Optimisation of the design parameters of a domestic refrigerator using CFD and artificial neural networks

Avci H., KUMLUTAŞ D., Ozer O., Ozsen M.

INTERNATIONAL JOURNAL OF REFRIGERATION-REVUE INTERNATIONALE DU FROID, cilt.67, ss.227-238, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 67
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1016/j.ijrefrig.2016.02.018
  • Dergi Adı: INTERNATIONAL JOURNAL OF REFRIGERATION-REVUE INTERNATIONALE DU FROID
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.227-238
  • Anahtar Kelimeler: Household refrigerators, Experimentation, Computational fluid dynamics, Neural networks, HEAT-TRANSFER, THERMAL UNIFORMITY, EXCHANGERS, FLOW, PREDICTIONS, PERFORMANCE, SIMULATION, IMPROVE, SYSTEM
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

This paper reports the use of computational fluid dynamics (CFD) and artificial neural networks (ANN) for optimising and improving the performance of a static type domestic refrigerator. The effects of the hydrodynamic and thermal fields on the air flow inside of the refrigerator compartment were considered by exact modelling of the fan. The interior volume of air of the refrigerator was modelled using a CFD method, and analyses were performed. The numerical results were validated by comparing experimental results with the calculated interior design parameters. An optimisation study of the obtained design parameters was performed using a parametric method. The optimum design case was predicted by the ANN. The performance of the refrigerator improved by approximately 7.7% based on the amount of heat taken from the evaporator surface assuming thermal uniformity of the interior volume of air and the ISO 15502 standards. As a result, the annual energy consumption will decrease by 17.52 kWh. (C) 2016 Elsevier Ltd and IIR. All rights reserved.