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Anomalous Scaling in the Anisotropic Sectors of the Kriachnan Model of Passive Scalar Advection

1999/07/12 by Itai Arad Victor S. L'vov, L'vov, Itai Arad Victor S., Evgenii Podivilov +3
Physics and Astronomy · #Chaotic Dynamics (nlin.CD) #FOS: Physical sciences #chao-dyn #nlin.CD

paper · pdf · doi:10.48550/arxiv.chao-dyn/9907017

PRE, submitted, 17 pages, 3 eps. figures

arxiv created 1999/07/12 · arxiv updated 2009/11/30

Abstract

Kraichnan's model of passive scalar advection is studied as a case model for understanding the anomalous scaling in the anisotropic sectors. We show here that the solutions of the Kraichnan equation for the n order correlations foliate into sectors that are classified by the irreducible representations of the SO(d) group. We find a discrete spectrum of universal anomalous exponents in every sector. Generically the correlations and structure functions appear as sums over all the contributions, with non-universal amplitudes which are determined by the anisotropic boundary conditions. The isotropic sector is always characterized by the smallest exponent, and therefore for sufficiently small scales local isotropy is always restored. The calculation of the anomalous exponents is done in two complementary ways. In the first they are obtained from the analysis of correlations of gradient fields. The corresponding theory involves the control of logarithmic divergences which translate into anomalous scaling with the ratio of the inner and the outer scales appearing in the final result. In the second way we compute the exponents from the zero modes of the Kraichnan equation for the correlations of the scalar field itself. In this case the renormalization scale is the outer scale. The two approaches lead to the same scaling exponents for the same statistical objects, illuminating the relative role of the outer and inner scales as renormalization scales. We derive fusion rules which govern the small scale asymptotics of the correlation functions in the sectors of the symmetry group, in all dimensions.

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