Multiscale modeling of particle in suspension with smoothed dissipative particle dynamics

Abstract

We apply smoothed dissipative particle dynamics (SDPD) [Español and Revenga, Phys. Rev. E 67, 026705 (2003)] to model solid particles in suspension. SDPD is a thermodynamically consistent version of smoothed particle hydrodynamics (SPH) and can be interpreted as a multiscale particle framework linking the macroscopic SPH to the mesoscopic dissipative particle dynamics (DPD) method. Rigid structures of arbitrary shape embedded in the fluid are modeled by frozen particles on which artificial velocities are assigned in order to satisfy exactly the no-slip boundary condition on the solid-liquid interface. The dynamics of the rigid structures is decoupled from the solvent by solving extra equations for the rigid body translational/angular velocities derived from the total drag/torque exerted by the surrounding liquid. The correct scaling of the SDPD thermal fluctuations with the fluid-particle size allows us to describe the behavior of the particle suspension on spatial scales ranging continuously from the diffusion-dominated regime typical of sub-micron-sized objects towards the non-Brownian regime characterizing macro-continuum flow conditions. Extensive tests of the method are performed for the case of two/three dimensional bulk particle-system both in Brownian/non-Brownian environment showing numerical convergence and excellent agreement with analytical theories. Finally, to illustrate the ability of the model to couple with external boundary geometries, the effect of confinement on the diffusional properties of a single sphere within a micro-channel is considered, and the dependence of the diffusion coefficient on the wall-separation distance is evaluated and compared with available analytical results.

Progress in particle-based multiscale and hybrid methods for flow applications

“…For H > 10, both parallel and normal component collapsed onto one single curve, showing the diminishing effect of channel confinement and return to isotropic behaviour. In a similar study, Bian et al (2012) investigated a single particle in suspension and applied it to several test cases ranging from typical examples found in microto macroscale flows which included flow through porous media, a particle with initially imposed velocity (kick started), particles under shear, interaction of two approaching spheres and a neutrally buoyant particle under Brownian motion close to and sufficiently far away from a solid boundary. Their test cases agreed well with reference data.…”

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

“…where D is the problem dimensionality, this will lead to the angular momentum preserving SDPD formulation proposed by Bian et al [7] and Hu and Adams [59] , which is linked to a particular SPH discretization of the viscous dissipation in the NSEs [60][61] . In fact, with the choice made in Eq.…”

“…The numerical technique was introduced in 2003 by Español and Revenga [1] to remedy some deficiencies of the classical dissipative particle dynamics (DPD) [2][3][4] . Since then, it has been applied successfully to a wide range of problems in microfluidics [5] , nanofluidics [6] , colloidal suspensions [7][8] , blood [9][10] , tethered DNA [11] , and dilute polymeric solutions [12][13][14] . It has also been used for the simulation of fluid mixtures [15][16][17][18] , mesoscopic viscoelastic flows [19] , and multiscale coupling strategies [18,[20][21] .…”

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

“…Many applications of DPD method 75 or its variants in the simulations of complex fluids have been reported, e.g., sphere colloidal suspensions ( [15]; [16]; [17]; [18]; [19]; [20]), colloidal suspensions of spheres, rods, and disks [21], viscoelastic fluid [22], ferromagnetic colloidal suspension [23], magnetic colloidal dispersions [24], soft matter and polymeric applications [25], [26], lipid bilayer [27], flows of DNA suspensions [28], poly-80 mer chains [29], red blood cell modelling [30], [31]; this list is not meant to be exhaustive.…”