate that the proposed reduced-order model can achieve a maximum prediction error of no more than 5% for blade creep life at five critical nodes

while the computational efficiencies of the reduced-order method for temperature field and stress field are 9.00×10

and 1.50×10

times that of traditional simulation method

respectively. Relevant research findings can provide si

gnificant theoretical and methodological support for the online operation and maintenance monitoring of rotating blades in heavy-duty gas turbines.

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