control and data acquisition for fusion experiments€¦ · b. carvalho | eiroforum school on...

Instituto de Plasmas e Fusão Nuclear Instituto Superior Técnico Lisbon, Portugal http://www.ipfn.ist.utl.pt

B. Carvalho | EIROforum School on Instrumentation, ESI 2011 | Grenoble

Control and Data Acquisition for Fusion experiments

Bernardo Brotas Carvalho [email protected]

Film by Jean-Luc Godard, (1967) “2 ou 3 choses que je sais d'elle”

“The stakes are considerable, not to say vital for our planet.“ José Manuel Barroso, President of the European Commission

Fusion – a Global Challenge

Fusion powers the sun and the stars

•  Essentially limitless fuel, available all over the world

•  No greenhouse gases

•  Intrinsic safety

•  No long-lived radioactive waste

•  Large-scale energy production

On Earth,

fusion could provide:

The Fusion ReacBon on Earth “... is not the same as in the Sun“

+ 3.5 MeV

+ 14.1 MeV

41H + 2e --> 4He + 2 υ+ 6 γ + 26.7 MeV (solar process)

Why D-‐T ?: Cross secBon!

Fusion Fuel

Raw fuel of a fusion reactor is water and lithium*

Lithium in one laptop battery + half a bath-full of ordinary water (-> one egg cup full of heavy water) 200,000 kW-hours = (current UK electricity average consumption) for 30 years * Deuterium/hydrogen = 1/6700

+ tritium from: neutron (from fusion) + lithium → tritium + helium

CH4 + 2O2 --> CO2 + 2H2O + 5.5 eV (Chemical) 2D + 3T --> He + n + 17.6 MeV (Fusion)

Basic Principle of Stable MoBon of Ions in MagneBcally confined Plasma

Reactor conditions

ITER

Progress in fusion performance

Author’s name | Place, Month xx, 2007 | Event 10 B. Carvalho | EIROforum School on Instrumentation, ESI 2011 | Grenoble

Control for Fusion Performance

Fusion Control System is a tool to achieve and maintain plasma condi6ons with best performance for •  plasma physics invesBgaBons •  energy confinement and stability

•  and -‐ at the end -‐ fusion power yield


Benchmark for Fusion Performance

•  The aim is to generate power: Pfusion/Pheat↑ –  Pfusion~(nT)2 : power expelled (lost) with fusion neutrons –  Pheat : power needed to sustain plasma

•  from external heaBng •  from α heaBng (dominaBng in a reactor)

•  For present-‐day experiments alpha α heaBng can be neglected: Pheat=Wplasma/τE and Wplasma~nT –  Wplasma: thermal energy –  τE :energy confinement Bme (thermal insulaBon)

•  So: Pfusion/Pheat ~n⋅ T ⋅ τE (fusion product)


Strategies to Improve the Fusion Product

•  Simply increasing each individual factor does not work: Complex limits restrict operational space.

•  Limits depend on spatial distribution of the quantities (profiles).

•  Each actuator affects multiple factors. •  We need to find transition paths to

plasmas with suitable combinations of n, T and τE.

Optimise the fusion product n⋅T⋅τE by •  n↑ : increasing density •  n⋅T ↑ : increasing pressure •  τE ↑ : increasing confinement •  Ip↑ : increasing current

Tip: PLAY with the virtual tokamak at http://w3.pppl.gov/~dstotler/SSFD


Applications of Performance Control in Fusion

Presently, Performance Control is not a monolithic application but a composition of various tools.

