Prediction and Optimization of Thermally Induced Structural Stress in the Rotor of a Solar Thermal Turbine

LI Weiye; WEN Siguo; DING Ying; QI Rui; YUAN Qi; LI Pu

doi:10.7652/xjtuxb202604015

您当前的位置：

首页 >

文章列表页 >

Prediction and Optimization of Thermally Induced Structural Stress in the Rotor of a Solar Thermal Turbine

Special Topic Dynamic Characteristics Research of Key Steam Turbine Components | 更新时间：2026-04-10

- Prediction and Optimization of Thermally Induced Structural Stress in the Rotor of a Solar Thermal Turbine
- Journal of Xi'an Jiaotong University Vol. 60, Issue 4, Pages: 187-198(2026)
- 作者机构：
  
  西安交通大学能源与动力工程学院，710049，西安
  哈尔滨汽轮机厂有限责任公司，150046，哈尔滨
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202604015
  CLC： TK14
- Received：15 August 2025，
  
  Online First：25 December 2025，
  
  Published：10 April 2026
- 稿件说明：
移动端阅览
LI Weiye, WEN Siguo, DING Ying, et al. Prediction and Optimization of Thermally Induced Structural Stress in the Rotor of a Solar Thermal Turbine[J]. Journal of Xi'an Jiaotong University, 2026, 60(4): 187-198.
DOI：

LI Weiye, WEN Siguo, DING Ying, et al. Prediction and Optimization of Thermally Induced Structural Stress in the Rotor of a Solar Thermal Turbine[J]. Journal of Xi'an Jiaotong University, 2026, 60(4): 187-198. DOI： 10.7652/xjtuxb202604015.

摘要

针对太阳能光热汽轮机中低压转子在冷态启动过程中的应力集中问题，通过有限元建模与混合残差编码网络（HRENet）相结合的方法，开展了瞬态热固耦合分析、应力预测及优化研究。首先，建立了转子有限元模型，模拟冷态启动工况。其次，提出一种新型HRENet模型，用于精准预测不同启动参数下转子应力集中点的应力。最后，通过启动优化与结构优化降低转子应力。其中，启动过程的划分和优化是基于初始温度、升速率、初始升温时间和温升率4个参数；结构优化则采用自适应多目标方法对转子中心打孔进行设计。研究结果表明：最大温度应力出现在第1级叶根槽倒圆角位置，这主要源于该区域的高温度梯度；与人工神经网络、卷积神经网络、残差神经网络和Transformer模型相比，HRENet的综合加载等效应力预测平均绝对误差分别降低了65.23%、78.01%、88.23%和85.65%，均方误差降低了82.13%、90.43%、98.94%和96.22%，从而显著提升了预测精度；启动优化结果发现，温升率对应力影响最大，同时启动时间缩短了23.08%，危险点应力降低了5.3%；结构优化后，第1级叶根槽倒圆角应力分别降低了33.33%和28.43%。该研究可为光热汽轮机转子的安全运行与优化设计提供有效方法。

Abstract

In response to the issue of stress concentration in the low-pressure rotor of solar thermal turbines during cold start-up，transient thermo-structural coupling analysis，stress prediction，and optimization were performed by combining finite element modeling with a hybrid residual encoding network（HRENet）.First，a finite element model of the rotor was established to simulate cold start-up conditions.Second，a novel HRENet model was proposed for accurately predicting the stress at stress-concentration points in the rotor under different start-up parameters.Finally，rotor stress was reduced through start-up optimization and structural optimization.The start-up process was divided and optimized based on four parameters：initial temperature，heating rate，initial heating duration，and temperature rise rate；structural optimization employed an adaptive multi-obj ective method to design center-hole drilling in the rotor.Results show that the maximum thermal stress occurs at the fillet of the first-stage blade-root groove，primarily due to the high temperature gradient in this region.Compared with artificial neural networks，convolutional neural networks，residual neural networks，and Transformer models，the mean absolute error of HRENet in predicting the comprehensive loading equivalent stress was reduced by 65.23%，78.01%，88.23%，and 85.65%，respectively，and the mean squared error was reduced by 82.13%，90.43%，98.94%，and 96.22%，thereby significantly improving prediction accuracy.From start-up optimization，it was found that the temperature rise rate exerts the greatest influence on stress；simultaneously，start-up time was shortened by 23.08% and stress at critical points was reduced by 5.3%.After structural optimization，the stress at the fillet of the first-stage blade-root groove was reduced by 33.33% and 28.43%，respectively.This research provides an effective approach for the safe operation and optimized design of solar thermal turbine rotors.

