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arXiv · 2608.22720

Areal-time disruption prediction and mitigation system for the EXL-50U spherical torus

J. P. Zhou·S. F. Liu·J. Q. Cai·H. Y. Zhao·J. Li·Y. P. Zhang·D. Guo·C. Wu·A. Wang·H. Y. Li·C. Zhang·Z. Y. Chen·Y. J. Shi
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Abstract

This work presents a real-time disruption prediction and mitigation system developed for high-current operations in the EXL-50U Spherical Torus. By leveraging Reflective Memory (RFM) technology, the system establishes a low-latency real-time data path, creating a fully integrated pipeline that synchronizes multi-channel diagnostic acquisition, online preprocessing, real-time inference, and Massive Gas Injection (MGI) triggering. At its core, a lightweight prediction model based on a Temporal Convolutional Network (TCN) with a channel attention mechanism extracts disruption precursor features while adaptively weighting the importance of different diagnostic channels. {Tested across discharges \#14036--\#14790, the system achieves a true positive rate of 82.4\% and a false positive rate of 16.5\%, with end-to-end latency below $1~\mathrm{ms}$ in online operation.} Mitigation experiments further show that the MGI system can supply the required gas inventory and trigger a rapid post-injection plasma response, supporting the operational requirements of EXL-50U and providing engineering guidance for future devices such as EHL-2. These results confirm the engineering feasibility of integrated real-time disruption control on EXL-50U, offering a robust basis for future research in higher-parameter fusion devices.

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J. P. Zhou, S. F. Liu, J. Q. Cai, H. Y. Zhao, J. Li, Y. P. Zhang, D. Guo, C. Wu, A. Wang, H. Y. Li, C. Zhang, Z. Y. Chen, Y. J. Shi. 2026-08-24. Areal-time disruption prediction and mitigation system for the EXL-50U spherical torus. https://arxiv.org/abs/2608.22720

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