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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.
Çolak et al. (Sat,) studied this question.