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Published July 2011 | Published
Journal Article Open

Modeling hydrodynamic self-propulsion with Stokesian Dynamics. Or teaching Stokesian Dynamics to swim

Abstract

We develop a general framework for modeling the hydrodynamic self-propulsion (i.e., swimming) of bodies (e.g., microorganisms) at low Reynolds number via Stokesian Dynamics simulations. The swimming body is composed of many spherical particles constrained to form an assembly that deforms via relative motion of its constituent particles. The resistance tensor describing the hydrodynamic interactions among the individual particles maps directly onto that for the assembly. Specifying a particular swimming gait and imposing the condition that the swimming body is force- and torque-free determine the propulsive speed. The body's translational and rotational velocities computed via this methodology are identical in form to that from the classical theory for the swimming of arbitrary bodies at low Reynolds number. We illustrate the generality of the method through simulations of a wide array of swimming bodies: pushers and pullers, spinners, the Taylor=Purcell swimming toroid, Taylor's helical swimmer, Purcell's three-link swimmer, and an amoeba-like body undergoing large-scale deformation. An open source code is a part of the supplementary material and can be used to simulate the swimming of a body with arbitrary geometry and swimming gait.

Additional Information

© 2011 American Institute of Physics. Received 22 September 2010; accepted 26 April 2011; published online 14 July 2011. Online Publication Date: 14 July 2011. The motivation for this work arose from a special topics course, ChE 174, on self-propulsion at low Reynolds number given in the spring of 2010 at the California Institute of Technology—hence the title "Teaching Stokesian Dynamics to swim." Participants were Lawrence Dooling (pushers and pullers), Nicholas Hoh (pushers and pullers), Jonathan Choi (spinners) and Roseanna Zia (toroids). We thank them for their contributions to the course and this work. This work was supported in part by NSF Grant Nos. CBET 0506701 and CBET 074967.

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Created:
August 19, 2023
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October 24, 2023