Supporting Systems from 293 K to 1.9 K for the Large Hadron Collider (LHC) Cryo-Magnets

  • M. Mathieu
  • V. Parma
  • T. Renaglia
  • P. Rohmig
  • L. R. Williams
Part of the Advances in Cryogenic Engineering book series (ACRE, volume 43)


The LHC machine will incorporate some 2000 main ring super-conducting magnets cooled at 1.9 K by super-fuid pressurized helium, mainly 15m-long dipoles with their cryostats and 6m-long quadrupoles housed in the Short Straight Section (SSS) units. This paper presents the design of the support system of the LHC arc cryo-magnets between 1.9 K at the cold mass and 293 K at the cryostat vacuum vessel. The stringent positioning precision for magnet alignment and the high thermal performance for cryogenic efficiency are the main conflicting requirements, which have lead to a trade-off design. The systems retained for LHC are based on column-type supports positioned in the vertical plane of the magnets inside the cryostats. An ad-hoc design has been achieved both for cryo-dipoles and SSS.

Each column is composed of a main tubular thin-walled structure in composite material (glass-fibre/epoxy resin, for its low thermal conductivity properties), interfaced to both magnet and cryostat via stainless steel flanges. The thermal performance of the support is improved by intercepting part of the conduction heat at two intermediate temperature levels (one at 50–75 K and the other at 4.5–20 K). These intercepts, on the composite column, are thermally connected to the helium gas cooled thermal shield and radiation screen of the cryo-magnet.

An overview of the design requirements is given, together with an appreciation of the system design. Particular attention is dedicated to the support system of the SSS where the positioning precision of the quadrupole magnet is the most critical.


Large Hadron Collider Heat Load Thermal Contraction Vacuum Vessel Thermal Shield 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • M. Mathieu
    • 1
  • V. Parma
    • 1
  • T. Renaglia
    • 1
  • P. Rohmig
    • 1
  • L. R. Williams
    • 1
  1. 1.European Organization for Nuclear ResearchCERNGeneva 23Switzerland

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