Preliminary Study: A New Method to Assess the Effective Frontal
Area of Cyclists
Anthony Bouillod
1,2,3
, Luca Oggiano
4
, Georges Soto-Romero
3,5
, Emmanuel Brunet
2
and Frederic Grappe
1,6
1
EA4660, C3S Health - Sport Department, Sports University, Besancon, France
2
French Cycling Federation, Saint Quentin en Yvelines, France
3
LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France
4
Norwegian University of Science and Technology, Department of Energy and Process Engineering, Trondheim, Norway
5
ISIFC - Génie Biomédical, 23 Rue Alain Savary, Besançon, France
6
Professional Cycling Team FDJ, Moussy le Vieux, France
Keywords: Aerodynamics, 3D Scanning, CFD, Wind Tunnel, Field Cycling.
Abstract: The present work aimed to assess the effective frontal area (AC
d
, m
2
) of a cyclist using both 3D scanning
and Computational Fluid Dynamics (CFD) simulation and compare the results with wind tunnel and field
measurements. One elite cyclist was recruited to complete a 3D scanning, a wind tunnel test and a field test.
The 3D scanning was analyzed using CFD simulation to determine the AC
d
of the cyclist. The CFD AC
d
was compared to those measured in both wind tunnel and field tests. The 3D scanning method provides
useful data for cycling science and TT position or equipment optimization, by using iterative approach.
Indeed, the AC
d
obtained after CFD simulation was in accordance with those obtained in both wind tunnel
and field testing sessions. Resolution, scanning time and post processing are compatible with an extensive
use in real conditions and with a larger number of cyclists.
1 INTRODUCTION
Aerodynamic drag is the main resistance (80-90%)
among the total resistive forces (R
T
, N) opposing
motion on level ground in cycling (Debraux et al.,
2011). To reduce air resistance, cyclists adopt a
characteristic time trial (TT) position on the bicycle
to decrease the effective frontal area (AC
d
, m
2
). The
body of cyclist accounts for about 70% of the total
drag while the remaining 30% is due to the bicycle
frame and the components (Oggiano et al., 2008,
Blocken et al., 2013). Thus, measures of a cyclist’s
ability to supply mechanical power do not always
predict performance time in TT racing (Hoogeveen
and Schep, 1997, Balmer, 2000). The cyclist
position has a significant impact in the performance
on flat terrain to overcome at the maximum the air
resistance (Oggiano et al., 2008).
In order to optimize the position by reducing AC
d
,
experimental tests became common in cycling. The
measurement techniques of AC
d
are now well
recognised. This parameter can be reliably evaluated
in laboratory or real cycling conditions (Debraux et
al., 2011). The measures include wind tunnel tests
(Davies, 1980, Garcia-Lopez et al., 2008, Martin et
al., 1998), dynamometric (di Prampero et al., 1979,
Capelli et al., 1993), deceleration (Candau et al.,
1999) and linear regression (Grappe et al., 1997)
methods. All of these have pros and cons. In
addition to these methods, Computational Fluid
Dynamics (CFD) simulations uses numerical
analysis and algorithms to solve and analyze
problems that involve fluid flows. Significant results
from CFD simulations applied to cycling can be
found in several studies (Defraeye et al., 2010a,
Defraeye et al., 2010b, Defraeye et al., 2011,
Blocken et al., 2013). While wind tunnel and field
tests are able to provide the total AC
d
acting on the
cyclist, CFD also provide drag information on
individual body segments or bicycle components,
increasing the insight in drag reduction mechanisms
and allowing local modifications.
This study aimed to 1) assess the AC
d
of a cyclist
using both 3D scanning and CFD simulation and 2)
compare the results with both wind tunnel and field
measurements. In this preliminary analysis, we
Bouillod, A., Oggiano, L., Soto-Romero, G., Brunet, E. and Grappe, F.
Preliminary Study: A New Method to Assess the Effective Frontal Area of Cyclists.
DOI: 10.5220/0006104400670071
In Proceedings of the 4th International Congress on Sport Sciences Research and Technology Support (icSPORTS 2016), pages 67-71
ISBN: 978-989-758-205-9
Copyright
c
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
67
decided to leave out of account the effect of the
bicycle on AC
d
, and focus on the cyclist position.
2 METHODS
One elite cyclist was recruited to participate in the
study. Prior to testing and after having received a
full explanation of the nature and purpose of the
study, the participant gave his written informed
consents. The participant performed three testing
sessions (3D scanning, wind tunnel test and field
test) with the same road-racing bicycle (Look L96,
Look Cycle International, Nevers, France).
