A Model for (Non-Lognormal) Density Distributions in Isothermal Turbulence
Philip F. Hopkins (Caltech/Berkeley)

TL;DR
This paper introduces a new physically motivated model for density probability distribution functions in isothermal turbulence, accurately capturing deviations from lognormality across various Mach numbers and simulation methods.
Contribution
The authors propose a novel distribution function that improves modeling of density PDFs in turbulence, accounting for intermittency and deviations from lognormality with fewer parameters.
Findings
Accurately fits density PDFs in simulations with Mach numbers 0.1-15.
Parameter T correlates with compressive Mach number and turbulence intermittency.
Explains discrepancies in density PDF moments and measurement methods.
Abstract
We propose a new, physically motivated fitting function for density PDFs in turbulent gas. Although it is known that when gas is isothermal, the PDF is approximately lognormal in the core, high-resolution simulations show large deviations from exact log-normality. The proposed function provides an extraordinarily accurate description of the density PDFs in simulations with Mach numbers ~0.1-15 and dispersion in log(rho) from ~0.01-4 dex. Compared to a lognormal or lognormal-skew-kurtosis model, the fits are improved by orders of magnitude in the wings of the distribution (with fewer free parameters). This is true in simulations using distinct numerical methods, including or excluding magnetic fields. Deviations from lognormality are represented by a parameter T that increases with the compressive Mach number. The proposed distribution can be derived from intermittent cascade models of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
