Ozkat, Erkan Caner; Abdioglu, Murat; Ozturk, U. Kemal

This study presents a finite element analysis (FEA)-driven optimization for the design of an electrodynamic levitation (EDL) system incorporating permanent magnet Halbach arrays (PM-HAs). The primary objective is to maximize the net force difference between lift (Fy) and drag (Fx) forces under realistic physical and manufacturing constraints. The nonlinear electromagnetic behavior of the EDL system is modeled using high-fidelity FEA, capturing the complex interaction between the PM-HA and a conductive plate. At each iteration of the population-based optimization algorithm, full magnetoquasistatic simulations are performed to ensure a physically accurate evaluation of key performance metrics. A comprehensive parametric study involving 256 distinct PM-HA configurations reveals a non-monotonic relationship between the number of magnets and levitation performance, highlighting the critical role of magnetic field distribution. Configurations with 9 to 11 magnets achieve the highest net levitation force. The optimization results indicate that the optimal configuration includes four horizontally aligned permanent magnets (PMs) with a width of 74.00 mm and three vertically aligned PMs with a width of 74.67 mm, along with a 7 mm thick conductive plate having a conductivity of 5 ×107 S/m. This design delivers a maximum lift force of 3550 N and a minimum drag force of 279 N at 100 m/s. The findings demonstrate that effective EDL system design requires a carefully arranged magnet configuration rather than a simple maximization of magnet count or size. The proposed FEA-guided framework offers a robust and scalable methodology for the optimization of high-performance Maglev systems.

İncele

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 β1 and β2, 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 (β3), 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.

AtıfScopus21·WoS18

İncele

Ozturk, Ufuk Kemal; Abdioglu, Murat; Ozkat, Erkan Caner; Mollahasanoglu, Hakki

A 2-D numerical finite-element model of a linear synchronous motor (LSM) is extended based on the magnetic field and moving mesh properties to investigate the magnetic flux and magnetic force characteristics of the high-speed electromagnetic levitation Maglev system, by using high-temperature superconductor (HTS) coils rather than lower current-carrying copper coils on the onboard unit and permanent magnets (PMs) on the ground. In this 2-D LSM model, the transient time-dependent solver is used to obtain the magnetic flux densities. Both the propulsion and levitation forces increased with increasing supercurrent Jc0 values indicating the advantage of using the superconducting windings in PM-LSM of Maglev vehicles as compared with the copper wires. It was also determined that, although the propulsion force is obtained on a comparable level with the studies in the literature, the lower levitation force issue than the electrodynamic suspension (EDS), despite the better propulsion to levitation force ratio, can be overcome by using the high flux trapping capacity bulk HTSs on the onboard unit together with the superconducting coils. The determined higher propulsion force in this PM-LSM Maglev model indicates that the Maglev vehicle can reach higher velocities in a short distance, and thus, this vehicle can be effectively used in short-distance travels in addition to the long-distance transportation. On the other hand, since the EDS system to be accelerated in a certain time interval via conventional wheels to achieve sufficient levitation, PM-LSM system can be integrated into the EDS Maglev system to ensure higher acceleration in a short time interval in addition to the higher levitation and propulsion force performances.

AtıfScopus10·WoS10·OpenAlex8

İncele