Share

IIR document

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

Author(s) : KORKMAZ M. E., GÜMÜSEL L., MARKAL B.

Type of article: Article, IJR article

Summary

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 by using experimental data. In the model, inlet pressure, conical valve angle, length to diameter ratio (L/D) and cold mass fraction are used as input parameters while total temperature difference 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.

Available documents

Format PDF

Pages: 1690-1696

Available

  • Public price

    20 €

  • Member price*

    Free

* Best rate depending on membership category (see the detailed benefits of individual and corporate memberships).

Details

  • Original title: Using artificial neural network for predicting performance of the Ranque–Hilsch vortex tube.
  • Record ID : 30004824
  • Languages: English
  • Source: International Journal of Refrigeration - Revue Internationale du Froid - vol. 35 - n. 6
  • Publication date: 2012/09

Links


See other articles in this issue (28)
See the source

Indexing