Artificial Neural Networks (ANNs): A New Paradigm for Thermal Science and Engineering

Abstract

The use of artificial neural network (ANN), as one of the artificial intelligence methodologies, in a variety of real-world applications has been around for some time. However, the application of ANN to thermal science and engineering is still relatively new, but is receiving ever-increasing attention in recent published literature. Such attention is due essentially to special requirement and needs of the field of thermal science and engineering in terms of its increasing complexity and the recognition that it is not always feasible to deal with many critical problems in this field by the use of traditional analysis. The purpose of the present review is to point out the recent advances in ANN and its successes in dealing with a variety of important thermal problems. Some current ANN shortcomings, the development of recent advances in ANN-based hybrid analysis, and its future prospects will also be indicated.

Artificial Neural Network modelling of sorption chillers

“…This approach represents a novelty within the CTSS method. There are several reasons why ANNs are such a powerful tool for modelling dynamic systems (Yang, 2008) based on experimental data:…”

Simulation Study on the Effect of Reduced Inputs of Artificial Neural Networks on the Predictive Performance of the Solar Energy System

“…The most popular training procedure for fully connected feed-forward networks is known as the supervised back-propagation learning scheme where the weights and biases are adjusted layer by layer from the output layer toward the input layer. The whole process of feeding forward with backward learning is then repeated until a satisfactory error level is reached or becomes stationary, as detailed in Section 2 [17,20,31,32].…”

“…Over the last two decades, an extensive number of studies using ANNs in energy systems have been published [18][19][20][21][22][23][24], comprising recent investigations by the authors [25,26]. The development of accurate ANN models depends on a range of different factors and algorithms; therefore, a number of challenging efforts regarding the performance predictive methodology must be addressed.…”

Modified neural network correlation of refrigerant mass flow rates through adiabatic capillary and short tubes: Extension to CO2 transcritical flow

“…Therefore, it has better data-fitting capability than the traditional power-law or other polynomial functions. In particular, NN's powerful modeling capability has been verified by numerous investigations including those in thermal and HVACR area (Kalogirou, 1999(Kalogirou, , 2000Yang, 2008). A three-layer perceptron network is employed in this work.…”