AI 中文总结
针对EXL-50U球形托卡马克，开发了基于TCN与RFM技术的实时破裂预测与缓解系统，在对应放电实验中表现出高准确率与低延迟，验证了其工程可行性。
AI 中文摘要
本研究针对EXL-50U球形托卡马克的大电流运行工况，开发了一套实时等离子体破裂预测与缓解系统。该系统利用反射内存（RFM）技术构建低延迟实时数据通路，形成了集多通道诊断信号采集、在线预处理、实时推理及大体积气体注入（MGI）触发于一体的全集成流程。系统核心为基于带通道注意力机制的时间卷积网络（TCN）的轻量级预测模型，可提取等离子体破裂前兆特征并自适应加权不同诊断通道的重要性。在14036至14790次放电实验中，该系统的真阳性率达82.4%，假阳性率为16.5%，在线运行时端到端延迟低于1毫秒。缓解实验进一步表明，MGI系统可提供所需气体储量并触发注入后快速的等离子体响应，满足EXL-50U的运行要求，并为EHL-2等未来装置提供工程指导。这些结果证实了EXL-50U上集成实时破裂控制的工程可行性，为更高参数聚变装置的后续研究提供了坚实基础。
英文摘要
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.
