Machine learning approach to predict the heat transfer coefficients pertaining to a radiant cooling system coupled with mixed and forced convection - Acikgoz, Ozgen; Colak, Andac Batur; Camci, Muhammet; Karakoyun, Yakup; Dalkilic, Ahmet Selim (ELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER, 2022)
Mixed convection phenomenon over radiant cooled surfaces with displacement ventilation in living environments is becoming a popular issue due to the airborne viruses and energy economy. Artificial neural networks are one of the machine learning methods that are widely evaluated as an engineering tool. In the current study, heat transfer coefficients for a radiant wall cooling system coupled with mixed and forced convection have been predicted by a machine learning approach. This approach should be noted as a first experimental investigation couple with an artificial neural network analysis in the open sources in which mixed convection systems in real sized living environments is examined. Experimentally obtained heat transfer coefficients have been used in the development of the feed forward back propagation multi-layer perceptron network structure. So as to analyze the impact of the input factors on the prediction performance, two neural network structures with dissimilar input parameters such as various temperatures, velocities, and heat transfer rates have been developed. By means of feed forward back propagation multi-layer perceptron neural network algorithms, convection, radiation, and total heat transfer coefficients have been predicted using the experimentally acquired dataset including 35 data points belonging to the mixed and forced convection conditions. Training, validation, and test data groups include 70%, 15%, and 15% of the dataset, in turn. Training algorithm has been computed via LevenbergMarquardt one with 10 neurons in the hidden layer. The findings obtained from the computational solution have been evaluated as a result of the contrast with the target data with in the +/- 5% deviation band for all heat transfer coefficients. The performance factors have been computed and the estimation precision of the numerical models has been thoroughly examined.
Keywords: Machine learning; Levenberg-marquardt; Mixed convection; Forced convection; ARTIFICIAL NEURAL-NETWORKS; THERMAL-CONDUCTIVITY; HYBRID NANOFLUID; ANN; OPTIMIZATION; TEMPERATURE; PERFORMANCE; VALIDATION; MODEL; FLOOR; Thermodynamics; Engineering