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  • Küçük Resim
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    Artificial neural networks based computational and experimental evaluation of thermal and drying performance of partially covered PVT solar dryer
    (Institution of Chemical Engineers, 2024) Gupta, Ankur; Das, Biplab; Arslan, Erhan; Das, Mehmet; Koşan, Meltem; Can, Ömer Faruk
    This study proposes a mixed-mode dryer with a semi-transparent photovoltaic thermal (PVT) collector for the assessment of drying and thermal performance using computational and experimental findings. The thermal behavior and fluid flow characteristics have been analyzed to optimize the air flow rate in the PVT solar dryer by considering three different inlet velocities of 0.048 m/s (Case 1), 0.096 m/s (Case 2), and 0.144 m/s (Case 3). The temperature distribution is obtained more uniformly for the PVT collector and dryer cabin in Case 2. The results of the investigation show that Case 3 has a positive impact on the PVT solar dryer performance. In numerical and experimental methods, the enhanced thermal efficiency is attained as 30.78% and 29.78% for Case 2, and 33.20% and 31.14% for Case 3, respectively, in comparison to Case 1. Case 3 has improved Reynolds and Nussselt numbers by 3.06 and 2.45 times, respectively compared to Case 1. Experimental results varied by 2.24 to 4.90% from simulated outcomes obtained from CFD. The machine learning approach of ANN has been implemented with different hidden layers network models to choose the best drying conditions by predicting the drying performance parameters.
  • Küçük Resim
    Öğe
    Artificial neural networks based computational and experimental evaluation of thermal and drying performance of partially covered PVT solar dryer
    (Institution of Chemical Engineers, 2024) Gupta, Ankur; Das, Biplab; Arslan, Erhan; Daş, Mehmet; Koşan, Meltem; Can, Ömer Faruk; 0000-0001-5037-6119; 0000-0003-3752-6308; 0000-0002-7540-7935; 0000-0002-4143-9226; 0000-0003-0799-889X
    This study proposes a mixed-mode dryer with a semi-transparent photovoltaic thermal (PVT) collector for the assessment of drying and thermal performance using computational and experimental findings. The thermal behavior and fluid flow characteristics have been analyzed to optimize the air flow rate in the PVT solar dryer by considering three different inlet velocities of 0.048 m/s (Case 1), 0.096 m/s (Case 2), and 0.144 m/s (Case 3). The temperature distribution is obtained more uniformly for the PVT collector and dryer cabin in Case 2. The results of the investigation show that Case 3 has a positive impact on the PVT solar dryer performance. In numerical and experimental methods, the enhanced thermal efficiency is attained as 30.78% and 29.78% for Case 2, and 33.20% and 31.14% for Case 3, respectively, in comparison to Case 1. Case 3 has improved Reynolds and Nussselt numbers by 3.06 and 2.45 times, respectively compared to Case 1. Experimental results varied by 2.24 to 4.90% from simulated outcomes obtained from CFD. The machine learning approach of ANN has been implemented with different hidden layers network models to choose the best drying conditions by predicting the drying performance parameters.
  • Küçük Resim
    Öğe
    Numerical and experimental assessment of a photovoltaic thermal collector using variable air volume
    (Elsevier Ltd., 2023) Arslan, Erhan; Can, Ömer Faruk; Koşan, Meltem; Demirtaş, Mehmet; Aktekeli, Burak; Aktaş, Mustafa
    Photovoltaic thermal collectors (PV-T’s) produce both thermal and electrical power simultaneously by using solar radiation. In this study, a new type of air-cooled PV-T’s with copper fins was designed and tested with variable airflows to control voltage of the fans (6, 8, 10, 12 V) electrical and thermal loads in solar photovoltaic (PV) systems. The novelty of this research is to design a fin with an increasing number of holes and rising height from the inlet to the outlet of the PV-T. In this way, it is aimed to enhance the heat transfer by increasing the turbulence of the air in the PV-T and to cool the PV-T homogeneously. As a result, average thermal, electrical, exergy and enviroeconomic efficiency of the experiments were found as 32.71 %, 12.77 %, 12.97 % and 0.76 kgCO2/h for PV-T panels. Due to cooling of PV with air circulation, 0.38 % increase in electrical efficiency was achieved. The surface temperatures PV-T panels were obtained with 3.2 % error rate by using computational fluid dynamic (CFD) before the experiments. The amount of power consumed by the fans against the increased electrical power was determined as 16.1 % and 4.4 % for the 12 and 6 V experiments, respectively. As a result, increasing the voltage of the fans increased both thermal and electrical performance and the best results were obtained in 12 V experiments. The outputs obtained about the PV-T collector for sustainable energy systems will contribute to researchers and industry in this field.