2.1 3D Scanning
Our focused application induces some technical
specifications, among which:
- The scanned volume, including both cyclist and
bicycle (around 4 m
3
).
- High resolution scanning (around 1 mm), including
ability to scan small details on equipment (bicycle
frame, wheels, crank, helmet, skinsuit) or hard-to-
reach places (like inside legs or under pedals areas).
The resolution must also be compatible with a CFD
meshing step.
- Different texture, colour and reflectance on unique
scanning operation, including composite material,
metal, textile, human skin, etc. No painting or
irreversible operation on equipment were allowed.
- High speed scanning must be in accordance to the
cyclist ability to stay in the same position (less than
10 minutes).
- Lightweight and portable equipment, in order to be
packaged and carried according to cyclist
availability.
By these considerations, contact scanners,
scanning rooms and robotized arms with several
scanners were excluded from tests. Photogrammetry
was considered out of scope in this study.
Structured light scanners tested (Vialux,
Chemnitz, Germany and Cobalt, Faro, Villepinte,
France) in static position were unable to combine a
high resolution and scanning volume specifications
in a single operation. It would be necessary to
perform several scanning and reconstruction, or to
have several scanners operating in same time.
However, this technique can be used to perform
design and 3D printed prototyping for smaller parts,
like helmets or handlebars.
At last, static laser scanner tested (Focus 3D,
Faro, Villepinte, France) was unable to reach all the
cyclist and bicycle areas in a single operation,
because of masking the direct line of sight.
Figure 1: Picture of the 3D scanning setup.
Finally, the technical solution in accordance with
specifications was a handle laser scanner (Freestyle
3D, Faro, Villepinte, France) with real time
feedback by a scatter plot, resolution of 1 mm at 1
meter from scene, up to 8 m
3
scene volume and less
than 1 kg.
Figure 2: Model obtained with the Faro freestyle 3D.
This system could be used for both CFD
modeling on bicycle parts, bicycle and rider profile,
but also for mannequin or 3D printing parts.
Reflective or composite parts could be treated with
some caution for 3D scanning, sometimes by
applying talcum powder on surfaces to improve
quality of obtained scatter plot.
2.2 CFD Simulation
The CFD simulations was performed with the
STARCCM+ solver from Cd-Adapco. The 3D
model of the cyclist obtained from 3D scanning was
imported into the software, the bicycle was digitally
removed and the cyclist surface was discretized
using a polyhedral surface mesh. A prismatic layer
of 20 cells from the surface with the first cell placed
icSPORTS 2016 - 4th International Congress on Sport Sciences Research and Technology Support
68
so that a y+<1 was achieved in the whole model was
created in order to correctly resolve the boundary
layer. The numerical wind tunnel consisted of a box
with a cross section of 6x6 m
2
and a total length of
21 m (Blocken et al., 2013). The cyclist was placed
at 3 m from the inlet and in the center of the test
section. The k-ε turbulence model, due to its ability
to correcly model complex geometries, was used
throughtout the simulations.
Figure 3: Numerical wind tunnel.
A steady state simulation as suggested by
Blocken and Oggiano was used (Blocken et al.,
2013, Defraeye et al., 2010a, Defraeye et al., 2010b,
Defraeye et al., 2011, Oggiano et al., 2015). A
dynamic Courant number approach was adopted in
order to enhance convergence and reduce
computational cost. Two different meshes were used
in order to ensure grid independence, a coarse mesh
consisting of ca.3.3millions cells and the reference
mesh consisting of 5.5millions cells. Velocity inlet
and pressure outlet boundary conditions were used
respectively for the inlet and the outlet. Symmetry
plane boundary conditions were used in the
sidewalls, top and bottom.
2.3 Wind Tunnel Test
A wind tunnel (up to 67 m.s
-1
) was used to measure
AC
d
. It was a ¾ open circuit type with a nozzle
section of 1.47 m high and 2.6 m wide and a testing
section of 4.15 m high, 6.6 m wide and 9.3 m long,
where a six-components force balance was placed
(S2A, Montigny-le-Bretonneux, France). The force
balance was circular (2.8 m diameter) and equipped
with strain gauges and force measurement systems.
The bikes were placed on the force balance by
means of 4 vertical supports for both front and rear
wheel axles. Before the test, the force balance was
calibrated. A first measurement was done with the
bicycle only and a second measurement was done
with the bicycle and the cyclist. After a 10-min
warm-up, the cyclists pedalled at moderate intensity
with a self-selected cadence at a wind speed of 50
km.h
-1
. Measurements were recorded over 30s once
the wind speed was stabilized (Garcia-Lopez et al.,
2008).
