Transport and mixing in control volumes through the lens of probability

TL;DR

This paper develops a global probabilistic framework for analyzing transport and mixing in control volumes, emphasizing the role of boundary fluxes and internal mixing, with applications to uncertainty and energy considerations.

Contribution

It introduces a novel global integral approach to model the evolution of joint probability distributions in control volumes, incorporating boundary fluxes and internal mixing effects.

Findings

Derivation of a PDE for joint probability distribution evolution

Identification of conditions for negative semidefinite diffusion coefficients

Application to models involving uncertainty and energy in mixing processes

Abstract

A partial differential equation governing the global evolution of the joint probability distribution of an arbitrary number of local flow observations, drawn randomly from a control volume, is derived and applied to examples involving irreversible mixing. Unlike local probability density methods, this work adopts a global integral perspective by regarding a control volume as the sample space. Doing so enables the divergence theorem to be used to expose contributions made by uncertain or stochastic boundary fluxes and internal cross-gradient mixing in the equation governing the joint probability distribution's evolution. Advection and diffusion across the control volume's boundary result in source and drift terms, respectively, whereas internal mixing, in general, corresponds to the sign-indefinite diffusion of probability density. Several typical circumstances for which the corresponding diffusion coefficient is negative semidefinite are identified and discussed in detail. The global joint probability perspective is the natural setting for available potential energy and the incorporation of uncertainty into bulk, volume integrated, models of transport and mixing. Finer-grained information in space can be readily obtained by treating coordinate functions as observables. By extension, the framework can be applied to networks of interacting control volumes of arbitrary size.