Abstract
Thermonuclear fusion is one of the alternative sources of energy. Fusion reactors use a device called tokamak. Classification of favorable and non-favorable discharges in a tokamak is very important for plasma operation point of view. Non-favorable discharges are mainly disruptive in nature which causes certain losses of confinement that take place abruptly and affect the integrity of tokamak. During disruptions, the plasma energy gets transferred to the surrounding structures of vacuum vessel which causes to massive heat and serious damage. The objective of the proposed work is to classify such plasma discharges in tokamak among the other favorable discharges and make some suitable classifiers. The convolutional neural network can be implemented as one of the most viable and responsive tools to classify the disruption. In this paper, along with review on the various existing approaches, a proposed CNN-based disruption classification method specifically for Aditya tokamak is presented.
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Sharma, P. et al. (2018). A Proposed Method for Disruption Classification in Tokamak Using Convolutional Neural Network. In: Chakraverty, S., Goel, A., Misra, S. (eds) Towards Extensible and Adaptable Methods in Computing. Springer, Singapore. https://doi.org/10.1007/978-981-13-2348-5_14
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