多维迭代滤波：数值模拟研究等离子体turbulence的新方法
Multidimensional Iterative Filtering: a new approach for investigating plasma turbulence in numerical simulations
摘要：
湍流空间和天体物理等离子体表现出复杂的动力学，其中涉及不同的时间和空间尺度的非线性耦合。越来越多的证据表明，脉冲事件，如磁重联不稳定性，导致空间局部增强的能量耗散，从而加快在小尺度上的能量转移。捕捉如此多样化的动态是具有挑战性的。在这里，我们采用多维迭代滤波（MIF）方法，一种新的技术，用于分析非平稳多维信号。与其他传统方法（例如，基于傅立叶或小波分解），MIF不需要对要识别的信号的函数形式进行任何先前的假设。使用MIF，我们进行了多尺度分析的霍尔磁流体动力学（HMHD）和混合粒子在细胞（HPIC）的数值模拟衰减等离子体湍流。结果评估的能力，MIF空间识别和分离的不同尺度（MHD惯性范围内，子离子动力学，和耗散尺度）的等离子体动力学。此外，MIF分解允许检测本地化的电流结构，并表征其对湍流的统计和光谱特性的贡献。总体而言，MIF出现作为一个非常有前途的技术研究湍流等离子体环境。
英文摘要：
Turbulent space and astrophysical plasmas exhibit a complex dynamics, which involves nonlinear coupling across different temporal and spatial scales. There is growing evidence that impulsive events, such as magnetic reconnection instabilities, lead to a spatially localized enhancement of energy dissipation, thus speeding up the energy transfer at small scales. Capturing such a diverse dynamics is challenging. Here, we employ the Multidimensional Iterative Filtering (MIF) method, a novel technique for the analysis of nonstationary multidimensional signals. Unlike other traditional methods (e.g., based on Fourier or wavelet decomposition), MIF does not require any previous assumption on the functional form of the signal to be identified. Using MIF, we carry out a multiscale analysis of Hall-magnetohydrodynamic (HMHD) and hybrid particle-in-cell (HPIC) numerical simulations of decaying plasma turbulence. The results assess the ability of MIF to spatially identify and separate the different scales (the MHD inertial range, the sub-ion kinetic, and the dissipation scales) of the plasma dynamics. Furthermore, MIF decomposition allows to detect localized current structures and to characterize their contribution to the statistical and spectral properties of turbulence. Overall, MIF arises as a very promising technique for the study of turbulent plasma environments.
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