Performance prediction of adiabatic capillary tubes by conventional and ann approaches: A comparison

Abstract

An experimental study of adiabatic capillary tubes was conducted to evaluate the flow characteristics of refrigerant HFC-134a. The effect of various input parameters, such as capillary tube diameter, length, and inlet subcooling on the mass flow rate of HFC-134a, were investigated. Moreover, a comparison was made for the mass flow rate of refrigerant HFC-134a in instrumented and noninstrumented capillary tubes. It was found that the provision of taps for pressure measurement on the capillary tube surface has a negligible effect on the massjlow rate of HFC-134a. The data obtained from the experiments were analyzed, and a semi-empirical correlation using a multiple-variable regression analysis was developed. The proposed correlation predicts that more than 86% of the data lies in the error band of ±10%. Furthermore, an artificial neural network (ANN) model using a feed-forward backpropagation algorithm was developed to predict, the mass flow rate from the given set of input parameters. These two approaches were compared, and ANN was found to predict the mass flow rate far more accurately than the conventional empirical correlation developed by regression. © 2009 ASHRAE.