PyECLOUD was originally developed as a tool for the simulation of electron cloud build-up in particle accelerators. Over the last five years the code has become part of a wider set of modular and scriptable Python tools that can be combined to study different effects of the e-cloud in increasingly complex scenarios. The Particle In Cell solver originally included in PyECLOUD later developed into a stand-alone general purpose library (PyPIC) that now includes advanced features like a refined modeling of curved boundaries and optimized resolution based on the usage of nested grids. The effects of the e-cloud on the beam dynamics can be simulated interfacing PyECLOUD with the PyHEADTAIL code. These simulations can be computationally very demanding due to the multi-scale nature of this kind of problems. Hence, a dedicated parallelization layer (PyPARIS) has been recently developed to profit from parallel computing resources in order to significantly speed up the computation.
EVOLUTION OF PYTHON TOOLS FOR THE SIMULATION OF ELECTRON CLOUD EFFECTS / Iadarola, Giovanni; Belli, Eleonora; Li, Kevin; Mether, Lotta; Romano, Annalisa; Rumolo, Giovanni. - (2017), pp. 3803-3806.
EVOLUTION OF PYTHON TOOLS FOR THE SIMULATION OF ELECTRON CLOUD EFFECTS
Eleonora Belli;
2017
Abstract
PyECLOUD was originally developed as a tool for the simulation of electron cloud build-up in particle accelerators. Over the last five years the code has become part of a wider set of modular and scriptable Python tools that can be combined to study different effects of the e-cloud in increasingly complex scenarios. The Particle In Cell solver originally included in PyECLOUD later developed into a stand-alone general purpose library (PyPIC) that now includes advanced features like a refined modeling of curved boundaries and optimized resolution based on the usage of nested grids. The effects of the e-cloud on the beam dynamics can be simulated interfacing PyECLOUD with the PyHEADTAIL code. These simulations can be computationally very demanding due to the multi-scale nature of this kind of problems. Hence, a dedicated parallelization layer (PyPARIS) has been recently developed to profit from parallel computing resources in order to significantly speed up the computation.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.