传感器丢失下基于扩散先验的KSTAR平衡重建

中文摘要 AI 辅助

我们研究了韩国超导托卡马克先进研究（KSTAR）装置在磁传感器丢失情况下的平衡重建问题，并以扩散模型作为先验。磁测量使得环向电流密度 $J_\phi$ 的大部分分量无法确定，而传感器丢失则使更多分量依赖于先验。扩散模型仅学习 $J_\phi$，并以线圈电流、等离子体电流以及大半径与环向磁场的乘积为条件，这些量不依赖于传感器丢失。物理信息通过由LIUQE代码的传感器响应矩阵构建的线性正演算子引入。由于观测是线性的，解耦退火后验采样的变体通过闭式线性修正来拟合观测。在37次KSTAR放电的实测信号上，我们随机关闭所使用的124个磁通道中的一部分（比例从0.0到0.9，共十种设置），并在相同掩码下与LIUQE进行比较，以全传感器LIUQE重建作为标签。两种方法对剩余通道的拟合水平相似。在所有设置下，扩散重建的 $J_\phi$ 与标签的中位相对距离保持在3.22%--4.07%，而LIUQE在丢失率为0.9时达到11.89%。在丢失率为0.5--0.9时，扩散重建在37次放电中的27--37次上更接近标签。在低丢失率下，LIUQE在大多数放电上更接近。对于导出的磁通，在丢失率为0.5--0.9时，扩散重建在21--36次放电上更接近，且其在高丢失率下的误差来源于容器电流估计，而非先验。

英文摘要

We study equilibrium reconstruction for the Korea Superconducting Tokamak Advanced Research (KSTAR) device under magnetic sensor dropout, with a diffusion model as the prior. Magnetic measurements leave most components of the toroidal current density $J_ϕ$ undetermined, and sensor loss leaves more of them to the prior. The diffusion model learns only $J_ϕ$, conditioned on the coil currents, the plasma current and the product of major radius and toroidal field, which do not depend on the dropout. The physics enters through a linear forward operator built from sensor response matrices of the LIUQE code. Because the observations are linear, a variant of decoupled annealing posterior sampling fits them with a closed-form linear correction. On measured signals of 37 KSTAR shots, we switch off a random fraction (0.0 to 0.9, ten settings) of the 124 magnetic channels in use and compare with LIUQE under the same masks, taking the full-sensor LIUQE reconstruction as the label. Both methods fit the remaining channels to a similar level. The median relative distance of $J_ϕ$ from the label stays at 3.22--4.07\% for the diffusion reconstruction in all settings, whereas that of LIUQE reaches 11.89\% at dropout 0.9. At dropout 0.5--0.9, the diffusion reconstruction is closer to the label on 27--37 of the 37 shots. At low dropout, LIUQE is closer on most shots. For the derived flux, the diffusion reconstruction is closer on 21--36 shots at dropout 0.5--0.9, and its error at high dropout comes from the vessel current estimate, not from the prior.

发表机构

• Chung-Ang University（中央大学）

机构由 AI 辅助整理，请以论文原文为准。