压气机特性曲线精确分步拟合方法研究
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摘要
针对压气机特性曲线分步拟合中坐标变换方法不明确的问题,提出先对压比进行坐标变换,坐标变换中的参数对折合转速进行拟合,之后折合流量或效率分两步对压比和折合转速进行拟合的改进方法。用多个指标对拟合结果进行了评估,给出了拟合阶次的选取方法。实例说明了改进方法的有效性,进一步说明了拟合中坐标变换的意义,以及第二步系数拟合中牛顿插值法和最小二乘法的适用情形。
Aiming at the problem that the coordinate transformation method in compressor characteristic map is not clear, an improved method was proposed. In this method, the coordinate of pressure ratio was transformed first, and the coordinate transformation parameters were fitted with the corrected rotating speed.Then the compressor corrected mass flow rate or efficiency was fitted with the pressure ratio or corrected rotating speed in two steps. The fitting results were evaluated by multiple indexes, and the selection method of fitting order was given. The examples illustrate the effectiveness of the proposed method, and further illustrate the significance of the coordinate transformation in the fitting. The choices of interpolation method and the least square method in the second step coefficient fitting are also discussed.
Aiming at the problem that the coordinate transformation method in compressor characteristic map is not clear, an improved method was proposed. In this method, the coordinate of pressure ratio was transformed first, and the coordinate transformation parameters were fitted with the corrected rotating speed.Then the compressor corrected mass flow rate or efficiency was fitted with the pressure ratio or corrected rotating speed in two steps. The fitting results were evaluated by multiple indexes, and the selection method of fitting order was given. The examples illustrate the effectiveness of the proposed method, and further illustrate the significance of the coordinate transformation in the fitting. The choices of interpolation method and the least square method in the second step coefficient fitting are also discussed.
引文
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[4]Yu Y H,Chen L G,Sun F R,et al.Neural-network based analysis and prediction of a compressor’s characteristic performance map[J].Applied Energy,2007,81(1):48-55.
[5]Ghorbanian K,Gholamrezaei M.An artificial neural network approach to compressor performance prediction[J].Applied Energy,2009,86(7):1210-1221.
[6]贺星,刘永葆,孙丰瑞.基于改进Levenberg-Marquardt算法的燃气轮机特性拟合优化[J].海军工程大学学报,2012,24(4):35-40.
[7]赵雄飞,刘永葆,贺星.燃气轮机部件特性方程的拟合研究[J].燃气轮机技术,2011,24(3):50-55.
[8]刘喜超.基于偏最小二乘法的压气机特性曲线的拟和[J].汽轮机技术,2006,48(5):327-329.
[9]涂环.基于Kriging算法的压气机特性建模[J].内燃机学报,2014,32(4):377-383.
[10]Yang Q C,Li S Y,Cao Y P.A new component map generation method for gas turbine adaptation performance simulation[J].Journal of Mechanical Science and Technology,2017,31(4):1947-1957.
[11]崔茂佩.压缩机特性线的系数拟合法[J].热能动力工程,1999,14(1):43-46.
[2]Sieros G,Stamatis A,Mathioudakis K.Jet engine component maps for performance modeling and diagnosis[J].Journal of propulsion and power,1997,13(5):665-674.
[3]Tsoutsanis E,Meskin N,Benammar M,et al.A component map tuning method for performance prediction and diagnostics of gas turbine compressors[J].Applied Energy,2014,135:572-585.
[4]Yu Y H,Chen L G,Sun F R,et al.Neural-network based analysis and prediction of a compressor’s characteristic performance map[J].Applied Energy,2007,81(1):48-55.
[5]Ghorbanian K,Gholamrezaei M.An artificial neural network approach to compressor performance prediction[J].Applied Energy,2009,86(7):1210-1221.
[6]贺星,刘永葆,孙丰瑞.基于改进Levenberg-Marquardt算法的燃气轮机特性拟合优化[J].海军工程大学学报,2012,24(4):35-40.
[7]赵雄飞,刘永葆,贺星.燃气轮机部件特性方程的拟合研究[J].燃气轮机技术,2011,24(3):50-55.
[8]刘喜超.基于偏最小二乘法的压气机特性曲线的拟和[J].汽轮机技术,2006,48(5):327-329.
[9]涂环.基于Kriging算法的压气机特性建模[J].内燃机学报,2014,32(4):377-383.
[10]Yang Q C,Li S Y,Cao Y P.A new component map generation method for gas turbine adaptation performance simulation[J].Journal of Mechanical Science and Technology,2017,31(4):1947-1957.
[11]崔茂佩.压缩机特性线的系数拟合法[J].热能动力工程,1999,14(1):43-46.