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arXiv 2603.17366physics.plasm-ph
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低维几何学习用于优化稳态器中的湍流预测

Low-dimensional geometry learning for turbulence prediction in optimized stellarators

University of California, Irvine(加州大学尔湾分校);Zhejiang University(浙江大学);Princeton University(普林斯顿大学);Lawrence Berkeley National Laboratory(劳伦斯伯克利国家实验室)

Xishuo Wei, Handi Huang, Haotian Chen, Hongxuan Zhu, Zhe Bai, Samuel Williams, Zhihong Lin

AI总结:

本文通过低维几何学习方法,利用深度学习发现稳态器设计在低维潜在空间中的分布,从而优化湍流传输、等离子体不稳定性及MHD模态。

摘要:

优化的稳态器是一种具有平均粒子径向漂移为零且单粒子损失显著减少的有吸引力概念。然而,对于反应堆设计,除了受限的单粒子轨道外,还需优化全局物理如湍流传输。第一原理全局传输代码计算成本过高,难以融入优化过程。基于机器学习的快速替代全球传输模型是可行选择,但因稳态器设计自由度高,需大量数据训练。本文发现近螺旋对称(QH)几何的稳态器设计近似分布在低维潜在空间中,可通过深度学习显式找到。这一发现使生成全局gyrokinetic模拟数据以训练替代模型,直接优化稳态器几何用于湍流传输、等离子体不稳定性及MHD模态成为可能。利用低维潜在空间和数据分析方法,发现线性区带残差与轴向位移的关系,为优化低湍流传输QH稳态器提供简单指导。

英文摘要:

The optimized stellarator is an attractive concept for which the averaged particle radial drift is zero, and the single particle loss can be significantly reduced. But for the reactor design, global physics such as turbulent transport also need to be optimized besides the confined single particle orbit, or properties estimated using local estimations and heuristic formulations. The first-principle global transport code is too computationally expensive to integrate into the optimization process. The fast surrogate global transport model based on machine learning is a good alternative choice, but the amount of data required to train the surrogate model is numerous due to the high degree-of-freedom of the stellarator design. The work shows that the stellarator design with quasi-helically(QH) symmetric geometry is approximately distributed in a low dimensional latent space, which can be explicitly found by deep learning. This discovery makes it possible to generate global gyrokinetic simulation data for training surrogate models to directly optimize the stellarator geometry for turbulent transport, energetic particle instability, and MHD modes. Using the low dimensional latent space and data analysis methods, the relation between linear zonal residues and axis-excursion is found, providing a simple guide to optimize low turbulent transport QH stellarators.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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