A dissipative particle dynamics model for thixotropic materials exhibiting pseudo-yield stress behaviour

Abstract

Many materials (e.g., gels, colloids, concentrated cohesive sediments, etc.) exhibit a stable solid form at rest, and liquify once subjected to an applied stress exceeding a critical value -a yield-stress behaviour. This can be qualitatively explained by the forming and destruction of the fluid microstructure [1], and it may be modelled as a thixotropic and yield stress material. In this paper, we propose a mesoscopic model which is able to mimic a thixotropic and yield stress behaviour using a particle-based technique known as dissipative particle dynamics (DPD). The DPD technique satisfies conservation of mass and momentum and it has been applied successfully for a number of problems in-

Investigation on surface roughness of piston in mini-magnetorheological damper using dissipative particle dynamics modeling

“…Few studies have been carried out to calculate shear stress using molecular models. Recently, Le-Cao et al (2017) developed a model to calculate shear properties of a fluid by utilizing DPD method. They divided DPD particles into two categories by applying the indirect linkage dissipative particle (ILDP) model.…”

“…Also, they defined particular weight functions and calculated exerted force by particles so that shear stress can be computed by utilizing this force. Further details on their study can be found in the study by Le-Cao et al (2017).…”

Everything you always wanted to know about SDPD⋆ (⋆but were afraid to ask)

“…In fact, new forces (elastic, rigid, etc.) can be added between particles in order to describe long polymers [71][72][73][74] , rigid or deformable particles in suspension [75] , porous media [76] , droplets [77] , cells [78][79][80] , or even more complex thixotropic materials [81] (see Fig. 1).…”

Coarse-graining, compressibility, and thermal fluctuation scaling in dissipative particle dynamics employed with pre-determined input parameters

“…DPD conserves momentum locally and therefore preserves hydrodynamics. The method has been used to simulate various fluid systems, for example, particulate suspensions [5,6,7], microfluidic systems [8], red blood cells [9], thixotropic materials [10], polymer solutions [11], nanoporous shales [12], to name a few. In DPD, the solvent phase is simply modelled by a set of particles (called DPD particles) under their Newton second law motions, while the suspended phases (e.g., solid particles, droplets, bubbles and polymer chains) can all be constructed from the DPD particles through appropriate constraints.…”