Using artificial neural network for predicting performance of the Ranque–Hilsch vortex tube

In this study, effects of conical valve angle and length to diameter ratio on the performance of a counter flow Ranque–Hilsch vortex tube are predicted with artificial neural networks (ANNs) by using experimental data. In the model, inlet pressure (Pi), conical valve angle (ϕ), length to diameter ratio (L/D) and cold mass fraction (yc) are used as input parameters while total temperature difference (ΔT) is chosen as the output parameter. The multilayer feed forward model and the Levenberg–Marquardt learning algorithm are used in the network and the hyperbolic tangent function is chosen as a transfer function. The artificial neural network is designed via the NeuroSolutions 6.0 software. Finally, it’s disclosed that ANN can be successfully used to predict effects of geometrical parameters on the performance of the Ranque–Hilsch vortex tube with a good accuracy.

► Effects of conical valve angle and length to diameter ratio on the performance of a counter flow Ranque–Hilsch vortex tube are predicted with artificial neural networks (ANNs).
► The multilayer feed forward model and the Levenberg–Marquardt learning algorithm are used in the network.
► The hyperbolic tangent function is chosen as a transfer function.
► ANN can be successfully used to predict effects of geometrical parameters on the performance of the Ranque–Hilsch vortex tube.