 Combining CFD and artificial neural network techniques to predict the thermal performance of all-glass straight evacuated tube solar collectorBin Du, Peter D. Lund and Jun Wang Energy, 2021, vol. 220, issue C Abstract: Thermal performance modelling and performance prediction of a novel all-glass straight-through evacuated tube collector is analyzed here. A mathematical model of the tube was developed and incorporated into CFD software for numerical performance simulation. To improve the thermal performance prediction of the collector, different artificial neural network (ANN) models were considered. A comprehensive experimental dataset with more than 200 samples were employed for testing of the models. Integrating the thermal simulation model with the ANN models by using modelled collector output as one of the input models, significantly improved the prediction accuracy of the ANN models. The predictions based on the CFD model alone gave the poorest accuracy compared to the ANN models. The convolutional neural network (CNN) model proved to be the best ANN model in terms of prediction accuracy. Keywords: Solar collector; Evacuated tube; Neural network; Multiple linear regression; CFD; Thermal performance; Prediction (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:220:y:2021:i:c:s0360544220328206 DOI: 10.1016/j.energy.2020.119713 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
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