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Overview

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This page summarizes the design & implementation of the interface between the live FACET-II controls system and the FACET2E Bmad model.

GitHub mirror: F2_live_model.git

The infrastructure consists of two layers:

• BmadLiveModel: a python class responsible for watching the accelerator and updating a live instance of PyTao based on extant machine settings
• The live model PVA service: a process that runs it's own BmadLiveModel and publishes data to table PVs

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BmadLiveModel

The goal of this class is to provide access to an instance of Tao that can view/manipulate the FACET2 Bmad model with settings that match those of the actual production accelerator.

Device Monitoring

Connection The BmadLiveModel python class loads the FACET2E lattice and runs a local copy of pytao.SubprocessTao This object connects to the production controls system is handled mainly via EPICS Channel Access , and connects callback functions to each live quantity (PV) of interest. When live quantities change, these callbacks will submit update instructions to a shared queue. Additionally, a daemon thread (model-update) will periodically empty the queue and execute all the scheduled update commands and then update the BmadLiveModel.live data structure.

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Modes of use

The BmadLiveModel can be used in three distinct modes, depending on the input flags at construction:

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f2m = BmadLiveModel(design_only=True)

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f2m = BmadLiveModel(instanced=True)
f2m.refresh_all() 
# --> your code here

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f2m = BmadLiveModel()
f2m.start()
# --> your code here
f2m.stop

or:

with BmadLiveModel() as f2m:
    # --> your code here

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PVs (as well as limited use of AIDA-PVA for klystron timing statuses). During real-time updating, there are three "watcher" daemon threads that monitor extant machine settings. Each thread will submit instructions for updates to the simulation to one of two shared update queues.

acc1-watcher and acc2-watcher

These are the main passive device monitoring threads. acc1 is responsible for monitoring all quadrupole magnets in the accelerator, while acc2 id responsible for monitoring dipoles, sextupoles and other miscellaneous devices.

LEM

LEM (LINAC energy management) is a set of algorithms responsible for estimating the beam momentum profile and calculating the corresponding lattice quad settings. This code only calculates the momentum profile, errors and magnet settings. Actually setting magnets in the accelerator will be handled by a LEM server GUI (coming soon...).

• BmadLiveModel will monitors the klystron complement and bend magnet settings to calculate the live momentum profile of the beam
• Periodically, the server will use the live momentum profile to calculate energy errors and magnet settings, which are published to a table PV

Detailed overview of LEM implementation: LEM_notes-3.pdf

LEM-watcher

Daemon thread spawned by BmadLiveModel, responsible for calculating the live beam momentum profile as described below, and for submitting update requests for cavity voltages and phases to the command queue, as well as linac amplitude, chirp and fudge values

PyTao updates

The BmadLiveModel python class loads the FACET2E lattice and runs a local copy of pytao.Tao. This instance of tao is both the engine used by the class to update the live lattice, as well as a part of the public interface of the code. BmadLiveModel.tao is a publicly accessible attribute of the class itself

model-update

This thread is responsible for executing all the submitted update requests using Tao, and for updating derived quantities (BLEM) and python data structures.

1. empty the device update

Daemon process design

This class makes use of both thread-level and process-level parallelism. Controls system monitoring, processing of update requests and data structure changes are handled asynchronously by callback functions and a model-update daemon thread. The Tao instance itself is contained in a separate subprocess via pytao.SubprocessTao.  Controls monitoring and device update management are handled asynchronously to optimize performance and minimize delays between changes being broadcast over the network and being mirrored in the model. Tao itself is wrapped in a subprocess for handling Fortran errors, and calls to BmadLiveModel.tao will still block the main python executable

PV callback functions are responsible for the following actions:

1. convert the new value into Bmad units (i.e. from kGm → T, GeV → eV etc)
2. submit a tuple of (element name, attribute, value) to the queue

Separately, the model-update thread will run forever in a loop, at each iteration it will:

1. empty the command queue of (approximately*) all submitted updates
2. run all the set ele commands, then re-calculate lattice parameters
3. update the live.p0c, live.e_tot, live.twiss, live.quads, live.xcors, live.ycors, live.bends, live.rf data structures (← or update on request?)

