A hybrid physics/data-driven logic to detect, classify, and predict anomalies and disruptions in tokamak plasmas

Abstract

Disruptions are abrupt collapses of the configuration that have afflicted all tokamaks ever operated. Reliable observers are a prerequisite to the definition and the deployment of any realistic strategy of countermeasures to avoid or mitigate disruptions. Lacking first principle models of the dynamics leading to disruptions, in the past decades empirical predictors have been extensively studied and some were even installed in JET real-time network. Having been conceived as engineering tools, they were often very abstract. In this work, physics and data-driven methodologies are combined to identify the main macroscopic precursors of disruptions: magnetic instabilities, abnormal kinetic profiles and radiation patterns. Machine learning predictors utilising these observers can not only detect and classify these anomalies but also determine their probability of occurrence and estimate the time remaining before their onset. These tools have been applied to a database of about two thousand JET discharges with various isotopic compositions including DT, in conditions simulating in all respects real time deployment. Their performance would meet ITER requirements, and they are expected to be easily transferrable to larger devices, because they rely only on normalised quantities, form factors, and physical/empirical scaling laws.

Enhancing disruption prediction through Bayesian neural network in KSTAR

“…Disruption prediction models, including this work, aim to be used for disruption avoidance [61][62][63] or mitigation [64]. This work focused on developing a reliable event alarm to predict the occurrence of disruptions with the Bayesian framework.…”