Simple •  Electron/Neutral

Density Control •  Radiation Control •  H/D (Isotopes)

Control •  beta control

Advanced •  Gap/Shape Control •  VS Control •  Profile Control

(current, density, temperature)

•  MHD Control

Protection •  Disruption Prediction,

Avoidance and Mitigation

•  Hot-Spot Detection •  Radiation Peaking


Rationale for Fusion Performance Control

Performance Control is a tool •  to guide plasma state to a desired domain (scenario,

regime) on prescribed paths •  to simplify the plant operation scheme

  replacing actuator inputs by higher level control variables

  linearizing and decoupling the system behaviour •  to increase the safety margin to critical limits •  to counteract external disturbances •  compensate for incomplete system knowledge

For This

Feedback from measured quantities is Essential.


r ControllerC(s)

e u PlantP(s)

y

FeedbackF(s)

-

reference error command Outputr Closed-Loop

Hcl(s)y

=

Feedback Control System Basics: LTI Systems

r ControllerC(s)

u PlantP(s)

y

reference error command Output

)()()( sRsCsU =

)()()()( sYsFsRsE −= )()()()()()(1

)()()( sRsHsRsCsPsF

sCsPsY cl=+

=

)()()(1)()()(

sCsPsFsCsPsHcl +

=

Closed-‐loop transfer funcBon of the system

)()()( sCsPsHo =

Open-‐Loop transfer funcBon of the system

)()()()()()()()( sRsHsRsCsPsUsPsY o===

Transfer Functions are represented in frequency (Laplace) domain rather than in time domain.

{ } dttfetfLsF st )()()(0∫∞ −==

iws +=σ


Control Loop with Disturbance

C l o s e d -‐ l o o p transfer funcBon

r ControllerC(s)

e u PlantP(s)

y

FeedbackF(s)

-

reference errordisturbance

Outputd

))()()()(()()()()()()()()( sYsFsRsCsPsDsPsUsPsDsPsY −+=+=

)()()()()()()()()()( sRsCsPsDsPsYsFsCsPsY +=+

)()()()(1

)()()()()()(1

)()( sRsFsCsP

sCsPsDsFsCsP

sPsY+

++

=

)()()()()()( sRsHsD

sCsHsY cl

cl +=


Feed-Forward Control

r ControllerC(s)

e u PlantP(s)

y

FeedbackF(s)

-

reference errordisturbance

Outputdff

feedfoward

•  same entry point in the loop as standard disturbance input •  difference: synchronized with the reference •  predicBon of required actuator command values

GOAL: • test control scenarios without stability concerns • provide adequate iniBal values when switching on a controller • shortcut and speed-‐up control reacBon


Transfer Function: Poles and Zeros

)sin()()(2

)(

)()(

222 ϕ+=⇒+++

+=

=⇒+

=

−

−

wtCetywaass

BAssY

Aetyas

AsY

at

at

)(...)()(...)(

......)(

1

1

10

10

n

mmn

n

mm

pspsqsqsb

sbasaasbsbbsH

+⋅⋅+

+⋅⋅+=

+++

+++=

Zeros q1,..., qm : M complex roots of the transfer funcBon numerator

Poles p1,..., pn : N complex roots of the transfer funcBon denominator

n>= m CAUSALITY CONSTRAIN

Single real pole (|a|>0; p = -‐a)

Pair of complex poles (|a|>0; p = -‐a ± i w)

Examples:

Re

Im

p1 qi pp

Pp*

pa


Pole Positioning

Can be roughly denoted as follows:

Re

Im

RHP Not allowed

LHP

Too oscillatory

Good/ok Good/fast

OK

Too slow

Debatable


Control Stability: Effects of Closing the Loop

)()()()()()(1

)()()( sRsHsRsCsPsF

sCsPsY cl=+

=

)()()(1)()()(

sCsPsFsCsPsHc +

=

•  Feedback preserves the zeros •  moves the poles (alters the denominator) •  can stabilize but also destabilize!

r ControllerC(s)

e u PlantP(s)

y

FeedbackF(s)

-


)()()( sCsPsHo =

A controller changes the dynamic behavior of the closed loop system – But how ? There is no simple analytical formula to translate controller parameters to closed loop poles and zeros • Ideal method: pole-placement

 requires full feedback of all state variables, or reconstruction by observers  potentially complex  can be compromised by parasitic delays

• Pragmatic method: frequency response shaping

 infer characteristic properties from open loop to closed loop  live with approximations and incomplete models  more robust, less performing


