Predicting solar wind streams from the inner-heliosphere to Earth via shifted operator inference
Predicting solar wind streams from the inner-heliosphere to Earth via shifted operator inference
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DOI：
10.1016/j.jcp.2022.111689
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发表时间：
2022-03
期刊：
影响因子：
--
通讯作者：
Opal Issan;B. Kramer
中科院分区：
文献类型：
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作者：
Opal Issan;B. Kramer
太阳风条件主要是通过三维数值磁流体动力学（MHD）模型预测。尽管它们能够产生高度准确的预测，但MHD模型需要计算密集型的高维模拟。这使得它们不足以进行时间敏感的预测和不确定性量化所需的大集合分析。本文提出了一种新的数据驱动的降阶模式（ROM）预测日光层太阳风速度的能力。传统的基于Galerkin投影的模型降阶方法在对流主导的系统（如太阳风）中存在困难，因为它们需要大量的基函数并且可能变得不稳定。这项工作的核心贡献解决了这一挑战，通过扩展非侵入性的运营商推理ROM框架，利用平移对称性所造成的太阳风太阳的旋转。数值结果表明，该方法能够很好地模拟MHD模拟，并且比简化物理代理模型--日球逆风外推模型更精确。
Solar wind conditions are predominantly predicted via three-dimensional numerical magnetohydrodynamic (MHD) models. Despite their ability to produce highly accurate predictions, MHD models require computationally intensive high-dimensional simulations. This renders them inadequate for making time-sensitive predictions and for large-ensemble analysis required in uncertainty quantification. This paper presents a new data-driven reduced-order model (ROM) capability for forecasting heliospheric solar wind speeds. Traditional model reduction methods based on Galerkin projection have difficulties with advection-dominated systems—such as solar winds—since they require a large number of basis functions and can become unstable. A core contribution of this work addresses this challenge by extending the non-intrusive operator inference ROM framework to exploit the translational symmetries present in the solar wind caused by the Sun's rotation. The numerical results show that our method can adequately emulate the MHD simulations and is more accurate than a reduced-physics surrogate model, the Heliospheric Upwind Extrapolation model.