Celal Bayar University

About: Celal Bayar University is a education organization based out in Magnesia ad Sipylum, Turkey. It is known for research contribution in the topics: Population & Heat transfer. The organization has 2960 authors who have published 6024 publications receiving 100646 citations.

Energy and exergy analysis of a hybrid photovoltaic/thermal-air collector modified with nano-enhanced latent heat thermal energy storage unit

Abstract: Electrical and thermal energy can be generated simultaneously by using photovoltaic-thermal (PVT) systems. Also, electrical efficiency can be enhanced by cooling the PV panel. In this study, three different PVT-air collectors have been designed, manufactured and experimentally analyzed including conventional (PVT), with paraffin-based thermal energy storage unit (PVT-TES) and with nano-enhanced paraffin-based thermal energy storage unit (PVT-NeTES). Copper oxide (CuO) nanoparticles (1 wt%) have been utilized to upgrade the thermal conductivity of the phase change material. Tests have been performed in two flow rates (0.007 and 0.014 kg/s). According to experimental results, overall exergy efficiencies for PVT, PVT-TES and PVT-NeTES were achieved between 10.52–13.59%, 11.08–14.36% and 12.52–15.44%, respectively. Moreover, sustainability index (SI) values were attained in the range of 1.12–1.16, 1.13–1.17 and 1.14–1.18, respectively. Obtained findings showed that utilizing nano-enhanced thermal energy storage system and increasing flow rate significantly upgraded both electrical and thermal performances of the PVT system.

Use of neural networks for the prediction of the CBR value of some Aegean sands

Abstract: This study deals with the development of an artificial neural network (ANN) and a multiple regression (MR) model that can be employed for estimating the California bearing ratio (CBR) value of some Aegean sands. To achieve this, the results of CBR tests performed on the compacted specimens of nine different Aegean sands with varying soil properties were used in the development of the ANN and MR models. The results of the ANN and MR models were compared with those obtained from the experiments. It is found that the CBR values predicted from the ANN model matched the experimental values much better than the MR model. Moreover, several performance indices, such as coefficient of determination, root-mean-square error, mean absolute error, and variance, were used to evaluate the prediction performance of the ANN and MR models. The ANN model has shown higher prediction performance than the MR model based on the performance indices, which demonstrates the usefulness and efficiency of the ANN model. Thus, the ANN model can be used to predict CBR value of the Aegean sands included in this study as an inexpensive substitute for the laboratory testing, quite easily and efficiently.