Biomechanics and Medicine in Swimming XI

A Full Body Computational Fluid Dynamic Analysis of
the Freestyle Stroke of a Previous Sprint Freestyle
World Record Holder
Keys, M. 1 ; lyttle, A. 2 ; Blanksby, B.A. 1 & cheng, l. 1
1 The University of Western Australia, Australia
2 Western Australian Institute of Sport, Australia
Computational Fluid Dynamics (CFD) allows simulation of complex
fluid flow regimes and geometry. A case-study examined propulsive and
drag forces experienced across the body during full-body freestyle swimming
using CFD. An elite male swimmer was scanned using a whole
body 3D scanner and manual digitising provided the 3D freestyle kinematics
to animate the model. A Finite Volume Method of CFD modelling
was used which incorporated a realisable K-ε turbulence model. The
CFD analysis enabled an examination of the forces distributed across
the body throughout the full freestyle stroke. This project increases the
level of foundational knowledge and presents practical points that may
improve swimming performance.
Key words: computational Fluid dynamics; Freestyle; drag; Propulsion
IntroductIon
Typically, current techniques of elite swimmers are derived from a mix
of natural genetics, feel for the water, knowledge of experienced coaches,
and trial and error methods. Although this is considered to be effective,
little is known of the hydrodynamic factors making one technique faster
than another.
It is widely accepted that an increased understanding of fluid flow
patterns in swimming should enhance performance. However, it is very
difficult to quantify the relative effects of these flow patterns experimentally
when swimming with, at best, only approximations of total body
effects were being provided. To date, research has incorporated one or
more of the following methods to estimate the drag/propulsion effects
and flow patterns:
• Physical testing using either force plates, drag lines or towing
devices.
• Analysis and numerical modelling of recorded flow lines and
vortex patterns measured by injecting dye or Particle Image Velocimetry
(PIV) methods, based on swimmers in a test pool or
swimming flume.
• Entirely numerical modelling which use estimations of drag and
inertia effects on shapes similar to those of human limbs.
Each method provided valuable information and some empirical data
concerning a few of the questions raised. However, inherent limitations
exist and fluid flows around an irregularly shaped human form that is
always changing in shape and position, are highly complex. Hence, none
of these techniques can provide a full understanding of what is actually
occurring throughout a full swimming stroke cycle.
Computational Fluid Dynamics (CFD) can be used to model and
solve complex problems of fluid flow and is ideally suited to analysing
drag and propulsion across the body when swimming. Based on fundamental
fluid mechanics principles, CFD allows complex fluid flow
regimes and geometry to be simulated, providing visualization of the
resulting variables across the entire solution domain. This can provide
insights into problems thus far unobtainable via known physical testing
techniques. To date, CFD predictions of forces acting on a swimmer
have been limited to passive drag studies (Bixler et al., 2007), hand motion
through the water (Bixler & Riewald, 2001; Sato & Hino, 2002)
and underwater kicking (Lyttle et al., 2006; Von Loebbecke et al., 2009).
The current study was the culmination of several years spent devel-
chaPter2.Biomechanics
oping a full body stroking CFD model. The objective was to examine the
distribution of forces across the human body for an elite sprint freestyler.
A better understanding of the interaction of propulsive and drag forces
across the body will increase the foundational knowledge of swimming
hydrodynamics.
Methods
A CFD case-study examined the propulsion and drag forces across the
body during a full freestyle swimming stroke. At the time of the kinematic
recordings, the swimmer used held both the 50m and 100m
freestyle World Records. Therefore the technique examined was highly
evolved.
The 3D kinematics recorded one full stroke cycle with a separate
above- and below-water camera used on each side of the swimmer.
Each camera was pointed at 45-60° to the horizontal plane. The swimmer
performed regular freestyle at race pace and a full 3D kinematic
analysis was performed using manual video digitizing. Medial and lateral
body landmarks, rather than joint centres, were digitized for each
body segment to allow for calculating full segment rotations. Cartesian
coordinates for the derived joint centres were then converted to a polar
coordinate system.
A full 3D surface scan of the swimmer provided accurate 3D geometry
for the CFD simulations. The laser scanning of the swimmer was
performed using a Cyberware WBX whole body laser scanner, with a
density of one point every 4mm. Higher resolution scans were also conducted
of the hands, head and feet (density of one point every 0.67mm).
The higher resolutions acknowledged the importance of these areas in
setting the initial flow conditions and in developing thrust. The higher
resolution scans were then aligned and merged seamlessly into the full
body scan to provide more accuracy at these locations (see Figure 1).
The 3D model was then processed to extract 288 non-uniform rational
b-splines (NURBS), curved surfaces forming a 3D solid model of the
swimmers.
The computer simulation was performed using the CFD software
package “FLUENT” (version 6.3.26; Fluent Inc., Lebanon, NH) which
utilizes the Finite Volume Method of CFD modelling. A realizable K-ε
turbulence model, together with a multi-phase fluid domain, was used
with standard wall functions to simulate the boundary layer. This was
combined with the use of prism cells near the wall boundaries and tetrahedral
cells in the main fluid domain. The domain surfaces contained
varying mesh densities to define the detail around highly curved areas
while still maintaining a workable mesh size. The surface mesh on the
swimmer comprised approximately 100000 triangular surface elements
with the total simulation consisting of close to 5 million cells (see Figure
1 for sample triangulated meshing surrounding the hand).
Figure 1. 3D laser scanned image of the subject (top) and sample triangulated
mesh surrounding the hand (bottom).
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