首页 | 本学科首页   官方微博 | 高级检索  
     


Self-Similar Decay in the Kraichnan Model of a Passive Scalar
Authors:Gregory L. Eyink  Jack Xin
Affiliation:(1) Department of Mathematics, University of Arizona, Tucson, Arizona, 85721;(2) Department of Mathematics, University of Texas at Austin, Austin, Texas, 78712
Abstract:We study the two-point correlation function of a freely decaying scalar in Kraichnan's model of advection by a Gaussian random velocity field that is stationary and white noise in time, but fractional Brownian in space with roughness exponent 0<zeta<2, appropriate to the inertial-convective range of the scalar. We find all self-similar solutions by transforming the scaling equation to Kummer's equation. It is shown that only those scaling solutions with scalar energy decay exponent ale(d/gamma)+1 are statistically realizable, where d is space dimension and gamma=2–zeta. An infinite sequence of invariants Jp, p=0, 1, 2,..., is pointed out, where J0 is Corrsin's integral invariant but the higher invariants appear to be new. We show that at least one of the invariants J0 or J1 must be nonzero (possibly infinite) for realizable initial data. Initial datum with a finite, nonzero invariant—the first being Jp—converges at long times to a scaling solution PHgrp with a=(d/gamma)+p, p=0, 1. The latter belongs to an exceptional series of self-similar solutions with stretched-exponential decay in space. However, the domain of attraction includes many initial data with power-law decay. When the initial datum has all invariants zero or infinite and also it exhibits power-law decay, then the solution converges at long times to a nonexceptional scaling solution with the same power-law decay. These results support a picture of a ldquotwo-scalerdquo decay with breakdown of self-similarity for a range of exponents (d+gamma)/gamma<a<(d+2)/gamma, analogous to what has recently been found in the decay of Burgers turbulence.
Keywords:passive scalar  Kraichnan model  self-similar decay
本文献已被 SpringerLink 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号