WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8653
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Article Citation - WoS: 1Numerical Investigation for Enhancement of Heat Transfer in the Cooling Water Jacket of CI Engine(Taylor & Francis inc, 2025) Altug, Hakan; Yapici, Ekin OzgirginDiesel engines are essential in heavy industries and agriculture, especially in vehicles like tractors that operate under challenging conditions, often causing engine faults. Effective thermal management systems are vital for faultless operation preventing overheating, extending engine life, reducing emissions, and improving efficiency. The core of these systems is the water jacket around the cylinder head, which regulates temperatures, facilitates lubrication, prevents friction-related faults, increase durability and thermal performance of the engine. Computational Fluid Dynamics techniques are crucial for analyzing engine thermal behavior and designing cooling systems with complex flows. This study simulates the engine block's temperature distribution under extreme conditions to prevent overheating and improve thermal performance. Geometrical modifications, such as optimizing outlet water ports are employed to achieve enhanced thermal performance by reducing the temperature of coolant. 3D model of the engine block is developed using STAR CCM+ to calculate water temperatures, flow rates and outlet pressures. Numerical validation is conducted with a test bench, and three geometric improvements are analyzed for temperature distribution and heat transfer coefficient. Results showed that, 6.2% improvement on thermal performance is achieved based on the average coolant temperatures and 10% enhancement is achieved in terms of heat transfer coefficient values.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.