Water pool boiling in metal foams: From experimental results to a generalized model based on artificial neural network

Abstract

This paper shows the promising capabilities that the aluminum metal foams offer in enhancing the water pool boiling on flat surfaces. Different metal foam samples, with pore density ranging between 5 and 40 Pore Per Inch (PPI), with similar porosity values, around 0.92, and two different thickness values, 5 and 10 mm, were tested to investigate their pertinent pool boiling performance. The saturated boiling curves at atmospheric pressure were obtained. Furthermore, using a high speed camera, heat and fluid flow inside and above the porous layer were investigated. The results showed that boiling heat transfer can be greatly enhanced by the use of metal foams partly because of an earlier onset of the nucleate boiling. Moreover, over a wide range of operating conditions foams lead to remarkably higher heat transfer coefficients, compared with a smooth aluminum reference surface. For a more comprehensive understanding of the problem, we tried to correlate the existing data in the literature for different foam geometries and base materials. However, the existing correlations and independent test data in the literature do not agree well; not even within the same order of magnitude. Hence, in order to offer a generic solution, predictions based on the results of an artificial neural network (ANN) are sought. A large database, comprising 758 experimental data points available in the open literature, was collected and then used to develop, train and validate a model based on ANN to estimate the heat transfer performance of metal foams during water pool boiling. Our data show that the predicted Nusselt number is within 10% of the measured experimental data. Moreover, we demonstrate that the developed ANN tool can be successfully implemented to predict the intrinsic complexity of water pool boiling inside metal foams that in most cases cannot be articulated by merely relying on conventional semi-empirical methods.