WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8653
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Article Performance Analysis of a Flat Plate Solar Collector Utilizing Different Nanofluids(Korean Soc Mechanical Engineers, 2025) Topak, Aysu Deniz; Yapici, Ekin OzgirginGiven the risks of fossil fuel utilization, interest in renewable energy sources like solar power is growing, particularly with solar collectors. Flat plate solar collectors are common in solar thermal applications, though conventional heat transfer fluids have low thermal conductivity. To improve efficiency, nanofluids are employed. This study involves thermal analysis of a solar collector system using different nanofluids prepared in laboratories. Design parameters of the collector and the impact of utilizing nanofluids with different concentrations on the thermal performance of collector system are investigated through both analytical and experimental approaches. Results show nanofluids enhance thermo-physical properties, improving collector efficiency even at low concentrations. Comparing commonly used oxides (Al2O3) and rarely used nitrides (AlN), AlN-based nanofluids showed superior thermal properties. Additionally, MXene-water nanofluid with MAX (Ti3AlC2) synthesized from Titanium (II) hydride further increased efficiency. Experimental results demonstrated up to a 55.3 % efficiency improvement for nanofluids over water.Article Citation - WoS: 10Citation - Scopus: 11Analysis of Heat Transfer Enhancement of Passive Methods in Tubes With Machine Learning(Sage Publications Ltd, 2024) Ayli, Ece; Turkoglu, Hasmet; Yapici, Ekin Ozgirgin; Özgirgin Yapıcı, EkinThis study investigates the efficacy of machine learning techniques and correlation methods for predicting heat transfer performance in a dimpled tube under varying flow conditions, including the presence of nanoparticles. A comprehensive numerical analysis involving 120 cases was conducted to obtain Nusselt numbers and friction factors, considering different dimple depths and velocities for both pure water and water-Al2O3 nanofluid at 1%, 2%, and 3% volume concentrations. Utilizing the data acquired from the numerical simulations, a correlation equation, SVM ANN architectures were developed. The predictive capabilities of the statistical approach, ANN, and SVM models for Nusselt number distribution and friction factor were meticulously assessed through mean average percentage error (MAPE) and correlation coefficients (R2). The research findings reveal that machine learning techniques offer a highly effective approach for accurately predicting heat transfer performance in a dimpled tube, with results closely aligned with Computational Fluid Dynamics (CFD) simulations. Particularly noteworthy is the superior performance of the ANN model, demonstrating the most precise predictions with an error rate of 2.54% and an impressive R2 value of 0.9978 for Nusselt number prediction. In comparison, the regression model achieved an average error rate of 6.14% with an R2 value of 0.8623, and the SVM model yielded an RMSE value of 2.984% with an R2 value of 0.9154 for Nusselt number prediction. These outcomes underscore the ANN model's ability to effectively capture complex patterns within the data, resulting in highly accurate predictions. In conclusion, this research showcases the promising potential of machine learning techniques in accurately forecasting heat transfer performance in dimpled tubes. The developed ANN model exhibits notable superiority in predicting Nusselt numbers, making it a valuable tool for enhancing thermal system analyses and engineering design optimization.