Nuclear fusion power created by tokamak devices holds one of the most promising ways as a sustainable source of clean energy. One main challenge research field of tokamak is to predict the last closed magnetic flux surface (LCFS) determined by the interaction of the actuator coils and the internal tokamak plasma. This work requires high-dimensional, high-frequency, high-fidelity, real-time tools, further complicated by the wide range of actuator coils input interact with internal tokamak plasma states. In this work, we present a new machine learning model for reconstructing the LCFS from the Experimental Advanced Superconducting Tokamak (EAST) that learns automatically from the experimental data of EAST. This architecture can check the control strategy design and integrate it with the tokamak control system for real-time magnetic prediction. In the real-time modeling test, our approach achieves over 99% average similarity in LCFS reconstruction of the entire discharge process. In the offline magnetic reconstruction, our approach reaches over 93% average similarity.
翻译:托卡马克装置所创造的核聚变动力是作为可持续的清洁能源来源的最有希望的方式之一。托卡马克的主要挑战研究领域之一是预测由激活器共和体和内托卡马克等离子体相互作用决定的最后一个封闭磁通流表面(LCFS)。这项工作需要高维、高频、高纤维、高时空工具,并由于一系列的动作器共和体输入与内部托卡马克等离子体状态相互作用而进一步复杂化。在这项工作中,我们提出了一个从实验高级超导托卡马克(East)中重建LCFS(LCFS)的新机器学习模型,该模型可以自动从东方实验数据中学习。这个结构可以检查控制战略设计,并将其与托卡马克控制系统整合,用于实时磁性预测。在实时模型测试中,我们的方法在整个排放过程重建LCFS中达到平均99%以上的相似性。在离子体磁重建中,我们的方法达到了平均相似性超过93%。