Automated Collimation Operation

1
Automated Collimation Operation

• LHC-MAC 2007 06 15
• M.Jonker
• On behalf of the collimation project
• (as the controls coordinator)

2
Collimation Controls

• Collimation Controls Steering team (Cocost)
• Stefano Redaelli (high level control
  applications)
• Maciej Sobczak (css, middle level)
• Roberto Losito (low level control)
• Alessandro Masi (low level control)
• Ralph Assmann (project leader)
• Michel Jonker (controls coordinator)
• Invited
• Philippe Gayet (plcs)
• Rudiger Schmidt (machine protection)
• Bernd Dehning (beam loss monitors)

3
Outline

• 94 (up to 160 in final upgrade) collimators, to
  protect against machine damage and magnet
  quenches.
• The collimation process is a multi-staged process
  that require precise (0.1 ?beam) setting of the
  jaws with respect to the beam envelope.Goal for
  positioning accuracy is ?20 ?m (0.1 ?beam at 7
  TeV).
• Actual beam envelope (position and size) may
  change (from fill to fill ?, by how much?)

4
Collimation Optimisation

• Adapt to changing beam parameters to guarantee
  machine protection and to keep good cleaning
  efficiency
• There are 376 degrees of freedom (4 motors per
  collimator) (188 if not considering the angle of
  the jaws)
• 30 seconds per degree of freedom (a very
  efficient operator) still requires about 3 hours.
• ? We need automated tools and procedures

5
Setup Procedures

• Beam probing
• Determine beam positions and size at every
  collimator by touching the beam.
• Required for initial setup of a machine optics
  (injection and top energies ), or after
  substantial changes in beam parameters.
• Setup at with a low intensity beam 5 nominal LHC
  bunches (equivalent to the Tevatron Beam in
  stored energy).Extrapolation from 5 to 3000
  bunches(bunch train effects?)
• Fast beam based setup
• Position collimators based on loss patterns, not
  on measured beam positions and sizes
• Further systematic optimisation with nominal
  intensity beam
• Response matrix corrections
• Correct collimator positions guided by loss
  patterns.

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Response matrix corrections

• Fine tune and optimise the cleaning efficiency
  (at injection or top energy).
• Collimator response matrices to translate a given
  beam loss pattern into an adjustment of multiple
  collimator positions PC Mblc ? BL
• Theoretical matrices have been calculated based
  on Fluka and Struct programs.
• Ambitious procedure, commissioning of this
  process will require many machine studies.
• Not for the beginning
• Not discussed here

BLM loss pattern
Collimator Settings
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Beam Probing

• Establishes the beam positions, angle and size by
  probing the actual beam.A traditional method
• Starts with producing a well-defined cut-off in
  the beam distribution.
• Each collimator jaw is moved until the beam edge
  is touched. This step defines an absolute
  reference position for each jaw. (and angle if
  two motors are moved independently)

Beam Loss Monitor
Beam Loss Monitor

• Note Best done from the last element in the
  cleaning insertion to the first
• Collimators may stay in place
• Machine is better protected against quenches
• Disadvantages
• Only possible with low intensity beam (i.e. 5
  bunches).
• Slow if done manually (188 positions )
• Delicate (e.g. moving a collimator too far
  changes the cut-off in the beam distribution).

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Beam Probing in SPS MD

• Beam probing was tested in the collimation MD at
  the SPS in 2006 with the collimation control
  system.
• The jaws were driven in by the control
  application either manually or in repetitive
  steps.
• The control application simultaneously displays
  jaw position and Beam Loss Data

In the MD, to speed up we used successively
smaller steps, and while doing so we scraped the
beam away bit by bit.
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Fast beam based setup

• Complements the traditional set-up method.
• Adjust positions to reproduce known beam loss
  pattern.
• Based on experience of other acceleratorsCollima
  tion efficiency is more closely related to beam
  loss patterns than to absolute collimator
  positions, which are sensitive to orbit
  deviations, beta beat, etc.
• Move jaws in hierarchical order into the beam
  halo up to the point where a specified beam loss
  level is recorded in the adjacent beam loss
  monitors.

