IPAC2019 - List of Authors (Gatera, A.)

Paper	Title	Page
MOPTS003	Superconducting LINAC Design Upgrade in View of the 100 MeV MYRRHA Phase I	837
	F. Bouly, M.A. Baylac LPSC, Grenoble Cedex, France A. Gatera SCK•CEN, Mol, Belgium D. Uriot CEA-DRF-IRFU, France
	Funding: Part of this work supported by the European Atomic Energy Community (EURATOM) H₂020 Programme under grant agreement n°662186 (MYRTE project). The goal of the MYRRHA project is to demonstrate the technical feasibility of transmutation in a 100 MW Accelerator Driven System (ADS) by building a new flexible irradiation complex at Mol (Belgium). The MYRRHA facility requires a 600 MeV accelerator delivering a maximum proton current of 4 mA in continuous wave operation, with an additional requirement for exceptional reliability. Supported by SCK•CEN and the Belgium government the project has entered in its phase I: which consists in the development and the construction of the linac first part, up to 100 MeV. We review the design updates of the superconducting linac, with its enhanced fault-tolerance capabilities. The linac capabilities at 100 MeV (Phase I) and 600 MeV (ADS operation) are exposed and discussed.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2019-MOPTS003
About •	paper received ※ 23 April 2019 paper accepted ※ 20 May 2019 issue date ※ 21 June 2019
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPTS006	Modelization of an Injector With Machine Learning	3096
	M. Debongnie, M.A. Baylac, F. Bouly LPSC, Grenoble Cedex, France N. Chauvin, D. Uriot CEA-IRFU, Gif-sur-Yvette, France A. Gatera SCK•CEN, Mol, Belgium T. Junquera Accelerators and Cryogenic Systems, Orsay, France
	Modern particle accelerator projects, such as MYRRHA, have very high stability and/or reliability requirements. To meet those, it is necessary to optimize or develop new methods for the control systems. One of the difficulties lies in the relatively long computation time of current beam dynamics codes. In this context, the very low computation time of neural network is of great attraction. However, a neural network has to be trained in order to be of any use. The training of a beam dynamic predictor uses a large dataset (experimental or simulated) that represents the dynamics over the parameter space of interest. Therefore, choosing the right training dataset is crucial for the quality of the neural network predictions. In this work, a study on the sampling choice for the training data is performed to train a neural network to predict the transmission of a beam through a low energy beam transport line and a Radiofrequency Quadrupole. We show and discuss the results obtained on training data set to model the IPHI and MYRRHA injectors. https://myrrha.be/
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2019-WEPTS006
About •	paper received ※ 15 May 2019 paper accepted ※ 23 May 2019 issue date ※ 21 June 2019
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Superconducting LINAC Design Upgrade in View of the 100 MeV MYRRHA Phase I

837

F. Bouly, M.A. Baylac
LPSC, Grenoble Cedex, France
A. Gatera
SCK•CEN, Mol, Belgium
D. Uriot
CEA-DRF-IRFU, France

Funding: Part of this work supported by the European Atomic Energy Community (EURATOM) H₂020 Programme under grant agreement n°662186 (MYRTE project).
The goal of the MYRRHA project is to demonstrate the technical feasibility of transmutation in a 100 MW Accelerator Driven System (ADS) by building a new flexible irradiation complex at Mol (Belgium). The MYRRHA facility requires a 600 MeV accelerator delivering a maximum proton current of 4 mA in continuous wave operation, with an additional requirement for exceptional reliability. Supported by SCK•CEN and the Belgium government the project has entered in its phase I: which consists in the development and the construction of the linac first part, up to 100 MeV. We review the design updates of the superconducting linac, with its enhanced fault-tolerance capabilities. The linac capabilities at 100 MeV (Phase I) and 600 MeV (ADS operation) are exposed and discussed.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2019-MOPTS003

About •

paper received ※ 23 April 2019 paper accepted ※ 20 May 2019 issue date ※ 21 June 2019

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPTS006

Modelization of an Injector With Machine Learning

3096

M. Debongnie, M.A. Baylac, F. Bouly
LPSC, Grenoble Cedex, France
N. Chauvin, D. Uriot
CEA-IRFU, Gif-sur-Yvette, France
A. Gatera
SCK•CEN, Mol, Belgium
T. Junquera
Accelerators and Cryogenic Systems, Orsay, France

Modern particle accelerator projects, such as MYRRHA, have very high stability and/or reliability requirements. To meet those, it is necessary to optimize or develop new methods for the control systems. One of the difficulties lies in the relatively long computation time of current beam dynamics codes. In this context, the very low computation time of neural network is of great attraction. However, a neural network has to be trained in order to be of any use. The training of a beam dynamic predictor uses a large dataset (experimental or simulated) that represents the dynamics over the parameter space of interest. Therefore, choosing the right training dataset is crucial for the quality of the neural network predictions. In this work, a study on the sampling choice for the training data is performed to train a neural network to predict the transmission of a beam through a low energy beam transport line and a Radiofrequency Quadrupole. We show and discuss the results obtained on training data set to model the IPHI and MYRRHA injectors.
https://myrrha.be/

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2019-WEPTS006

About •

paper received ※ 15 May 2019 paper accepted ※ 23 May 2019 issue date ※ 21 June 2019

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)