Yazar "Ozturk, U. Kemal" seçeneğine göre listele

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    Enhancing magnetic levitation and guidance force and weight efficiency of high-temperature superconducting maglev systems by using sliced bulk YBCO
    (Wiley, 2023) Abdioglu, Murat; Ozturk, U. Kemal; Guner, Sait Baris; Ozturk, Mehmet; Mollahasanoglu, Hakki; Yanmaz, Ekrem
    We aimed to enhance the magnetic force efficiency of Maglev systems without increasing total weight. For this aim, we divided YBCO bulks into three slices horizontally to utilize the YBCO-permanent magnetic guideway (PMG) interaction surface as much as possible. We used whole YBCO above PMGs with different magnetic pole directions (PMG-A and PMG-B) in two lying positions of transversal and longitudinal and investigated levitation and guidance force performances. It is determined that levitation and guidance forces by using YBCO in transversal lying mode are bigger compared to the longitudinal mode. For sliced YBCO, the maximum levitation force increased by 69% and 78%, while the guidance force enhancements are determined as 212% and 91%, compared to the whole YBCO above PMG-A and PMG-B, respectively. The levitation and guidance force density with respect to the total mass of unit a set of slices YBCO increased by 92% and 106%, respectively, compared to the whole YBCO above PMG-B in transversal mode. Since the higher levitation force and the lower total weight of the onboard unit are important parameters in point of the energy efficiency in Maglev and other levitation applications, the result of this study supplies useful data for the engineers and industrial partners.
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    Machine learning driven optimization and parameter selection of multi-surface HTS Maglev
    (Elsevier, 2024) Ozkat, Erkan Caner; Abdioglu, Murat; Ozturk, U. Kemal
    This research aims to tackle the challenges posed by precise force measurement for high temperature superconducting (HTS) Maglev systems, including mechanical constraints, step motor limitations, and sensor resolutions. For this aim, six machine learning (ML) models namely Support Vector Machine (SVM), Gaussian Process Regression (GPR), Extreme Gradient Boosting (XGB), Long Short-Term Memory (LSTM), Extreme Machine Learning (EML), and Convolutional Neural Network (CNN) were developed to predict levitation force (Fz) and lateral force (Fx) based on process parameters including permanent magnet width (PMW), field cooling height (FCH), the movement in the z-axis (vertical distance), and the movement in the x-axis (lateral distance). Among six ML models, CNN emerged as the most accurate model, demonstrating smaller root mean square deviation (RMSD) without compromising correlation coefficients. Furthermore, an innovative process window approach was introduced to select process parameters that simultaneously meet the minimum value of Fz and maximum value of Fx, named beta 1 and beta 2, set at 90 N and 0 N, respectively. Within this window, PMW of 30 mm and z values less than 10 mm were found to be consistent for all FCH and x values. The novelty of this study is to formulate the optimisation problem in HTS Maglev using the developed ML model by addressing two specific objectives one of which focuses on maximizing Fz while ensuring Fx remains within a defined tolerance (beta 3), representing the minimum allowable ratio of the levitation force to the total force, and the second problem aims to maximize Fz while obtaining zero Fx. The optimum PMW, FCH, x, and z values were obtained at 30 mm, 30 mm, 4 mm and 5 mm, corresponding to Fz and Fx values of 224.2 N and -53.8 N for option 1. As for option 2, the process parameters were obtained as 28.6 mm, 25.9 mm, 0 mm, and 5 mm, corresponding to Fz and Fx values of 194.2 N and 0 N. It was obtained both experimentally and by the optimization that Fz reaches close its maximum as the Fx gains attractive character. Hence, it is expected that the outcomes of this study will significantly benefit the design of HTS Maglev systems and find valuable applications across various transportation engineering projects.
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    Magnetic Force Performance of Hybrid Multisurface HTS Maglev System With Auxiliary Onboard PMs
    (IEEE-Inst Electrical Electronics Engineers Inc, 2023) Ozturk, U. Kemal; Abdioglu, Murat; Mollahasanoglu, Hakki
    The vertical levitation force, guidance force, and magnetic stiffness values, and thus the loading capacity and movement stability of high-temperature superconducting (HTS) Maglev systems, are aimed to be increased in this study by using auxiliary permanent magnets (PMs) in the onboard unit together with the multisurface HTS-permanent magnetic guideway (PMG) arrangement (hybrid multisurface arrangement). First, the magnetic levitation force, guidance force, and stiffness performances of the hybrid multisurface arrangement were investigated at different field cooling heights (FCH). Then, to compensate for the negation of instability that results from the higher repulsive force between the onboard PMs and the PMG and to obtain an optimal magnetic field medium, we have changed the vertical position of the auxiliary onboard PMs (Z(PM)) to Z(PM) = 0, 2, and 4 mm, at the cost of a bit of adecrement in the vertical levitation force. The bigger levitation force, together with the guidance force values for FCH = 25 mm and Z(PM) = 0 mm, indicates that the hybrid multisurface HTS-PMG arrangements are beneficial to increasing the practical applicability of Maglev systems.