Lagrangian Liouville models of multiphase flows with randomly forced inertial particles

TL;DR

This paper introduces a novel Lagrangian Liouville model for multiphase flows with inertial particles, employing polynomial chaos expansion and a new spectral scheme to efficiently capture stochastic particle dynamics.

Contribution

It develops a deterministic PDE framework for stochastic particle dynamics using PCE and the method of distributions, with a high-order spectral scheme for efficient computation.

Findings

Lower computational cost than traditional methods

Avoids CFL condition and Gibbs oscillations

Demonstrated effectiveness on test cases

Abstract

Eulerian-Lagrangian models of particle-laden (multiphase) flows describe fluid flow and particle dynamics in the Eulerian and Lagrangian frameworks respectively. Regardless of whether the flow is turbulent or laminar, the particle dynamics is stochastic because the suspended particles are subjected to random forces. We use a polynomial chaos expansion (PCE), rather than a postulated constitutive law, to capture structural and parametric uncertainties in the particles' forcing. The stochastic particle dynamics is described by a joint probability density function (PDF) of a particle's position and velocity and random coefficients in the PCE. We deploy the method of distributions (MoD) to derive a deterministic (Liouville-type) partial-differential equation for this PDF. We reformulate this PDF equation in a Lagrangian form, obtaining PDF flow maps and tracing events and their probability in the phase space. That is accomplished via a new high-order spectral scheme, which traces, marginalizes and computes moments of the high-dimensional joint PDF and comports with high-order carrier-phase solvers. Our approach has lower computational cost than either high-order Eulerian solvers or Monte Carlo methods, is not subjected to a CFL condition, does not suffer from Gibbs oscillations and does not require (order-reducing) filtering and regularization techniques. These features are demonstrated on several test cases.