Stochastic dynamics of active swimmers in linear flows
Mario Sandoval, Navaneeth K.M., Ganesh Subramanian, Eric Lauga

TL;DR
This paper develops a theoretical framework to analyze the stochastic long-time dynamics of active swimmers in linear flows, accounting for translational and rotational diffusion, and demonstrates how swimming enhances displacement compared to passive particles.
Contribution
It introduces a comprehensive model for active particle motion in linear flows, including effects of rotary diffusion and run-and-tumble dynamics, extending previous passive particle analyses.
Findings
Active swimming increases mean-square displacement by orders of magnitude.
Long-time scalings for active and passive particles are similar, with active particles having larger coefficients.
In solid-body rotation, active and passive diffusion are equivalent, showing flow independence.
Abstract
Most classical work on the hydrodynamics of low-Reynolds-number swimming addresses deterministic locomotion in quiescent environments. Thermal fluctuations in fluids are known to lead to a Brownian loss of the swimming direction. As most cells or synthetic swimmers are immersed in external flows, we consider theoretically in this paper the stochastic dynamics of a model active particle (a self-propelled sphere) in a steady general linear flow. The stochasticity arises both from translational diffusion in physical space, and from a combination of rotary diffusion and run-and-tumble dynamics in orientation space. We begin by deriving a general formulation for all components of the long-time mean square displacement tensor for a swimmer with a time-dependent swimming velocity and whose orientation decorrelates due to rotary diffusion alone. This general framework is applied to obtain the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