Beam Loss Monitor
Beam Loss Monitor

• Fast if implemented as an automated procedure
• Start at a fixed offset relative to a previously
  known position (only have to move short
  distances, no need to be retracted.
• Two beam can be tuned in parallel in the two
  cleaning insertions IR3 and IR7

10
Fast beam based setup

• Procedure in practice
• The collimators are set at 1.5 s retracted with
  respect to the last optimised value.
• The jaws are optimised one by one in a precise
  order.
• Optimization by moving in steps of 0.05 s until
  the associated set of Beam Loss Monitors (BLM)
  detects a predefined value of beam loss.The BLM
  reference levels are found empirically and may be
  updated from fill to fill.
• Timing implications
• Starting position 1.5 s, step size of 0.05 s
  (?50 µm _at_ 450 GeV)
• ? 30 steps/motor ? 9600 steps in total (only
  position, no angles, final upgrade).
• Available time 5 min. two rings in parallel ? 60
  ms per step (16 Hz)
• _at_ 2mm/s 50 µm ? 25 ms per step needed for motor
  movement
• gt 35 ms for driving, data collection, reading
  BLM, deciding

11
Controls Architecture

• Control room software
• Management of (critical) settings (LSA)
• Preparation for ramp
• Assistance in collimator tuningPost Mortem data
  collection and Analysis
• Based on standard LSA components
• Dedicated graphical interface for collimator
  control and tuning
• Collimator Supervisor System (CSS)
• Support building, VME / FESA
• Fesa Gateway to Control Room Software
• Synchronization of movements
• Beam Based Alignment primitives
• Takes action on position errors (FB)
• Receives timing, send sync signals over fiber to
  low level (Ramp Beam Based Alignment)
• Synchronization and communication with BLM
• Low level control systems
• 3 distinct systems
• Motor drive (PXI)
• Position readout and survey (PXI)
• Environment Survey (PLC)

BLM system
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Fast Optimisation Primitives

• Collimator Supervisory System (CSS)
• Send a trigger to adjacent BLM system on every
  motor movement
• BLM system sends a short transient data to the
  CSS
• Optimization primitive command (on CSS)
• Move until BLM-level
• Parameters
• Motors and step size
• BLM signals and limits
• Repetition frequency
• Maximum steps
• Example
• Move Jaw-left in steps of 10 um every 30 ms until
  BL signal reaches 103
• This optimization primitive can be used by a
  central application for
• Beam Probing
• Fast beam based optimization

Beam Loss Monitor
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Fast Optimisation Primitives
During optimization, positions are continuously
measured, If the position gets out of tolerance,
the procedure will be interrupted.
15 µm
25 µm mechanical play
R. Losito et al
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BLM Transient in SPS MD

• Adjacent BLM triggered by collimator movements.
• Collected data
• Transient Data Buffer (2.5 ms sampling 80 ms for
  BLM based FB).
• Post mortem data (40 us sampling over 1.7
  seconds)
• (For analysis)
• Data needs carefull interpretation.
• LHC acquisition chain tested, including link with
  collimators (trigger and data transmission).
• Plots from Daniel Kramer

15
Conclusion

• Fast collimator optimisation is technically
  possible.
• The controls architecture contains the necessary
  elements to deal with these requirement
  (synchronisation lines, BLM data acquisition and
  connection)
• The principles have been tested during an SPS MD
  in 2006
• Fully automated steering application and
  procedures to be developed and tested (2008)
• However, the real challenge
• beam dynamics
• SPS md BLM responds to collimator movement over
  time scales of 100th of ms

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Conclusion
During the SPS MD, not able to make clean cut in
the beam distribution Re-populatution of tails
over 100th of ms.
Long tails after collimator movement, Large noise
components
50, 150, 300, 450 600 Hz noise
SPS MD in 2007 to investigate the origins of
these problems If these effect are also present
in the LHC, optimisation will me more challenging.
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