Machine learning-based modelling using ANN for performance prediction of a solar air heater design with jet impingement

Abstract

The prediction of heat transfer performance for solar air heater (SAH) designs is important to assess its potential without relying on extensive experimental studies. This work aimed to predict the heat transfer characteristics of a SAH design with jet impingement using an extensive artificial neural network (ANN) study and covering a complete year-long ambient and various operating parameters (commensurate and non-commensurate). Total 84 ANN models were developed using different network types, transfer functions, and training functions. They were tested on 174 representative datasets collected throughout a year. The best ANN model was found to be the feed forward backpropagation model with tangent-sigmoid transfer function and LM training algorithm, which produced accurate predictions for thermal efficiency, exergy efficiency, and thermo-hydraulic efficiency, with sample errors within ±5 %. The numerical values of R2, MAE, RMSE, and COV from the ANN model results were respectively 0.9972, 0.9053, 1.1884, and 2.0618 for thermal efficiency, 0.9947, 0.2631, 0.3363, and 1.7157 for exergy efficiency, and 0.996, 0.0139, 0.0193, and 0.8076 for thermo-hydraulic efficiency. The study concluded that ANN models can accurately predict the heat transfer characteristics of an SAH design, requiring less time and less computational resources compared to conventional experimental or computational methods.