基于面向对象方法的压气机性能计算

郑洪涛; 潘福敏; 杨仁

doi:10.13224/j.cnki.jasp.2014.01.018

基于面向对象方法的压气机性能计算

doi: 10.13224/j.cnki.jasp.2014.01.018

哈尔滨工程大学动力与能源工程学院, 哈尔滨 150001

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出版历程
- 收稿日期: 2012-12-17
- 刊出日期: 2014-01-28

Performance calculation of compressor based on object-oriented method

College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China

摘要

摘要: 提出一套预测压气机未知特性的方法，并基于面向对象思想采用变比热容计算方法进行压气机性能计算的分析和编程.结合粒子群优化（PSO）的全局寻优能力和反向传播（BP）神经网络的局部寻优能力提出基于PSO的BP神经网络（PSO-BP神经网络）预测压气机特性，分析了其预测误差和拟合误差：拟合误差基本都小于0.5%，预测误差基本都小于0.8%.其拟合精度和预测精度满足要求.采用变比热容计算方法来计算压气机性能，并采用面向对象方法编写了压气机性能计算程序.对几个压气机变工况点进行验证，各输出参数的最大误差为1.12%.因此，特性预测方法和性能计算的数学模型适用于压气机性能计算，这套方法同样适用于燃气轮机性能计算.
- 压气机特性 /
- 性能计算 /
- 面向对象 /
- 粒子群优化 (PSO) /
- 神经网络 /
- 变比热容
Abstract: 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.
- compressor characteristics /
- performance calculation /
- object-oriented /
- particle swarm optimization(PSO) /
- neural network /
- variable specific heat

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参考文献(18)

[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.