supported by the fusion energy sciences tokamak disruption
TRANSCRIPT
12021 IAEA-PPPL TSDW: Tokamak Disruption Event Characterization and Forecasting Research - Real-Time Expansion (S.A. Sabbagh, et al. 7/23/21)
Supported by theU.S. Department of Energy Office of ScienceFusion Energy Sciences
Supported by US DOE grants DE-SC0016614, DE-SC0018623, DE-FG02-99ER54524
and contract DE-AC02-09CH11466
MAST-U
ASDEX-U
S.A. Sabbagh1, J.W. Berkery1, Y.S. Park1, J.M. Bialek1, J. Butt1, Y.
Jiang1, V. Klevarova1, J.D. Riquezes1, J.G. Bak2, M.D. Boyer3, K.
Erickson3, A.H. Glasser4, C. Ham5, S.H. Hahn2, J. Kim2, A. Kirk5, J. Ko2,
W.H. Ko2, L. Kogan5, J.H. Lee2, M. Podesta3, D. Ryan5, A. Thornton5,
S.W. Yoon2, Z.R. Wang3
V4
1Department of Applied Physics, Columbia University, New York, NY2Korea Institute of Fusion Energy, Daejeon, Republic of Korea
3Princeton Plasma Physics Laboratory, Princeton, NJ4Fusion Theory and Computation, Inc., Kingston, WA
5Culham Centre for Fusion Energy, UKAEA, Abingdon, UK
Tokamak Disruption Event Characterization and Forecasting
Research and Progress on Expansion to Real-Time Application
2021 IAEA-PPPL Workshop:
Theory/Sim of Disruptions
23 July 2021
Virtual Meeting
22021 IAEA-PPPL TSDW: Tokamak Disruption Event Characterization and Forecasting Research - Real-Time Expansion (S.A. Sabbagh, et al. 7/23/21)
Disruption prediction and avoidance research progressing
for ITER and future tokamaks – expanding to real-time
Motivation: Disruption prediction/avoidance is a critical need
Why? A disruption stops plasma operation, might cause device damage
A highest priority DOE FES (Tier 1) initiative - present “grand challenge”
in tokamak stability research:
• Can be done! (JET: < 4% disruptions with carbon wall)
• ITER disruption allowance: < 1 - 2% (energy + E&M loads); << 1% (runaways)
Outline
Disruption Event Characterization and Forecasting (DECAF) analysis
• Disruption event chains, early forecasting, brief results, continued development
Recent focus on real-time DECAF design and implementation on KSTAR
Expanded physics analysis supporting DECAF
• e.g. KSTAR high bN, D’ analysis, high to ~100% non-inductive CD transport
analysis
32021 IAEA-PPPL TSDW: Tokamak Disruption Event Characterization and Forecasting Research - Real-Time Expansion (S.A. Sabbagh, et al. 7/23/21)
DECAF is a physics-based approach to disruption event
understanding / forecasting to enable disruption avoidance
Physical event modules encapsulate disruption chain events
Continued development
focuses on improving
these modules
Structure eases parallel
development incl. real-time
KEY: Offline and real-time analysis INTEGRATED
The SAME researchers
that oversee the offline
code/analysis are
responsible for real-time
code specifications
Main data
structure
Code control
workbooksDensity Limits
Confinement
Stability
Tokamak
dynamics
Power/current
handling
Technical issues
Physical event
modules
Output
processing
Tokamak
databases
DECAF
database
42021 IAEA-PPPL TSDW: Tokamak Disruption Event Characterization and Forecasting Research - Real-Time Expansion (S.A. Sabbagh, et al. 7/23/21)
DECAF is structured to ease parallel development of
disruption characterization, event criteria, and forecasting
Physical event modules encapsulate disruption chain events. Examples:
Main data
structure
Code control
workbooksDensity Limits
Confinement
Stability
Tokamak
dynamics
Power/current
handling
Technical issues
Physical event
modules
Output
processing
Tokamak
databases
DECAF
database
VDE
DIS
IPR
HLB
GWL
IPB
LON
PRP
LTM
RWMRKM
MHD BIF
LOQ
WPC
Greenwald limit
Island power balance
Low density
H-L back-transition
MHD
Bifurcation
Locked modeVDE
Pressure peaking
Low qRWM and
Kinetic RWM
forecastingNot at requested Ip
Wall proximity control
Disruption
52021 IAEA-PPPL TSDW: Tokamak Disruption Event Characterization and Forecasting Research - Real-Time Expansion (S.A. Sabbagh, et al. 7/23/21)
DECAF provides an early disruption forecast - on transport
timescales – giving potential for disruption avoidance
DECAF event chain reveals physics Rotating MHD slows, bifurcates, and locks
126962
Disruption forecast level
DECAF
MHD
events
MHD-n1 PRP DISIPR WPC VDE
(0.490s)
BIF-n1 LTM-n1
(+.068s) (+.073s) (+.073s) (+.077s) (+.080s)(+.005s) (+.045s)
DECAF
event chain
Then, plasma has an H-L back-transition (pressure peaking warning PRP) before DIS
Important: Early warning occurs in apparently SAFE region of operating space!
