Keyword: cryogenics
Paper Title Other Keywords Page
TUBR04 Control System of Cryomodule Test Facilities for SHINE* controls, cryomodule, power-supply, monitoring 353
  • H.Y. Wang, G.H. Chen, J.F. Chen, J.G. Ding, M. Li, Y.J. Liu, Q.R. Mi, H.F. Miao, C.L. Yu
    SSRF, Shanghai, People’s Republic of China
  Funding: Work supported by Shanghai Municipal Science and Technology Major Project (Grant No. 2017SHZDZX02)
Shanghai HIgh repetition rate XFEL aNd Extreme light facility (SHINE) is under construction. The 8 GeV superconducting Linac consists of seventy-five 1.3 GHz and two 3.9 GHz cryomodules. A cryomodule assembling and test workshop is established. Multiple platforms have been built for cryomodule and superconducting cavity test, including two vertical test platforms, two horizontal test platform, one multiple test platform and one liquid helium visualization platform. The local control systems are all based on Yokogawa PLC, which monitor and control the process variables such as temperature, pressure, liquid level and power of the heater. PID and other algorithms are used to keep liquid level and power balance. EPICS is adopt to integrate these platforms’along with vacuum devices, solid state amplifiers, LLRF and RF measurement system, etc. The details of the control system design, development and commissioning will be reported in this paper.
slides icon Slides TUBR04 [22.084 MB]  
DOI • reference for this paper ※  
About • Received ※ 22 October 2021       Accepted ※ 11 February 2022       Issue date ※ 24 February 2022  
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
TUPV006 Control System of the SPIRAL2 Superconducting Linac Cryogenic System controls, cryomodule, PLC, cavity 382
  • A.H. Trudel, G. Duteil, A. Ghribi, Q. Tura
    GANIL, Caen, France
  • P. Bonnay
    CEA/INAC, Grenoble Cedex 9, France
  The SPIRAL2 cryogenic system has been designed to cool down and maintain stable operation conditions of the 26 LINAC superconducting resonating cavities at a temperature of 4.5 K or lower. The control system of the cryogenic system of the LINAC is based on an architecture of 20 PLCs. Through an independent network, it drives the instrumentation, the cryogenic equipment, the 26 brushless motors of the frequency tuning system, interfaces the Epics Control System, and communicates process information to the Low Level Radio Frequency, vacuum, and magnet systems. Its functions are to ensure the safety of the cryogenic system, to efficiently control the cooldown of the 19 cryomodules, to enslave the frequency tuning system for the RF operation, and to monitor and analyze the data from the process. A model based Linear Quadratic regulation controls simultaneously both phase separators the liquid helium level and pressure. This control system also makes it possible to perform a number of virtual verification tests via a simulator and a dedicated PLC used to develop advanced model based control, such as a real time heat load estimator based on a Luenberger Filter  
poster icon Poster TUPV006 [2.393 MB]  
DOI • reference for this paper ※  
About • Received ※ 08 October 2021       Accepted ※ 23 February 2022       Issue date ※ 14 March 2022  
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
WEPV001 Temperature Control for Beamline Precision Systems of Sirius/LNLS controls, operation, hardware, experiment 607
  • J.L. Brito Neto, R.R. Geraldes, F.R. Lena, M.A.L. Moraes, A.C. Piccino Neto, M. Saveri Silva, L.M. Volpe
    LNLS, Campinas, Brazil
  Funding: Ministry of Science, Technology and Innovation (MCTI)
Precision beamline systems, such as monochromators and mirrors, as well as sample stages and sample holders, may require fine thermal management to meet performance targets. Regarding the optical elements, the main aspects of interest include substrate integrity, in case of high power loads and densities; wavefront preservation, due to thermal distortions of the optical surfaces; and beam stability, related to thermal drift. Concerning the sample, nanometer positioning control, for example, may be affected by thermal drifts and the power management of some electrical elements. This work presents the temperature control architecture developed in house for precision elements at the first beamlines of Sirius, the 4th-generation light source at the Brazilian Synchrotron Light Laboratory (LNLS). Taking some optical components as case studies, the predictive thermal-model-based approach, the system identification techniques, the controller design workflow and the implementation in hardware are described, as well as the temperature stability results.
poster icon Poster WEPV001 [0.914 MB]  
DOI • reference for this paper ※  
About • Received ※ 15 October 2021       Accepted ※ 22 December 2021       Issue date ※ 21 February 2022  
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
THPV041 Innovative Methodology Dedicated to the CERN LHC Cryogenic Valves Based on Modern Algorithm for Fault Detection and Predictive Diagnostics controls, operation, diagnostics, experiment 959
  • M. Pezzetti, A. Amodio, Y. Donon, L. Iodice
    CERN, Geneva, Switzerland
  • P. Arpaia
    Naples University Federico II, Science and Technology Pole, Napoli, Italy
  • F. Gargiulo
    University of Naples Federico II, Naples, Italy
  The European Organization for Nuclear Research (CERN) cryogenic infrastructure is composed of many equipment, among them there are the cryogenic valves widely used in the Large Hadron Collider (LHC) cryogenic facility. At present time, diagnostic solutions that can be integrated into the process control systems, capable to identify leak failures in valves bellows, are not available. The authors goal has been the development of a system that allows the detection of helium leaking valves during normal operation using available data extracted from the control system. The design constraints has driven the development towards a solution integrated in the monitoring systems in use, not requiring manual interventions. The methodology presented in this article is based on the extraction of distinctive features (analyzing the data in time and frequency domain) which are exploited in the next phase of machine learning. The aim is to identify a list of candidate valves with a high probability of helium leakage. The proposed methodology, which is at very early stage now, with the evolution of the data set and the iterative approach is aiming toward a cryogenic valves targeted maintenance.  
poster icon Poster THPV041 [1.120 MB]  
DOI • reference for this paper ※  
About • Received ※ 06 October 2021       Revised ※ 26 October 2021       Accepted ※ 22 December 2021       Issue date ※ 02 March 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
FRAL03 CERN Cryogenic Controls Today and Tomorrow controls, PLC, radiation, SCADA 997
  • M. Pezzetti, Ph. Gayet
    CERN, Geneva, Switzerland
  The CERN cryogenic facilities demand a versatile, distributed, homogeneous and highly reliable control system. For this purpose, CERN conceived and developed several frameworks (JCOP, UNICOS, FESA, CMW), based on current industrial technologies and COTS equipment, such as PC, PLC and SCADA systems complying with the requested constraints. The cryogenic control system nowadays uses these frameworks and allows the joint development of supervision and control layers by defining a common structure for specifications and code documentation. Such a system is capable of sharing control variable from all accelerator apparatus. The first implementation of this control architecture started in 2000 for the Large Hadron Collider (LHC). Since then CERN continued developing the hardware and software components of the cryogenic control system, based on the exploitation of the experience gained. These developments are always aimed to increase the safety and to improve the performance. The final part will present the evolution of the cryogenic control toward an integrated control system SOA based CERN using the Reference Architectural Model Industrie 4.0 (RAMI 4.0).  
slides icon Slides FRAL03 [6.597 MB]  
DOI • reference for this paper ※  
About • Received ※ 10 October 2021       Revised ※ 25 October 2021       Accepted ※ 26 November 2021       Issue date ※ 01 March 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)