关键词

Keywords

references

刘春雨.太阳能光热电站储能系统热性能研究及技术经济分析[D].西安：西安理工大学，2023.

杜凤丽，原郭丰，常春，等.太阳能热发电技术产业发展现状与展望[J].储能科学与技术，2013，2（6）：551-564.

DU Fengli，YUAN Guofeng，CHANG Chun，et al. Concentrating solar power：current status and perspective [J].Energy Storage Science and Technology，2013，2（6）：551-564.

高嵩，许继刚.塔式光热电站设计国家标准与IEC标准的对比研究[J].电力勘测设计，2023（12）：6-9.

GAO Song，XU Jigang.Comparative research on design of solar power tower plant between national and IEC standard [J].Electric Power Survey & Design，2023（12）：6-9.

张忠涛.基于相变储能系统的槽式太阳能光热电站运行特性研究[D].保定：华北电力大学，2023.

焦玉雪，徐自力，尹刚，等.太阳能光热发电汽轮机转子热机械应力分析及启动优化[J].汽轮机技术，2020，62（6）：417-420.

JIAO Yuxue，XU Zili，YIN Gang，et al.Start-up optimization and analysis of thermo-mechanical stress of solar thermal steam turbine rotor[J].Turbine Technology，2020，62（6）：417-420.

麻永前，包一鸣，张伟康.光热发电中汽轮机运行的优化措施分析[J].电子技术，2022，51（1）：290-291.

MA Yongqian，BAO Yiming，ZHANG Weikang. Analysis on optimization measures of steam turbine operation in photothermal power generation[J].Electronic Technology，2022，51（1）：290-291.

赵锦，刘雄，岳书培，等.光热发电汽轮机频繁启停灵活性研究[J].自动化应用，2025，66（7）：161-164.

ZHAO Jin，LIU Xiong，YUE Shupei，et al.Research on frequent start and stop flexibility of steam turbines for concentrated solar power generation[J].Automation Application，2025，66（7）：161-164.

ŁUCZYN′SKI P，TOEBBEN D，PEHLE L，et al. Thermo-structural analysis of steam turbine in prewarming operation with hot air [C]//ASME Turbo Expo 2019：Turbomachinery Technical Conference and Exposition.New York，NY，USA：ASME，2019：V008T29A013.

MOROZ L，FROLOV B，KOCHUROV R.Steam turbine rotor transient thermo-structural analysis and lifetime prediction [C]//ASME Turbo Expo 2016：Turbomachinery Technical Conference and Exposition. New York，NY，USA：ASME，2016：V008T26A038.

ZHAO Nailong，WANG Weizhe，ZHANG Junhui，et al.Numerical investigation on life improvement of lowcycle fatigue for an ultra-supercritical steam turbine rotor [J].Journal of Mechanical Science and Technology，2016，30（4）：1747-1754.

WANG Wei，DONG Chao，SUN Yongjian.On startup optimization for a 300 MW steam turbine [J].Journal of Engineering，Technology and Applied Science，2023，1（1）：1-21 .

GUO Ding，LIU Tianyuan，ZHANG Di，et al.Fast reconstruction method of the stress field for the steam turbine rotor based on deep fully convolutional network [J].Journal of Engineering for Gas Turbines and Power，2022，144（2）：021023.

