Hybrid Analytical Modeling of Force Dense Segmented Magnetic Linear Actuator with Non-Dimensional Parametric Modeling of the Magnetic Flux Effects

Abstract

A new advanced two-dimensional hybrid analytical model of a segmented magnet linear actuator (MLA) comprised of surface permanent magnets (PM) is developed in this paper. This model is used to predict and evaluate the performance of the segmented MLA with proper correction on magnetic Flux Effects, validated by computational modeling. An MLA design with non-uniform PM segmentation was applied in this research to improve its performance compared with conventional radially magnetized MLA and uniform segmented Halbach Array based MLA. For MLA thrust force prediction, the previous published analytical model does not consider losses due to two observed magnetic Flux Effects: (1) the magnetic edge effect—the diminishing nature of the magnetic flux at the edge of the MLA, and (2) the observed magnetic interaction effect—the inconsistent peaks of individual magnetic flux lines, lower than the overall peak flux. In the proposed hybrid model for the segmented MLA, the shaft magnetic field distribution is based on a scalar potential theory subdomain method and the ring magnetic field is based on equivalent surface distributed currents. Collectively, these models are combined with three-dimensional finite element analysis (FEA), to estimate the magnetic thrust force. A data driven pole correction factor is introduced, based on the FEA computational database of three-dimensional MLA, to capture the losses associated with the magnetic flux, which is not considered in the analytical subdomain method. Finally, a normalized pole correction is proposed to generalize the model to different magnetic grades, different dimensional constraints, and varying magnet ratios of the segmented magnets. The developed model provides the design basis for manufacturing optimized force dense segmented MLAs for rotary to linear actuation, based on the force required for the application without the need for running FEA analysis after each design iteration, reducing costs and time required for the optimal design.