Performance predictions using Artificial Neural Network for isobutane flow in non-adiabatic capillary tubes

•A non-adiabatic capillary tube model has been derived and validated.•An Artificial Neural Network is capable of reproducing data generated by the 1d model.•The Artificial Neural Network is validated by experiments from literature.•The model is able to evaluate mass flow rates under non-choked conditions.•The model is able to evaluate mass flow rates under two phase inlet conditions.

This work presents an Artificial Neural Network (ANN) model of non-adiabatic capillary tubes for isobutane (R600a) as refrigerant. The basis therefore is data obtained by a 1d homogeneous model which has been validated by own measurements and measurements from literature. With this method it is possible to account for choked, non-choked, and also for two-phase inlet conditions, whereas most of the correlations reported in literature are not capable of predicting mass flow rates for non-choked and two-phase inlet conditions. The presented models are valid for a broad range of input parameters in respect to domestic applications – the mass flow rates range from 0 to 5 kg h−1, inlet pressure is from saturation pressure at ambient conditions up to 10 bar, the inlet quality is from 0.5 (capillary) and 0.7 (suction line) to 0 and subcooling (capillary) and superheating (suction line) from 0 K to 30 K.