Planar multi-pole magnetic field decomposition method based on genetic algorithm

By using data processing based on genetic algorithms and fitting based on least squares algorithms, the accuracy and efficiency issues of planar multipole magnetic field decomposition were solved, achieving fast and accurate magnetic field decomposition, which is applicable to accelerator magnet design and improves design efficiency and accuracy.

CN121960124APending Publication Date: 2026-05-01XIAN INSTITUE OF SPACE RADIO TECH
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Patent Information

Application Number
CN202511969057.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-24
Publication Date
2026-05-01

AI Technical Summary

Technical Problem

In existing technologies, planar multipole magnetic field decomposition methods have low accuracy, long computation time, and poor callability, making it difficult to effectively suppress high-order magnetic field components in accelerator magnet design.

Method used

A genetic algorithm-based method is used to standardize the data of the multipolar magnetic field. Through global and local search optimization, the parameters to be fitted are obtained, and the least squares algorithm is used for fitting, so as to finally realize the visualization of the multipolar magnetic field.

Benefits of technology

It achieves rapid and accurate planar multipole magnetic field decomposition, improving design efficiency. It is applicable to both permanent magnets and electromagnetic magnets, enhancing the precision and efficiency of magnet design. It can quickly determine the magnetic pole shape in the early stages of accelerator magnet design, increasing its versatility.

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Abstract

The invention discloses a planar multi-pole magnetic field decomposition method based on a genetic algorithm. Quantitative decomposition of multi-pole components of a magnetic field is realized by fitting constant coefficients in a two-dimensional complex magnetic potential expression in a Maxwell equation set. The method comprises the following steps: preprocessing a two-dimensional plane complex magnetic potential obtained by actual measurement / simulation to obtain an initial population of a genetic algorithm, selecting a proper population scale, crossover and mutation probabilities and a termination evolution algebra, constructing a fitness function based on an optimization target, and carrying out iterative calculation to obtain a global better decomposition result of a multipole magnetic field. And on the basis, least square method optimization is further carried out, and finally, a globally optimal multipole magnetic field decomposition result is obtained.
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