基于改进Swin Transformer的HL-3实时等离子体形状检测与控制
Adapted Swin Transformer-based Real-Time Plasma Shape Detection and Control in HL-3
AI总结：
本文提出基于改进Swin Transformer的PST模型，通过CCD图像实时检测HL-3托卡马克等离子体形状，实现高精度快速反馈，为实时控制奠定基础。
摘要：
在磁约束等离子体控制领域，等离子体位置和形状的精确反馈主要依赖于通过平衡重建或矩阵映射方法从磁测量中计算得出。然而，在高能中子辐射和高温等恶劣条件下，在装置内部安装磁探针变得具有挑战性。仅依赖外部磁探针可能会损害EFIT确定等离子体形状的精度。为解决这一问题，我们在HL-3托卡马克上引入了一种实时的非磁测量方法，通过成像诊断等离子体位置和形状。特别地，我们提出了一种改进的Swin Transformer模型，即Poolformer Swin Transformer（PST），以准确且快速地从电荷耦合器件相机（CCD）图像中解释等离子体形状。通过采用多任务学习和知识蒸馏技术，该模型能够在诸如偏滤器形状和气体注入等干扰条件下稳健地检测六个形状参数，规避全局亮度变化和繁琐的人工标注。具体而言，训练良好的PST模型能够在平均误差分别低于1.1厘米和1.8厘米的情况下推断R和Z，同时端到端反馈所需时间少于2毫秒，比最小的Swin Transformer模型提高了80%，为实时控制奠定了基础。最后，我们使用TensorRT将PST模型部署在等离子体控制系统（PCS）中，并基于PST计算的水平位移信息实现了500毫秒的稳定PID反馈控制。总之，这项研究为图像计算等离子体形状诊断方法在实时反馈控制领域的实际应用开辟了新途径。
英文摘要：
In the field of magnetic confinement plasma control, the accurate feedback of plasma position and shape primarily relies on calculations derived from magnetic measurements through equilibrium reconstruction or matrix mapping method. However, under harsh conditions like high-energy neutron radiation and elevated temperatures, the installation of magnetic probes within the device becomes challenging. Relying solely on external magnetic probes can compromise the precision of EFIT in determining the plasma shape. To tackle this issue, we introduce a real-time, non-magnetic measurement method on the HL-3 tokamak, which diagnoses the plasma position and shape via imaging. Particularly, we put forward an adapted Swin Transformer model, the Poolformer Swin Transformer (PST), to accurately and fastly interpret the plasma shape from the Charge-Coupled Device Camera (CCD) images. By adopting multi-task learning and knowledge distillation techniques, the model is capable of robustly detecting six shape parameters under disruptive conditions such as a divertor shape and gas injection, circumventing global brightness changes and cumbersome manual labeling. Specifically, the well-trained PST model capably infers R and Z within the mean average error below 1.1 cm and 1.8 cm, respectively, while requiring less than 2 ms for end-to-end feedback, an 80 improvement over the smallest Swin Transformer model, laying the foundation for real-time control. Finally, we deploy the PST model in the Plasma Control System (PCS) using TensorRT, and achieve 500 ms stable PID feedback control based on the PST-computed horizontal displacement information. In conclusion, this research opens up new avenues for the practical application of image-computing plasma shape diagnostic methods in the realm of real-time feedback control.
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