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 evaluated 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 results. 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 verified 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 other 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.
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 evaluated 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 results. 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 verified 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 other 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
Artificial neural network
CuO/H2O nano-fluids
Parabolic trough solar collector
Solar thermal performance
TiO2/ H2O nano-fluids.