Anomalous exponents in strong turbulence

PHYSICA D-NONLINEAR PHENOMENA

卷:384

Article

Yakhot, Victor¹  Donzis, Diego A.² 

[1] Boston Univ, Dept Mech Engn, Boston, MA 02215 USA

[2] Texas A&M Univ, Dept Aerosp Engn, College Stn, TX 77843 USA

关键词: Incompressible turbulence;    Anomalous scaling;    Transition;   

DOI  :  10.1016/j.physd.2018.07.005

来源: Elsevier

【 摘 要 】

To characterize fluctuations in a turbulent flow, one usually studies different moments of velocity increments and dissipation rate, <((v(x + r) - v(x))(n))over bar> proportional to r(zeta n) and (epsilon(n)) over bar proportional to Re-dn, respectively. In high Reynolds number flows, the moments of different orders cannot be simply related to each other which is the signature of anomalous scaling, one of the most puzzling features of turbulent flows. High-order moments are related to extreme, rare events and our ability to quantitatively describe them is crucially important for meteorology, heat, mass transfer and other applications. In this work we present a solution to this problem in the particular case of the Navier-Stokes equations driven by a random force. A novel aspect of this work is that, unlike previous efforts which aimed at seeking solutions around the infinite Reynolds number limit, we concentrate on the vicinity of transitional Reynolds numbers Re-tr where the first emergence of anomalous scaling is observed out of a low-Re Gaussian background. The obtained closed expressions for anomalous scaling exponents and cl, which depend on the transition Reynolds number, agree well with experimental and numerical data in the literature and, when n >> 1, d(n) approximate to 0.19n ln(n). The theory yields the energy spectrum E(k) proportional to k(-zeta 2-1) I with zeta(2) approximate to 0.699, different from the outcome of Kolmogorov's theory. It is also argued that fluctuations of dissipation rate and those of the transition point itself are responsible for both, deviation from Gaussian statistics and multiscaling of velocity field. (C) 2018 Elsevier B.V. All rights reserved.

【 授权许可】

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