Smoothed dissipative particle dynamics for mesoscale advection-diffusion-reaction problems

Area of Science:

• Computational physics
• Chemical engineering
• Materials science

Background:

• Smoothed dissipative particle dynamics (SDPD) is a particle-based simulation method.
• SDPD offers thermodynamic consistency and control over fluid transport properties.
• Modeling advection-diffusion-reaction (ADR) dynamics in complex systems is challenging.

Purpose of the Study:

• To develop an SDPD model that incorporates sub-particle scale reactant transport and compositional field evolution.
• To enable the simulation of complex systems governed by ADR dynamics.
• To validate the model's effectiveness in diverse simulation scenarios.

Main Methods:

• Implementation of the novel SDPD model within the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS).
• Validation through benchmark problems covering diffusion-dominated, reaction-dominated, and coupled ADR regimes.
• Analysis of simulation results for accuracy and predictive capability.

Main Results:

• The SDPD model successfully simulates systems with sub-particle scale reactant transport.
• The model accurately captures various ADR dynamics, including diffusion and reaction limitations.
• Demonstrated ability to reproduce complex phenomena like Turing pattern formation.

Conclusions:

• The developed SDPD model provides a robust framework for simulating ADR dynamics.
• This methodology is suitable for mesoscopic and macroscopic modeling of soft matter systems.
• The model has broad applicability in biology, chemistry, materials science, and environmental engineering.