Influence of different mutual friction models on two-way coupled quantized vortices and normal fluid in superfluid $^4$He

Hiromichi Kobayashi, Satoshi Yui, Makoto Tsubota
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Abstract

We study the influence of two mutual friction models on quantized vortices and normal fluid using two-way coupled simulations of superfluid $^4$He. The normal fluid is affected by quantized vortices via mutual friction. A previous study [Y. Tang, et al. Nat. Commun. 14, 2941 (2023)] compared the time evolutions of the vortex ring radius and determined that the self-consistent two-way coupled mutual friction (S2W) model yielded better agreement with the experimental results than the two-way coupled mutual friction (2W) model whose model parameters were determined through experiments with rotating superfluid helium. In this study, we compare the two models in more detail in terms of the quantized vortex ring propagation, reconnection, and thermal counterflow. We found that the S2W model exhibits better results than the 2W model on the microscopic scale near a quantized vortex, such as during quantized vortex ring propagation and reconnection, although the S2W model requires a higher spatial resolution. For complex flows such as a thermal counterflow, the 2W model can be applied even to a low-resolution flow while maintaining the anisotropic normal fluid velocity fluctuations. In contrast, the 2W model predicts lower normal fluid velocity fluctuations than the S2W model. The two models show probability density functions with $- 3$ power-law tails for the normal fluid velocity fluctuations.
不同相互摩擦模型对超流体 $^4$He 中双向耦合量子化涡流和法向流体的影响
我们利用超流体 $^4$He 的双向耦合模拟,研究了两种相互摩擦模型对量子化涡流和正常流体的影响。正常流体通过相互摩擦受到量子化涡流的影响。之前的一项研究[Y. Tang, et al. Nat. Commun. 14, 2941 (2023)]比较了涡旋环半径的时间变化,并确定自洽双向耦合相互摩擦(S2W)模型比双向耦合相互摩擦(2W)模型与实验结果的一致性更好,后者的模型参数是通过旋转超流体实验确定的。在本研究中,我们从量化涡旋环传播、再连接和热逆流等方面更详细地比较了这两个模型。我们发现,在量子化涡旋附近的微观尺度上,例如在量子化涡旋环传播和重联过程中,S2W模型比2W模型显示出更好的结果,尽管S2W模型需要更高的空间分辨率。对于热逆流等复杂流动,2W 模型甚至可以应用于低分辨率流动,同时保持各向异性的正常流体速度波动。相比之下,2W 模型预测的正态流体速度波动低于 S2W 模型。这两个模型都显示了正态流体速度波动的概率密度函数具有 $- 3$ 的幂律尾。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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