Performance prediction for non-adiabatic capillary tube suction line heat exchanger: an artificial neural network approach

Islamoglu Y., Kurt A., Parmaksizoglu C.

ENERGY CONVERSION AND MANAGEMENT, cilt.46, sa.2, ss.223-232, 2005 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 46 Sayı: 2
  • Basım Tarihi: 2005
  • Doi Numarası: 10.1016/j.enconman.2004.02.015
  • Dergi Adı: ENERGY CONVERSION AND MANAGEMENT
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.223-232
  • İstanbul Üniversitesi Adresli: Hayır

Özet

This study presents an application of the artificial neural network (ANN) model using the back propagation (BP) learning algorithm to predict the performance (suction line outlet temperature and mass flow rate) of a non-adiabatic capillary tube suction line heat exchanger, basically used as a throttling device in small household refrigeration systems. Comparative studies were made by using an ANN model, experimental results and correlations to predict the performance. These studies showed that the proposed approach could successfully be used for performance prediction for the exchanger. (C) 2004 Elsevier Ltd. All rights reserved.