NSTX
Safe
n
1
2
3
62021 IAEA-PPPL TSDW: Tokamak Disruption Event Characterization and Forecasting Research - Real-Time Expansion (S.A. Sabbagh, et al. 7/23/21)
DECAF MHD events also produce early disruption
warnings for KSTAR; aim to compute in real-timeDECAF automated MHD objects DECAF “heat map” (for MHD)
Mode locking at reduced plasma rotation
Key notables of MHD warning
“Safe”/“unsafe” MHD periods
Early disruption warning (300
ms) on transport timescale
MHD warning level
Safe
KSTAR16299
MHD-n1
MHD-n2
Very low frequency mode
Plasma rotation profileand dynamics
High amplitude mode
}
Decreasing bN
co
nditio
n
time (s)
fre
que
ncy (
kH
z)
wa
rnin
g le
ve
l
n
1
2
3
72021 IAEA-PPPL TSDW: Tokamak Disruption Event Characterization and Forecasting Research - Real-Time Expansion (S.A. Sabbagh, et al. 7/23/21)
Continued development of DECAF builds from an
extrapolable approach with strong initial success
Fully automated, physics-based analysis of existing tokamak databases from multiple devices
Analysis of all plasma states, continuous and asynchronous events, continuous “warning level” determination
“Safe”: events indicating steady operation (e.g. determination of L-mode,
H-mode, steady ELMing, etc.)
“Proximity”: expected paths to “critical” events
“Critical”: event chains leading to disruption if no action taken
“Forecaster events” using models to provide the earliest possible indication of issues
High success found to date, determined quantitatively
> 91% true positive, ~ 8% false positive rate (~1e4 shots, ~1e6 samples)
Research continues focused on improving forecasting to needed accuracy (98%+ goal, w/low false positives),
LTMMHD HLB DIS
82021 IAEA-PPPL TSDW: Tokamak Disruption Event Characterization and Forecasting Research - Real-Time Expansion (S.A. Sabbagh, et al. 7/23/21)
Recent focus: implementation of real-time (r/t)
diagnostic hardware at KSTAR enabling DECAF
Implementation of real-time diagnostic capabilities
rtMHD system taking data; FPGA card real-time processing of FFTs with
programming for all 16 channels channels completed (W. Que)
rtVf system installed, data taken in 2020, Vf(R) profile measured with
temporary calibration; new system designed, to install 2021 (M. Podesta)
rtECE system (Te(R)) installed at KSTAR, accessible, on new Dolphin r/t
network (10x bandwidth of present RFM network)
rtECEI system (2D dTe) installed at KSTAR, accessible, on new Dolphin
network
rtMSE computer and interface in design (B pitch angle, dB) (F. Levinton)
DOE “Reach: Milestone (for KSTAR 2021 run campaign)
DEPARTE-2021-4R: Utilize first real-time DECAF events to actuate the
KSTAR shattered pellet injectors (SPI) for disruption mitigation
DECAF “LTM event forecaster” planned to be used for this demonstration
92021 IAEA-PPPL TSDW: Tokamak Disruption Event Characterization and Forecasting Research - Real-Time Expansion (S.A. Sabbagh, et al. 7/23/21)
Overall setup for KSTAR real-time diagnostic
integration and DECAF analysis for the PCS
All software development under GIT version control
Main Diagnostics RoomPCS Room
KSTAR Test Cell / ECE Screen Room
A-to-D
(192 ch)Expansion box connected
to main ECEI r/t computer
r/t ECEI and
r/t DECAF
development
computer
rtDECAF
Optical
isolation
(Dolphin)1G to KSTAR imaging data server & MDSPlus
r/t MHD
computer
rtDECAF
r/t ECE computer
(includes Te(R)
calibrations 73 ch)
r/t Vf computer
(includes profile
calibration 16 ch)
r/t MSE computer
(includes profile
calibration 25 ch)
KSTAR
PCS
RFM
r/t DECAF
development
computer
rtDECAF
1G to MDSPlus
RFM
RFM
1G to MDSPlus
RFM
RFM
RFM
Dolphin
Dolphin
Dolphin
1G to MDSPlus
1G to MDSPlus
1G to MDSPlus
Main Diagnostics Rm
= installed
Dolphin
network switch
102021 IAEA-PPPL TSDW: Tokamak Disruption Event Characterization and Forecasting Research - Real-Time Expansion (S.A. Sabbagh, et al. 7/23/21)
Real-time toroidal velocity diagnostic (rtVf) installation
completed on KSTAR (Oct. 29th), first light the next day!
