Effect of finite Reynolds number on self-similar crossing statistics and fractal measurements in turbulence

TL;DR

This paper uses stochastic simulations to study how finite Reynolds numbers influence self-similar crossing statistics and fractal measurements in turbulence, revealing distortions due to finite-size effects.

Contribution

It introduces an analysis of finite Reynolds number effects on turbulence statistics and provides an expression for the effective exponent accounting for finite-size distortions.

Findings

Finite Reynolds number distorts scale-invariance in turbulence statistics.

An effective exponent expression recovers expected relations between parameters.

Finite-size effects impact indicators of self-similarity and intermittency.

Abstract

Stochastic simulations are used to create synthetic one-dimensional telegraph approximation (TA) signals based on turbulent zero crossings, where the interval between crossings is governed by a power law probability distribution with exponent $\alpha$. The power law exponent is determined for statistics of simulated TA signals, namely the box-counting fractal dimension $D_1$, energy spectrum exponent $\beta_{TA}$, and an intermittency exponent $\mu_{TA}$. For the binary TA signal with no variability in amplitude, the parameters are related linearly as $D_1 = 2 - \beta_{TA} = 1 - \mu_{TA}$. The relations are unchanged if the crossing interval distribution has a finite power law region (i.e. inertial subrange) representing a flow with finite Reynolds number. However, the finite distribution yields statistics that are not truly scale-invariant, and distorts the linear relation between the statistic exponents and $\alpha$. The behavior is due to finite-size effects apparent from the survival function, or the complementary cumulative distribution, which for finite Reynolds number is only approximately self-similar and has an effective exponent differing from $\alpha$. An expression presented for the effective exponent recovers the expected relations between $\alpha$ and the TA statistics. The findings demonstrate how a finite Reynolds number can affect indicators of self-similarity, fractality, and intermittency observed from single-point measurements.