Numerical and ensemble machine learning-based investigation of the energy and exergy yields of a concentrating photovoltaic thermal device equipped with a perforated twisted tube turbulator

Abstract

The current research was carried out with the aim of numerically investigating the effect of employing a perforated twisted tube turbulator on the energy and exergy yields of a concentrating photovoltaic thermal (PVT) device. The simulations were performed in 4 different Reynolds numbers (Re) (i.e. 500, 1000, 1500 and 2000) and considering 4 different twist distance (TD) (i.e. L/25, L/50, L/70, and L/100) for the non-perforated turbulator and three different perforated turbulators (with 1, 2, and 3 holes) with TD = L/100. Among the examined cases, the best and worst performance belonged to the PVT device with perforated turbulator and without a turbulator, respectively. For the PVT device with non-perforated turbulator, the lowest PV panel temperature, the highest water outlet temperature, and the highest energy and exergy efficiencies occurred at the highest Re (i.e. 2000) and the lowest TD (i.e. L/100). Also, it was revealed that among the examined perforated turbulators, the best performance belongs to the turbulator with 3 holes in each pitch. In this case, the temperature of the PV panel, the overall energy efficiency and the overall exergy efficiency of the PVT device are respectively 3 ºC lower, 7.43% higher and 3.21% higher than the case without turbulator. As another novelty, a new ensemble machine learning model, namely boosted regression tree (BRT) was developed to simulation of the overall energy and exergy efficiencies based on the Reynolds number and volume fraction. The outcomes revealed the promising accuracy for both targets in terms various statistical metrics.