汽轮机通流间隙精度预测与修配规划方法研究

张嘉锟; 陈琨; 高建民; 高智勇; 戴宏玮; 杜欣霏

doi:10.7652/xjtuxb202112011

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汽轮机通流间隙精度预测与修配规划方法研究

更新时间：2025-07-09

- 汽轮机通流间隙精度预测与修配规划方法研究
- Accuracy Prediction and Repair Planning of Steam Turbine Flow Clearance
- 西安交通大学学报 2021年55卷第12期页码：87-97
- 作者机构：
  
  西安交通大学机械制造系统工程国家重点实验室,西安,710049
- 作者简介：
- 基金信息：
  
  : 2021-03-05。作者简介: 张嘉锟(1996—),男,硕士生;陈琨(通信作者),男,副教授。基金项目: 国家重点研发计划资助项目(2019YFB1703800,2017YFF0210500)
- DOI：10.7652/xjtuxb202112011
  中图分类号： TP391.7
- 网络首发：2021-12-10，
  
  纸质出版：2021
- 稿件说明：
移动端阅览
张嘉锟, 陈琨, 高建民, 等. 汽轮机通流间隙精度预测与修配规划方法研究[J]. 西安交通大学学报, 2021,55(12):87-97.

Accuracy Prediction and Repair Planning of Steam Turbine Flow Clearance[J]. 2021, 55(12): 87-97.
张嘉锟, 陈琨, 高建民, 等. 汽轮机通流间隙精度预测与修配规划方法研究[J]. 西安交通大学学报, 2021,55(12):87-97. DOI： 10.7652/xjtuxb202112011.

Accuracy Prediction and Repair Planning of Steam Turbine Flow Clearance[J]. 2021, 55(12): 87-97. DOI： 10.7652/xjtuxb202112011.

摘要

针对汽轮机通流间隙装配精度难以保障

需要反复装配及调整

缺乏事先的仿真、预判及修正

难以适应数字化装配转型需求的问题

提出一种汽轮机通流间隙精度预测与修配规划方法。该方法首先基于规则推理进行一次修配

确保汽轮机可以满足除通流间隙以外的其他主要装配要求

防止因不能成功装配而引起的零部件返修; 其次根据产品装配工艺信息

通过构建装配特征网络

引入深度优先搜索算法

生成通流间隙装配尺寸链

进而实现通流间隙的精度预测; 然后考虑尺寸链之间的耦合特性

基于案例推理进行二次修配

通过案例检索获得修配环的修配范围

以及基于粒子群算法的最优修配量

确保通流间隙能够满足设计要求。以某型号汽轮机装配单元为例进行应用验证

结果表明

提出的方法可成功根据采集到的实测数据实现通流间隙的精度预测

并输出对应的修配建议

为实物装配提供参考

进而提高汽轮机的一次装配成功率及装配效率。

Abstract

It is difficult to guarantee assembly accuracy of steam turbine flow clearance due to repeated assembly and adjustments in the case of lack of prior simulation

prediction and correction. To meet the requirements of digital assembly transformation

a steam turbine flow clearance accuracy prediction and repair planning method is proposed. The primary repair is carried out following the rule-based reasoning to ensure the turbine

except the flow clearance

to meet the main assembly requirements and prevent the parts from being repaired due to assembly failure. The assembly feature network is constructed according to the product assembly process information

and the depth first search algorithm is used to realize the generation of the flow clearance assembly dimension chains

then the accuracy prediction of flow clearance is realized. Considering the coupling characteristics of the dimension chains

the secondary repair is carried out following the case-based reasoning

and via case retrieval the repair range of repair ring is obtained and the optimal repair quantity is achieved with particle swarm optimization algorithm to ensure the flow clearance to meet the design requirements. A turbine assembly unit is used as an example for application verification. The results show that this method can successfully predict the accuracy of flow clearance according to the collected measurement data and can output corresponding repair suggestions

and then improve the first-time assembly success rate and assembly efficiency of steam turbine.

关键词

Keywords

references

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