Simple models of turbulent flows

Abstract

Stochastic Lagrangian models provide a simple and direct way to model turbulent flows and the processes that occur within them. This paper provides an introduction to this approach, aimed at the nonspecialist, and providing some historical perspective. Basic models for the Lagrangian velocity (i.e., the Langevin equation) and composition are described and applied to the simple but revealing case of dispersion from a line source in grid turbulence. With simple extensions, these models are applied to inhomogeneous turbulent reactive flows, where they form the core of probability density function (PDF) methods. The use of PDF methods is illustrated for the case of a lifted turbulent jet flame. Lagrangian time series are now accessible both from experiments and from direct numerical simulations, and this information is used to scrutinize and improve stochastic Lagrangian models. In particular, we describe refinements to account for the observed strong Reynolds-number effects including intermittency. It is emphasized that all models of turbulence are necessarily approximate and incomplete, and that simple models are valuable in many applications in spite of their limitations.