A Review of Traditional and Data-Driven Approaches for Disruption Prediction in Different Tokamaks

Abstract

Tokamak is a nuclear fusion reactor; inside, the two lighter nuclei known as deuterium and tritium are first ionized together to form plasma, which is heated up to 150 million degrees Celsius, and then they are confined by the torus-shaped magnetic field. During this process, it releases a massive amount of energy, making fusion a feasible option for a long-term and renewable source of energy. On the other hand, plasma leads to disruptions as a consequence of the sudden implosion of the system, which halts the fusion process. Disruptions can irrevocably harm current fusion devices and are predicted to have a more catastrophic impact on feature devices such as ITER since they cause a rapid loss of confinement. To control, and prevent disruptions, or at least lessen their negative impact by mitigating them, various traditional and data-driven models obtained with machine learning and deep learning techniques have been used, an overview of some of which is presented in this article. These models are commonly used to forecast their occurrence and give sufficient time to take some counteractive measures.