Browsing by Author "Cetinceviz, Y."

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Design and numerical analysis of thermal capability of power transformer using coupled electromagnetic field-thermal model
(Springer Science and Business Media Deutschland GmbH, 2024) Cetinceviz, Y.; Sehirli, E.
In this study, by using the co-simulation technique, the dry-type transformer is designed for a wind turbine with 3.8 MVA, 690 V/36 kV power, and voltages. Dry-type transformers used in wind turbines require extra effort at the design stage concerning oil-immersed type because of the limited cooling capacity of air, possible higher inrush currents, resonant overvoltage, higher voltage, and load fluctuations that degrade thermal behavior. Furthermore, transformer design and analysis of their thermal behavior cannot be realized through one simulation software. Therefore, another approach using different simulation software simultaneously is needed, called co-simulation or multi-physics solution. This paper investigates transformer design and its effect on the system using a co-simulation technique including ANSYS Maxwell 3D, Simplorer, and Mechanical. After realizing the three-dimensional transformer design, its lumped parameter consisting of inductance matrix, leakage inductances, and resistances are determined analytically and using an eddy current solver. Core losses of the transformer, high- and low-voltage windings voltages, and their harmonic spectrums under different load conditions, including ohmic and inductive loads, are defined, the steady-state thermal solution is performed, and conclusions are made. It is proved that the transformer design works as desired.
Design and numerical analysis of thermal capability of power transformer using coupled electromagnetic field-thermal model
(2024.01.01) Cetinceviz, Y.; Sehirli, E.
In this study, by using the co-simulation technique, the dry-type transformer is designed for a wind turbine with 3.8 MVA, 690 V/36 kV power, and voltages. Dry-type transformers used in wind turbines require extra effort at the design stage concerning oil-immersed type because of the limited cooling capacity of air, possible higher inrush currents, resonant overvoltage, higher voltage, and load fluctuations that degrade thermal behavior. Furthermore, transformer design and analysis of their thermal behavior cannot be realized through one simulation software. Therefore, another approach using different simulation software simultaneously is needed, called co-simulation or multi-physics solution. This paper investigates transformer design and its effect on the system using a co-simulation technique including ANSYS Maxwell 3D, Simplorer, and Mechanical. After realizing the three-dimensional transformer design, its lumped parameter consisting of inductance matrix, leakage inductances, and resistances are determined analytically and using an eddy current solver. Core losses of the transformer, high- and low-voltage windings voltages, and their harmonic spectrums under different load conditions, including ohmic and inductive loads, are defined, the steady-state thermal solution is performed, and conclusions are made. It is proved that the transformer design works as desired.
Modeling, Analysis, and Winding Loss Calculation of Different Litz and Solid Wire on Coupled Inductor in DC-DC Cuk Converter for DCM
(2024.01.01) Sehirli, E.; Cetinceviz, Y.
In this article, the coupled inductor is designed using a litz wire, an enameled litz wire, and a solid wire separately. All the coupled inductors wound with litz and solid wires are tested in a Cuk converter operated in DCM with a 25 kHz switching frequency for 15 W power, and winding losses of each wire, including proximity and skin effect loss, are calculated analytically without using any software, by using different techniques presented in literature, and the results are compared. Also, state-space modeling of the converter using a coupled inductor for DCM is derived as a contribution of the article because such a modeling is not derived for the Cuk converter in the literature. Thanks to the applications, inductor currents, switch voltage, input-output voltage, and output current are measured for coupled inductors with different wires. In addition, FEM simulation and co-simulation of all coupled inductors are realized, and the results obtained by applications are verified by them. Besides, it is shown that while the number of turns is the same, increasing the bundle of wires increases efficiency and inductance, reducing the window area and coupling coefficient.
Python-based machine learning estimation ofthermo-hydraulic performance along varying nanoparticle shape, nanofluid and tube configuration
(2025.01.01) Gürsoy, E.; Tan, M.H.M.; Gürdal, M.; Cetinceviz, Y.
In this research article, a Python-based machine learning model prediction study was conducted based on the study results obtained from sudden expansion tubes containing different expansion angles, dimpled fin structures and nanofluids, whose thermo-hydraulic performance was previously examined. In the study, Artificial Neural Network and Ridge regression models were used to make predictions on the average Nusselt number (Nu), average Darcy friction factor (f) and performance evaluation criteria (PEC). Physical variations of the sudden expansion tube were taken into account and a detailed comparison of the results was made. A superior average Nu was acquired as 172.45 %, 22.05 %, 17.18 %, 13.65 %, and 7.76 % compared to Ag-MgO/H2O, Al2O3/H2O (blade), CoFe2O4/H2O, Al2O3/H2O (cylindrical), and Al2O3/H2O (platelet), respectively. The highest Performance Evaluation Criteria (PEC) for Re= 2000 based on Al2O3/H2O (platelet) shows an increase of 4.84%, 12.08 %, 11.76 %, 66.05 %, and 148.94 % compared to Al2O3/H2O (cylindrical), Al2O3/H2O (blade), CoFe2O4/H2O, Fe3O4/H2O, and Ag-MgO/H2O, respectively. From the results obtained, it was determined that Python-based Machine Learning approach which facilitates custom optimizations showed a significant performance with small margins of error in predicting the heat transfer parameters. The lowest error rates of machine learning and polynomial ridge regression models ranged from 0.2 % to 5.4 % for the unseen test set and the application of Python-based algorithms provided considerable savings in calculation time compared to conventional methods. On the other hand, using machine learning models with feature engineering has been found to increase model performance by at least 30%. In these years when studies on the predictions of thermo-hydraulic studies are very rare in the literature, this study is intended to facilitate scientists, engineers and academicians who will further study on this subject.