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arXiv 1010.3754astro-ph.GA
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超音速等温turbulence中密度方差-马赫数关系

The density variance -- Mach number relation in supersonic, isothermal turbulence

Daniel J. Price, Christoph Federrath, Christopher M. Brunt

摘要:

我们研究的密度方差和均方马赫数之间的关系,在超音速,等温湍流,假设在最近的几个分析模型的恒星形成过程。从一系列使用纯螺线管傅立叶模式驱动的超音速流体动力学湍流的计算中,我们发现密度对数方差与马赫数平方之间的“标准”关系,即,σ ^2_(ln rho/rhobar)=ln(1+b^2 M^2),b = 1/3,在马赫数至少达到20的超音速范围内与数值结果很好地吻合,这与以前在较低马赫数下的测定结果相似。虽然发现线性密度方差的直接测量值被有限分辨率效应严重低估,但可以通过概率分布函数中的对数正态假设来推断线性密度方差。推导出的与马赫数的关系,与sigma_然而,b=1/3的(rho/rhobar)~ bM比金牛座分子云和IC 5146中观测确定的关系明显要浅(两者均与b~ 0.5一致),这意味着额外的物理学,如重力,在这些云中很重要,或者ISM中的湍流驱动包含显著的压缩分量。没有发现磁场会显著改变这幅图,一般来说,会减少测量的方差,从而使与观测的差异恶化。

英文摘要:

We examine the relation between the density variance and the mean-square Mach number in supersonic, isothermal turbulence, assumed in several recent analytic models of the star formation process. From a series of calculations of supersonic, hydrodynamic turbulence driven using purely solenoidal Fourier modes, we find that the `standard' relationship between the variance in the log of density and the Mach number squared, i.e., sigma^2_(ln rho/rhobar)=ln (1+b^2 M^2), with b = 1/3 is a good fit to the numerical results in the supersonic regime up to at least Mach 20, similar to previous determinations at lower Mach numbers. While direct measurements of the variance in linear density are found to be severely underestimated by finite resolution effects, it is possible to infer the linear density variance via the assumption of log-normality in the Probability Distribution Function. The inferred relationship with Mach number, consistent with sigma_(rho/rhobar) ~ b M with b=1/3, is, however, significantly shallower than observational determinations of the relationship in the Taurus Molecular Cloud and IC5146 (both consistent with b~ 0.5), implying that additional physics such as gravity is important in these clouds and/or that turbulent driving in the ISM contains a significant compressive component. Magnetic fields are not found to change this picture significantly, in general reducing the measured variances and thus worsening the discrepancy with observations.

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