Simulation analysis and experiment of magnetic pole shape influence on betatron magnetic field

Abstract

Betatrons have the advantages of small size, lightweight, and simple operation. They are widely used in non-destructive testing, cargo, and vehicle safety inspection systems. Magnetic field distribution is an essential parameter of betatrons and has been investigated via experimental methods. Recently, simulations have been performed for the magnetic field distribution generated by different magnetic pole parameters. In this study, the finite element method is employed to simulate the magnetic field distribution. The effects of the different magnetic end face opening angles, pole protrusion sizes, number of central magnetic pads, and magnetic pole bottom width on the magnetic field distribution are simulated. Based on the simulation results, magnetic poles are developed, and the magnetic field distribution is measured by a gauss meter. The relative error of the measured and simulated equilibrium orbit is 2.1 %, and the relative error of the magnetic field decay index is 3.3 %. The magnetic field distribution can satisfy the essential conditions that the magnetic field at equilibrium orbit of the betatron is equal to half of the average magnetic field within equilibrium orbit and the magnetic field decay index is greater than 0 and less than 1. The results show that the finite element simulation method and established model have high reliability and effectively improve the design accuracy of magnetic poles.

Recognition of Power Quality Disturbance Based on RCNN

“…With the adjustment of the energy structure, new energy technologies play an increasingly important role, such as various distributed photovoltaic solar power generation, wind power generation, and so on. However, due to the instability of new energy power generation, the power quality is affected to a certain extent, resulting in a series of power quality problems such as voltage and frequency [2]. It is of great significance to effectively solve the negative impact of power quality disturbances, improve the governance ability for power quality problems, and enhance the ability to identify disturbance types accurately [3].…”