Impact of sensor dynamics in the feedback loop

r ControllerC(s)

e u PlantP(s)

y

FeedbackF(s)

-


Gain inversion: )(1)( ∞→=∞→ trK

tyf

Sensors are in the feedback branch of the loop

)()()()()()()(

)()(

)()(1

)()()()()(1

)()()(sQsCsPsP

sPsCsP

sPsQ

sCsP

sCsPsFsCsP

sCsPsHff

f

f

fc +

=+

=+

=

)()(

)(sPsQ

sFf

f=

Poles of F(s) become Zeros of Hc(s)


Impact of delays in the control loop

Originators of delays: • Digital control systems • Digital data processors (real-‐Bme diagnosBcs) • Event counBng sensors • Switching power supplies (e.g. thyristor converters)

Transfer function for a delay in time dsTd esFT −=⇒ )(

• Constant gain, no damping at all frequencies

1)( =ωiF

• But conBnuously increasing phase delay : limits the achievable bandwidth of the closed loop • Transcendent funcBon (not representable by poles and zeros)

ωω ⋅−=∠ dTiF )(

• TIP: keep measurement delays short (e.g. filtering, computer network communicaBon latencies)


Example: Plasma Density Control

3) IdenBfy Parameters and Simplify

Plant

F Density build-up

no Transportne(core)

Pumping

-

Commandgas flux

disturbance (wall influx)Outputd

01.01 =K

sssP

025.011

1.0101.0)(

+⋅

+=

1) Describe behaviour

Plant

F no ne

-

Commandgas flux

disturbanceOutput

d

sKdens

1

pumpK

transp

transp

sTK+1

2) Formulate Model

Plant P(s)

F no ne

Commandgas flux Output

transp

transp

sTK+1

1

1

1 sTK

+

sec1.01 =T1=transpK sec025.01 =T

)40()10(4

)025.01()1.01(01.0)(

+⋅+=

+⋅+=

sssssP

DC gain (s=0): K= 0.01


Controlling the example: Proportional Control

nref

-

Plant P(s)

e no ne


transp

transp

sTK+1

1

1

1 sTK

+

1=K

F100=PK

Unity Feedback

P-controller

Add some: Feedback (Unity) Controller (ProporBonal)

Simulate! Beeer sBll: if you have a Tokamak nearby: TRY-‐IT!!

Steady State Error (SSE): Lets increase KP to 500?

P

P

P

P

cl KssK

ssK

ssK

sH⋅++⋅+

⋅=

+⋅+⋅+

+⋅+⋅

=4)40()10(

4

)40()10(41

)40()10(4

)(

SSE error


Increasing K by Trial & Error

Now we have Overshoot

P

Pcl Kss

KsH⋅++⋅+

⋅=

4)40()10(4)(

K = 500


Improving the Density control: Integration Controller

nref

-

Plant P(s)

e no ne


transp

transp

sTK+1

1

1

1 sTK

+

1=K

F

100=IK

Unity Feedback

I-controllersKI

I

I

I

I

cl KsssK

sssK

sssK

sH⋅++⋅+⋅

⋅=

+⋅+⋅+

+⋅+⋅

=4)40()10(

4

)40()10(41

)40()10(4

)(

AeenBon: the controlled loop could get unstable !


Real-Time Diagnostics

Robust Density control

ne* Controller

C(s)e u Plant

P(s)ne

DCN interferometer

-

reference error Outputff

F

feedfoward

Bremstrahlung

Coton MoutonEffect

Reconstructor:Compute

best estimate

Command

Requirement • DCN signals can be compromised by fringe jumps. • Density measurement from a single central DCN line-of-sight (LOS) is unsecure. • Density from Bremsstrahlung has drifts. • A valid density value is required for control and monitoring (NBI interlocks)

RealisaBon: Compute a validated density from several diagnosBc sources o detect sensor failures o replace with other inputs


ITER CODAC is the primary tool for operation


ITER CODAC is a challenging endeavour

ITER will require a far higher level of availability and reliability than previous/

existing Tokamaks .

