Wendelstein 7-X弹丸加料放电中等离子体约束损失的神经网络预测
Neural network predictions of plasma confinement loss in Wendelstein 7-X pellet-fueled discharges
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中文总结 AI 辅助
本研究开发数据驱动模型，预测W7-X弹丸加料放电中等离子体失去增强约束的剩余时间，模型预测精度达90%以上且评估迅速，可用于优化弹丸注入的实时控制系统。
中文摘要 AI 辅助
能量约束时间是磁化聚变等离子体的关键参数，有助于确定能否实现点火。托卡马克和仿星器的实验表明，通过弹丸注入可提升约束时间。特定弹丸带来的增强约束状态会随时间恶化，除非注入后续弹丸。本研究开发了一种数据驱动模型，可在任意时刻预测Wendelstein 7-X（W7-X）中等离子体失去增强约束状态前的剩余时间，该“剩余时间”指标有效设定了维持高约束时间所需注入下一个弹丸的截止时间。我们描述了该模型的开发与训练过程，并将其预测结果与过往实验观测数据对比，模型至少90%的预测精度在51毫秒以内，该值低于W7-X典型能量约束时间，也低于后续弹丸注入的最小时间间隔。该模型可快速评估，适用于实时优化弹丸注入速率的控制系统。
英文摘要
The energy confinement time is a key parameter of a magnetized fusion plasma, helping to determine whether ignition can occur. Experiments in tokamaks and stellarators have shown that the confinement time can be improved via pellet injection. The state of enhanced confinement brought about by a given pellet typically deteriorates over time unless and until a subsequent pellet is injected. In this work, we develop a data-driven model that predicts, at any moment, the remaining time before a plasma in Wendelstein 7-X (W7-X) will lose its enhanced confinement state. This "remaining time" metric effectively sets a deadline for when the next pellet must be injected in order to steadily maintain a high confinement time. We describe the development and training of the model and compare its predictions to observations from previous experiments. At least 90% of the model predictions are accurate to within 51 ms, which is below the typical W7-X energy confinement time as well as the minimum time separation between subsequent pellet injections. The model can be evaluated rapidly and could be suitable for use in a control system that optimizes the pellet injection rate in real time.
