Prediction of frost deposition on a horizontal circular cylinder under natural convection using artificial neural networks

In the present work Artificial Neural Network is used to predict frost thickness and density around a cooled horizontal circular cylinder having constant surface temperature under natural convection for different ambient conditions. The database for ANN generated from the experimental measurements. In the present work a multilayer perceptron network is used and it is found that the back-propagation algorithm with Levenberg–Marquardt learning rule is the best choice to estimate frost growth due to accurate and faster training procedure. Experimental measurements are used for training and testing the ANN approach and comparison is performed among the soft programming ANN and experimental measurements. It is observed that ANN soft programming code can be used more efficiently to determine frost thickness and density around a cold horizontal cylinder. Based on the developed ANN wide range of frost formation over various cylinder diameters are determined and presented for various conditions.