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A Statistical Analysis of Root Causes of Disruptions at JET Peter de Vries, Mike Johnson, Barry Alper and JET EFDA Collaborators IEA Disruption Workshop (W70) Culham 8 October 2009. Motivation.

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slide1

A Statistical Analysis of

Root Causes of Disruptions at JET

Peter de Vries, Mike Johnson, Barry Alper

and JET EFDA Collaborators

IEA Disruption Workshop (W70)

Culham 8 October 2009

motivation
Motivation
  • A disruption of a Tokamak discharge often induces large forces on the surrounding structure and large heat loads on in-vessel components. It is therefore important to prevent or mitigate these events, especially in large devices as ITER
    • Hence: Study the causes and consequences of disruptions
  • In order to find ways to prevent or mitigate disruptions a detailed picture of all the precursors or causes is necessary.
    • What are the main causes of disruptions in JET?
    • What are the characteristics and ‘precursor signs’?
  • This information may help us to work out better ways to avoid or mitigate disruptions in JET and possibly ITER.
statistics disruption rate
Statistics: Disruption rate
  • Disruption rate1: the fraction of discharges that disrupt?
    • Variation  but also signs of a downward trend:

Period All (Uninten.)

<1987: 24% (22%)

1987-1992: 21% (19%)

1992-1996: 31% (28%)

1996-1998: 21% (19%)

1998-2001: 19% (17%)

2001-2004: 12% (8%)

2004-2007: 8% (6%)

2008-2009: 5.5% (3.4%)

[1] P.C. de Vries, Nucl. Fusion 49 (2009) 055011

statistics ptn triggers
Statistics: PTN triggers
  • A simple protection system (PTN) at JET is able to detect disruptive plasmas, terminate the pulse, mitigating Forces1.
    • 49% of all unintentional disruptions with a warning time of 200ms or more
    • 66% of all unintentional disruptions with a warning time of 30ms or more
    • 25% of all unintentional disruptions is not detected at all

[1] P.C. de Vries, Nucl. Fusion 49 (2009) 055011

causes of disruptions
Causes of disruptions
  • Many causes of disruptions have been studied for many years and the basic classes are ‘known’. However, these studies often focus on special examples or the detailed physics behind the disruptions and do not often deal with more complex cases or those with technical causes.
  • At JET a dedicated disruption database exist:
    • Record of all disruptions since 1985 but no information on causes
    • A detailed analysis of the causes of all disruptions with Ip>1MA between 2000 and 2007 (= 1707 cases) has been carried out.
    • With the aim of building a realistic/complete picture of chain of events leading to disruptions at JET
what has been done
What has been done?
  • All 1707 disruptions have been analysed manually by:
    • Checking the session leader pulse schedule (XPSEDIT).
    • Reading main session and pulse information:
      • DCO display overview for a session overview
      • Session Leader JOTTER comments (session + pulse)
      • Other technical information, meeting minutes, etc.
    • Checking if all auxiliary heating systems worked as requested.
    • Analysing plasma control systems:
      • PPCC and shape control (XLOC, EFIT, KL1 video, …)
      • Density, Gas and other RT control
    • Doing basic signal analysis:
      • MHD: n=1, n=2 and locked mode, Radiation and impurity levels, …
    • Carrying out more detailed analysis:
      • Detailed MHD analysis (Kink modes, NTMs,…), MARFEs, etc.
  • Each step towards the disruption labelled…
what labels
What labels?

Physics problems

Technical problems

classification of disruptions
Classification of disruptions
  • Although in some cases a clear unambiguous cause can be identified, the process that leads to a disruption is often:
    • Complex, with several problems occurring at the same time
    • Disruption happen very fast and are not always well diagnosed
  • Thus the analysis and the resulting classification may be subjective, depending on who did it and which diagnostics were available.
    • But, this analysis is done for many disruptions, and we can build a statistical picture.
  • Disruptions can be classified according to the root cause or the specific path or chain of events that lead to the disruption.
what can be studied
What can be studied?
  • Chain of events that leads to the disruption.
    • Example: SC  WAL  IMP  RC  MHD  ML  DISR
    • A flow chart of all events can be built
  • Statistics of causes and links between certain events.
    • How often is the root cause a human mistake?
    • How often does an SC error cause a RC?
    • What are the main ‘chain of events’ or classes that occur in JET?
  • Details on specific classes of disruptions
    • How fast is the chain of events after an NTM is triggered?
    • What is the operational space in which a class occurs?
    • Can we improve the detection?
slide11

