Numerical investigation and neural network modeling of the performance of a dual-fluid parabolic trough solar collector containing non-Newtonian water-CMC/Al2O3 nanofluid

The performance of a novel parabolic trough collector (PTC) equipped with a non-circular absorber tube and a solid insulation is investigated numerically using the Eulerian-Eulerian two-phase method. The water-CMC/Al2O3 nanofluid is considered as the working fluid. The impacts of the Reynolds number, nanoparticle volume concentration, nanoparticle diameter and insulation angle on the performance metrics are examined. The results showed that the changes of collector efficiency with Reynolds number has an ascending-descending pattern. In addition, it was found that the highest collector efficiency is 61.7%, which belongs to the case of dual-fluid collector with a novel tube and insulation angle of 90 degrees containing nanofluid with phi = 1.5% and nanoparticle diameter of 100 nm. Finally, the artificial neural network was employed to provide a predictive model for the collector efficiency. It was found that the neural network consisting of eight neurons has a high potential in forecasting the efficiency of the novel collector. The maximum deviation was less than 0.2% and this high accuracy caused the R-square for the neural network to be 0.9998.

Automated summary

A novel parabolic trough collector equipped with a non-circular absorber tube and solid insulation was numerically studied using a two-phase method. The study examined the impacts of various factors on the collector performance metrics. An artificial neural network model showed high potential in predicting the collector efficiency accurately.