Artificial Neural Networks Modeling of Heat Transfer Characteris-tics in a Parabolic Trough Solar Collector using Nano-Fluids | ||
| Anbar Journal of Engineering Sciences | ||
| Article 15, Volume 12, Issue 2, November 2021, Pages 245-255 PDF (2.35 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.37649/aengs.2021.171192 | ||
| Authors | ||
| T. A. Salih; S. A. Mutlag; H. K. Dawood | ||
| Mechanical Engineering Department, College of Engineering, University of Anbar, Ramadi, Anbar, Iraq | ||
| Abstract | ||
| In the current article, an experimental investigation has been implemented of flow and heat transfer characteristics in a parabolic trough solar collector (PTSC) using both nano-fluids and artificial neural networks modeling. Water was used as a standard working fluid in order to compare with two different types of nano-fluid namely, nano-CuO /H2O and nano-TiO2/ H2O, both with a volume concentration of 0.02. The performance of the PTSC system was eval-uated using three main indicators: outlet water temperature, useful energy and thermal efficiency under the influence of mass flowrate ranging from 30 to 80 Lt/hr. In parallel, an artificial neural network (ANN) has been proposed to predict the thermal efficiency of PTSC depending on the experimental re-sults. An Artificial Neural Network (ANN) model consists of four inputs, one output parameter and two hidden layers, two neural network models (4-2-2-1) and (4-9-9-1) were built. The experimental results show that CuO/ H2O and TiO2/H2O have higher thermal performance than water. Overall, it was veri-fied that the maximum increase in thermal efficiency of TiO2/H2O and CuO/H2O compared to water was 7.12% and 19.2%, respectively. On the oth-er hand, the results of the model 4-9-9-1 of ANN provide a higher reliability and accuracy for predicting the Thermal efficiency than the model 4-2-2-1. The results revealed that the agreement in the thermal efficiency between the ANN analysis and the experimental results about of 91% and RMSE 3.951 for 4-9-9-1 and 86% and RMSE 5.278 for 4-2-21. | ||
| Keywords | ||
| Solar thermal perfor-mance; Parabolic trough solar collector; artificial neural network; CuO/H2O nano fluids; TiO2/ H2O nano fluids | ||
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