Data-Driven Control for Radiative Collapse Avoidance in Large Helical Device
نویسندگان
چکیده
A radiative collapse predictor has been developed using a machine-learning model with high-density plasma experiments in the Large Helical Device (LHD). The is based on likelihood, which quantified by parameters selected sparse modeling, including ne, CIV, OV, and Te,edge. control system implementing this constructed single-board computer to apply LHD experiment. controller calculates likelihood regulates gas-puff fueling boosts electron cyclotron resonance heating real-time. In density ramp-up hydrogen plasma, maintained while avoiding collapse. This result shown that capability predict
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ژورنال
عنوان ژورنال: Plasma and Fusion Research
سال: 2022
ISSN: ['1880-6821']
DOI: https://doi.org/10.1585/pfr.17.2402042