Makine Mühendisliği Bölümü Yayın Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/263
Browse
4 results
Search Results
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.Article Citation - WoS: 2Citation - Scopus: 2Analysis of Heat Transfer Enhancement in Tubes With Capsule Dimpled Surfaces and Al2o3-Water Nanofluid(Turkish Soc thermal Sciences Technology, 2022) Ibrahim, Mahmoud Awni A. Haj; Turkoglu, Hasmet; Yapici, Ekin Ozgirgin; Haj Ibrahim, Mahmoud Awni A.This study aims to numerically investigate and evaluate the enhancement of heat transfer by new capsule dimples on tube surfaces for flow of water and Al2O3-water nanofluid with different concentrations, under uniform surface heat flux. The originality of this work lies in combining two passive heat transfer enhancement methods such as geometrical improvements and nanofluids together. Capsule dimples with different depths were considered. Al2O3- water nanofluid was modeled as a single-phase flow based on the mixture properties. The effects of dimple depth and nanoparticle concentrations on Nusselt number, friction factor and performance evaluation criteria (PEC) were studied. Numerical computations were performed using ANSYS Fluent commercial software for 2000-14000 Reynolds number range. It was found that when laminar, transient and fully developed turbulent flow cases are considered, increase in the dimple depth increases the Nusselt number and friction factor for both pure water and Al2O3-water nanofluids cases. Also, the friction factor increases as dimple depth increases. Results show that increase in PEC is more pronounced in the laminar region than in the transition region, it starts to decrease for turbulent flows. For nanofluid, PEC values are considerably higher than pure water cases. The variation of PEC for capsule dimpled tubes are dependent on flow regimes and dimple depths. Increasing the nano particle volume concentration and dimple depth in laminar flows increase the PEC significantly.Article Citation - WoS: 4Citation - Scopus: 4Performance Optimization of Finned Surfaces Based on the Experimental and Numerical Study(Asme, 2023) Ayli, Ece; Kocak, Eyup; Turkoglu, HasmetThis paper presents the findings of numerical and experimental investigations into the forced convection heat transfer from horizontal surfaces with straight rectangular fins at Reynolds numbers ranging from 23,600 to 150,000. A test setup was constructed to measure the heat transfer rate from a horizontal surface with a constant number of fins, fin width, and fin length under different flow conditions. Two-dimensional numerical analyses were performed to observe the heat transfer and flow behavior using a computer program developed based on the openfoam platform. The code developed was verified by comparing the numerical results with the experimental results. The effect of geometrical parameters on heat transfer coefficient and Nusselt number was investigated for different fin height and width ratios. Results showed that heat transfer can be increased by modifying the fin structure geometrical parameters. A correlation for Nusselt number was developed and presented for steady-state, turbulent flows over rectangular fin arrays, taking into account varying Prandtl number of fluids such as water liquid, water vapor, CO2, CH4, and air. The correlation developed predicts the Nusselt number with a relative root mean square error of 0.36%. This research provides valuable insights into the effects of varying Prandtl numbers on the efficiency of forced convection cooling and will help in the design and operation of cooling systems. This study is novel in its approach as it takes into account the effect of varying Prandtl numbers on the heat transfer coefficient and Nusselt number and provides a correlation for the same. It will serve as a valuable reference for engineers and designers while designing and operating cooling systems.Article Citation - WoS: 3Citation - Scopus: 5Optimization of Vortex Promoter Parameters To Enhance Heat Transfer Rate in Electronic Equipment(Asme, 2020) Ayli, Ece; Bayer, OzgurIn this paper, optimization of the location and the geometry of a vortex promoter located above in a finned surface in a channel with eight heat sources is investigated for a Reynolds number of 12,500 < Re < 27,700. Heat transfer rates and the corresponding Nusselt number distributions are studied both experimentally and numerically using different vortex promoter geometries (square, circular, and triangular) in different locations to illustrate the effect of vortex promoter on the fluid flow. Optimization study considered a range of following parameters: blockage ratio of 0.30<(y/C) < 0.45 and interpromoter distance ratio of 0.2277 <(x/L) < 0.3416. Results show that fins over which rectangular and circular promoters are integrated perform better in enhancing the heat transfer. According to the numerical and experimental results, higher blockage ratios cause significantly higher heat transfer coefficients. According to the observations, as the interpromoter distances increase, shedding gains strength, and more turbulence is created. All vortex promoters enhance heat transfer resulting in lower temperature values on the finned surface for different (y/C) and (x/L) values and Reynolds numbers. The use of promoters enhances the heat transfer, and the decrease in the maximum temperature values is recorded on the finned surface changing between 15% and 27%. The biggest decrease in maximum surface temperature value is 500 K-364 K and observed in circular promoter case with (y/C) = 0.43, (x/L) = 0.3416, and Reynolds numbers of 22,200.