We have modeled the dynamics of a 3-D system consisting of red blood cells (RBCs), plasma and capillary walls using a discrete-particle approach. The blood cells and capillary walls are composed of a mesh of particles interacting with harmonic forces between nearest neighbors. We employ classical mechanics to mimic the elastic properties of RBCs with a biconcave disk composed of a mesh of spring-like particles. The fluid particle method allows for modeling the plasma as a particle ensemble, where each particle represents a collective unit of fluid, which is defined by its mass, moment of inertia, translational and angular momenta. Realistic behavior of blood cells is modeled by considering RBCs and plasma flowing through capillaries of various shapes. Three types of vessels are employed: a pipe with a choking point, a curved vessel and bifurcating capillaries. There is a strong tendency to produce RBC clusters in capillaries. The choking points and other irregularities in geometry influence both the flow and RBC shapes, considerably increasing the clotting effect. We also discuss other clotting factors coming from the physical properties of blood, such as the viscosity of the plasma and the elasticity of the RBCs. Modeling has been carried out with adequate resolution by using 1 to 10 million particles. Discrete particle simulations open a new pathway for modeling the dynamics of complex, viscoelastic fluids at the microscale, where both liquid and solid phases are treated with discrete particles. Figure A snapshot from fluid particle simulation of RBCs flowing along a curved capillary. The red color corresponds to the highest velocity. We can observe aggregation of RBCs at places with the most stagnant plasma flow.

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Abstract. We present a novel technique based on a multiresolutional clustering and nonlinear multi-dimensional scaling of earthquake patterns to investigate observed and synthetic seismic catalogs. The observed data represent seismic activities around the Japanese islands during 1997-2003. The synthetic data were generated by numerical simulations for various cases of a heterogeneous fault governed by 3-D elastic dislocation and power-law creep. At the highest resolution, we analyze the local cluster structures in the data space of seismic events for the two types of catalogs by using an agglomerative clustering algorithm. We demonstrate that small magnitude events produce local spatiotemporal patches delineating neighboring large events. Seismic events, quantized in space and time, generate the multidimensional feature space characterized by the earthquake parameters. Using a non-hierarchical clustering algorithm and nonlinear multi-dimensional scaling, we explore the multitudinous earthquakes by real-time 3-D visualization and inspection of the multivariate clusters. At the spatial resolutions characteristic of the earthquake parameters, all of the ongoing seismicity both before and after the largest events accumulates to a global structure consisting of a few separate clusters in the feature space. We show that by combining the results of clustering in both low and high resolution spaces, we can recognize precursory events more precisely and unravel vital information that cannot be discerned at a single resolution.

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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.

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