Experimental Validation Of A CFD-based
Resistive Force Coefficient Set For Rotating Helical
Tails In Cylindrical Channels
Author(s)
A. Fatih Tabak & S. Yesilyurt
Abstract
Real-time manipulation of objects in micro flows is important due to an ever
increasing interest in demanding medical applications. Specialized swimming
micro robots are expected to perform minimal invasive surgery consisting of
various in vivo tasks. Moreover, fluid forces exerted on moving surfaces are
crucial for maneuverability if swimming micro robotic tools are ever to play a
key role in therapeutic applications. Hydrodynamic forces acting on an isolated
object of regular blunt shapes immersed in micro flows are calculated by
resistive force coefficients based on the Stokes Flow approximation derived by
analytical means. However, force coefficients presented in literature often lack
the accuracy to predict the hydrodynamic interactions between rotating and
translating objects of irregular shapes, and rigid concave surfaces such as a rigid
helix moving inside a cylindrical channel. In this study a set of parameterized 3D
CFD simulations are carried out using a novel geometric representation for rigid
helical tails of varying sizes and of varying distances with respect to the wall of a
cylindrical channel. Furthermore, the complex rigid-body motion of the helix is
represented by a series of simple translations and rotations. Each simulation is
governed by Stokes equations and carried out by a commercial CFD package. A
generic resistive force coefficient set is obtained via surface-fit procedures based
on the hydrodynamic forces computed by the CFD simulations along the
tangential, normal and binormal directions on the moving and rotating helical
surface. Finally, using a reduced-order microhydrodynamic model, the proposed
coefficient set is validated with empirical data collated using bio-inspired cm-
Keywords
cylindrical channels, bio-inspired robots, untethered swimming, low Reynolds number flows, CFD analysis, resistive force coefficients, reduced-order models
Related Book
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