A01
A16
B01
B16
No light
in B13
No light
in A03
26333
First Light! 32 channels
M. Podesta, J. Yoo (PPPL),
Y.S. Park (CU), WH. Ko (KFE) Initial real-time KSTAR Vf profile data taken 2020
112021 IAEA-PPPL TSDW: Tokamak Disruption Event Characterization and Forecasting Research - Real-Time Expansion (S.A. Sabbagh, et al. 7/23/21)
Real-time Vf profile shows very good agreement
with KSTAR CER system
122021 IAEA-PPPL TSDW: Tokamak Disruption Event Characterization and Forecasting Research - Real-Time Expansion (S.A. Sabbagh, et al. 7/23/21)
KSTAR real-time ECE and ECEI data acquisition
hardware installed earlier this year (2021)
rtECE computer near heterodyne radiometer (76 channels)
rtECEI computer connected to diagnostic by PCIe expansion box and custom interface in test cell (2D: 192 channels!)
rtECEI
rtECE
rtECE
interface
H.K. Park, Adv. in Physics: X, 4:1, 1633956 (2019)
K.D. Lee, KFE
132021 IAEA-PPPL TSDW: Tokamak Disruption Event Characterization and Forecasting Research - Real-Time Expansion (S.A. Sabbagh, et al. 7/23/21)
The first real-time DECAF module in KSTAR PCS
recently measured Te profile (1st time last week)
Te
(eV
)
First real-time ECE data (Te(R))
(red: real-time; black: off-line)
0 3 6 9 12 15
Te
(eV
)T
e(e
V)
Te
(eV
)
core
edge
t (s)
rtECE
rtECE
interface
Calibrated system, agrees with offline data acquisition
142021 IAEA-PPPL TSDW: Tokamak Disruption Event Characterization and Forecasting Research - Real-Time Expansion (S.A. Sabbagh, et al. 7/23/21)
rtECEI DAQ system installed in the KSTAR test cell
rtECEIcomputer located in the ECE rack (diagnostics room)
LEMO cables installed for buffer chassis hook-up
Dolphin cable run from ECEI DAQ in test cell to rtECEIcomputer
Buffer chassis (192 channels) PCIe expansion chassis
In ECEI DAQ room (test cell) In ECEI DAQ room (test cell)
152021 IAEA-PPPL TSDW: Tokamak Disruption Event Characterization and Forecasting Research - Real-Time Expansion (S.A. Sabbagh, et al. 7/23/21)
The first real-time ECEI data on KSTAR was
recently taken as well (1st time last week)
3 of 192 channels shown
rtECEI
Te
sig
nal
(V)
Te
sig
nal
(V)
Te
sig
nal
(V)
0 2 4 6 8 10t (s)
162021 IAEA-PPPL TSDW: Tokamak Disruption Event Characterization and Forecasting Research - Real-Time Expansion (S.A. Sabbagh, et al. 7/23/21)
New DECAF edge localized mode event created to
start examining correlations to other MHD
DECAF ELM event
Presently determines
ELM triggering times,
along with frequency and
relative amplitude
Algorithm compatible with real-time use
Distinguishes true “ELMs” from other events (global MHD, etc.) that generate Dalight
Magenta dashed lines at
t = 0.6s is a global mode
ELM
KSTAR
NSTX
16325
Da
(arb
)
t (s)
Da
(arb
)
0 2 4 6 8 10 12 14
1.0
2.0
3.0
4.0
0.0
0.5
1.0
1.5
2.0
0.013984
0.1 0.2 0.3 0.4 0.5 0.6 t (s)0.7
172021 IAEA-PPPL TSDW: Tokamak Disruption Event Characterization and Forecasting Research - Real-Time Expansion (S.A. Sabbagh, et al. 7/23/21)
Te profile provides critical addition to Da ELM detection by determining radial extent of perturbation – useful for real-time
Te ↓ in core
Te ↓ in edge
GLOBAL decrease
• Global Te drop, Slow Neutrons (&
