基于航空发动机工况的叶尖间隙智能预测方法

杨阳; 张建超; 项洋; 陆海鹰

doi:10.13224/j.cnki.jasp.20220757

基于航空发动机工况的叶尖间隙智能预测方法

doi: 10.13224/j.cnki.jasp.20220757

中国航空发动机集团有限公司中国航空发动机研究院，北京 101304

基金项目: 国家科技重大专项（2017-Ⅰ-0005-0006)

详细信息

作者简介:
杨阳（1995-），男，工程师，硕士，主要从事航空发动机空气系统方面的研究。E-mail：yangyang1@buaa.edu.cn

中图分类号: V233.93
计量
- 文章访问数: 683
- HTML浏览量: 381
- PDF量: 140
- 被引次数: 0
出版历程
- 收稿日期: 2022-09-30
- 网络出版日期: 2023-04-19

Intelligent prediction method of tip clearance under different aero-engine operating conditions

Aero Engine Academy of China，Aero Engine Corporation of China，Beijing 101304，China

摘要

摘要:
在实际工程中保持航空发动机高效运行的有效措施之一是应用叶尖间隙主动控制技术，其前提是建立精确的叶尖间隙模型以实现叶尖间隙预测。建立叶尖间隙的简化物理模型和数学模型，将叶尖间隙计算转化为热变形与传热问题，通过机器学习模型对发动机工况参数进行特征提取，利用有效特征求解传热问题的边界，从而实现基于发动机工况参数快速预测实时叶尖间隙。机器学习模型的十折交叉验证集的平均准确率为98.9%，叶尖间隙模型的验证误差为4.3%，得到了不同工况下的叶尖间隙计算结果和冷气流量大小变化规律，计算耗时小于0.03 s。
- 空气系统 /
- 叶尖间隙 /
- 机器学习 /
- 特征提取 /
- 零维仿真
Abstract:
One of the effective measures to maintain efficient operation of aero-engine in practical engineering is to apply active tip clearance control technology, provided that an accurate tip clearance model is established for achieving tip clearance prediction. A simplified physical model and a mathematical model of tip clearance were established. The tip clearance calculation was transformed into a problem of thermal deformation and heat transfer. The operating parameters of the engine was extracted through machine learning model, and the boundary of heat transfer was solved using the effective features, thus realizing rapid prediction of real-time tip clearance based on the operating parameters of the engine. The cross validation accuracy of machine learning model was 98.9%, and the verification accuracy of the tip clearance model was 4.3%. The tip clearance calculation results under different working conditions and the change rule of the cold air flow rate were obtained, and the calculation time was less than 0.03 s.
- air system /
- tip clearance /
- machine learning /
- feature extraction /
- zero-dimensional simulation

HTML

图 1 研究方案流程图

Figure 1. Flow chart of research scheme

下载: 全尺寸图片幻灯片

图 2 叶尖间隙简化模型与求解方法流程图

Figure 2. Simplified model of tip clearance and flow chart of solution conditions

下载: 全尺寸图片幻灯片

图 3 叶尖间隙计算方法流程图

Figure 3. Flow chart of calculation method of tip clearance

下载: 全尺寸图片幻灯片

图 4 冷却气流流量与换算转速对应关系

Figure 4. Correspondence between cooling air mass flow and corrected speed

下载: 全尺寸图片幻灯片

图 5 冷却气流温度与换算转速对应关系

Figure 5. Correspondence between cooling air temperature and corrected speed

下载: 全尺寸图片幻灯片

图 6 模型训练效果

Figure 6. Model training effect

下载: 全尺寸图片幻灯片

图 7 轮盘冷气流量的机器学习验证

Figure 7. Machine learning verification of cooling air mass flow in rotor

下载: 全尺寸图片幻灯片

图 8 机匣冷气流量的机器学习验证

Figure 8. Machine learning verification of cooling air mass flow in case

下载: 全尺寸图片幻灯片

图 9 轮盘冷气温度的机器学习验证

Figure 9. Machine learning verification of cooling air temperature in rotor

下载: 全尺寸图片幻灯片

图 10 机匣冷气温度的机器学习验证

Figure 10. Machine learning verification of cooling air temperature in case

下载: 全尺寸图片幻灯片

图 11 十折交叉验证评估结果

Figure 11. Ten fold cross validation evaluation results

下载: 全尺寸图片幻灯片

图 12 叶尖间隙计算模型与数值模拟模型的对比

Figure 12. Comparison between tip clearance calculation model and numerical simulation model

下载: 全尺寸图片幻灯片

图 13 径向相对坐标与换算转速对应关系

Figure 13. Correspondence between radial relative coordinates and converted speed

下载: 全尺寸图片幻灯片

图 14 叶尖间隙与换算转速对应关系

Figure 14. Correspondence between tip clearance and corrected speed

下载: 全尺寸图片幻灯片

图 15 调整机匣冷气流量后的叶尖间隙变化规律

Figure 15. Change rule of tip clearance after adjusting the cooling air mass flow of the casing

下载: 全尺寸图片幻灯片

参考文献(19)

