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首页> 外文期刊>International Journal of Refrigeration >A comparison between the modeling of a reciprocating compressor using artificial neural network and physical model
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A comparison between the modeling of a reciprocating compressor using artificial neural network and physical model

机译：基于人工神经网络的往复式压缩机建模与物理模型的比较。

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This article presents the development, validation, and comparison of two methods for modeling a reciprocating compressor. Initially, the physical mode is based on eight internal sub-processes that incorporate infinitesimal displacements according to the piston movement. Next, the analysis and modeling of the compressor through the application of artificial neural networks are presented. The input variables are: suction pressure, suction temperature, discharge pressure, and compressor rotation speed. The output parameters are: refrigerant mass flow rate, discharge temperature, and energy consumption. Both models are validated with experimental data for the refrigerants R1234yf and R134a; computer simulations show that mean relative errors are below +/- 10% with the physical model, and below +/- 1% when artificial neural networks are used. Additionally, the performance of the models is evaluated through the computation of the squared absolute error. Finally, these models are used to compute an energy comparison between both refrigerants. (C) 2015 Elsevier Ltd and International Institute of Refrigeration. All rights reserved.
机译：本文介绍了对往复式压缩机建模的两种方法的开发，验证和比较。最初，物理模式基于八个内部子过程，这些子过程根据活塞的运动并入了最小的位移。接下来，介绍了通过人工神经网络对压缩机进行的分析和建模。输入变量为：吸气压力，吸气温度，排气压力和压缩机转速。输出参数为：制冷剂质量流量，排放温度和能耗。两种模型均已通过制冷剂R1234yf和R134a的实验数据进行了验证;计算机仿真显示，物理模型的平均相对误差低于+/- 10％，而使用人工神经网络时的平均相对误差则低于+/- 1％。另外，通过计算平方的绝对误差来评估模型的性能。最后，这些模型用于计算两种制冷剂之间的能量比较。 （C）2015 Elsevier Ltd和国际制冷学会。版权所有。

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