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Active microrheology has emerged in recent years as a new technique to probe microscopically the mechanical properties of materials , particularly, viscoelastic ones. In this technique, a colloidal tracer is pulled through the material, and its dynamics is monitored. The interpretation of results usually relies on the Stokes–Einstein approximation, which is valid for a continuous medium in equilibrium. In this work, we have studied with simulations a suspension of quasi-hard colloidal spheres, where a large tracer is pulled by a constant force. The Navier–Stokes equation for a continuous bath predicts important finite size effects, decaying as the inverse box size, which require simulations of different systems to extract the microviscosity of a bulk system. A strategy to optimize the scheduling of the simulation tasks on a multi GPU–CPU cluster based on the adaptation of a genetic algorithm is presented here, and used to study the effect of different conditions on the friction experienced by the tracer (adding the tracer volume to the total system volume, fixing the center of mass of the system, varying the fluid friction coefficient and tracer size). It is observed that the theoretical prediction is not followed, but deviations are observed for large systems in all cases. These are attributed to the finite size of the bath particles, and the intrinsic dynamics of colloidal systems, as shown by the analysis of the velocity profile in the bath.

Results from Brownian Dynamics simulation of aggregation in mesoscopic systems are presented. The evolution of clusters is studied using a constant kernel kinetics, which produces a value for the constant kernel comparable to the theoretical one. Finally, the pair correlation function is employed to study the cluster fractal structure.