Machine learning driven optimization and parameter selection of multi-surface HTS Maglev

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dc.authoridOzkat, Erkan Caner/0000-0003-0530-5439
dc.contributor.authorOzkat, Erkan Caner
dc.contributor.authorAbdioglu, Murat
dc.contributor.authorOzturk, U. Kemal
dc.date.accessioned2024-10-04T18:51:22Z
dc.date.available2024-10-04T18:51:22Z
dc.date.issued2024
dc.departmentBayburt Üniversitesien_US
dc.description.abstractThis 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.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkiye (TUBITAK) [122F432, 118F426]en_US
dc.description.sponsorshipThis work was supported by the Scientific and Technological Research Council of Turkiye (TUBITAK), with project numbers of 122F432 and 118F426.en_US
dc.identifier.doi10.1016/j.physc.2023.1354430
dc.identifier.issn0921-4534
dc.identifier.issn1873-2143
dc.identifier.scopus2-s2.0-85179895417en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1016/j.physc.2023.1354430
dc.identifier.urihttp://hdl.handle.net/20.500.12403/3474
dc.identifier.volume616en_US
dc.identifier.wosWOS:001137977000001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofPhysica C-Superconductivity and Its Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHTS Magleven_US
dc.subjectMulti -surfaceen_US
dc.subjectOptimizationen_US
dc.subjectMachine learningen_US
dc.subjectDeep learningen_US
dc.titleMachine learning driven optimization and parameter selection of multi-surface HTS Magleven_US
dc.typeArticleen_US

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