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

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 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.