Makine Mühendisliği Bölümü Yayın Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/263
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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: 6Citation - Scopus: 7Machine Learning Based Developing Flow Control Technique Over Circular Cylinders(Asme, 2023) Turkoglu, Hasmet; Ayli, Ece; Kocak, EyupThis paper demonstrates the feasibility of blowing and suction for flow control based on the computational fluid dynamics (CFD) simulations at a low Reynolds number flows. The effects of blowing and suction position, and the blowing and suction mass flowrate, and on the flow control are presented in this paper. The optimal conditions for suppressing the wake of the cylinder are investigated by examining the flow separation and the near wake region; analyzing the aerodynamic force (lift and drag) fluctuations using the fast Fourier transform (FFT) to separate the effects of small-scale turbulent structures in the wake region. A method for stochastic analysis using machine learning techniques is proposed. Three different novel machine learning methods were applied to CFD results to predict the variation in drag coefficient due to the vortex shedding. Although, the prediction power of all the methods utilized is in the acceptable accuracy range, the Gaussian process regression (GPR) method is more accurate with an R-2(coefficient of determination) > 0.95. The results indicate that by optimizing the blowing and suction parameters like mass flowrate, slot location, and the slot configuration, up to 20% reduction can be achieved in the drag coefficient.Article Citation - WoS: 16Citation - Scopus: 17A Comparative Study of Multiple Regression and Machine Learning Techniques for Prediction of Nanofluid Heat Transfer(Asme, 2022) Ayli, Ece; Turkoglu, Hasmet; Kocak, EyupThe aim of this article is to introduce and discuss prediction power of the multiple regression technique, artificial neural network (ANN), and adaptive neuro-fuzzy interface system (ANFIS) methods for predicting the forced convection heat transfer characteristics of a turbulent nanofluid flow in a pipe. Water and Al2O3 mixture is used as the nanofluid. Utilizing fluent software, numerical computations were performed with volume fraction ranging between 0.3% and 5%, particle diameter ranging between 20 and 140 nm, and Reynolds number ranging between 7000 and 21,000. Based on the computationally obtained results, a correlation is developed for the Nusselt number using the multiple regression method. Also, based on the computational fluid dynamics results, different ANN architectures with different number of neurons in the hidden layers and several training algorithms (Levenberg-Marquardt, Bayesian regularization, scaled conjugate gradient) are tested to find the best ANN architecture. In addition, ANFIS is also used to predict the Nusselt number. In the ANFIS, number of clusters, exponential factor, and membership function (MF) type are optimized. The results obtained from multiple regression correlation, ANN, and ANFIS were compared. According to the obtained results, ANFIS is a powerful tool with a R-2 of 0.9987 for predictions.Article Citation - WoS: 11Citation - Scopus: 14Numerical Study on Effects of Computational Domain Length on Flow Field in Standing Wave Thermoacoustic Couple(Elsevier Sci Ltd, 2019) Turkoglu, Hasmet; Mergen, Suhan; Yildirim, EnderFor the analysis of thermoacoustic (TA) devices, computational methods are commonly used. In the computational studies found in the literature, the flow domain has been modelled differently by different researchers. A common approach in modelling the flow domain is to truncate the computational domain around the stack, instead of modelling the whole resonator to save computational time. However, where to truncate the domain is not clear. In this study, we have investigated how the simulation results are affected by the computational domain length (I-d) when the truncated domain approach is used. For this purpose, a standing wave TA couple which undergoes a refrigeration cycle was considered. The stack plate thickness was assumed to be zero and the simulations were performed for six different dimensionless domain length (I-d/lambda) varying between 0.029 and 0.180. Frequency and Mach number were taken as 100 Hz and 0.01, respectively, and kept constant for all the cases considered. The mean pressure and the pressure amplitude were taken as 10 kPa and 170 Pa, respectively (Drive ratio of 1.7%). Helium was considered as the working fluid. To assess the accuracy of the simulation results, the pressure distributions across the domain were compared with that of the standing wave. In addition to the pressure variation, the effects of the domain length on the phase delay of the pressure and velocity waves along the stack plate were also investigated. The results showed that with the increasing I-d/lambda. ratio, the simulated pressure distribution compares better with the standing wave pressure distribution. With the lowest I-d/lambda ratio (0.029) considered, the difference between the amplitudes of the computed pressure distribution and theoretical standing wave pressure distribution was approximately 50 Pa. However, as I-d/lambda value increases, the simulation results approach to the theoretical standing wave pressure distribution better. The computational results obtained with Id/lambda = 0.132 and 0.180, were almost identical with standing wave acoustic field. Hence, it was concluded that the domain length has a significant effect on the accuracy of the computational results when the truncated domain approach is used. It was also observed that for a given TA device and operating parameters, there is a minimum I-d/lambda value for obtaining reliable results.