In this study, artificial neural networks (ANNs) have been used to model the
effects of four important parameters consist of the ratio of the length to
diameter(L/D), the ratio of the cold outlet diameter to the tube
diameter(d/D), inlet pressure(P), and cold mass fraction (Y) on the cooling
performance of counter flow vortex tube. In this approach, experimental data
have been used to train and validate the neural network model with MATLAB
software. Also, genetic algorithm (GA) has been used to find the optimal
network architecture. In this model, temperature drop at the cold outlet has
been considered as the cooling performance of the vortex tube. Based on
experimental data, cooling performance of the vortex tube has been predicted
by four inlet parameters (L/D, d/D, P, Y). The results of this study indicate
that the genetic algorithm-based artificial neural network model is capable
of predicting the cooling performance of vortex tube in a wide operating
range and with satisfactory precision.