•  ITER will generate a huge quantity of

experimental data

–  150 plant systems –  1 000 000 diagnostic channels –  300 000 slow control channels –  5 000 fast control channels –  40 CODAC systems –  5 Gb/s data –  3Pb/year data (e.g. 12 IR cameras in a 10 minutes discharge: 1.728

Tbytes) In addition...


International ITER Agreement

140 slices

Procurement “IN KIND”

Need for Standards in HW & SW Architecture

IO Team in charge of the integration on site and the operation

Author’s name | Place, Month xx, 2007 | Event 31 B. Carvalho| EIROforum School on Instrumentation, ESI 2011 | Grenoble

ITER Subsystem is a set of related plant system I&C

ITER Instrumentation & Control System physical architecture


ITER subsystem # of PS I&C # of PSH+controllers # of servers+terminals

Tokamak 6 55 6

Cryo and cooling water 5 40 3

Magnets and coil power supply 8 30 3

Building and power 37 66 3

Fuelling and vacuum 6 45 3

Heating 8 55 4

Remote handling 2 15 2

Hot cell and environment 3 20 2

Test blanket 6 24 7

Diagnostics 89 400 20

Central 0 0 170

TOTAL 167 750 220

Estimate of ITER CODAC system size

~1000 computers connected to CODAC


Plant System I&C Is a deliverable by ITER member state. Set of standard components selected from catalogue. One and only one plant system host.

ITER Instrumentation & Control System physical architecture


CODAC Servers and Terminals are servers running Red Hat Enterprise Linux (RHEL) and EPICS/CSS/???. These servers implements supervision, monitoring, coordination, configuration, automation, data handling, archiving, visualization, HMI…


Plant Operation Network is the work horse general purpose flat network utilizing industrial managed switches and mainstream IT technology


High Performance Networks are physically dedicated networks to implement functions not achievable by the conventional Plant Operation Network. These functions are distributed real-time feedback control, high accuracy time synchronization and bulk video distribution.


Slow Controller is a Siemens Simatic S7 industrial automation Programmable Logic Controller (PLC A Slow Controller runs software and plant specific logic programmed on STEP 7. A Slow Controller has normally I/O and IO supports a set of standard I/O modules.


Fast Controller is a dedicated industrial controller implemented in PCI family form factor and There may be zero, one or many Fast Controllers in a Plant System I&C. A Fast Controller runs LINUX RHEL and EPICS IOC. A Fast Controller has normally I/O and IO supports a set of standard I/O modules with associated EPICS drivers. A Fast Controller may have interface to High Performance Networks (HPN),


High Performance Networks are physically dedicated networks to implement functions not achievable by the conventional Plant Operation Network..


High Performance Computer are dedicated computers (multi core, GPU) running plasma control algorithms.


Fast Controllers for Fusion Devices

Actuators

Plasma

SensorsHeating systems

Fueling systems

Corrective systems

Diagnostics

Analysis codes

Magnetic coils

ControllersPlasma Shaping

&Current Control

Machine protection

Profiles control

High performance communication networks

Supporting InfrastructureSimulation environment

Scheduler

R-‐T signal servers

Instabilities control


Vertical Stabilization | an example

Elongated plasmas are vertically unstable MIMO systems designed to make plasma vertically stable while other controllers control plasma position and shape

Growth Rate


ITER Vertical Position Control How important are control systems?

•  Loss of vertical plasma position control in ITER will cause thermal loads on Plasma Facing Components of 30-60 MJ/m2 for ~0.1s.

–  PFCs cannot be designed to sustain such (repetitive) thermal loads

•  Vertical Displacement Events also generates the highest electromagnetic loads –  A phenomenological extrapolation of horizontal forces estimates loads

~45MN on ITER vacuum vessel. –  Simulations of MHD predicts ~20MN –  Vertical loads ~90MN

Plasma vertical position in ITER must be robust & reliable to ensure a vertical plasma position control

loss is a very unlikely event


•  192 signals acquired by ADCs and transferred at each cycle

•  50 µs control loop cycle time with jitter < 1 µs archieved by MARTe.