[1]	刘松龄. 燃气涡轮发动机的传热和空气系统[M]. 上海: 上海交通大学出版社, 2018.
[2]	陈光. 航空燃气涡轮发动机结构设计[M]. 北京: 北京航空航天大学出版社, 1988.
[3]	廉筱纯. 航空发动机原理[M]. 西安: 西北工业大学出版社, 2005.
[4]	孙健国. 航空燃气涡轮发动机控制[M]. 上海: 上海交通大学出版社, 2014.
[5]	漆文凯,陈伟. 某型航空发动机高压涡轮叶尖间隙数值分析[J]. 南京航空航天大学学报,2003,35(1): 63-67. doi: 10.3969/j.issn.1005-2615.2003.01.013 QI Wenkai,CHEN Wei. Numerical analysis of tip clearance of high pressure turbine of an aeroengine[J]. Journal of Nanjing University of Aeronautics and Astronautics,2003,35(1): 63-67. (in Chinese) doi: 10.3969/j.issn.1005-2615.2003.01.013
[6]	PILLIDIS P, MACCALLUM N R L. Models for predicting tip clearance changes in gas turbines[R]. NASA N83-229258, 1983.
[7]	PENG Kai,FAN Ding,YANG Fan,et al. Active generalized predictive control of turbine tip clearance for aero-engines[J]. Chinese Journal of Aeronautics,2013,26(5): 1147-1155. doi: 10.1016/j.cja.2013.07.005
[8]	FEI Chengwei,BAI Guangchen. Distributed collaborative probabilistic design for turbine blade-tip radial running clearance using support vector machine of regression[J]. Mechanical Systems and Signal Processing,2014,49(1/2): 196-208.
[9]	CHAPMAN J W, GUO T H, KRATZ J L, et al. Integrated turbine tip clearance and gas turbine engine simulation[R]. AIAA-2016-5047, 2016.
[10]	KRATZ J L, CHAPMAN J W. Active turbine tip clearance control trade space analysis of an advanced geared turbofan engine[R]. AIAA-2018-4822, 2018.
[11]	KRATZ J L, CHAPMAN J W, GUO T H. A parametric study of actuator requirements for active turbine tip clearance control of a modern high bypass turbofan engine[R]. ASME Paper GT2017-63472, 2017.
[12]	STEINETZ B M, LATTIME S B, DECASTRO J A, et al. Evaluation of an active clearance control system concept[R]. AIAA-2005-3989, 2005.
[13]	王鹏飞. 高压涡轮主动间隙控制系统的供气流路与机匣传热研究[D]. 南京: 南京航空航天大学, 2018. WANG Pengfei. Research on air supply flow path and casing heat transfer of HP turbine active clearance control system[D]. Nanjing University of Aeronautics and Astronautics, 2018. (in Chinese)
[14]	朱之丽,廖阔. 燃气轮机过渡过程中叶尖间隙估算及间隙变化对加速过程的影响[J]. 航空动力学报,1995,10(2): 66-67, 95-96. doi: 10.13224/j.cnki.jasp.1995.02.018 ZHU Zhili,LIAO Kuo. Estimation of tip clearance during gas turbine transition and the effect of clearance change on acceleration process[J]. Journal of Aerospace Power,1995,10(2): 66-67, 95-96. (in Chinese) doi: 10.13224/j.cnki.jasp.1995.02.018
[15]	张少平,苏廷铭,罗秋生,等. 航空发动机压气机径向间隙设计方法研究[J]. 燃气涡轮试验与研究,2011,24(4): 25-27, 31. doi: 10.3969/j.issn.1672-2620.2011.04.007 ZHANG Shaoping,SU Tingming,LUO Qiusheng,et al. Research on design method of radial clearance of aeroengine compressor[J]. Gas Turbine Test and Research,2011,24(4): 25-27, 31. (in Chinese) doi: 10.3969/j.issn.1672-2620.2011.04.007
[16]	刘兆颖,毛军逵,夏木云,等. 叶尖间隙控制系统中横流效应的试验研究[J]. 推进技术,2019,40(9): 2030-2039. doi: 10.13675/j.cnki.tjjs.180577 LIU Zhaoying,MAO Junkui,XIA Muyun,et al. Experimental study on cross flow effect in tip clearance control system[J]. Propulsion Technology,2019,40(9): 2030-2039. (in Chinese) doi: 10.13675/j.cnki.tjjs.180577
[17]	王立峰, 李正熙. 前向神经网络快速学习算法在发动机模型辨识中的应用[J]. 航空动力学报, 2003, 18(5): 705-708. WANG Lifeng, LI Zhengxi. Application of fast learning algorithm of feedforward neural network in engine model identification[J]. Journal of Aerodynamics, 2003, 18(5): 705-708. (in Chinese)
[18]	张鹏, 黄金泉. 航空发动机神经网络内模控制[J]. 航空动力学报, 2005, 20(6): 1061-1065. ZHANG Peng, HUANG Jinquan. Golden spring aeroengine neural network internal model control [J]. Journal of Aerospace Power, 2005, 20(6): 1061-1065. (in Chinese)
[19]	何清,李宁,罗文娟,等. 大数据下的机器学习算法综述[J]. 模式识别与人工智能,2014,27(4): 327-336. doi: 10.3969/j.issn.1003-6059.2014.04.007 HE Qing,LI Ning,LUO Wenjuan,et al. Overview of machine learning algorithms under big data[J]. Pattern Recognition and Artificial Intelligence,2014,27(4): 327-336. (in Chinese) doi: 10.3969/j.issn.1003-6059.2014.04.007