Paper | Title | Other Keywords | Page |
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TUPLM12 | Method for a Multiple Square Well Model to Study Transverse Mode Coupling Instability | DTL, space-charge, longitudinal-dynamics, simulation | 395 |
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In the high intensity limit it can become difficult to simulate intense beams sufficiently within a short time scale due to collective effects. Semi-Analytic methods such as the Square Well Model*/AirBag Square Well** (SWM/ABS) exist to estimate collective effects within a short time scale. SWM/ABS discretizes the longitudinal confining potential into a single square well enforcing linearity for the case of linear transverse optics. A method is proposed here to extend the Square Well Method multiple square wells. This method preserves linearity properties that make it easily solvable within a short time scale as well as including nonlinear effects from the longitudinal potential shape.
*M. Blaskiewicz PRSTAB 1, 044201. 1998 **A. Burov PRAB 22, 034202. 2019 |
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Poster TUPLM12 [1.818 MB] | ||
DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-NAPAC2019-TUPLM12 | ||
About • | paper received ※ 27 August 2019 paper accepted ※ 05 September 2019 issue date ※ 08 October 2019 | ||
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WEPLS14 | A C++ TPSA/DA Library With Python Wrapper | multipole, simulation, operation, framework | 796 |
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Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under contract DE-AC05-06OR23177. Truncated power series algebra (TPSA) or differential algebra (DA) is often used by accelerator physicists to generate a transfer map of a dynamic system. The map then can be used in dynamic analysis of the system or in particle tracking study. TPSA/DA can also be used in some fast algorithms, eg. the fast multipole method, for collective effect simulation. This paper reports a new TPSA/DA library written in C++. This library is developed based on Dr. Lingyun Yang’s TPSA code, which has been used in MAD-X and PTC. Compared with the original code, the updated version has the following changes: (1) The memory management has been revised to improve the efficiency; (2) A new data type of DA vector is defined and supported by most frequently used operators; (3) Support of inverse trigonometric functions and hyperbolic functions for the DA vector has been added; (4) function composition is revised for better efficiency; (5) a python wrapper is provided. The code is hosted at github and available to the public. |
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DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-NAPAC2019-WEPLS14 | ||
About • | paper received ※ 20 September 2019 paper accepted ※ 16 November 2020 issue date ※ 08 October 2019 | ||
Export • | reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml) | ||