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

Korkmaz, MURAT; GÜMÜŞEL, LEVENT; MARKAL, BURAK

doi:10.1016/j.ijrefrig.2012.04.013

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

Atıf İçin Kopyala

Korkmaz M. E., GÜMÜŞEL L., MARKAL B.

INTERNATIONAL JOURNAL OF REFRIGERATION-REVUE INTERNATIONALE DU FROID, cilt.35, sa.6, ss.1690-1696, 2012 (SCI-Expanded)

Yayın Türü: Makale / Tam Makale
Cilt numarası: 35 Sayı: 6
Basım Tarihi: 2012
Doi Numarası: 10.1016/j.ijrefrig.2012.04.013
Dergi Adı: INTERNATIONAL JOURNAL OF REFRIGERATION-REVUE INTERNATIONALE DU FROID
Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
Sayfa Sayıları: ss.1690-1696
Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

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 (P-i), conical valve angle (phi), length to diameter ratio (LID) and cold mass fraction (y(c)) are used as input parameters while total temperature difference (Delta T) 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. (C) 2012 Elsevier Ltd and IIR. All rights reserved.