Optimization of beam transport magnetic field in linear induction accelerator based on genetic algorithm
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摘要: 以直线感应加速器(LIA)匹配磁场设计和束线调谐为背景,提出解决强流相对论电子束长距离、小波动、多元件磁约束的输运优化问题的数值优化办法,建立基于遗传算法的优化程序。结合束质心轨迹及束包络耦合模型,设计描述束传输半径波动大小的评价函数,采用励磁元件馈入电流为优化对象,解决LIA磁场配置组合爆炸优化问题。计算结果表明:优化程序可依据不同的初始束流,有针对性地快速给出一组符合束输运要求的励磁电流配置。研究成果为在建的LIA装置束线调谐提供一种重要的数值分析工具。Abstract: The beam transport system of the Dragon-Ⅰ linear induction accelerator(LIA) consists of hundreds of solenoid coils and dipole steering coils, which are designed to reduce corkscrew amplitude and transverse motion of electron beam. In order to improve the beam quality, a genetic optimization model of solenoid currents is proposed in this paper and the optimization code GABC based on genetic algorithm and beam transport models is designed, which contains both beam centroid track and the beam envelope model. The matched magnetic field in five blocks of the Dragon-Ⅰ LIA, including twenty induction acceleration cells and five connection cells, are analyzed using the optimization code. The numerical results reveal that the GABC is effective to solve transport magnetic field optimization problems and could play an important role for beam tuning simulation and experiment.
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Key words:
- linear induction accelerator /
- beam tuning /
- genetic algorithm /
- matched magnetic field /
- optimization
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