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Database-wide hazard modelling of the onset of DIII-D tearing modes with field features

Part of: Machine Learning for Plasma Physics and Fusion Energy

Published online by Cambridge University Press:  17 October 2022

K.E.J. Olofsson Open the ORCID record for K.E.J. Olofsson [Opens in a new window] ,
C. Akçay Open the ORCID record for C. Akçay [Opens in a new window]  and
B.S. Sammuli Open the ORCID record for B.S. Sammuli [Opens in a new window]
K.E.J. Olofsson*
Affiliation:
General Atomics, San Diego, CA, USA
C. Akçay
Affiliation:
General Atomics, San Diego, CA, USA
B.S. Sammuli
Affiliation:
General Atomics, San Diego, CA, USA
*
†Email address for correspondence: olofsson@fusion.gat.com
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Abstract

The rate of onset (hazard) of tearing modes is modelled probabilistically using statistical learning algorithms. Axisymmetric energy-density equilibrium fields are taken as raw high-dimensional input features which are reduced with principal component analysis. Signal processing of non-axisymmetric magnetics fluctuation array data provides the target information from which to learn. Model selection, visualization and calibration assessment procedures are detailed. The analysis is deployed at large scale across the DIII-D tokamak database. Standard model selection criteria suggest that the energy-density post-processed feature is a better choice for modelling the onset rate compared to the non-processed equilibrium reconstruction solution. Two example applications of the learned rate function are demonstrated: (i) proximity-to-onset discharge monitoring and (ii) database analysis showing an (expected) observational global trend that the general hazard increases as a plasma performance metric increases. An important connection between the hazard function and its use as a conditional probability generator is reviewed in the Appendix.

Keywords

plasma instabilitiesfusion plasma
Type
Research Article
Information
Journal of Plasma Physics , Volume 88 , Issue 5 , October 2022 , 895880503
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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References

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