A generalized neural network model of refrigerant mass flow through adiabatic capillary tubes and short tube orifices

作者：Zhao, Ling Xiao; Zhang, Chun Lu^*; Shao, Liang Liang; Yang, Liang

来源：Journal of Fluids Engineering-Transactions of the ASME, 2007, 129(12): 1559-1564.

DOI：10.1115/1.2801352

• adiabatic capillary tube
• short tube orifice
• mass flow rate
• dimensionless parameter
• neural network

Adiabatic capillary tubes and short tube orifices are widely used as expansive devices in refrigeration, residential air conditioners, and heat pumps. In this paper a generalized neural network has been developed to predict the mass flow rate through adiabatic capillary tubes and short tube orifices. The input/output parameters of the neural network are dimensionless and derived from the homogeneous equilibrium flow model. Three-layer backpropagation (BP) neural network is selected as a universal function approximator Log sigmioid and pure linear transfer functions are used in the hidden layer and the output layer respectively. The experimental data of R12, R22, R134a, R404A, R407C, R410A, and R600a from the open literature covering capillary and short tube geometries, subcooled and two-phase inlet conditions, are collected for the BP network training and testing. Compared with experimental data, the overall average and standard deviations of the proposed neural network are 0.75% and 8.27% of the measured mass flow rates, respectively.

• 出版日期2007-12
• 单位上海交通大学