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Model-based neural network correlation for refrigerant mass flow rates through adiabatic capillary tubes.

Author(s) : ZHANG C. L., ZHAO L. X.

Type of article: Article, IJR article

Summary

A capillary tube is a common expansion device widely used in small-scale refrigeration and air-conditioning systems. A generalized correlation of refrigerant mass flow rate through adiabatic capillary tubes covering both subcooled and two-phase inlet conditions is expected for multiple purposes. Based on the homogeneous equilibrium flow model, a new group of dimensionless parameters has been proposed. To express the nonlinear relationship between the mass flow rate and the associated parameters, the multi-layer perceptron neural network is employed as a universal function approximator. Simulated data from a validated homogeneous equilibrium model are used for the neural network training and testing. A 5-6-1 network trained with the simulated data of R-600a and R-407C shows good generality in predicting the simulated data of R-12, R-22, R-134a, R-290, R-410A, and R-404A. Also, the deviations between the trained neural network and the experimental data from the open literature fall into plus or minus 10%.

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Pages: 690-698

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Details

  • Original title: Model-based neural network correlation for refrigerant mass flow rates through adiabatic capillary tubes.
  • Record ID : 2007-1082
  • Languages: English
  • Source: International Journal of Refrigeration - Revue Internationale du Froid - vol. 30 - n. 4
  • Publication date: 2007/06

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