Deep electric field predictions by drift-reduced Braginskii theory with plasma-neutral interactions based upon experimental images of boundary turbulence

We present 2-dimensional turbulent electric field calculations via physics-informed deep learning consistent with (i) drift-reduced Braginskii theory under the framework of an axisymmetric fusion plasma with purely toroidal field and (ii) experimental estimates of the fluctuating electron density and temperature obtained from analysis of gas puff imaging of a discharge on the Alcator C-Mod tokamak. The inclusion of effects from the locally puffed atomic helium on particle and energy sources within the reduced plasma turbulence model are found to strengthen correlations between the electric field and electron pressure. The neutrals are also directly associated with an observed broadening in the distribution of turbulent field amplitudes and increased E×B shearing rates.

本工作报道了基于物理信息深度学习（physics-informed deep learning）的二维湍流电场计算结果，该计算结果同时满足两项约束条件：其一为纯环向磁场轴对称聚变等离子体框架下的漂移约化布拉金斯基理论（drift-reduced Braginskii theory）；其二为通过对Alcator C-Mod托卡马克（Alcator C-Mod tokamak）某次放电的气体喷注成像（gas puff imaging）数据分析得到的涨落电子密度与温度的实验估算值。
研究表明，在约化等离子体湍流模型中纳入局部喷注原子氦气对粒子源与能量源的影响，能够增强电场与电子压强之间的相关性。
此外，中性粒子还与观测到的湍流场振幅分布展宽以及升高的E×B剪切速率直接相关。