• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 56 Issue 5
Oct.  2021
Turn off MathJax
Article Contents
ZENG Li, CHEN Renxiang, DONG Shaojiang. Master-Slave Interpolation Modeling of Compressor Healthy Parameters Based on Kriging Algorithm[J]. Journal of Southwest Jiaotong University, 2021, 56(5): 967-972. doi: 10.3969/j.issn.0258-2724.20191118
Citation: ZENG Li, CHEN Renxiang, DONG Shaojiang. Master-Slave Interpolation Modeling of Compressor Healthy Parameters Based on Kriging Algorithm[J]. Journal of Southwest Jiaotong University, 2021, 56(5): 967-972. doi: 10.3969/j.issn.0258-2724.20191118

Master-Slave Interpolation Modeling of Compressor Healthy Parameters Based on Kriging Algorithm

doi: 10.3969/j.issn.0258-2724.20191118
  • Received Date: 26 Nov 2019
  • Rev Recd Date: 30 Jun 2020
  • Available Online: 13 Aug 2021
  • Publish Date: 15 Oct 2021
  • In order to solve the problem that the accuracy of the model is low due to the lack of compressor flow coefficient in the process of digital modeling of gas turbine compressor, a master-slave interpolation model for flow coefficient is constructed based on the Kriging interpolation algorithm. The distribution characteristics of flow coefficient in high-dimensional space are studied. The mapping relationship among flow coefficient, conversion speed and pressure ratio are explored, and a high-dimensional sample construction method for these three parameters is proposed. A high-precision flow coefficient master-slave interpolation model of gas turbine under transient conditions is established based on the Kriging algorithm. The results show that compared with the traditional Kriging interpolation method and the Newton interpolation method, the calculation result of flow coefficient based on master-slave model is closer to the actual value, and the calculation accuracy is improved by nearly 10%. In addition, the master model can output the estimated vector. Compared with the traditional Kriging model, the interpolation efficiency is improved by nearly 15%.

     

  • loading
  • KULKTLA, RDEDDY P, PRASAD B. Numerical study on the secondary air performance of the film holes for the combined impingement and film cooled first stage of high pressure gas turbine nozzle guide vane[J]. International Journal of Turbo and Jet Engines, 2020, 37(3): 221-240. doi: 10.1515/tjj-2017-0022
    谭治学,钟诗胜,林琳. 民航发动机性能诊断方法[J]. 哈尔滨工业大学学报,2019,51(1): 22-26. doi: 10.11918/j.issn.0367-6234.201801117

    TAN Zhixue, ZHONG Shisheng, LIN Lin. Method for performance diagnosis of commercial aero-engine[J]. Journal of Harbin Institute of Technology, 2019, 51(1): 22-26. doi: 10.11918/j.issn.0367-6234.201801117
    潘率诚,李刚团,丁朝霞,等. 基于部件特性的航空发动机性能模型修正[J]. 燃气涡轮试验与研究,2016,29(6): 26-29. doi: 10.3969/j.issn.1672-2620.2016.06.006

    PAN Shuaicheng, LI Gangtuan, DING Zhaoxia, et al. Aero-engine performance model correction based on component performance map[J]. Gas Turbine Experiment and Research, 2016, 29(6): 26-29. doi: 10.3969/j.issn.1672-2620.2016.06.006
    涂环,陈辉. 基于Kriging算法的压气机特性建模[J]. 内燃机学报,2014,32(4): 377-383.

    TU Huan, CHEN Hui. Modeling of compressor characteristica using Kriging method[J]. Transactions of CSICE, 2014, 32(4): 377-383.
    尹大伟,李本威. 基于Kriging方法的航空发动机压气机特性元建模[J]. 航空学报,2011,32(1): 99-106.

    YIN Dawei, LI Benwei. Aeroengine compressor characteristics metamodeling using Kriging method[J]. Acta Aeronautia et Astronautica Sinica, 2011, 32(1): 99-106.
    赵勇,李本威. 基于QPSO算法的压气机特性代理模型优化[J]. 推进技术,2014,35(11): 1538-1543.

    ZHAO Yong, LI Benwei. Surrogate model optimization of compressor characteristics based on QPSO algorithm[J]. Journal of Propulsion Technology, 2014, 35(11): 1538-1543.
    GHOLAMREZAEI M, GHORBANIAN K. Compressor map generation using a feed-forward neural network and rig data[J]. Power and Engine, 2009, 224(A): 97-106.
    MAREK O, SLAWOMIR S. Modeling of turbine engine axial-flow compressor and turbine characteristics[J]. Journal of Propolsion and Power, 2000, 16(2): 336-347. doi: 10.2514/2.5574
    柳强,王成恩. 基于Kringing模型的复杂产品管线敷设顺序粒子群优化[J]. 机械工程学报,2011,47(13): 140-145. doi: 10.3901/JME.2011.13.140

    LIU Qiang, WANG Chengen. Kriging mode-based routing sequence planning for complex products by particle swarm optimization[J]. Journal of Mechanical Engineering, 2011, 47(13): 140-145. doi: 10.3901/JME.2011.13.140
    赵海龙,岳珠峰,刘伟. 矩独立重要性分析的Kriging代理模型方法[J]. 航空学报,2016,37(7): 2234-2241.

    ZHAO Hailong, YUE Zhufeng, LIU Wei. A Kriging surrogatemodel method for moment-independent importanceanalysis[J]. Acta Aeronautics et Astronautica Sinica, 2016, 37(7): 2234-2241.
    申静,苏天赟,王国宇. 基于Kriging算法的海底地形插值设计与实现[J]. 海洋科学,2012,36(5): 24-28.

    SHEN Jing, SU Tianyun, WANG Guoyu. Submarine topography visualization based on Kriging algorithm[J]. Marine Sciences, 2012, 36(5): 24-28.
    TIMOTHY W S, JOHN J K. Kriging models for global approximation in simulation-based multidisciplinary design optimization[J]. AIAA Journal, 2001, 39(12): 2233-2240. doi: 10.2514/2.1234
    杨永存,辛庆伟. 一种基于试验数据的发动机特性曲线拟合[J]. 海军航空工程学院学报,2016,31(3): 312-316. doi: 10.7682/j.issn.1673-1522.2016.03.003

    YANG Yongcun, XIN Qingwei. A Fitting on engine characteristic map based on text data[J]. Journal of Naval Aeronautical and Astronautical University, 2016, 31(3): 312-316. doi: 10.7682/j.issn.1673-1522.2016.03.003
    宋文艳,孟乒乒,柴政. 基于飞机/发动机性能一体化的发动机控制规律优化设计方法研究[J]. 推进技术,2018,39(12): 2661-2668.

    SONG Wenyan, MENG Pingping, CHAI Zheng. Research on aero-engine control law optimization design based on integrated fighter/aero-engine performance[J]. Journal of Propulsion Technology, 2018, 39(12): 2661-2668.
    曾力,龙伟. 基于SVR的航空发动机滑油金属含量预测方法[J]. 四川大学学报(工程科学版),2016,48(增刊2): 161-164.

    ZENG Li, LONG Wei. Prediction method of metal content of aero engine lubricating oil based on SVR[J]. Journal of Sichuan University (Engineering Science Edition), 2016, 48(S2): 161-164.
    骆广琦, 桑增产. 航空燃气涡轮发动机数值仿真[M]. 北京: 国防工业出版社, 2007.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(4)  / Tables(3)

    Article views(389) PDF downloads(14) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return