Numerical investigation and ANN modeling of performance for hexagonal boron Nitride-water nanofluid PVT collectors - Büyükalaca, Orhan; Kılıç, Hacı Mehmet; Olmuş, Umutcan; Güzelel, Yunus Emre; Çerçi, Kamil Neyfel (Thermal science and engineering progress, 2023)
[Display omitted] •hBN/water nanofluid based PVT collectors has been investigated for the first time.•hBN can be utilized as an alternative nanoparticle to enhance the performance of PVT.•hBN/water is exhibited greater efficiencies compared to pure water and graphene/water.•ANN is successful for predicting the outcomes of hBN-based PVT systems. In this study, performance of hexagonal boron nitride (hBN)/water nanofluid used as a coolant in a PVT collector for the first time in the open literature was numerically analyzed based on various input parameters. Numerical analyzes were carried out by varying the flow rate between 14.5 and 43.4 l/h, solar radiation intensity between 200 and 1000 W/m2, hBN nanoparticle volumetric ratio between 0 and 0.22% and nanoparticle diameter between 20 and 80 nm. The results revealed that the thermal efficiency increases up to 0.18 volumetric ratio and then decreases, while the electrical efficiency continuously increases as the volumetric ratio increases. Additionally, an increase in the volumetric ratio leads to an improvement in all exergy parameters. The utilization of 20 nm diameter hBN nanoparticles results in an increase of 0.7%, 3.01%, 2.71%, and 1.80% in electrical, thermal, overall, and exergy efficiency, respectively, in comparison to pure water. In addition to the numerical analysis conducted with hBN/water nanofluid, simulations were also performed for graphene/water nanofluid, which is commonly studied for PVT collectors in the literature, and it was shown that the former exhibits better performance than the latter, albeit to a minimal extent. Finally, two different sets of ANN models were developed to predict five performance parameters of the PVT collector using hBN/water nanofluid. In the first set, each model predicted only one of the five performance parameters, while in the second set, a single ANN model predicted all output parameters. Different numbers of neurons and training functions were tested in the ANN models, and the Feed Forward Backpropagation algorithm was used as the training algorithm for all the models. Additionally, Logsig and Purelin transfer functions were used for the hidden and output layers, respectively. The proposed models were able to successfully reproduce the performance parameters.