Time‐varying magnetic field analysis using an improved meshless method based on interpolating moving least squares
Mehdi Arehpanahi, Hamid Reza Jamalifard
Abstract
Mehdi Arehpanahi, Hamid Reza Jamalifard
Abstract
In this study, analysis of 2D time‐varying magnetic field using an improved meshless method based on interpolating moving least squares (IMLS) is proposed. In this study, solving the time‐domain magnetic field equation in a meshless method carried out by selection of magnetic field intensity as a variable instead of magnetic vector potential which is widely used as a variable. Using this approach, the post‐processing process will be fast and the boundary condition is implemented simply. For performance evaluation of the proposed method in eddy current analysis, two other meshless techniques, i.e. moving least squares (MLS) and radial point interpolation method (RPIM) have been considered. The results of improved IMLS are compared with MLS, RPIM and finite element method (FEM) results. Verification of improved meshless results is performed by FEM.
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In this study, analysis of 2D time‐varying magnetic field using an improved meshless method based on interpolating moving least squares (IMLS) is proposed. In this study, solving the time‐domain magnetic field equation in a meshless method carried out by selection of magnetic field intensity as a variable instead of magnetic vector potential which is widely used as a variable. Using this approach, the post‐processing process will be fast and the boundary condition is implemented simply. For performance evaluation of the proposed method in eddy current analysis, two other meshless techniques, i.e. moving least squares (MLS) and radial point interpolation method (RPIM) have been considered. The results of improved IMLS are compared with MLS, RPIM and finite element method (FEM) results. Verification of improved meshless results is performed by FEM.
Key concepts: Moving least squares, Field (mathematics), Meshfree methods, Magnetic field, Regularized meshless method, Least-squares function approximation, Mathematics, Computer science