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University of Victoria

1. Jung, Paul Matthew. Hybrid macro-particle moment accelerator tracking algorithm.

Degree: Department of Physics and Astronomy, 2020, University of Victoria

A particle accelerator simulation which straddles the gap between multi-particle and moment codes is derived. The hybrid approach represents the beam using macro-particles which contain discrete longitudinal coordinates and transverse second moments. The discretization scheme for the macro-particles is derived using variational principles, as a natural extension of well known variational approaches. This variational discretization allows for exact transverse emittance conservation. The electrostatic self-potential is discrete in the longitudinal direction and solved semi-analytically in the transverse direction using integrated Green’s functions. The algorithm is implemented and tested against both a moment and multi-particle code. Advisors/Committee Members: Baartman, Richard (supervisor), Karlen, Dean Albert (supervisor).

Subjects/Keywords: Particle Accelerator; Simulation; variational principles

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Jung, P. M. (2020). Hybrid macro-particle moment accelerator tracking algorithm. (Masters Thesis). University of Victoria. Retrieved from http://hdl.handle.net/1828/12036

Chicago Manual of Style (16th Edition):

Jung, Paul Matthew. “Hybrid macro-particle moment accelerator tracking algorithm.” 2020. Masters Thesis, University of Victoria. Accessed September 20, 2020. http://hdl.handle.net/1828/12036.

MLA Handbook (7th Edition):

Jung, Paul Matthew. “Hybrid macro-particle moment accelerator tracking algorithm.” 2020. Web. 20 Sep 2020.

Vancouver:

Jung PM. Hybrid macro-particle moment accelerator tracking algorithm. [Internet] [Masters thesis]. University of Victoria; 2020. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/1828/12036.

Council of Science Editors:

Jung PM. Hybrid macro-particle moment accelerator tracking algorithm. [Masters Thesis]. University of Victoria; 2020. Available from: http://hdl.handle.net/1828/12036

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