TY - JOUR
T1 - A comprehensive approach to analyze the discrepancies in heat transfer characteristics pertaining to radiant ceiling heating system
AU - Karakoyun, Yakup
AU - Acikgoz, Ozgen
AU - Çebi, Alican
AU - Koca, Aliihsan
AU - Çetin, Gürsel
AU - Dalkilic, Ahmet Selim
AU - Wongwises, Somchai
N1 - Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2021/3/25
Y1 - 2021/3/25
N2 - Radiant heating/cooling systems are being popular thanks to their ability of regulating the living-environment with the use of low temperature heating and high temperature cooling. In this work, an artificial neural network investigation is carried out to predict heat transfer characteristics over a heated radiant ceiling. Experimental tests consisting of 28 case studies, obtained through varying supply water temperature, are conducted. A computational method, including the Boussinesq approach using k-ɛ RNG model, is also employed to increase the number of case studies in order to use them in artificial neural networks investigation that applies Levenberg-Marquardt training function. Thus, total data number have been increased from 28 to 74 by a simulation software. Estimations of artificial neural networks method are compared with experimental data, and seen that the outputs are compatible with each other, where most of deviations are within the range of ±15%. According to this result, experimental data can be increased by a numerical simulation software and evaluated by one of the artificial intelligence techniques, successfully. In conclusion, the heat transfer coefficients to use in the radiant ceiling heating applications are proposed as 0.9 W/m2 K, 5.3 W/m2 K, and 7.0 W/m2 K for convective, radiative, and total heat transfer coefficients, respectively.
AB - Radiant heating/cooling systems are being popular thanks to their ability of regulating the living-environment with the use of low temperature heating and high temperature cooling. In this work, an artificial neural network investigation is carried out to predict heat transfer characteristics over a heated radiant ceiling. Experimental tests consisting of 28 case studies, obtained through varying supply water temperature, are conducted. A computational method, including the Boussinesq approach using k-ɛ RNG model, is also employed to increase the number of case studies in order to use them in artificial neural networks investigation that applies Levenberg-Marquardt training function. Thus, total data number have been increased from 28 to 74 by a simulation software. Estimations of artificial neural networks method are compared with experimental data, and seen that the outputs are compatible with each other, where most of deviations are within the range of ±15%. According to this result, experimental data can be increased by a numerical simulation software and evaluated by one of the artificial intelligence techniques, successfully. In conclusion, the heat transfer coefficients to use in the radiant ceiling heating applications are proposed as 0.9 W/m2 K, 5.3 W/m2 K, and 7.0 W/m2 K for convective, radiative, and total heat transfer coefficients, respectively.
KW - ANN
KW - CFD
KW - Heat transfer coefficients
KW - Radiant ceiling heating
KW - Thermal output
UR - http://www.scopus.com/inward/record.url?scp=85099691275&partnerID=8YFLogxK
U2 - 10.1016/j.applthermaleng.2020.116517
DO - 10.1016/j.applthermaleng.2020.116517
M3 - Article
AN - SCOPUS:85099691275
SN - 1359-4311
VL - 187
JO - Applied Thermal Engineering
JF - Applied Thermal Engineering
M1 - 116517
ER -