LEM

LEM (LINAC energy management) is a set of algorithms responsible for estimating the beam momentum profile and calculating the corresponding lattice quad settings. This code only calculates the momentum profile, errors and magnet settings. Actually setting magnets in the accelerator will be handled by a LEM server GUI (coming soon...)

Calculating the live momentum profile

1. start with an estimated momentum profile based on reported klystron amplitudes & phases

   Latex
   \[ E_{est}(s) = \sum_1^{N_{klys}(s)} ENLD_i cos(PDES_{SBST} + PDES_i)

2. The estimated momentum profile is unlikely to add up to exactly the correct energy, so we need to scale things by a fudge factor to match reality. Since the actual final energy of the beam in each linac is known form the bend magnet settings, we can calculate this number by comparing the estimated final energy to the actual energyuse the live momentum profile to calculate quad settings for the linac

   Latex
   \[ f = E_{design}(end) / E_{est}(end) \quad \rightarrow \quad E_{live}(s) = f * E_{est}(s)

Calculating magnet settings

1. From the design momentum profile

   1. calculate a dimensionless "LEM error":

      Latex
      \sigma_{LEM}(s) = E_{live}(s) / E_{design}(s)

1. 1. calculate new magnet BDESes: 

      Latex
      BDES_{LEM} = \sigma_{LEM}(s) * BDES_{design}

2. from design multipole coefficients (lcls style)
   1. get design multipole coefficients (k0_des, k1_des for bends/quads), and (effective) magnet lengths l_eff from the model

   2. calculate the rigidity like so (where E_live is in GeV): 

      Latex
      \[ B\rho(s) = \frac{E_{live}(s)}{299.792458*10^4}

   3. calculate new magnet BDESes

      Latex
      BDES_{LEM} = B\rho * k_{design} * l_{eff}

F2 Live Model Server

2. empty the LEM update queue and set the live.LEM amplitude, chirp and fudge values

PVA model server(s)

The principal use case for the BmadLiveModel is running a real-time live model server. This The live model server runs its own BmadLiveModel, and periodically writes then publishes live model data for NTTables to scalar and table PVs accessible on the controls system via EPICS PVAccess. The code for the live model itself is relatively simple, and is derived from the lcls_live_model server.

For performance, there are actually two production services. Both recalculate and update PVs once per second.

• Model Server 1: This server is responsible for updating live twiss parameters and LEM data.
• Model Server 2: This service is responsible for publishing R matrices

Access to the live Bmad model server (for twiss/rmat data), should be provided through is mediated by the python meme service

The format of the publish tables are as folllows:

Implementation: F2_live_model code

The code for all the infrastructure described in this document lives together. Source code can be viewed on GitHub, and API documentation is available online.

• SLAC network master repository: /afs/slac/g/cd/swe/git/repos/slac/FACET/F2_live_model.git
• Production deployment: /usr/local/tools/python/F2_live_model
• GitHub mirror: F2_live_model.git
• The public interface to the code is documented here: f2-live-model.readthedocs.io/

Details

Model server data description

Implementation details

Additionally, the service will publish scalar PVs of the estimated linac amplitudes/chirps/fudges by calculated by LEM. Their names are as follows:

• BMAD:SYS0:1:FACET2E:LEM:<XX>_FUDGE
• BMAD:SYS0:1:FACET2E:LEM:<XX>_AMPL
• BMAD:SYS0:1:FACET2E:LEM:<XX>_CHIRP

where <XX> is a "LEM region", either L0, L1, L2 or L3, for a total of an additional 12 PVs. (ex: BMAD:SYS0:1:FACET2E:LEM:L2_FUDGE). There is an additional read/write PV: BMAD:SYS0:1:FACET2E:LEM:PROFILE which stores the last known extant momentum profile to which current rf settings are compared. 

Dependencies

• numpy, scipy
• pyEPICS
• p4p
• Bmad + Tao
• Pytao
• facet2-lattice

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• The live model needs to run on the production network (primarily on facet-srv02)

• numpy, spicy
• pyEPICS
• p4p
• Bmad + Tao
• Pytao
• facet2-lattice

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Source file descriptions