Deep sequence to sequence learning-based prediction of major disruptions in ADITYA tokamak

Abstract

Major disruptions in tokamak plasmas need to be identified well before their occurrence and appropriately mitigated. Otherwise, it may dump the heat and electromagnetic load to the vessel and its surrounding plasma-facing components. A predictor system based on precursor diagnostics may help in forecasting the disruptive events in tokamak plasma and raise the alert beforehand to take necessary actions to prevent the major damages inside the vacuum vessel. This paper describes a predictor system built with a few selected diagnostic signals from the ADITYA tokamak and trained on a time-sequence long short-term memory network to predict the occurrence of disruption to 7–20 ms in advance with an accuracy of 89% on the testing set of 36 disruptive and 6 non-disruptive shots. This real-time network can infer to one time-step results under 170 µs on an Intel Xeon processor running python, suggesting minimal computation cost and best suited for the real-time plasma control applications.