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arXiv 1105.2912astro-ph.SR
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来自直接数值模拟的全球发电机模型及其平均场对应物

Global dynamo models from direct numerical simulations and their mean-field counterparts

M. Schrinner

摘要:

上下文最近开发的测试场方法允许计算发电机系数从全球,直接数值模拟。随后使用这些参数的平均场模型,使我们能够比较自洽发电机模型与他们的平均场。到目前为止,这已经完成了旋转磁对流和一个简单的基准发电机,这两者都是(准)静态的模拟。目标。结果表明,在低Rossby数制度的混沌时间依赖发电机可以适当地描述相应的平均场结果。此外,它指出在哪些条件下平均场模型不匹配直接数值模拟。方法.本文在Boussinesq近似下求解了旋转球壳中的磁流体动力学方程。在此基础上,我们计算的平均场系数的几个模型的帮助下,以前开发的测试场方法。这些系数的平均电动势的参数化测试对直接数值模拟。此外,我们使用确定的发电机系数的平均场模型和比较结果与方位平均场直接数值模拟。结果快速旋转发电机的方位角和时间平均电动势是足够好的参数化的一组确定的平均场系数。在以前考虑的(准)平稳发电机相比,混沌的时间依赖性导致一个改进的尺度分离,从而直接数值模拟和平均场结果之间的协议。

英文摘要:

Context. The recently developed test-field method permits to compute dynamo coefficients from global, direct numerical simulations. The subsequent use of these parameters in mean-field models enables us to compare self-consistent dynamo models with their mean-field counterparts. So far, this has been done for a simulation of rotating magnetoconvection and a simple benchmark dynamo, which are both (quasi-)stationary. Aims. It is shown that chaotically time-dependent dynamos in a low Rossby number regime may be appropriately described by corresponding mean-field results. Also, it is pointed out under which conditions mean-field models do not match direct numerical simulations. Methods. We solve the equations of magnetohydrodynamics (MHD) in a rotating, spherical shell in the Boussinesq approximation. Based on this, we compute mean-field coefficients for several models with the help of the previously developed test-field method. The parameterization of the mean electromotive force by these coefficients is tested against direct numerical simulations. In addition, we use the determined dynamo coefficients in mean-field models and compare the outcome with azimuthally averaged fields from direct numerical simulations. Results. The azimuthally and time-averaged electromotive force in fast rotating dynamos is sufficiently well parameterized by the set of determined mean-field coefficients. In comparison to the previously considered (quasi-)stationary dynamo, the chaotic time-dependence leads to an improved scale separation and thus to a better agreement between direct numerical simulations and mean-field results.

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