Wtot) exhibit catastrophic drop D⍺ info now to be passed to
other DECAF events• Low frequency, large-amplitude low-f
mode • Strong RWM sensor (BR, Bp) signal
increase• Apparent RWM, not ELM
Te profile evolution
J. Butt (this meeting)
NSTX
182021 IAEA-PPPL TSDW: Tokamak Disruption Event Characterization and Forecasting Research - Real-Time Expansion (S.A. Sabbagh, et al. 7/23/21)
Real-time MHD system taking data on KSTAR to be
used for real-time DECAF application in 2021
Real-time MHD analysis computer installed at KFE
Part of plasma control system
System FGPA chip now
computing FFTs in real-time
Offline Magnetic probe spectrogram analysis
DECAF analysis of real-time signals
Fre
qu
en
cy (
kH
z)
192021 IAEA-PPPL TSDW: Tokamak Disruption Event Characterization and Forecasting Research - Real-Time Expansion (S.A. Sabbagh, et al. 7/23/21)
Simple island rotation dynamics model used to
forecast the bifurcation point to signal disruption
Cylindrical, rigid body model
Possible model of drag for both a “slip” and a “no slip” condition:
At very low angular speed, mode can
reach a stable steady state,
observed in KSTAR
First real-time model, assume“no slip” condition
𝑇𝑚𝑜𝑑𝑒 =𝑘2Ω
1 + 𝑘3Ω2
R. Fitzpatrick et al., Nucl. Fusion 33 (1993) 1049
𝑑 𝐼Ω
𝑑𝑡= 𝑇𝑎𝑢𝑥 −
𝑘2Ω
1 + 𝑘3Ω2 −
𝐼Ω
𝜏2𝐷
Ω0
𝑘2 = 0
Bifurcation
𝑇𝑚𝑜𝑑𝑒 =𝑘1Ω
J. Riquezes (this meeting)
202021 IAEA-PPPL TSDW: Tokamak Disruption Event Characterization and Forecasting Research - Real-Time Expansion (S.A. Sabbagh, et al. 7/23/21)
New locked mode (LTM) forecaster “measures” key
parameters, provides early warning, high success
Disruption forecasted when mode freq. < 0.5x computed inflection freq.
13 KSTAR shots from 2020 analysed, 100% success rate; 1.5s warning!
KSTAR 23874
KSTAR 25829
Safe plasma discharge Disrupting plasma discharge
forecasted critical frequency
forecasted critical frequency
mode frequencymode frequency 1.5s
212021 IAEA-PPPL TSDW: Tokamak Disruption Event Characterization and Forecasting Research - Real-Time Expansion (S.A. Sabbagh, et al. 7/23/21)
DECAF is fueled by coordinated research that
continues to validate/develop physics models, e.g.:
Resistive MHD
Detection / forecasting: available magnetic diagnostics, plasma rotation
Forecasting: examination of MRE start with D’ evaluation
Density limits
Detection: rad. power, global empirical limit
Forecasting: examination of rad. island power balance model
Global MHD
Detection: available magnetic diagnostics, plasma rotation, equilibrium
Forecasting: Kinetic MHD model has high success in NSTX, DIII-D
Physics analysis / experiments to build DECAF models
Interpretive and “predict-first” TRANSP analysis of KSTAR long-pulse,
high beta plasmas with high non-inductive fraction
222021 IAEA-PPPL TSDW: Tokamak Disruption Event Characterization and Forecasting Research - Real-Time Expansion (S.A. Sabbagh, et al. 7/23/21)
16325
Classical tearing stability index, D′, computed at q = 2 surface using outer layer
solutions
At higher q95, D′ is mostly positive predicting unstable classical tearing mode
• Indicates neoclassical effects, additional physics needed to reproduce XP
• KEY POINT: Conclusions regarding D’ evolution can be made!
