Abstract
We develop a Lagrangian approach to conservation-law anomalies in weak solutions of inviscid Burgers equation, motivated by previous work on the Kraichnan model of turbulent scalar advection. We show that the entropy solutions of Burgers possess Markov stochastic processes of (generalized) Lagrangian trajectories backward in time for which the Burgers velocity is a backward martingale. This property is shown to guarantee dissipativity of conservation-law anomalies for general convex functions of the velocity. The backward stochastic Burgers flows with these properties are not unique, however. We construct infinitely many such stochastic flows, both by a geometric construction and by the zero-noise limit of the Constantin–Iyer stochastic representation of viscous Burgers solutions. The latter proof yields the spontaneous stochasticity of Lagrangian trajectories backward in time for Burgers, at unit Prandtl number. It is conjectured that existence of a backward stochastic flow with the velocity as martingale is an admissibility condition which selects the unique entropy solution for Burgers. We also study linear transport of passive densities and scalars by inviscid Burgers flows. We show that shock solutions of Burgers exhibit spontaneous stochasticity backward in time for all finite Prandtl numbers, implying conservation-law anomalies for linear transport. We discuss the relation of our results for Burgers with incompressible Navier–Stokes turbulence, especially Lagrangian admissibility conditions for Euler solutions and the relation between turbulent cascade directions and time-asymmetry of Lagrangian stochasticity.
Original language | English (US) |
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Pages (from-to) | 386-432 |
Number of pages | 47 |
Journal | Journal of Statistical Physics |
Volume | 158 |
Issue number | 2 |
DOIs | |
State | Published - Jan 1 2015 |
All Science Journal Classification (ASJC) codes
- Statistical and Nonlinear Physics
- Mathematical Physics
Keywords
- Admissibility condition
- Burgers equation
- Dissipative anomaly
- Kraichnan model
- Spontaneous stochasticity
- Weak solution