Using the artificial neural network model for modeling the performance of the counter flow vortex tube

In this study, the effect of the nozzle number and the inlet pressure on the heating and cooling performance of the counter flow type vortex tube has been modeled with artificial neural networks (ANN) by using the experimentally obtained data. ANN has been designed by Pithiya software. In the developed system output parameter temperature gradient between the cold and hot outlets (ΔT) has been determined using inlet parameters such as the inlet pressure (Pinlet), nozzle number (N), and cold mass fraction (μc). The back-propagation learning algorithm with variant which is Levenberg–Marquardt (LM) and Fermi transfer function have been used in the network. In addition, the statistical validity of the developed model has been determined by using the coefficient of determination (R2), the root means square error (RMSE) and the mean absolute percentage error (MAPE). R2, RMSE and MAPE have been determined for ΔT as 0.9947, 0.188224, and 0.0460, respectively.