• Always in real-time (24 hours per day)

• 1.728 x 109 50 µs cycles/day

• Crucial for ITER very long pulses

Example: JET Vertical Stabilization system

192 input signals

Front view


ATCA @ JET Ver6cal Stabilisa6on Controller

•  x86-based ATCA controller

•  Up to 12 DGP cards (PCIe links through the ATCA full mesh backplane)

•  32 18 bit ADC channels / board , separately isolated (1 kV) •  Parallel execution on FPGAs for MIMO signal processing (Control loop delay < 50 µs, aim < 10 µs)

•  Linux RT operating system (RTAI)

•  Aurora and PCI Express communication protocols allow data transport, between modules - expected latencies below 2 µs.


IPFN’s ATCA-MIMO-ISOL I/O Processing Boards

RTM ADC module


ATCA JET Gamma ray Spectroscopy

•  19 lines of sight 10 Horizontal + 9 Vertical Channels

•  2 FPGA ( Virtex II-Pro) ATCA Boards Digitizing at 200 MSPS, 13bit, 8 channels


Gamma and X-ray Diagnostics Real time Processing

Analog-to-Digital

Converter Block

Analog-to-Digital Converter BlockAnalog-to-Digital Converter Block

DDR SODIMM 1 GBYTES

XilinxTMFPGA

VirtexII-ProXC2VP30FF1152 -6

Clock Synthesis

SYSTEM ACE COMPACT

FLASH

Analog-to-Digital Converter Block 4x 12 bits

Sync

Ref CLK

Analog Inputs

Analog-to-Digital

Converter Block

Analog-to-Digital Converter BlockAnalog-to-Digital Converter Block

DDR SODIMM 1 GBYTES

XilinxTMFPGA

VirtexII-ProXC2VP30FF1152 -6

Clock Synthesis

PCI Express x4 link

Analog-to-Digital Converter Block 4x 12 bitsAnalog Inputs

PCI EXPRESS SWITCHPex 8516

4X Rocket IO

4X Rocket IO

PCI Express x4 link

Channel 11

Channel 12

•  Parallel DPP in FPGA •  Real-Time PHA at 1MHz average pulse

rate. •  20 ns resolution timestamp •  Data reduction rate of at least 80%

attainable 95% of total pulses resolved


Why ATCA?

ATCA platform is gaining traction in the physics community because of •  Advanced communication bus architecture (serial gigabit replacing parallel buses) •  very high data throughput options and its suitability for real-time applications •  Scalable shelf capacity to 2.5Tb/s •  Scalable system availability to 99.999% •  Robust power infrastructure (distributed 48V power system) and large cooling

capacity (cooling for 200W per board) •  Ease of integration of multiple functions and new features •  The ability to host large pools of DSPs, NPs, processors and storage •  Full redundancy support •  Reliable mechanics (serviceability, shock and vibration) •  Hardware management interface (IPMI Bus)


Who else is using ATCA?

The group of experimenters includes several major laboratories representing different fields of use and a range of applications.

•  Active programs are showing up most notably at –  DESY for XFEL and JET

•  Other laboratories –  ILC, IHEP, KEK, SLAC, FNAL, ANL, BNL, FAIR, ATLAS at CERN, AGATA, large telescopes,

Ocean Observatories

•  Investigating ATCA solutions for future upgrades –  Both the CMS and ATLAS detectors

•  Setting up prototype experiments to test its potential –  ILC and ITER

ATCA is being adapted without significant change as a platform for generic data acquisition processors requiring high throughput and bandwidth.

Most of these programmes put the emphasis on High Availability


SOFTWARE TOOLS FOR CONTROL: EPICS and ITER

In February 2009 ITER Organization decided to use EPICS for the control system. This decision was based on three independent studies In February 2010 ITER-IO released the first version (V1.0) of CODAC Core System, which basically is a package of selected EPICS products


What is EPICS?

