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Electron Cloud Simulations with PyECLOUD

PyECLOUD is a newly developed code for the simulation of the electron cloud (EC) build-up in particle accelerators. Almost entirely written in Python, it is mostly based on the physical models already used in the ECLOUD code but, thanks to the implementation of new optimized algorithms, it exhibits...

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Detalles Bibliográficos
Autores principales: Iadarola, G, Rumolo, G
Formato: info:eu-repo/semantics/article
Lenguaje:eng
Publicado: 2012
Materias:
Acceso en línea:http://cds.cern.ch/record/1498082
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author Iadarola, G
Rumolo, G
author_facet Iadarola, G
Rumolo, G
author_sort Iadarola, G
collection CERN
description PyECLOUD is a newly developed code for the simulation of the electron cloud (EC) build-up in particle accelerators. Almost entirely written in Python, it is mostly based on the physical models already used in the ECLOUD code but, thanks to the implementation of new optimized algorithms, it exhibits a significantly improved performance in accuracy, speed, reliability and flexibility. PyECLOUD simulations have been already broadly employed for benchmarking the EC observations in the Large Hadron Collider (LHC). Thanks to the new feature of running EC simulations with bunch-by-bunch length and intensity data from machine measurements, the scrubbing process of the LHC beam pipes could be reconstructed from heat load measurements in the cryogenic dipoles. In addition, PyECLOUD simulations also provide the estimation of the bunch-by-bunch energy loss, which can be compared with the measurements of the stable phase shift.
format info:eu-repo/semantics/article
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spelling cern-14980822022-08-17T13:32:31Z http://cds.cern.ch/record/1498082 eng Iadarola, G Rumolo, G Electron Cloud Simulations with PyECLOUD Accelerators and Storage Rings 4: AccNet: Accelerator Science Networks PyECLOUD is a newly developed code for the simulation of the electron cloud (EC) build-up in particle accelerators. Almost entirely written in Python, it is mostly based on the physical models already used in the ECLOUD code but, thanks to the implementation of new optimized algorithms, it exhibits a significantly improved performance in accuracy, speed, reliability and flexibility. PyECLOUD simulations have been already broadly employed for benchmarking the EC observations in the Large Hadron Collider (LHC). Thanks to the new feature of running EC simulations with bunch-by-bunch length and intensity data from machine measurements, the scrubbing process of the LHC beam pipes could be reconstructed from heat load measurements in the cryogenic dipoles. In addition, PyECLOUD simulations also provide the estimation of the bunch-by-bunch energy loss, which can be compared with the measurements of the stable phase shift. info:eu-repo/grantAgreement/EC/FP7/227579 info:eu-repo/semantics/openAccess Education Level info:eu-repo/semantics/article http://cds.cern.ch/record/1498082 2012
spellingShingle Accelerators and Storage Rings
4: AccNet: Accelerator Science Networks
Iadarola, G
Rumolo, G
Electron Cloud Simulations with PyECLOUD
title Electron Cloud Simulations with PyECLOUD
title_full Electron Cloud Simulations with PyECLOUD
title_fullStr Electron Cloud Simulations with PyECLOUD
title_full_unstemmed Electron Cloud Simulations with PyECLOUD
title_short Electron Cloud Simulations with PyECLOUD
title_sort electron cloud simulations with pyecloud
topic Accelerators and Storage Rings
4: AccNet: Accelerator Science Networks
url http://cds.cern.ch/record/1498082
http://cds.cern.ch/record/1498082
work_keys_str_mv AT iadarolag electroncloudsimulationswithpyecloud
AT rumolog electroncloudsimulationswithpyecloud