Adrian Oeftiger edited this page Mar 27, 2018 · 23 revisions

Welcome to the PyHEADTAIL wiki

PyHEADTAIL is

  • a python library to numerically simulate particle beam dynamics;
  • based on macro-particles to model collective effects of charged beam plasmas;
  • developed and maintained at CERN.

Find a quick start tutorial in our playground repository of script snippets and PyHEADTAIL module explanations. There is also an impedance-driven head-tail instability example for the LHC as a fully working simulation case.

Contents

This wiki explains how to

  1. set up PyHEADTAIL
  2. use PyHEADTAIL
  3. exploit the GPU for simulations
  4. contribute to development (and how the workflow is devised)

What is PyHEADTAIL used for?

PyHEADTAIL is used at CERN and other labs to study a wide range of collective effects in synchrotrons.

In particular, it is used to study impedance effects such as coherent tune shifts, the development of coherent modes and instabilities and TMCI thresholds. It can employ both low beta wakes as well as ultra-relativistic wake fields.

In combination with PyECLOUD it is used to explore electron cloud instabilities in a vast variety of configurations, i.e. investigating the impact of electron clouds within different chamber geometries and in different magnetic field environments.

The impact of sophisticated transverse feedback systems including bandwidth limitation and the underlying digital signal processing chain can be included and studied in conjunction with the aforementioned collective effects.

Novel instability mitigation techniques such as wideband feedback systems or longitudinal-to-transverse Landau damping are also being assessed with PyHEADTAIL.

Further studies make used of the feature rich space charge module to explore the impact of space charge or the strong support for flexible longitudinal tracking to investigate the impact of multi-harmonic RF systems on the coherent beam stability.

The collective effects course at the US Particle Accelerator School 2015 has been taught using PyHEADTAIL. The exercises contain jupyter notebooks with instructive examples how to use PyHEADTAIL.


NB: the exercises work with PyHEADTAIL v1.2.0 (the current version in March 2015). In the meantime, a few structural improvements in the library broke backwards compatibility and the notebooks would need some polishing to run with current versions -- this is left as an exercise to the interested reader ;-)

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