Exploratory Machine Learning Studies for Disruption Prediction Using Large Databases on DⅢ-D

摘要

Using data-driven methodology, we exploit the time series of relevant plasma parameters for a large set of disrupted and non-disrupted discharges from the DIII-D tokamak with the objective of developing a disruption classification algorithm. We focus on a subset of disruption predictors, most of which are dimensionless and/or machine-independent parameters such as the plasma internal inductance l(i) and the Greenwald density fraction n(G), coming from both plasma diagnostics and equilibrium reconstructions. The utilization of dimensionless indicators will facilitate a more direct comparison between different tokamak devices.