UTILIZATION OF THREE-PARTICLE (TERNARY) NANOFLUID FOR COOLING IN PHOTOVOLTAIC/THERMAL COLLECTORS /
Adun, Humphrey
UTILIZATION OF THREE-PARTICLE (TERNARY) NANOFLUID FOR COOLING IN PHOTOVOLTAIC/THERMAL COLLECTORS / Humphrey ADUN; Supervisor: Prof. Dr. Mustafa DAGBASI - 181 sheets; 31 cm. Includes CD
Thesis (PHD) - Cyprus International University. Institute of Graduate Studies and Research Department of Energy Systems Engineering
Includes bibliography (sheets 151-181)
ABSTRACT The current situation of significantly higher fossil fuel production for meeting growing energy demands, as against renewable energy sources, despite the global consensus on their adverse environmental effects, is a monumental issue. Also, considering depleting fossil fuel reserves, the need to produce efficient renewable energy technologies is crucial. The abundance of solar energy makes it an important renewable energy in meeting growing global energy demands. Solar radiation is a source of both heat and light, for which photovoltaic (PV) cells have been constructed to utilize the visual part of the light to produce electricity. The incorporation of thermal collectors in PV systems has further increased their total efficiencies. While significant progress has been made in the structural designs of photovoltaic/thermal (PV/T) systems, the invention of nanofluids, have allowed extracting more heat from panels, due to their significant higher thermophysical properties, as compared to conventional heat transfer fluids (HTFs). This study undergoes a comprehensive experimental and numerical study on novel ternary hybrid nanofluids (THNF) (3-particle nanofluids), and their application as HTFs in PV/T systems. A novel Al2O3-ZnO-Fe3O4 nanofluid is synthesized, with characterization and stability tests, carried out. The effect of mixture ratio, volume fraction, particle size, and temperature on their thermophysical properties are investigated. Furthermore, machine learning algorithms are developed to accurately estimate the behaviour of the fluid. This study also numerically investigates the efficiency of the PV/T system, assessing the effects of mass flow rate, solar radiation, and volume fraction. Meteorological data retrieved from the Cyprus International University was used in validating the model designed for the PV/T system. The result in this study showed that the mixture ratio of THNF has a huge effect in achieving an optimum thermophysical property for HTF application in the PV/T system. The optimum mixture ratio retrieved was for the 1:1:1 mixture ratio. Also, this study concludes that the advantage of THNF over conventional and hybrid nanofluids is their higher efficiency at higher volume fractions. Furthermore, this study developed an accurate artificial neural network model, for estimating the thermal conductivity of hybrid nanofluids, across different combinations of nanoparticles, and base fluids. Conclusively, this study gives a clear path for future experimental works that will incorporate hybrid nanofluids, as the optimum volume fraction, temperature, and mixture ratio is discussed.
Neural networks (Computer science)--Dissertations, Academic
Nanofluids--Dissertations, Academic
Photovoltaics--Dissertations, Academic
Thermal conductivity--Dissertations, Academic
UTILIZATION OF THREE-PARTICLE (TERNARY) NANOFLUID FOR COOLING IN PHOTOVOLTAIC/THERMAL COLLECTORS / Humphrey ADUN; Supervisor: Prof. Dr. Mustafa DAGBASI - 181 sheets; 31 cm. Includes CD
Thesis (PHD) - Cyprus International University. Institute of Graduate Studies and Research Department of Energy Systems Engineering
Includes bibliography (sheets 151-181)
ABSTRACT The current situation of significantly higher fossil fuel production for meeting growing energy demands, as against renewable energy sources, despite the global consensus on their adverse environmental effects, is a monumental issue. Also, considering depleting fossil fuel reserves, the need to produce efficient renewable energy technologies is crucial. The abundance of solar energy makes it an important renewable energy in meeting growing global energy demands. Solar radiation is a source of both heat and light, for which photovoltaic (PV) cells have been constructed to utilize the visual part of the light to produce electricity. The incorporation of thermal collectors in PV systems has further increased their total efficiencies. While significant progress has been made in the structural designs of photovoltaic/thermal (PV/T) systems, the invention of nanofluids, have allowed extracting more heat from panels, due to their significant higher thermophysical properties, as compared to conventional heat transfer fluids (HTFs). This study undergoes a comprehensive experimental and numerical study on novel ternary hybrid nanofluids (THNF) (3-particle nanofluids), and their application as HTFs in PV/T systems. A novel Al2O3-ZnO-Fe3O4 nanofluid is synthesized, with characterization and stability tests, carried out. The effect of mixture ratio, volume fraction, particle size, and temperature on their thermophysical properties are investigated. Furthermore, machine learning algorithms are developed to accurately estimate the behaviour of the fluid. This study also numerically investigates the efficiency of the PV/T system, assessing the effects of mass flow rate, solar radiation, and volume fraction. Meteorological data retrieved from the Cyprus International University was used in validating the model designed for the PV/T system. The result in this study showed that the mixture ratio of THNF has a huge effect in achieving an optimum thermophysical property for HTF application in the PV/T system. The optimum mixture ratio retrieved was for the 1:1:1 mixture ratio. Also, this study concludes that the advantage of THNF over conventional and hybrid nanofluids is their higher efficiency at higher volume fractions. Furthermore, this study developed an accurate artificial neural network model, for estimating the thermal conductivity of hybrid nanofluids, across different combinations of nanoparticles, and base fluids. Conclusively, this study gives a clear path for future experimental works that will incorporate hybrid nanofluids, as the optimum volume fraction, temperature, and mixture ratio is discussed.
Neural networks (Computer science)--Dissertations, Academic
Nanofluids--Dissertations, Academic
Photovoltaics--Dissertations, Academic
Thermal conductivity--Dissertations, Academic