BAKER M，ROSIC B.The hybrid pathway to flexible power turbines：part I novel autoencoder methods for the automated optimization of thermal probes and fast sparse data reconstruction，enabling real-time thermal analysis [J].Journal of Engineering for Gas Turbines and Power，2024，146（3）：031020.

RUSIN A，NOWAK G，LIPKA M.Practical algorithms for online thermal stress calculations and heating process control [J].Journal of Thermal Stresses，2014，37（11）：1286-1301.

ZHU Guangya，GUO Ding，LI Jinxing，et al.Intelligent steam turbine start-up control based on deep reinforcement learning [J].Energy，2025，320：135335.

史进渊.大功率电站汽轮机寿命预测与可靠性设计[M].北京：中国电力出版社，2011.

袁奇，李浦.燃气轮机拉杆转子结构动力学[M].西安：西安交通大学出版社，2021.

徐佳敏，李汪繁.高低齿汽封表面换热系数计算方法研究[J].热力发电，2022，51（6）：147-153.

XU Jiamin，LI Wangfan.Research on calculation method of heat transfer coefficient of stepped seal surface[J]. Thermal Power Generation，2022，51（6）：147-153.

赵翔，茹东恒，王鹏，等.基于NARX神经网络方法的汽轮机转子关键部位应力预测[J].应用数学和力学，2021，42（8）：771-784.

ZHAO Xiang，RU Dongheng，WANG Peng，et al.On the stress prediction of key components in steam turbine rotors based on the NARX neural network[J]. Applied Mathematics and Mechanics，2021，42（8）：771-784.

HARRINGTON P.Machine learning in action [M]. Greenwich，CT，USA：Manning Publications Co，2012.

KINGMA D P，BA J.Adam：a method for stochastic optimization [EB/OL].（2017-01-30）[2025-06-20].https://arxiv.org/abs/1412.6980.

李敏，李红娇，陈杰.差分隐私保护下的Adam优化算法研究[J].计算机应用与软件，2020，37（6）：253-258.

LI Min，LI Hongjiao，CHEN Jie.Adam optimization algorithm based on differential privacy protection [J]. Computer Applications and Software，2020，37（6）：253-258.

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 thermomechanical coupling stress prediction and life assessment[J].Journal of Xi'an Jiaotong University，2025，59（10）：87-95.

陈志峰，支德胜.汽轮机启动过程优化分析[J].技术与市场，2019，26（3）：124.

CHEN Zhifeng，ZHI Desheng.Optimization analysis of steam turbine start-up process[J].Technology and Market，2019，26（3）：124.

刘萌，王印松，牟文彪，等.基于多策略分区勘探粒子群算法的主蒸汽温度优化控制[J].热力发电，2021，50（7）：23-30.

LIU Meng，WANG Yinsong，MOU Wenbiao，et al. Optimization control of main steam temperature system based on multi strategy partition exploration particle swarm optimization algorithm [J].Thermal Power Generation，2021，50（7）：23-30.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Z-Pinch Dynamic Axial Magnetization Model of Coupled Rotating Reflux Column and Its Structural Optimization Method

Effect on the Next Stage Inlet Flow of the Complementary Gas Flow Between Centrifugal Compressor Stages

Study on the Multiparameter Difference of Voltage Equalization Characteristics ofInsulator Strings of 750 kV Substation End Portal Frame

Effect of Internal Partition in Square Cavity Unit on Heat Storage Process of Phase Change Material

Knowledge Graph Representation Learning Driven by Semantic Discrimination and Subgraph Integration

Related Author

LI Pu

DING Ying

WEN Siguo

LI Weiye

YUAN Qi

QI Rui

WU Jian

ZHAO Yiming

Related Institution

School of Energy and Power Engineering, Xi'an Jiaotong University

西安交通大学

哈尔滨汽轮机厂有限责任公司

State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University

School of Electrical Engineering, Xi'an Jiaotong University

⁰