Turbulent field fluctuations in gyrokinetic and fluid plasmas

A key uncertainty in the design and development of magnetic confinement fusion energy reactors is predicting edge plasma turbulence. An essential step in overcoming this uncertainty is the validation in accuracy of reduced turbulent transport models. Drift-reduced Braginskii two-fluid theory is one such set of reduced equations that has for decades simulated boundary plasmas in experiment, but significant questions exist regarding its predictive ability. To this end, using a novel physics-informed deep learning framework, we demonstrate the first ever direct quantitative comparisons of turbulent field fluctuations between electrostatic two-fluid theory and electromagnetic gyrokinetic modelling with good overall agreement found in magnetized helical plasmas at low normalized pressure. This framework is readily adaptable to experimental and astrophysical environments, and presents a new technique for the numerical validation and discovery of reduced global plasma turbulence models.

磁约束聚变能反应堆的设计与研发中，一项核心不确定性在于边缘等离子体湍流的预测。破解该不确定性的必要步骤，是对简化湍流输运模型的精度开展验证。约化漂移玻尔金斯基两流体理论（Drift-reduced Braginskii two-fluid theory）作为一类约化方程，数十年来一直被用于模拟实验中的边界等离子体，但学界对其预测能力仍存在诸多疑问。为此，我们采用一种全新的物理感知深度学习（physics-informed deep learning）框架，首次实现了静电两流体理论与电磁回旋动理学（gyrokinetic）建模之间湍流场涨落的直接定量对比，发现在低归一化压强下的磁化螺旋等离子体中，二者整体吻合度良好。该框架可灵活适配实验与天体物理环境，为简化全局等离子体湍流模型的数值验证与理论发现提供了全新的技术手段。