Extending the Langevin model to variable-density pressure-gradient-driven turbulence

TL;DR

This paper extends the Langevin model to variable-density turbulence driven by pressure gradients, capturing large density fluctuations and small-scale anisotropy, enabling more accurate modeling of complex VD flows.

Contribution

It introduces a generalized Langevin model for VD turbulence that accounts for density fluctuations and anisotropy, reducing to the original model for constant density flows.

Findings

The extended model captures large density fluctuations and anisotropy.

Coupling with a mass-density particle model provides a closed-form statistical representation.

The framework sets the stage for future validation with Rayleigh-Taylor flow simulations.

Abstract

We extend the generalized Langevin model, originally developed for the Lagrangian fluid particle velocity in constant-density shear-driven turbulence, to variable-density (VD) pressure-gradient-driven flows. VD effects due to non-uniform mass concentrations (e.g. mixing of different species) are considered. In the extended model large density fluctuations leading to large differential fluid accelerations are accounted for. This is an essential ingredient to represent the strong coupling between the density and velocity fields in VD hydrodynamics driven by active scalar mixing. The small scale anisotropy, a fundamentally "non-Kolmogorovian" feature of pressure-gradient-driven flows, is captured by a tensorial stochastic diffusion term. The extension is so constructed that it reduces to the original Langevin model in the limit of constant density. We show that coupling a Lagrangian mass-density particle model to the proposed extended velocity equation results in a statistical representation of VD turbulence that has important benefits. Namely, the effects of the mass flux and the specific volume, both essential in the prediction of VD flows, are retained in closed form and require no explicit closure assumptions. The paper seeks to describe a theoretical framework necessary for subsequent applications. We derive the rigorous mathematical consequences of assuming a particular functional form of the stochastic momentum equation coupled to the stochastic density field in VD flows. A previous article discussed VD mixing and developed a stochastic Lagrangian model equation for the mass-density. Second in the series, this article develops the momentum equation for VD hydrodynamics. A third, forthcoming paper will combine these ideas on mixing and hydrodynamics into a comprehensive framework: it will specify a model for the coupled problem and validate it by computing a Rayleigh-Taylor flow.