a statistical analysis of root causes of disruptions at jet

11 Root Causes of Disruptions at JET – IEA Disruption Workshop 2009 – Peter de Vries

A Statistical Analysis of A Statistical Analysis of Root Causes of Disruptions at JETRoot Causes of Disruptions at JET

Peter de Vries, Mike Johnson, Barry Alper

and JET EFDA Collaborators

IEA Disruption Workshop (W70) Culham 8 October 2009


MotivationMotivation 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 rateStatistics: 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 triggersStatistics: 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 disruptionsCauses 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?

Physics problems

Technical problems


Classification of disruptionsClassification 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?


Chain of Events (Unintentional)Chain of Events (Unintentional)

MAR

LOQ

IMP

RC

MHD

ML

VDE

GIM

Flow Chart of 417 Intentional JET Disruptions from 2000 to 2007

NTM

VSK

IMC

HUM

NC


MAR

IMC

UFO

LHC

ICH

HD

ROT

VST

LOQ

GWLNPK

HUM

AUX

IP

RECMSH

QED

PRPKNK

2ST

NC

IMP

HL

RC

MHD

ML

VDESTOP

WAL

ITB

LON

DIV

NBI

SC

GIM

RCY

Flow Chart of 1283 Unintentional JET Disruptions from 2000 to 2007

NTM



MAR

IMC

UFO

LHC

ICH

HD

ROT

VST

LOQ

GWLNPK

HUM

AUX

IP

RECMSH

QED

PRPKNK

2ST

NC

IMP

HL

RC

MHD

ML

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WAL

ITB

LON

DIV

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SC

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RCY

NTM

Disruption Process


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1000D

IAV

SK

LIB

MA

RR

CY

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UX

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UFO

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IIC

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LIM

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IVIT

BN

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CH

DH

UM

NTM

Root Cause

Eve

nts

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 CausesStatistics 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, …


CharacterisationCharacterisation 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?


MAR

IMC

UFO

LHC

ICH

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VST

LOQ

GWLNPK

HUM

AUX

IP

RECMSH

QED

PRPKNK

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IMP

HL

RC

MHD

ML

VDESTOP

WAL

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LON

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SC

GIM

RCY


NTM ROT


NTM triggered disruptionsNTM 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 N

– 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.


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NTM triggered disruptionsNTM 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

ALL ONLY NTM

PTN

ML

NTMTrigger


MAR

IMC

UFO

LHC

ICH

HD

ROT

VST

LOQ

GWLNPK

HUM

AUX

IP

RECMSH

QED

PRPKNK

2ST

NC

IMP

HL

RC

MHD

ML

VDESTOP

WAL

ITB

LON

DIV

NBI

SC

GIM

RCY


NTM


Statistics on Human ErrorsStatistics 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, ...


MAR

IMC

UFO

LHC

ICH

HD

ROT

VST

LOQ

GWLNPK

HUM

AUX

IP

RECMSH

QED

PRPKNK

2ST

NC

IMP

HL

RC

MHD

ML

VDESTOP

WAL

ITB

LON

DIV

NBI

SC

GIM

RCY


NTM


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Disruptions at Greenwald limitDisruptions 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

ALL ONLY GWL

HL

PTN


MAR

IMC

UFO

LHC

ICH

HD

ROT

VST

LOQ

GWLNPK

HUM

AUX

IP

RECMSH

QED

PRPKNK

2ST

NC

IMP

HL

RC

MHD

ML

VDESTOP

WAL

ITB

LON

DIV

NBI

SC

GIM

RCY


NTM


Overview Top 10Overview Top 10 Classed by Root Cause %

unint. <F/Ip2>

(MN/MA2) tPTN

(s) %

tPTN >0.04s

Improve prevention or mitigation at JET

1 NTM 17 0.12 0.4-1 86 Earlier detection of NTM

2 Human error 8.3 0.20 0.04-0.1 69 Improve preparation & procedures

3 Power switch-off at high density 6.5 0.31 0.01-0.02 20 Improve scenario design & detect too high density / radiation

4 Shape control error 6.4 0.18 0.2-0.4 50 Improve shape control & detect high recycling

5 Density control error 6.2 0.22 0.04-0.1 42 Improve density control & detect high radiation

6 Strong ITB 5.9 0.21 <0.001 5.2 Detect strong ITBs

7 No divertor cro-pumping 5.1 0.21 0.1-0.2 71 Detect high density / radiation

8 Low density error field mode 4.0 0.12 0.4-1 90 Improve scenario design

9 LHCD arc 3.9 0.13 0.1-0.2 83 Detect Iron influx / high radiation

10 Impurity control error 3.4 0.19 0.02-0.04 29 Detect high radiation / Impurities

Greenwald limit 2.6 0.37 <0.001 13 Detect HL transition / high radiation

Intentionally kicked VDE - 0.40 <0.001 3 -



unint. <F/Ip2>

(MN/MA2) tPTN

(s) %

tPTN >0.04s















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

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[1] J. Wesley, FEC (2006 IAEA) IT/P1-21[2] V. Riccardo, Plasma Phys. Control. Fusion 47 (2005) 117–129



– ITB, kicked VDEs and other clean disruptions have a fast quench

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Intentional VDEs

Overall statistics (All disruptions)

Disruption due to strong ITBs




– Other classes show significantly slower quench times

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Disruptions due to impurity control errors

Current rise and low li disruptions



Relation other devices and ITERRelation 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 MitigationDisruption 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


Reserve SlidesReserve Slides


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ITB triggered disruptionsITB 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’

ALL ONLY ITBs

PTNPTN


Normalised ForcesNormalised 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

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0.0 0.2 0.4 0.6 0.8 1.0Normalised Force F/Ip2 [MN/MA2]

%

All disruptionsKicked VDE's onlyImpurity control errorsToo strong ITBLow density locked modesCaused by NTMs

78% are VDEs


Example of labellingExample 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

NTM

ML triggered fast-stop t=52.199s

Sawtooth crash

Locking

52.1s 52.2s 52.3s 52.6s 52.7s

a statistical analysis of root causes of disruptions at jet

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