EPICS is: •  A collaboration •  A tool kit •  A control system architecture

EPICS is an abbreviation for: Experimental Physics and Industrial Control System


The History – In1989 started a collaboration between Los Alamos

National Laboratory (GTA) and Argonne National Laboratory (APS)

(Bob Dalesio & Marty Kraimer)

– More than 150 licenses agreements were signed, before EPICS became Open Source in 2004

– Team work on problems, for example over “Tech Talk” mailing list

– Database and network protocol (CA) basically unchanged since 1990.

– Collaborative efforts vary •  Assistance in finding bugs •  Share tools, schemes, and advice

GTA: Ground Test Accelerator APS: Advanced Photon Source

http://www.aps.anl.gov/epics


EPICS – who is using it?

Some members of the collaboration (very short List!):

– ANL (APS Accelerator, APS Beamlines, IPNS) in Chicago, USA – LANL in Los Alamos, USA – ORNL (SNS) in Oak Ridge, USA – SLAC (SSRL, LCLS) in Standford, USA – DESY in Hamburg, Germany – BESSY in Berlin, Germany – PSI (SLS) in Villigen, Switzerland – KEK in Tsukuba, Japan – DIAMOND Light Source (Rutherford Appleton Laboratory) in

Oxfordshire, England – In FUSION: NTSX, KSTAR, ITER and ISTTOK


Parts of EPICS

Commercial Instruments

IOC IOC

IOC

IOC CAS

CAS

Custom hardware

Technical Equipment

Out

put

Input

Client Software MEDM

ALH StripTool TCL/TK

Perl Scripts

OAG Apps

Many, many others …

Channel Access

CA Server Software EPICS Database

consists of Process Variables Custom Programs

Realtime control

Sequence Programs

Records


How does it do it?

Power Supply

Beam Position Monitor

Vacuum Gauge

Computer Interface

Computer Interface

Computer Interface

Process Variables:

Channel Access Server

S1A:H1:CurrentAO

S1:P1:x

S1:P1:y

S1:G1:vacuum

Channel Access Client



Network (Channel Access Protocol)

Machine

Operator

IOC


What is an IOC? •  A special CA Server and CA Client •  A computer running “IOC Core” •  This computer may be:

-  VME based, operating system vxWorks or RTEMS -  PC, operating system Windows, Linux, RTEMS -  Apple, operating system OSX -  UNIX Workstation, operating system Solaris

•  An IOC normally is connected to input and/or output hardware •  An EPICS control system is based on at least one Channel Access Server (normally an IOC) •  An IOC runs a record database, which defines what this IOC is doing

IOC means Input Output Controller


Sequencer

Inside an IOC

LAN (Network)

Device Support

I/O Hardware

IOC

The major software components of an IOC (IOC Core)

Database

Channel Access


Control and Data Acquisition for Next Generation Fusion Experiments

Challenges •  Increasing number of interdependent parameters to be controlled

•  Increasingly faster real-‐Bme loop-‐cycle response

• Stricter OperaBng Safety Margins

•  ConBnuous OperaBon generaBon huge data quanBBes

Implica6ons •  Massive processing power (parallel, mulB-‐processing support)

•  High bandwidth for data-‐transfer

•  Real-‐Bme mulB-‐input-‐mulB-‐output (MIMO) control

•  Advanced, intelligent, flexible Bming & syncronizaBon


Concluding remarks

•  High performance of fusion depends on real-time MIMO control systems

•  Control systems are critical for safe operation and reliability of Fusion Devices

•  ITER is a big challenge for its higher complexity and stricter safety margins

•  Likely there were will be a grater convergence between Neutron/High energy physics and Fusion on hardware technologies in hardware (ATCA) and software (EPICS)

control and data acquisition for fusion experiments€¦ · b. carvalho | eiroforum school on...

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