Inertial Coupling Method for particles in an incompressible fluctuating fluid

TL;DR

This paper introduces an inertial coupling method for simulating particles in an incompressible fluctuating fluid, ensuring physical consistency, stability, and efficiency, especially suitable for GPU implementation.

Contribution

It generalizes previous models to incompressible fluids, incorporating particle inertia and thermal fluctuations with a stable, efficient discretization and GPU-compatible solver.

Findings

Method preserves fluctuation-dissipation balance.

Discrete particles exhibit realistic volume and hydrodynamic properties.

Efficient GPU implementation with FFT-based solvers.

Abstract

We develop an inertial coupling method for modeling the dynamics of point-like 'blob' particles immersed in an incompressible fluid, generalizing previous work for compressible fluids. The coupling consistently includes excess (positive or negative) inertia of the particles relative to the displaced fluid, and accounts for thermal fluctuations in the fluid momentum equation. The coupling between the fluid and the blob is based on a no-slip constraint equating the particle velocity with the local average of the fluid velocity, and conserves momentum and energy. We demonstrate that the formulation obeys a fluctuation-dissipation balance, owing to the non-dissipative nature of the no-slip coupling. We develop a spatio-temporal discretization that preserves, as best as possible, these properties of the continuum formulation. In the spatial discretization, the local averaging and spreading operations are accomplished using compact kernels commonly used in immersed boundary methods. We find that the special properties of these kernels make the discrete blob a particle with surprisingly physically-consistent volume, mass, and hydrodynamic properties. We develop a second-order semi-implicit temporal integrator that maintains discrete fluctuation-dissipation balance, and is not limited in stability by viscosity. Furthermore, the temporal scheme requires only constant-coefficient Poisson and Helmholtz linear solvers, enabling a very efficient and simple FFT-based implementation on GPUs. We numerically investigate the performance of the method on several standard test problems...