Seshasayanan Kannabiran, Gallet Basile, Alexakis Alexandros
Laboratoire de Physique Statistique, École Normale Supérieure, CNRS UMR 8550, Université Paris Diderot, Université Pierre et Marie Curie, 24 rue Lhomond, 75005 Paris, France.
Service de Physique de l'État Condensé, CEA, CNRS UMR 3680, Université Paris-Saclay, CEA Saclay, 91191 Gif-sur-Yvette, France.
Phys Rev Lett. 2017 Nov 17;119(20):204503. doi: 10.1103/PhysRevLett.119.204503.
While the saturated magnetic energy is independent of viscosity in dynamo experiments, it remains viscosity dependent in state-of-the-art 3D direct numerical simulations (DNS). Extrapolating such viscous scaling laws to realistic parameter values leads to an underestimation of the magnetic energy by several orders of magnitude. The origin of this discrepancy is that fully 3D DNS cannot reach low enough values of the magnetic Prandtl number Pm. To bypass this limitation and investigate dynamo saturation at very low Pm, we focus on the vicinity of the dynamo threshold in a rapidly rotating flow: the velocity field then depends on two spatial coordinates only, while the magnetic field consists of a single Fourier mode in the third direction. We perform numerical simulations of the resulting set of reduced equations for Pm down to 2×10^{-5}. This parameter regime is currently out of reach to fully 3D DNS. We show that the magnetic energy transitions from a high-Pm viscous scaling regime to a low-Pm turbulent scaling regime, the latter being independent of viscosity. The transition to the turbulent saturation regime occurs at a low value of the magnetic Prandtl number, Pm≃10^{-3}, which explains why it has been overlooked by numerical studies so far.
虽然在发电机实验中饱和磁能与粘性无关,但在当前最先进的三维直接数值模拟(DNS)中,它仍然依赖于粘性。将这种粘性标度律外推到实际参数值会导致磁能被低估几个数量级。这种差异的根源在于完全三维的DNS无法达到足够低的磁普朗特数Pm值。为了绕过这一限制并研究极低Pm下的发电机饱和现象,我们聚焦于快速旋转流中发电机阈值附近的情况:此时速度场仅依赖于两个空间坐标,而磁场在第三个方向上由单一傅里叶模式组成。我们对由此得到的简化方程组进行了数值模拟,模拟的Pm低至2×10⁻⁵。目前完全三维的DNS无法达到这个参数范围。我们表明,磁能从高Pm粘性标度 regime转变为低Pm湍流标度 regime,后者与粘性无关。向湍流饱和 regime的转变发生在磁普朗特数的低值处,Pm≃10⁻³,这解释了为什么到目前为止它一直被数值研究所忽视。