Figure 4: Picture of the cyclist during the wind tunnel test.
Main limits of wind tunnel measurements: the air
flow around the bicycle is modified by the floor if
the cyclist is motionless; the effect of the wind is
altered if the wheels of the bicycle are stationary; the
cyclist’s position on the bicycle is not identical to
the position in real cycling conditions (Candau et al.,
1999); lateral sways that can occur in real cycling
locomotion are not present in a wind tunnel.
2.4 Field Test
To determine the AC
d
, the cyclist performed an
incremental exercise at speeds (V, m.s
-1
) from 30
km.h
-1
to 50 km.h
-1
, increasing by 2 km.h
-1
every 90
s, on a 250 m covered velodrome (Saint-Quentin-en-
Yvelines, France). In order to avoid great variations
of pedalling cadence, the gear ratio varied according
to V. R
T
was determined by the measurement of the
power output (PO, W) at constant V on each
increment as follows: R
T
= PO.V
-1
. R
T
was plotted
against V
2
to obtain the R
T
-V
2
linear regression
(Grappe et al., 1997). The equation of the linear
regression to determine AC
d
, using R
T
= αV
2
+ β,
was AC
d
= a / 0.5ρ where ρ (kg.m
-3
) is the air
density. PO and V were measured by a SRM
Figure 5: Picture of the cyclist during the field test.
Preliminary Study: A New Method to Assess the Effective Frontal Area of Cyclists
69
crankset (SRM, Schoberer Rad Messtechnich,
Julich, Germany) and a speed sensor, respectively.
The SRM power meter and the speed sensor were
paired with a Garmin power control (Garmin 810,
Olathe, USA). The data were analyzed with the use
of the TrainingPeaks software (WKO4, Peaksware,
Boulder, USA). A beeper was used to manage the
pace during the whole exercise.
3 RESULTS AND DISCUSSION
The AC
d
of the cyclist was assessed by combining
both 3D scanning and CFD simulation, and the
results were compared with both wind tunnel AC
d
and field AC
d
.
The CFD simulation (figure 6) focused only on
the cyclist, whereas wind tunnel test and field test
considered the total AC
d
. During both the wind
tunnel test and the field test, we subtracted the AC
d
of the bicycle measured in the wind tunnel (0.053
m
2
) to obtain the AC
d
of the rider for each, and to
compare them with the CFD AC
d
.
Figure 6: Pressure on the cyclist, computed by CFD
simulation.
The figure 7 shows that AC
d
of the cyclist
obtained by wind tunnel test was lower than AC
d
computed by CFD simulation (-10.9%). This result
was in accordance with the literature and can be
considered as a good agreement (Blocken et al.,
2013, Oggiano et al., 2015). Additionally, AC
d
obtained by field test was lower than AC
d
computed
by CFD simulation (-13.1%) and the wind tunnel
session (-2.4%). This result was also in accordance
with a previous study (Garcia-Lopez et al., 2014)
which showed that total AC
d
was 0.003 m
2
(1.3 %)
lower on the field when compared to wind tunnel.
Figure 7: effective frontal area (AC
d
) of the cyclist
obtained with the CFD, wind tunnel and field protocols.
4 CONCLUSIONS
Combining 3D scanning and CFD simulation
methods provide useful data for cycling science and
TT position optimization, despite of the big scanning
volume and wide range of materials. Indeed, this
preliminary study performed on one elite cyclist
showed that combined 3D scanning and CFD
simulation were in accordance with either wind
tunnel or field measurements.
Resolution, scanning time and post processing
are compatible with an extensive use in real
conditions with a larger number of cyclists. This step
could be used prior to wind tunnel step.
However, we also found some limits concerning
bicycle and some equipment’s scanning, specially on
reflective parts, where the 3D scan by structured
light give some shape errors.
ACKNOWLEDGMENTS
The authors would like to thank the participating
cyclist (M.D. Geoffrey Millour) for his cooperation,
as well as both companies, Kallisto, Toulouse and
Faro, France for their support and scanner providing.
We would also like to specially thank Dr. Antony
Costes, for participating in preliminary tests as
cyclist, and for his technical support. Last but not
least, kind acknowledgements to Cyril Fresillon, for
the accurate and honest touch of his photographic
eye.
0,1
0,11
0,12
0,13
0,14
0,15
0,16
0,17
CFD Wind tunnel Field
AC
d
(m
2
)
icSPORTS 2016 - 4th International Congress on Sport Sciences Research and Technology Support
70
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