Flow Chart of 1283 Unintentional JET Disruptions from 2000 to 2007

STOP

ML

VDE

NTM

ROT

VST

LON

GWL

NPK

RC

MHD

HL

HD

DIV

AUX

NBI

SC

WAL

RCY

NC

HUM

GIM

IMC

IMP

LHC

ICH

UFO

MAR

LOQ

IP

QED

MSH

REC

ITB

KNK

PRP

2ST

slide12

Flow Chart of 1283 Unintentional JET Disruptions from 2000 to 2007

STOP

ML

VDE

NTM

ROT

VST

LON

GWL

NPK

RC

MHD

HL

HD

DIV

AUX

NBI

SC

WAL

RCY

NC

HUM

GIM

IMC

IMP

LHC

ICH

UFO

MAR

LOQ

IP

QED

MSH

REC

ITB

KNK

PRP

2ST

Disruption Process

slide13

Flow Chart of 1283 Unintentional JET Disruptions from 2000 to 2007

STOP

ML

VDE

NTM

ROT

VST

LON

GWL

NPK

RC

MHD

HL

HD

DIV

AUX

NBI

SC

WAL

RCY

NC

HUM

GIM

IMC

IMP

LHC

ICH

UFO

MAR

LOQ

IP

QED

MSH

REC

ITB

KNK

PRP

Disruption Process

2ST

statistics of root causes
Statistics of Root Causes

Main root causes of Unintentional Disruptions (67%):

1. Neo-classical Tearing Modes (NTM) 17%

2. Human error (HUM) 8.3%

3. High density operation (HD) 6.5%

4. Shape control problems (SC) 6.4%

5. Density control problems (NC) 6.2%

6. Internal Transport Barrier (ITB) 5.9%

7. No divertor cryo-pumping (DIV) 5.1%

8. Low density error field mode (LON) 4.0%

9. Lower Hybrid Current Drive (LHC) 3.9%

10. Impurity control problems (IMC) 3.4%

statistics of root causes1
Statistics of Root Causes
  • No or unclear root cause: 3%
    • For 5.5% the analysis is uncertain
  • Human error: 8.3%
  • Real time control problem: 16.5%
    • SC, NC, IMC, RTC
  • Wall and impurity issue: 7.1%
    • WAL, IMP, UFO, RCY,MAR
  • Technical root cause: 19.3%
    • DIV, LHC, NBI, ICH, RMP (STOP)
  • Physics root cause: 45.2%
    • NTM, ITB, RC, LOQ, QED, GWL/HD, …
characterisation
Characterisation
  • Disruption classes defined either by root cause or the chain of events.
    • Characterise the precursors for each class
      • Best to detect the root or otherwise next steps in the chain of events
      • What are the typical warning times from for example PTN
    • Characterise the consequences (forces/current quench, heat loads)
  • Find methods to prevent or mitigate specific classes
    • Focus attention on those which are most relevant
    • What is the best action to be taken to mitigate the effects?
    • Which precursors could be used to detect specific classes?
slide17

Flow Chart of 1283 Unintentional JET Disruptions from 2000 to 2007

STOP

ML

VDE

NTM

ROT

VST

LON

GWL

NPK

RC

MHD

HL

HD

DIV

AUX

NBI

SC

WAL

RCY

NC

HUM

GIM

IMC

IMP

LHC

ICH

UFO

MAR

LOQ

IP

QED

MSH

REC

ITB

KNK

PRP

2ST

ntm triggered disruptions
NTM triggered disruptions
  • Of all disruptions with as root cause and NTM
    • 51% disrupted due to pure mode locking
      • Pure ICRH discharges with large sawteeth, NBI discharges at high bN
    • 34% disrupted as a consequence of the termination sequence
      • Mainly wall interaction leading to low q or high radiation
      • Looks bad but the termination sequence will have a mitigating effect
    • 10% disrupted due to a fast shut-down of VS (FRFA temperature)
      • This problem has been solved at JET
    • 2% disrupted due to density pump-out and an error field-mode
  • Detection:
    • These disruptions have a clear precursor that can be detected well in advance of the disruption
    • 34-44% disrupted during the fast-stop or emergency termination.
ntm triggered disruptions1
NTM triggered disruptions
  • Warning time from precursors
    • n=1 or n=2 MHD mode, Locked Mode (ML)
    • A larger fraction can be detected compared to average disruption statistics and also earlier: 90% more than 1s before tDISR
    • Improve detection and mitigation technique for these modes at JET

ONLY NTM

ALL

NTM

Trigger

PTN

ML

slide20

Flow Chart of 1283 Unintentional JET Disruptions from 2000 to 2007

STOP

ML

VDE

NTM

ROT

VST

LON

GWL

NPK

RC

MHD

HL

HD

DIV

AUX

NBI

SC

WAL

RCY

NC

HUM

GIM

IMC

IMP

LHC

ICH

UFO

MAR

LOQ

IP

QED

MSH

REC

ITB

KNK

PRP

2ST

statistics on human errors
Statistics on Human Errors
  • Breakdown of human mistakes
    • Density control: 35%
    • Impurity control: 26%
    • Too low density: 11%
    • Shape controller request: 11%
    • Too low q: 6%
    • NBI timing: 3%
  • Who is to blame
    • Session Leader: 89%
    • Gas Matrix set-up: 9%
    • Engineer-in-Charge: 1%
    • Laser ablation: 1%
  • Prevention methods
    • Better preparation, improved interfaces and operation procedures, ...
slide22

