利用机器学习对托卡马克等离子体中的热传输进行模拟预测

IF 3.5 2区 物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY
Hui Li, Yan-Lin Fu, Ji-Quan Li, Zheng-Xiong Wang
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引用次数: 0

摘要

机器学习为等离子体约束研究提供了新的可能性。具体来说,利用机器学习算法构建的模型可以有效简化模拟过程。以往的第一原理模拟可以提供基于物理的传输信息,但在实时应用或等离子体控制方面速度不够快。为解决这一问题,本研究提出了 Gyro-Landau 扩展流体代码(ExFC)的替代模型 SExFC。作为我们之前模型 ExFC-NN 的扩展版本,SEXFC 可以利用最初由 ExFC 仿真生成的扩展数据库,捕捉离子温度梯度模式和困电子模式驱动的传输的更多特征。除了预测主导不稳定性、径向平均通量和通量的径向剖面之外,训练有素的 SExFC 还适用于基于物理学的快速预测,可在未来的实时等离子体控制系统中加以考虑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Simulation Prediction of Heat Transport with Machine Learning in Tokamak Plasmas
Machine learning opens up new possibilities for research of plasma confinement. Specifically, models constructed using machine learning algorithms may effectively simplify the simulation process. Previous first-principles simulations could provide physics-based transport information, but not fast enough for real-time applications or plasma control. To address this issue, this study proposes SExFC, a surrogate model of the Gyro-Landau Extended Fluid Code (ExFC). As an extended version of our previous model ExFC-NN, SExFC can capture more features of transport driven by the ion temperature gradient mode and trapped electron mode, using an extended database initially generated with ExFC simulations. In addition to predicting the dominant instability, radially averaged fluxes and radial profiles of fluxes, the well-trained SExFC may also be suitable for physics-based rapid predictions that can be considered in real-time plasma control systems in the future.
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来源期刊
Chinese Physics Letters
Chinese Physics Letters 物理-物理:综合
CiteScore
5.90
自引率
8.60%
发文量
13238
审稿时长
4 months
期刊介绍: Chinese Physics Letters provides rapid publication of short reports and important research in all fields of physics and is published by the Chinese Physical Society and hosted online by IOP Publishing.
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