Abstract

Electromagnetic forces, thermal loads, and radiation loads experienced by the in-vessel components or vacuum vessels at the time of the tokamak plasma current quench (CQ) significantly affect the overall plasma device’s health. Thus the mitigation of plasma CQ is of paramount importance, which requires a proper identification of the disruption precursors. Using new Machine Learning (ML) and Artificial Intelligence (AI) approaches, it is possible to identify disruption precursors; however, such approaches require training the ML models. This training of models requires a massive amount of experimental data, which sometimes may not be available for different tokamaks. This necessitates the need for accurate synthetic disruption data generation presenting different types of the CQ profiles observed experimentally. A novel approach for synthetic CQ data generation, considering the experimental aspect of the CQ profile shape for a wide range of tokamak plasma discharges, is designed to train ML/AI models. The trained model results are also elaborated here, which includes identifying current before disruption and classification of CQ profile types in time-space.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.