Heat transfer analysis of phase change process in a finned-tube thermal energy storage system using artificial neural network

By K. Ermis, Aytunç Erek, Ibrahim Dincer. 2007

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Applications of artificial neural networks for thermal analysis of heat exchangers – A review (2015, methodology): “Thermal energy storage capacity of a heat exchanger was estimated using MLFFNwith four neurons in input layer representing a heat transfer area, Re, inlet temperature of heat transfer fluid and time [159].”
An investigation of melting/freezing characteristics of nanoparticle-enhanced phase change materials (2012, methodology): “Different approaches have been used to overcome this problem, including using porous matrix materials [5, 6], inserting the finned tubes [7], dispersing high-conductivity millimeter- or micrometer-sized particles, such as carbon fibers [8] and aluminum powders [9].”