Parallel implementation of the fluid particle model for simulating complex fluids in the mesoscale

Abstract

Dissipative particle dynamics (DPD) and its generalization -the fluid particle model (FPM) -represent the "fluid particle" approach for simulating fluid-like behavior in the mesoscale. Unlike particles from molecular dynamics (MD) method, the "fluid particle" can be viewed as a "droplet" consisting of liquid molecules. In FPM, "fluid particles" interact by both central and non-central, short-range forces with conservative, dissipative and Brownian character. In comparison to MD, FPM method in 3-D requires two to three times more memory load and three times more communication overhead. Computational load per step per particle is comparable to MD due to the shorter interaction range allowed between "fluid particles" than between MD atoms. The classical linked-cells technique and decomposing the computational box into strips allow for rapid modifications of the code and for implementing non-cubic computational boxes. We show that the efficiency of the FPM code depends strongly on the number of particles simulated, geometry of the box, and the computer architecture. We give a few examples from long FPM simulations involving up to 8 million fluid particles and 32 processors. Results from FPM simulations in 3-D of the phase separation in binary fluid and dispersion of colloidal slab are presented.Scaling law for symmetric quench in phase separation has been properly reconstructed. We show also that the microstructure of dispersed fluid depends strongly on the contrast between kinematic viscosities of this fluid phase and the bulk phase. This FPM code can be applied for simulating mesoscopic flow dynamics in capillary pipes or critical flow phenomena in narrow blood vessels.

Clustering revealed in high‐resolution simulations and visualization of multi‐resolution features in fluid–particle models

“…As shown in Figure 4, the parallel algorithm for MNN clustering has a similar structure to the algorithm for computing short-ranged forces in the particle ensemble [43]. We consider an isothermal twodimensional system, which consists of Np particles.…”

“…In Figure 5 we present a more detailed meta-code of an MNN parallel algorithm, which consists of parallel procedures for finding neighbors and merging local and medium cross-domain clusters. The communication between processor domains is more complex than in the fluid particle code [43]. In finding the NN we allow communication to take place only for the adjacent processor.…”

Modeling Incompressible Fluids by Means of the SPH Method: Surface Tension and Viscosity

“…Then, the character of interparticle interactions is shortrange; there exists cut-off radius r cut such, that for every pair of particles whose distance is larger than r cut , the force acting between them is equal to zero. For simulation with short-range interactions it is possible to use the structure of cubic cells, which rapidly accelerate calculations [2]. Every SPH particle undergoes the acceleration given by the formula:…”

“…Application of this equation requires the initialization of the density values at the beginning of the simulation. The incompressible character of the fluid is modeled by an appropriate equation of state, which is used for evaluating the pressure values in equation (2). The authors use the equation of state given by [12]:…”

“…In the case of the SPH method presented here, where the thermal energy of the fluid is not concerned, it is necessary to incorporate the dissipation of the fluid energy by means of viscosity. Also, the every day experience of viscosity in almost all real fluids demands incorporating the viscosity term Π ij into the equation (2).…”