Enhanced low-Reynolds-number propulsion in heterogeneous viscous environments

TL;DR

This paper demonstrates that microorganisms and artificial microswimmers can achieve faster and more efficient propulsion in heterogeneous viscous environments with obstacles, supported by numerical simulations and theoretical models.

Contribution

It introduces a theoretical and numerical framework showing propulsion enhancement in heterogeneous viscous media with stationary obstacles, extending understanding of microorganism locomotion.

Findings

Propulsion speed increases in heterogeneous media compared to pure viscous fluids.

Hydrodynamic efficiency of swimming improves in the presence of obstacles.

Numerical simulations agree with the resistive force theory predictions.

Abstract

It has been known for some time that some microorganisms can swim faster in high-viscosity gel-forming polymer solutions. These gel-like media come to mimic highly viscous heterogeneous environment that these microorganisms encounter in-vivo. The qualitative explanation of this phenomena first offered by Berg and Turner [Nature (London) 278, 349 (1979)], suggests that propulsion enhancement is a result of flagellum pushing on quasi-rigid loose polymer network formed in some polymer solutions. Inspired by these observations, inertia-less propulsion in a heterogeneous viscous medium composed of sparse array of stationary obstacles embedded into incompressible Newtonian liquid is considered. It is demonstrated that for prescribed propulsion gaits, including propagating surface distortions and rotating helical filament, the propulsion speed is enhanced when compared to swimming in purely viscous solvent. It is also shown that the locomotion in heterogenous viscous media is characterized by improved hydrodynamic efficiency. The results of the rigorous numerical simulation of the rotating helical filament propelled through a random sparse array of stationary obstructions are in close agreement with predictions of the proposed resistive force theory based on effective media approximation.