Data-Driven-Based Disruption Prediction in GOLEM Tokamak with Missing Values

Chandrasekaran, Jayakumar; Madhawa, Surendar; Sangeetha, J.

doi:10.1007/978-981-16-0730-1_9

Cited by 1 publication

(1 citation statement)

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“…The sensors and actuators fail at times in capturing the data during the experiments on the tokamak study which may lead to missing values. To overcome this problem, machine learning-based techniques [62] have been implemented to handle this kind of missing value by using mean value replacement. The researchers utilized 187 shots, where 132 shots were used for training, consisting of 82 normal and 50 disruptive shots.…”

Section: Classifier Techniquesmentioning

confidence: 99%

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

Priyanka,

Sangeetha,

Jayakumar

2024

E3S Web Conf.

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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.

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Section: Classifier Techniquesmentioning

confidence: 99%