Volume 29 Issue 1
Jan.  2014
Turn off MathJax
Article Contents
ZHENG Hong-tao, PAN Fu-min, YANG Ren. Performance calculation of compressor based on object-oriented method[J]. Journal of Aerospace Power, 2014, 29(1): 140-145. doi: 10.13224/j.cnki.jasp.2014.01.018
Citation: ZHENG Hong-tao, PAN Fu-min, YANG Ren. Performance calculation of compressor based on object-oriented method[J]. Journal of Aerospace Power, 2014, 29(1): 140-145. doi: 10.13224/j.cnki.jasp.2014.01.018

Performance calculation of compressor based on object-oriented method

doi: 10.13224/j.cnki.jasp.2014.01.018
  • Received Date: 2012-12-17
  • Publish Date: 2014-01-28
  • A characteristic prediction method was proposed and variable specific heat calculation was applied to the performance analysis and programming of compressor based on object-oriented theory.Also,a method named particle swarm optimization(PSO) based on back propagation (BP) neural network was presented by combining the global optimization ability of the PSO with the local optimization ability of the BP neural network,and the prediction error and fitting error were analyzed.The fitting error is mostly within 0.5% while the highest prediction error is within 0.8%;and both the fitting accuracy and prediction accuracy could meet the requirements.Variable specific heat calculation method was applied to the compressor performance calculation,and object-oriented method was used to build the compressor performance computing program.Compared with several working condition points of the compressor,the output parameter errors are less than 1.12%.Therefore,the characteristic prediction method and performance mathematical model are suitable for compressor performance calculation,and the compressor calculation procedure is also suitable for gas turbine performance calculation.

     

  • loading
  • [1]
    郑洪涛, 张玉龙, 杨仁.CRGT循环燃气轮机性能仿真[J].航空动力学报, 2012, 27(1):118-123. ZHENG Hongtao, ZHANG Yulong, YANG Ren.Simulation research of the CRGT cycle gas turbine performance[J].Journal of Aerospace Power, 2012, 27(1):118-123.(in Chinese)
    [2]
    Li Y G, Abdul Ghafir M F, Wang L, et al.Nonlinear multiple points gas turbine off-design performance adaptation using a genetic algorithm[J].Journal of Engineering for Gas Turbines and Power, 2011, 133(7):071701.1-071701.9.
    [3]
    He F, Li Z, Liu P, et al.Operation window and part-load performance study of a syngas fired gas turbine[J].Applied Energy, 2012, 89(1):133-141.
    [4]
    Colin K D, Gregory J, Charles W P, et al.Gas turbine system simulation:an object-oriented approach[R].NASA-TM-106044, 1992.
    [5]
    Brain P C, James L F.Object-oriented approach for gas turbine engine simulation[R].NASA-TM-106970, 1995.
    [6]
    Reed J A, Afjeh A A.Interactive secure web-enabled aircraft engine simulation using XML data binding integra-tion[R].AIAA-2002-4058, 2002.
    [7]
    Reed J A.Onyx:an objected-oriented framework for computational simulation of gas turbine systems[D].Toledo, US:University of Toledo, 1998.
    [8]
    Visser W P J, Broomhead M J.GSP:a generic object-oriented gas turbine simulation environment[R].NLR-TP-2000-267, 2000.
    [9]
    Kurzke J.GasTurb9:a program to calculate design and off-design performance of gas turbines[EB/OL].[2001-07-23].http://www.gasturb.de.
    [10]
    Cohen H, Rogers G F C, Saravanamuttoo H I H.Gas turbine theory[M].5th ed.Englewood Cliffs, US:Prentice Hall Press, 2001.
    [11]
    Ghorbanian K, Gholamrezaei M.An artificial neural network approach applied to compressor performance prediction[J].Applied Energy, 2009, 86(7/8):1210-1221.
    [12]
    López L F D M, Blas N G, Arteta A.The optimal combi-nation:grammatical swarm, particle swarm optimization and neural networks[J].Journal of Computational Science, 2012, 3(1/2):46-55.
    [13]
    Zhang J R, Zhang J, Lok T M, et al.A hybrid particle swarm optimization-back-propagation algorithm for feed forward neural network training[J].Applied Mathematics and Computation, 2007, 185(2):1026-1037.
    [14]
    Zhou J L, Duan Z C, Li Y, et al.PSO-based neural net-work optimization and its utilization in a boring machine[J].Journal of Materials Processing Technology, 2006, 178(9):19-23.
    [15]
    Chau K W.Application of a PSO-based neural network in analysis of outcomes of construction claims[J].Automation in Construction, 2007, 16(8):642-646.
    [16]
    刘志刚.工质热物理性质计算程序的编制及应用[M].北京:科学出版社, 1992.
    [17]
    邵维忠, 杨芙清.面向对象的系统分析[M].2版.北京:清华大学出版社, 2006.
    [18]
    钱能.C++程序设计[M].2版.北京:清华大学出版社, 2005.
  • 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

    Article Metrics

    Article views (1588) PDF downloads(990) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return