BxC 工具包:生成定制的湍流三维磁场

Daniela Maci, Rony Keppens and Fabio Bacchini
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引用次数: 0

摘要

湍流状态在等离子体中无处不在,对湍流的了解是现代天体物理学的基础。数值模拟是研究湍流的最先进方法,需要大量计算资源。最近,人们的注意力转移到了生成合成湍流磁场的方法上,这种磁场具有参数控制的湍流特征。在此背景下,我们开发了 BxC 工具包,并根据各向同性湍流磁场的直接数值模拟(DNS)进行了验证。在此,我们展示了 BxC 的新扩展,以快速、可控的几何方法生成逼真的湍流磁场。首先,我们进行了参数研究,以确定 BxC 输入参数与所需的湍流功率谱特征之间的定量关系,如惯性范围、光谱斜率以及注入和耗散尺度。其次,我们在模型中引入了一组结构化背景磁场 B0,作为纯各向同性湍流场的自然和更现实的扩展。第三,我们对模型进行了扩展,将各向异性的湍流特性纳入生成的场中。结合所有这些扩展,我们的工具可以快速生成任何所需的结构磁场,并具有可控的各向异性湍流波动,速度比 DNS 快几个数量级。这些数据可用于为 DNS 提供初始条件,或为许多天体物理环境轻松生成合成数据,而所有这些数据的分辨率都是难以承受的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
BxC Toolkit: Generating Tailored Turbulent 3D Magnetic Fields
Turbulent states are ubiquitous in plasmas, and the understanding of turbulence is fundamental in modern astrophysics. Numerical simulations, which are the state-of-the-art approach to the study of turbulence, require substantial computing resources. Recently, attention shifted to methods for generating synthetic turbulent magnetic fields, affordably creating fields with parameter-controlled characteristic features of turbulence. In this context, the BxC toolkit was developed and validated against direct numerical simulations (DNSs) of isotropic turbulent magnetic fields. Here, we demonstrate novel extensions of BxC to generate realistic turbulent magnetic fields in a fast, controlled, geometric approach. First, we perform a parameter study to determine quantitative relations between the BxC input parameters and the desired characteristic features of the turbulent power spectrum, such as the extent of the inertial range, its spectral slope, and the injection and dissipation scale. Second, we introduce in the model a set of structured background magnetic fields, B0, as a natural and more realistic extension to the purely isotropic turbulent fields. Third, we extend the model to include anisotropic turbulence properties in the generated fields. With all these extensions combined, our tool can quickly generate any desired structured magnetic field with controlled, anisotropic turbulent fluctuations, faster by orders of magnitude with respect to DNSs. These can be used, e.g., to provide initial conditions for DNSs or easily generate synthetic data for many astrophysical settings, all at otherwise unaffordable resolutions.
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