Assessment of ANN to accurate prediction of thermal efficiency of a Heat Pipe solar collector working with nano fluid

Abstract

assessing solar collectors with different working fluids and utilizing Artificial Neural Network (ANN) simulations plays a critical role in solar energy research. This investigation specifically delves into the examination of the thermal efficiency of a solar collector that incorporates a heat pipe system with both pure water and MgO/water nanofluid at various concentrations. Through the analysis of the effects of solar radiation heat flux and flow rates on system performance, the primary aim of this study is to improve predictive accuracy using ANN. The comparison between anticipated outcomes and actual results serves to demonstrate the accuracy of the ANN model in forecasting system behavior, with deviations remaining within acceptable parameters. Furthermore, this research highlights the efficiency and speed of ANN in generating predictions based on limited experimental data, presenting a promising strategy for optimizing solar thermal system efficiency.