Modeling the cooling performance of vortex tube using a genetic algorithm-based artificial neural network

Abstract

In this study, artificial neural networks have been used to model the effects of four important parameters consist of the ratio of the length to diameter, the ratio of the cold outlet diameter to the tube diameter, inlet pressure, and cold mass fraction 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 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. 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.