@Article{, title={Artificial Neural Networks Modeling of Heat Transfer Characteristics in a Parabolic Trough Solar Collector using Nano-Fluids}, author={T. A. Salih and S. A. Mutlag and H. K. Dawood}, journal={Anbar Journal of Engineering Sciences مجلة الأنبار للعلوم الهندسية}, volume={12}, number={2}, pages={245-255}, year={2021}, abstract={In the current article, an experimental investigation has been implemented offlow and heat transfer characteristics in a parabolic trough solar collector(PTSC) using both nano-fluids and artificial neural networks modeling. Waterwas used as a standard working fluid in order to compare with two differenttypes of nano-fluid namely, nano-CuO /H2O and nano-TiO2/ H2O, both with avolume concentration of 0.02. The performance of the PTSC system was evaluatedusing three main indicators: outlet water temperature, useful energyand thermal efficiency under the influence of mass flowrate ranging from 30to 80 Lt/hr. In parallel, an artificial neural network (ANN) has been proposedto predict the thermal efficiency of PTSC depending on the experimental results.An Artificial Neural Network (ANN) model consists of four inputs, oneoutput 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 andTiO2/H2O have higher thermal performance than water. Overall, it was verifiedthat the maximum increase in thermal efficiency of TiO2/H2O andCuO/H2O compared to water was 7.12% and 19.2%, respectively. On the otherhand, the results of the model 4-9-9-1 of ANN provide a higher reliabilityand accuracy for predicting the Thermal efficiency than the model 4-2-2-1.The results revealed that the agreement in the thermal efficiency between theANN analysis and the experimental results about of 91% and RMSE 3.951 for4-9-9-1 and 86% and RMSE 5.278 for 4-2-21.

} }