No abstract

We consider the flow of humid air over fin-tube multi-row multi-column compact heat exchangers with possible condensation. Previously published experimental data are used to show that a regression analysis for the best-fit correlation of a prescribed form does not provide an unique answer, and that there are small but significant differences between the predictions of the different correlations thus obtained. It is also shown that it is more accurate to predict the heat rate directly rather than through intermediate quantities like the j-factors. The artificial neural network technique is offered as an alternative technique. It is trained with experimental values of the humid-air flow rates, dry-bulb and wet-bulb inlet temperatures, fin spacing, and heat transfer rates. The trained network is then used to make predictions of the heat transfer. Comparison of the results demonstrates that the neural network is more accurate than conventional correlations.

No 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.

No abstract