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Data mining techniques for analyzing MHD fluctuation data from toroidal fusion plasmas. B.D. Blackwell , D.G. Pretty , S. Yamamoto , K. Nagasaki , E. Ascasibar , R. Jimenez-Gomez , S. Sakakibara , F. Detering . ). Datamining – extract new information from databases – (old and new).

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data mining techniques for analyzing mhd fluctuation data from toroidal fusion plasmas

Data mining techniques for analyzing MHD fluctuation data fromtoroidal fusion plasmas

B.D. Blackwell , D.G. Pretty , S. Yamamoto , K. Nagasaki , E. Ascasibar , R. Jimenez-Gomez , S. Sakakibara , F. Detering

)

datamining extract new information from databases old and new
Datamining – extract new information from databases – (old and new)

First steps: 1) design database: one entry per mode, per timestep2) preprocess raw data: each shot(~100MB) condensed by >100x

preprocessing stage 1 svd
Preprocessing Stage 1: SVD

Divide each time signal into 1ms pieces, then within these,

Singular value decomposition “separates variables” time and space” for each mode

27/18 probe signals  one time function (chronos) C(t)

one spatial function (topos) T(x)Fmode = C(t)  T(x)

per mode (actually 2 or 3 in practice, sin-like and cos-like, travelling wave)

preprocessing 2 svd s grouped into flucstruc s
Preprocessing 2: SVDs grouped into “flucstrucs“

Singular value decomposition “separates variables” time and space” for each mode

27/18 probe signals  one time function (chronos) C(t)

one spatial function (topos) T(x)Fmode = C(t)  T(x)

per mode (actually 2 or 3 in practice, sin-like and cos-like, travelling wave)

Group Singular Vectors with matching spectra

> 0.7

data mining classification by clustering
Data Mining Classification by Clustering

Cluster in delta-phase

Full dataset

h 1 identification with alfv n eigenmodes n e
H-1: Identification with Alfvén Eigenmodes: ne

phase

  • Coherent mode near iota = 1.4, 26-60kHz, Alfvénic scaling with ne
  • m number resolved by bean array of Mirnov coils to be 2 or 3.
  • VAlfvén = B/(o) B/ne
  • Scaling in ne in time (right) andover various discharges (below)

1/ne

ne

f 1/ne

Critical issue in fusion reactors:

VAlfvén ~ fusion alpha velocity fusion driven instability!

overall fit assuming radial location
Overall fit assumingradial location

Better fit of frequency to iota, ne obtained if the location of resonance is assumed be either at the zero shear radius, or at an outer radius if the associated resonance is not present.

Assumed mode location

 ~ 5/4

heliotron j poloidal modes from m 4 to 4
Heliotron J: Poloidal Modes from m=-4 to 4

Data set of > 2,000 shots, including both directions of B0

Corresponding phase variation

Clusters – Freq. vs time

tjii alfv nic non alfv nic scalings distinguished by kullback leibler divergence
TJII: Alfvénic/Non Alfvénic Scalings distinguished by Kullback-Leibler divergence

Alfvenic

Non-Alfvenic

lhd spectra complex huge data volume
LHD: spectra complex, huge data volume

N=2

0 Toroidal angle 5 rad

0 1 2 (sec) 3 4 5 6

jt60u tokamak initial results
JT60U Tokamak Initial Results
  • Clear separation into modes, based on phase differences

Nearest neighbour phase differences

real time mode identification

a)

Dashed Line is max likelihood mode before transition, solid line after

b)

c)

Real Time Mode Identification
  • Identify by cluster probability density functions
  • Multivariate nature produces huge range in values

Solution: modes are represented as multivariate von Misesdistributions

-trivially compute the likelihood of any new data being of a certain type of documented mode.

H-1 application:

Mode is clearly separatedfrom the rest

LHD

conclusions future work
Conclusions/Future Work

Discovery of new information

  • promising, but needs either very high quality of data, or human intervention (ideally both!)

Real time identification

  • Works well using Von Mises distribution to reduce problems in probability density function

Incorporation into IEA Stellarator CWGM MHD database

  • Needs further reduction to be most useful – several methods
    • Store cluster statistics for a concise overview
    • Store more complete data for some “canonical” shots
    • Develop importance criteria – relationship to transitions, confinement loss
  • Time dependence important! – (Detering, Blackwell, Hegland, Pretty)
  • Currently adding W7-AS data –
  • Tokamak data - JT60U - others?

See D. Pretty, B. Blackwell, A data mining algorithm for automated characterisation of fluctuations in multichannel timeseries.”Comput. Phys. Commun., 2009.

[ Open source python code “PYFUSION” http://pyfusion.googlecode.com ]

Supported an Australian Research Council grant and Kyoto University Visiting Professorship