应用遗传算法的汽轮机转子启动优化

李猛 1; 2; 郭勇 3; 马骏 3; 高进 3; 徐自力 1

doi:10.7652/xjtuxb201801009

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应用遗传算法的汽轮机转子启动优化

更新时间：2025-07-09

- 应用遗传算法的汽轮机转子启动优化
- Rotor Start-Up Optimization of Steam Turbine Based on Genetic Algorithm
- 西安交通大学学报 2018年52卷第1期页码：54-60
- 作者机构：
  
  1. 西安交通大学机械结构强度与振动国家重点实验室,西安,710049
  2. 上海航天精密机械研究所,上海,210699
- 作者简介：
- 基金信息：
  
  : 2017-06-05。作者简介: 李猛(1991—),男,硕士生;徐自力(通信作者),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(51675406)
- DOI：10.7652/xjtuxb201801009
  中图分类号： TK263
- 网络首发：2018-01-10，
  
  纸质出版：2018
- 稿件说明：
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李猛 1, 2, 郭勇 3, 等. 应用遗传算法的汽轮机转子启动优化[J]. 西安交通大学学报, 2018,52(1):54-60.

Rotor Start-Up Optimization of Steam Turbine Based on Genetic Algorithm[J]. 2018, 52(1): 54-60.
李猛 1, 2, 郭勇 3, 等. 应用遗传算法的汽轮机转子启动优化[J]. 西安交通大学学报, 2018,52(1):54-60. DOI： 10.7652/xjtuxb201801009.

Rotor Start-Up Optimization of Steam Turbine Based on Genetic Algorithm[J]. 2018, 52(1): 54-60. DOI： 10.7652/xjtuxb201801009.

摘要

为了对汽轮机启动过程进行优化

发展了一种转子热应力半解析递推计算模型。该模型考虑蒸汽换热系数的变化

将启动过程分解为多个换热系数不变的升温过程

各升温过程的换热系数值取为该升温过程开始时刻的换热系数。同时

将每个升温过程结束时刻转子的温度场拟合为只含偶数次幂的4次多项式

并将拟合的温度场作为下个升温过程的初始温度场

通过拉普拉斯变换法

计算出下个升温过程的瞬态温度场。利用半解析递推模型构造转子启动优化的目标函数

采用遗传算法对660 MW机组的冷态启动曲线进行了优化

优化后转子的最大热应力减小了19.4%

且启动时间减小了4.9%。为验证该半解析递推模型的计算精度和效率

分别采用有限元模型和半解析递推模型计算了660 MW机组转子冷态启动过程中的瞬态温度场、应力场

计算结果表明:两种模型计算的转子关键部位热应力变化趋势相同

最大热应力相差0.11%

而递推模型计算的时间约为有限元模型计算时间的2.8%。

Abstract

To optimize the start-up process of steam turbine

a semi-analytical recursive calculation model of rotor temperature field is developed. Considering the change of heat transfer coefficient with time

the start-up process is divided into multiple heating processes with constant heat transfer coefficient in the algorithm. The value of heat transfer coefficient of each heating process is taken as the initial value of heat transfer coefficient at the beginning of the process. The temperature field at the end of each process is fitted as 4th-order polynomial

and the 4th-order polynomial will be the initial temperature field of the next temperature rising process. Then

the transient temperature field of the next temperature rising process is calculated through the Laplace transform method. To verify the accuracy and efficiency of the semi-analytical recursive model

finite element method and recursive model are used to calculate the temperature field and stress field of the rotor during cold start of a 660 MW turbine unit. The calculation results show that the trend of the thermal stress calculated by two models is the same. The thermal stress calculated by two models differs by 0.11%

and the computation time of the recursive model is about 2.8% of the finite element model. An objective function of rotor start-up optimization is constructed by the semi-analytical recursive model

and the genetic algorithm is used to optimize the rotor cold start-up of a 660 MW unit. After optimization

the maximum thermal stress of the rotor is reduced by 19.4% and the starting time is reduced by 4.9%.

关键词

Keywords

references

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