A NUMERICAL STUDY AIMED AT FINDING OPTIMAL ARTIFICIAL NEURAL NETWORK MODEL COVERING EXPERIMENTALLY OBTAINED HEAT TRANSFER CHARACTERISTICS OF HYDRONIC UNDERFLOOR RADIANT HEATING SYSTEMS RUNNING VARIOUS NANOFLUIDS

Colak A. B., Karakoyun Y., AÇIKGÖZ Ö., YUMURTACI Z., DALKILIÇ A. S.

Heat Transfer Research, cilt.53, sa.5, ss.51-71, 2022 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 53 Sayı: 5
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1615/heattransres.2022041668
  • Dergi Adı: Heat Transfer Research
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.51-71
  • Anahtar Kelimeler: ANN, Levenberg-Marquardt, radiant floor heating, thermal characteristics
  • Van Yüzüncü Yıl Üniversitesi Adresli: Hayır

Özet

In this paper, three unique artificial neural network models have been developed for three different working fluid cases to predict the radiative, convective, and total heat transfer coefficients over the floor surface of radiant floor heating system in a real-size room. Pure water, multiwall carbon nanotube with 0.7 vol.% and 0.07 vol.% contents, and aluminium oxide with 1.26 vol.% content are the operating fluids having inlet temperatures ranging from 30°C to 60°C, while the mass flow rates are 0.056, 0.09, and 0.125 kg/s. The performances of multilayer perceptron networks with the Levenberg-Marquardt, Bayesian regularization, and scaled conjugate gradient as training algorithms and different neuron numbers have been developed and the Levenberg-Marquardt algorithm, having the highest prediction performance with 99% accuracy, is selected as a result of detailed computational numerical analyses. This study can be considered as a pioneer artificial neural network one on the floor heating systems having nanofluids.