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MOCOXBS04 | The Berlin Energy Recovery Linac Project BERLinPro - Status, Plans and Future Opportunities | 8 |
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Funding: Work supported by the German Bundesministerium für Bildung und Forschung, Land Berlin and grants of Helmholtz Association The Helmholtz-Zentrum Berlin is constructing the Energy Recovery Linac Prototype BERLinPro, a SRF based demonstration facility for the science and technology of ERLs for future high power, high brilliance electron beam applications. BERLinPro was designed to accelerate a high current (100 mA, 50 MeV), high brilliance (norm. emittance below 1 mm mrad) cw electron beam. Given the recent prioritization of the BESSY II upgrade to the BESSY VSR variable pulse length storage ring, HZB is forced to reduce the project goals of BERLinPro. As a result, the project had to be rescoped with the goal to maximize its scientific impact within the present boundary conditions. We report on the last year’s progress of the building, the warm and cold infrastructure and on the time line, goals nd opportunities for the remaining project run time. |
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Slides MOCOXBS04 [13.980 MB] | |
DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-ERL2019-MOCOXBS04 | |
About • | paper received ※ 16 September 2019 paper accepted ※ 06 November 2019 issue date ※ 24 June 2020 | |
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WECOYBS04 | Commissioning of theBERLinPro Diagnostics Line using Machine Learning Techniques | 123 |
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Funding: Work supported by German Bundesministerium für Bildung und Forschung, Land Berlin and grants of Helmholtz Association BERLinPro is an Energy Recovery Linac (ERL) project currently being set up at HZB, Berlin. Commissioning is planned for early 2020. HZB triggered and supported the development of release 2.0 of the particle tracking code OPAL, that is now also applicable to ERLs. OPAL is set up as an open source, highly parallel tracking code for large accelerator systems and many particles. Thus, it is idially suited to serve attempts of applying machine learning approaches to beam dynamics, as demonstrated in [1]. OPAL is used to calculate hundreds of randomized machines close to the commissioning optics of BERLinPro. This data base will be used to train a neural network, to establish a surrogate model of BERLinPro, much faster than any physical model including particle tracking. First steps, like the setup of the sampler and a sensitivity analysis of the resulting data are presented. The ultimate goal of this work is to use machine learning techniques during the commissioning of BERLinPro. Future steps are outlined. [1] A. Edelen, A. Adelmann, N. Neveu, Y. Huber, M. Frey, ’Machine Learning to enable orders of magnitude speedup in multi-objective optimization of particle accelerator systems’ |
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DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-ERL2019-WECOYBS04 | |
About • | paper received ※ 30 October 2019 paper accepted ※ 07 November 2019 issue date ※ 24 June 2020 | |
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FRCOXBS05 | Adjusting BERLinPro Optics to Commissioning Needs | 165 |
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Funding: Work supported by German Bundesministerium für Bildung und Forschung, Land Berlin and grants of Helmholtz Association BERLinPro is an Energy Recovery Linac (ERL) project currently being set up at HZB, Berlin. During the turn of the project, many adaptations of the optics to changing hardware realities were necessary. To name only one, commissioning of the recirculator will now be realized with the superconducting linac module fabricated for the Mainz ERL project MESA, as the BERLinPro linac is delayed. The Mainz linac will supply roughly 60% of the energy planned for the BERLinPro linac module and will be limited by higher order modes in the cavities to few mA of current. While the adapted optics shows similar parameters as the original 50MeV optics, studies of longitudinal space charge and coherent synchrotron radiation show that the energy leads to large emittance blow up due toμbunching. Furthermore, preparations for commissioning with gun fields much lower than the original 30MV/m in the 1.4 cell SRF gun are introduced and according optics are presented. |
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Slides FRCOXBS05 [9.766 MB] | |
DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-ERL2019-FRCOXBS05 | |
About • | paper received ※ 19 September 2019 paper accepted ※ 04 November 2019 issue date ※ 24 June 2020 | |
Export • | reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml) | |