Optimization of beam transport magnetic field in linear induction accelerator based on genetic algorithm

Wang Qiang; Zhao Hailong; Dai Zhiyong; Sun Wensui; Xie Long; Wang Wendou

doi:10.3788/HPLPB20132505.1256

Volume 25 Issue 05

Mar. 2013

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Article Navigation > High Power Laser and Particle Beams > 2013 > 25(05): 1256-1260

Zhang Ying, Pang Beibei, Xi Zhiguo, et al. Design of a in-situ stress loading system used for residual stress neutron diffractometer[J]. High Power Laser and Particle Beams, 2013, 25: 2865-2868. doi: 10.3788/HPLPB20132511.2865

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Wang Qiang, Zhao Hailong, Dai Zhiyong, et al. Optimization of beam transport magnetic field in linear induction accelerator based on genetic algorithm[J]. High Power Laser and Particle Beams, 2013, 25: 1256-1260. doi: 10.3788/HPLPB20132505.1256

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Optimization of beam transport magnetic field in linear induction accelerator based on genetic algorithm

doi: 10.3788/HPLPB20132505.1256

1.
Institute of Fluid Physics,CAEP,P.O.Box 919-105,Mianyang 621900,China

Received Date: 2012-08-09
Rev Recd Date: 2012-12-25
Publish Date: 2013-03-12

Abstract

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.
- linear induction accelerator,
- beam tuning,
- genetic algorithm,
- matched magnetic field,
- optimization

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