Swimming path statistics of an active Brownian particle with time-dependent self-propulsion

Abstract

Typically, in the description of active Brownian particles, a constant effective propulsion force is assumed, which is then subjected to fluctuations in orientation and translation leading to a persistent random walk with an enlarged long-time diffusion coefficient. Here, we generalize previous results for the swimming path statistics to a time-dependent and thus in many situations more realistic propulsion which is a prescribed input. We analytically calculate both the noise-free and the noise-averaged trajectories for time-periodic propulsion under the action of an additional torque. In the deterministic case, such an oscillatory microswimmer moves on closed paths that can be highly more complicated than the commonly observed straight lines and circles. When exposed to random fluctuations, the mean trajectories turn out to be self-similar curves which bear the characteristics of their noise-free counterparts. Furthermore, we consider a propulsion force which scales in time $t$ as $\propto t^{\alpha }$ (with $\alpha =0,1,2,\dots$) and analyze the resulting superdiffusive behaviour. Our predictions are verifiable for diffusiophoretic artificial microswimmers with prescribed propulsion protocols.

24 pages, 13 figures, J. Stat. Mech. (accepted)

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