Flow Chart of 1283 Unintentional JET Disruptions from 2000 to 2007

STOP

ML

VDE

NTM

ROT

VST

LON

GWL

NPK

RC

MHD

HL

HD

DIV

AUX

NBI

SC

WAL

RCY

NC

HUM

GIM

IMC

IMP

LHC

ICH

UFO

MAR

LOQ

IP

QED

MSH

REC

ITB

KNK

PRP

2ST

disruptions at greenwald limit
Disruptions at Greenwald limit
  • Not in the top 10 of causes at JET
    • Characterised by a H to L back transition prior to disruption
      • ELMs stop, density drops but also a drop in edge temperature
    • No clear mode lock, well before the disruption
    • Detection of H-L transition + new ideas for preventive actions

ONLY GWL

ALL

HL

PTN

slide24

Flow Chart of 1283 Unintentional JET Disruptions from 2000 to 2007

STOP

ML

VDE

NTM

ROT

VST

LON

GWL

NPK

RC

MHD

HL

HD

DIV

AUX

NBI

SC

WAL

RCY

NC

HUM

GIM

IMC

IMP

LHC

ICH

UFO

MAR

LOQ

IP

QED

MSH

REC

ITB

KNK

PRP

2ST

current quench time
Current quench time
  • Statistics on the current quench time1,2.

[1] J. Wesley, FEC (2006 IAEA) IT/P1-21

[2] V. Riccardo, Plasma Phys. Control. Fusion 47 (2005) 117–129

current quench time1
Current quench time
  • Statistics on the current quench time1,2.
    • ITB, kicked VDEs and other clean disruptions have a fast quench

[1] J. Wesley, FEC (2006 IAEA) IT/P1-21

[2] V. Riccardo, Plasma Phys. Control. Fusion 47 (2005) 117–129

current quench time2
Current quench time
  • Statistics on the current quench time1,2.
    • Other classes show significantly slower quench times

[1] J. Wesley, FEC (2006 IAEA) IT/P1-21

[2] V. Riccardo, Plasma Phys. Control. Fusion 47 (2005) 117–129

relation other devices and iter
Relation other devices and ITER
  • The statistics of causes and classes at other devices and ITER may be different because:
    • JET differs technically from these devices or ITER
      • Some problems are typical to JET (e.g. the VS n=2 interference)
      • Failure rates differ for VS or density control systems or other subsystems such as auxiliary heating
    • JET does not operate in the exact same operational domain as other devices and especially ITER
      • Operation at the Greenwald density and with high radiation fractions
      • Operation at high N , with large Sawteeth prone to trigger NTMs.
  • What will change when JET starts operating with the new ITER-like wall (i.e. W divertor + Be limiters)?
  • Thus, an comparison study with other devices would be very interesting.
disruption prevention and mitigation
Disruption Prevention and Mitigation
  • Focus on most relevant causes of disruptions but look into realistic chain of events
    • How do we handle the large fraction of disruptions caused by power switch-off and density/impurity control problems?
  • Characterisation of all disruption classes may lead to better overall understanding and more focussed prevention and mitigation methods
  • How to tackle disruption prevention/mitigation?
    • Limit the chance of mistakes by the operator (human errors)
    • Prevent disruptions by scenario development
    • Be aware that disruptions may be caused by simultaneous problems
    • Be aware of the failure rate of subsystems
    • Built a robust detection system with tailored response/actuators
itb triggered disruptions
ITB triggered disruptions
  • Very fast ITB triggered disruptions show no warning
    • Of the 76 cases only 7 tripped the termination network PTN
    • Often due to a pressure driven internal kink mode
    • At JET prevented by limiting the ‘strength of the ITB’

ONLY ITBs

ALL

PTN

PTN

normalised forces
Normalised Forces
  • The normalised forces differ per root-cause of class of disruption
    • Can be attributed to the triggering of the disruption avoidance scheme (PTN) at JET by the respective precursors
example of labelling
Example of labelling
  • This process has its limitations as some cause of events are rather complex. Example #69617 (predominant ICRH)
    • 1) NTM  ML  RMP  SC  WAL  IMP  RC
    • 2) NTM  ML  RMP  SC  LOQ

Sawtooth crash

NTM

Locking

ML triggered fast-stop t=52.199s

52.1s 52.2s 52.3s 52.6s 52.7s

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