New dissipative particle dynamics boundary condition for complex geometry

Abstract

Dissipative particle dynamics (DPD) is a thriving particle-based simulation method of modeling mesoscale fluids. After two decades of evolution, DPD has shown unique advantages in researches about polymer, red blood cell, droplets wetting, etc. However, DPD is limited to relatively simple geometries due to the lack of a satisfactory boundary method. In this paper, we propose an adaptive boundary method for complex geometry, which fulfills the three basic requirements of boundary method: no penetration into the solid, no-slip near boundary, negligible fluctuation of density or temperature near boundary. Specifically, first, a new vector attribution is added to each solid particle, the attribution is named local wall normal (LWN) attribution and it is a function of its neighbor solid particle’s position, the LWN attribution is used to correct the penetrating fluid particles’ velocity and position and is computed only once if the wall is stationary. Second the surface wall particles are identified by neighbor solid fraction (<i>φ</i>), which indicates the percentage of surrounding space occupied by solid particles, then the wall is reconstructed by only the surface particles instead of all solid particles. By doing so, the redundant bulk particles are removed from the simulation. Third, it is detected on-the-fly whether the moving fluid particle penetrates the wall by computing its <i>φ</i>, the fluid particles with <i>φ</i> greater than 0.5 are considered to enter into the solid wall, their position and velocity will be corrected based on the local wall normal attribution. We verify that the method causes negligible density and temperature fluctuation in Poiseuille flow. Then, we illustrate the implementation of LWNM in the cases of complex blood vessel network and micro-structured surface. With this method, the obstacles in flow are no longer restricted to shapes described by functions but can be generated by CAD software, and blood vessels can also be generated by CT scan images or other experimental data. Moreover, we show a case with a bent tube and droplets inside, demonstrating the practicability of constructing complex geometry and the effectiveness of LWNM. This new boundary approach empowered DPD to simulate more realistic problems.