TY - JOUR
T1 - Numerical investigation and ANN modeling of performance for hexagonal boron Nitride-water nanofluid PVT collectors
AU - Büyükalaca, Orhan
AU - Kılıç, Hacı Mehmet
AU - Olmuş, Umutcan
AU - Güzelel, Yunus Emre
AU - Çerçi, Kamil Neyfel
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Artificial neural network
KW - CFD simulation
KW - Energy and exergy analysis
KW - hBN/water nanofluid
KW - Numerical simulation
KW - Photovoltaic thermal (PVT)
UR - http://www.scopus.com/inward/record.url?scp=85164214841&partnerID=8YFLogxK
U2 - 10.1016/j.tsep.2023.101997
DO - 10.1016/j.tsep.2023.101997
M3 - Article
AN - SCOPUS:85164214841
VL - 43
JO - Thermal Science and Engineering Progress
JF - Thermal Science and Engineering Progress
M1 - 101997
ER -