• Recent paper with MRE evaluation Y.S. Park, et al., NF 60 (2020) 056007
Tearing mode classical D’ stability examined in
KSTAR plasmas (supports future DECAF models)
Experimentally
2/1 stable
*Resistive DCON Δ′
bN ~ 2
Ideal DCON dW
bN ~ 2
16325
*A.H. Glasser PoP (2016)
232021 IAEA-PPPL TSDW: Tokamak Disruption Event Characterization and Forecasting Research - Real-Time Expansion (S.A. Sabbagh, et al. 7/23/21)
Tearing mode classical D’ and ideal stability
sensitivity to models also studied in KSTAR
-50
-25
0
25
50
75
100
0 2 4 6 8 10 12 14
Δ’(
q =
2)
Times (s)
-4
-2
0
2
4
6
8
-δW
n=
1n
o-w
all
unstable
stable
1E-08
1E-07
1E-06
1E-05
1E-04
1E-03
1E-02
1E-01
Co
nve
rge
nce
err
or
-50
-25
0
25
50
75
100
0 2 4 6 8 10 12 14
Δ’(
q =
2)
Times (s)
-4
-2
0
2
4
6
8
-δW
n=
1n
o-w
all
1E-08
1E-07
1E-06
1E-05
1E-04
1E-03
1E-02
1E-01
Co
nve
rge
nce
err
or5th order of P’ 5th order of ff’
2 constraints 8 free parameters
Ideal stability Ideal stability
resistive stability resistive stability
16325 16325
unstable
stable
unstable
stable
unstable
stable
qmin ~ 1.5 qmin ~ 1.5
Y. Jiang (submitted to Nucl. Fusion)
242021 IAEA-PPPL TSDW: Tokamak Disruption Event Characterization and Forecasting Research - Real-Time Expansion (S.A. Sabbagh, et al. 7/23/21)
<TRANSP>
bN = 3.4
fNI = 96%
PNBI = 6.5 MW
Predictive TRANSP analysis shows KSTAR design
target 𝜷𝑵~5 can be approached with 𝒇𝑵𝑰~100%
Up to 75% NICF already reached in similar plasmas
By altering 𝐼𝑃 and 𝐵𝑇values, 𝛽𝑁 > 4 , up to KSTAR design target 5can be achieved with 100% NICF
n=1 no-wall limit
n=1 with-wall limit
βN/li=6 βN/li=5𝐁𝐓=1.5T Predicted
𝐁𝐓=2.0T
𝐁𝐓=1.7T
Interpretive
“Predict-first” analysis used to design high-β , 100% non-inductive current
fraction (NICF) experiments for present KSTAR run campaign
252021 IAEA-PPPL TSDW: Tokamak Disruption Event Characterization and Forecasting Research - Real-Time Expansion (S.A. Sabbagh, et al. 7/23/21)
DECAF application and research for disruption
prediction and avoidance expanding to real-time
Multi-faceted, integrated approach to disruption prediction and avoidance with several key characteristics
Physics-based approach yields key understanding of evolution toward
disruptions: confident extrapolation of forecasting, avoidance by control
Full multi-machine databases used (full databases needed!)
Open to all methods of data analysis (physics, machine learning, etc.)
DECAF analysis produces early warning disruption forecasts
Sufficiently early for potential disruption avoidance by profile control
Significant physics support efforts from multiple devices
KSTAR D’ analysis, high to ~100% non-inductive CD transport analysis
Implementing real-time DECAF analysis in KSTAR (4 out of 5 new real-time diagnostic data acquisition systems installed)
See A. Piccione poster (this meeting)
262021 IAEA-PPPL TSDW: Tokamak Disruption Event Characterization and Forecasting Research - Real-Time Expansion (S.A. Sabbagh, et al. 7/23/21)
Supporting slides
272021 IAEA-PPPL TSDW: Tokamak Disruption Event Characterization and Forecasting Research - Real-Time Expansion (S.A. Sabbagh, et al. 7/23/21)
A database of high-non-inductive fraction plasmas is
important for disruption forecasting ; NICF ~ 75% in KSTAR
18492
16498
18476
1632516295
TRANSP analysis
of experimental
plasmas
Non-inductive
fraction
Beam-driven
Bootstrap
Non-inductive
fraction is key for
stable high beta
steady state
operationVolume average electron density (m-3)
282021 IAEA-PPPL TSDW: Tokamak Disruption Event Characterization and Forecasting Research - Real-Time Expansion (S.A. Sabbagh, et al. 7/23/21)
“Predict-first” KSTAR TRANSP analysis shows
expected high performance plasmas at > 80% NICF
High non-inductive current fraction predicted for 6.5, 7.5, 8.5 MW NBI
The bN ranges from 3.0 – 3.5; based on KSTAR plasmas with NICF ~70%
Aim to generate a significant database of long pulse, high NICF plasmas in 2021 KSTAR run for disruption prediction studies
Predicted high non-inductive current fraction (NICF) current profiles
81.5% NICF 93% NICF 101% NICF