Proper Orthogonal Decomposition for Parameterized Macromodeling of a Longitudinal Electromagnetic Levitator

Abstract

This article presents the application of proper orthogonal decomposition (POD) and Gaussian process regression (GPR) to develop a fast and accurate macromodel for predicting electromagnetic fields and forces in an electromagnetically levitated aluminum billet. The finite element method (FEM) was used to create a 2-D model of the device, extracting the current density and magnetic field distributions in the billet for different positions and frequencies. POD was applied to reduce the dimensionality of the FEM data, while GPR was employed to predict the reduced-order model coefficients for new input parameters. The resulting surrogate model significantly reduces computation time from 8 min to 52 ms, while maintaining a high level of accuracy, providing full-field predictions of the quantities of interest. The model was validated for both field and force predictions, demonstrating its potential to accelerate device study and optimization, while paving the way toward its application as a digital twin of the device.