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

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.

Probabilistic locked mode predictor in the presence of a resistive wall and finite island saturation in tokamaks

“…An actual validation of this model against DIII-D will require a systematic study through the DIII-D database 25,27,33 to extract the relevant model parameters in a statistically meaningful manner. For certain, key plasma parameters shown to be correlated with locking, such as the normalized or poloidal beta, plasma density-and possibly edge safety factor-should enter the future versions of the locking model in question here.…”

“…Recent work more directly relevant to our investigation is by Murari et al 23 on the probabilistic locked-mode predictor, which uses support-vector machine classifiers 24 and adaptive training on the JET data. Another example by Olofsson et al [25][26][27] focuses on hazard/survival analysis for calculating the event onset intensity with statistical models.…”