2026/07/27 by Junru Wen, Yi Yu, Zongyu Yang +6
Engineering · Physics and Astronomy · #Magnetic confinement fusion research #Nuclear Engineering Thermal-Hydraulics #Nuclear reactor physics and engineering
paper · pdf · doi:10.1088/1361-6587/ae90ea
openalex publication_date 2026/07/27 · openalex created_date 2026/07/28 · openalex updated_date 2026/07/29
Abstract For tokamaks like the HL-3, which operates at reactor-grade parameters, major disruptions under high-performance conditions are intolerable. High-parameter tokamaks require not only algorithms and engineering solutions for disruption mitigation but also systematic analysis of disruption causes. Traditional disruption analysis relies on various diagnostic data, requiring both temporal evolution analysis and diagnostic cross-channel comparison, which demands substantial expert knowledge and manual effort. To address these limitations, a Plasma Event Identification System have been developed to detect key plasma events in the HL-3 tokamak. This system integrates artificial intelligence, threshold-based judgments to identify disruptions and pre-disruption plasma events including VDE, MHD instability, low-q disruption and high density. The performance of each module in the system was evaluated: The disruption identification module achieved 99% accuracy; The disruption time identification module reached 95% accuracy; The tearing mode and locked mode identification attained 95% overall accuracy, demonstrating the system’s robust performance. The system enables rapid statistics and analysis for HL-3 experiments, including disruption cause analysis based on expert-logic judgments and 200 ms pre-disruption event-chain. Furthermore, this system has been deployed in the HL-3 plasma display for disruption analysis. The analysis results are also upload to the Dig Data analysis database to support the research needs of engineering and physics researchers.