Keyword: collective-effects
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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
  • M.A. Balcewicz, Y. Hao
    FRIB, East Lansing, Michigan, USA
  • M. Blaskiewicz
    BNL, Upton, New York, USA
  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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DOI • reference for this paper ※  
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
  • H. Zhang, Y. Zhang
    JLab, Newport News, Virginia, USA
  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.
DOI • reference for this paper ※  
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)