Adaptive neuro-fuzzy modeling of transient heat transfer in circular duct air flow

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Haşıloğlu A., Yimaz M., Çomaklı Ö., Ekmekci I.

INTERNATIONAL JOURNAL OF THERMAL SCIENCES, cilt.43, sa.11, ss.1075-1090, 2004 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 43 Sayı: 11
  • Basım Tarihi: 2004
  • Doi Numarası: 10.1016/j.ijthermalsci.2004.01.010
  • Dergi Adı: INTERNATIONAL JOURNAL OF THERMAL SCIENCES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1075-1090
  • Anahtar Kelimeler: transient heat transfer, duct, forced convection, neural network, fuzzy, neuro-fuzzy, LAMINAR FORCED-CONVECTION, INLET TEMPERATURE, PERIODIC VARIATION, INFERENCE SYSTEM, NETWORKS, UNSTEADY, IDENTIFICATION
  • Atatürk Üniversitesi Adresli: Evet

Özet

The aim of this study is to demonstrate the usefulness of an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of transient heat transfer. An ANFIS has been applied for the transient heat transfer in thermally and simultaneously developing circular duct flow, subjected to a sinusoidally varying inlet temperature. The experiments covered Reynolds numbers in the 2528 less than or equal to Re less than or equal to 4265 range and inlet heat input in the 0.01 less than or equal to beta less than or equal to 0.96 Hz frequency range. The accuracy of predictions and the adaptability of the ANFIS were examined, and good predictions were achieved for the temperature amplitudes of the transient heat transfer in thermally and simultaneously developing circular duct flow. The results show that the neuro-fuzzy can be used for modeling transient heat transfer in ducts. The results obtained with the ANFIS are also compared to those of a multiple linear regression and a neural network with a multi-layered feed-forward back-propagation algorithm. (C) 2004 Elsevier SAS. All rights reserved.