Smoothed Dissipative Particle Dynamics for Mesoscale Advection-Diffusion-Reaction Problems

TL;DR

This paper introduces an SDPD model that incorporates reactive transport at mesoscale, validated through benchmarks, capable of capturing complex ADR phenomena like Turing patterns, with broad interdisciplinary applications.

Contribution

The paper presents a novel SDPD approach that models mesoscale advection-diffusion-reaction processes, including compositional field evolution, validated with diverse benchmarks.

Findings

Successfully captures Turing pattern formation.

Effective in diffusion, reaction, and coupled regimes.

Validated against multiple benchmark problems.

Abstract

Smoothed dissipative particle dynamics (SDPD) is a widely used particle-based method for modelling soft matter systems at mesoscopic and macroscopic scales, offering thermodynamic consistency and direct control over the fluid's transport properties. Here, we present an SDPD model that incorporates the transport of reactants on scales smaller than the discretising particles, including the evolution of compositional fields. The proposed methodology is well-suited for modelling complex systems governed by advection-diffusion-reaction (ADR) dynamics. Implemented in LAMMPS, the model is validated using a range of benchmark problems spanning diffusion-dominated, reaction-dominated, and coupled ADR regimes. Our simulation results demonstrate that the implemented SDPD model effectively captures complex behaviours, such as Turing pattern formation. The proposed model holds promise for applications across various fields, including biology, chemistry, materials science, and environmental engineering.