Preliminary Study: A New Method to Assess the Effective Frontal

Area of Cyclists

Anthony Bouillod

1,2,3

, Luca Oggiano

, Georges Soto-Romero

3,5

, Emmanuel Brunet

and Frederic Grappe

1,6

EA4660, C3S Health - Sport Department, Sports University, Besancon, France

French Cycling Federation, Saint Quentin en Yvelines, France

LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France

Norwegian University of Science and Technology, Department of Energy and Process Engineering, Trondheim, Norway

ISIFC - Génie Biomédical, 23 Rue Alain Savary, Besançon, France

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

, m

) 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

of the cyclist. The CFD AC

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

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

, 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

, m

). 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

experimental tests became common in cycling. The

measurement techniques of AC

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

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

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

decided to leave out of account the effect of the

bicycle on AC

, 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

- 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

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

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

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

. 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

, 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

was determined by the measurement of the

power output (PO, W) at constant V on each

increment as follows: R

= PO.V

-1

. R

was plotted

against V

to obtain the R

-V

linear regression

(Grappe et al., 1997). The equation of the linear

regression to determine AC

, using R

= αV

+ β,

was AC

= 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

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

of the cyclist was assessed by combining

both 3D scanning and CFD simulation, and the

results were compared with both wind tunnel AC

and field AC

The CFD simulation (figure 6) focused only on

the cyclist, whereas wind tunnel test and field test

considered the total AC

. During both the wind

tunnel test and the field test, we subtracted the AC

of the bicycle measured in the wind tunnel (0.053

) to obtain the AC

of the rider for each, and to

compare them with the CFD AC

Figure 6: Pressure on the cyclist, computed by CFD

simulation.

The figure 7 shows that AC

of the cyclist

obtained by wind tunnel test was lower than AC

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

obtained by field test was lower than AC

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

was 0.003 m

(1.3 %)

lower on the field when compared to wind tunnel.

Figure 7: effective frontal area (AC

) 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

)

icSPORTS 2016 - 4th International Congress on Sport Sciences Research and Technology Support

REFERENCES

Balmer, J. 2000. Peak power predicts performance power

during an outdoor 16.1 km cycling time trial. Med Sci

Sports Exerc, 32, 1485-1490.

Blocken, B., Defraeye, T., Koninckx, E., Carmeliet, J. &

Hespel, P. 2013. CFD simulations of the aerodynamic

drag of two drafting cyclists. Computers & Fluids, 71,

435-445.

Candau, R. B., Grappe, F., Menard, M., Barbier, B.,

Millet, G. Y., Hoffman, M. D., Belli, A. R. &

Rouillon, J. D. 1999. Simplified deceleration method

for assessment of resistive forces in cycling. Med Sci

Sports Exerc, 31, 1441-7.

Capelli, C., Rosa, G., Butti, F., Ferretti, G., Veicsteinas, A.

& Di Prampero, P. E. 1993. Energy cost and efficiency

of riding aerodynamic bicycles. Eur J Appl Physiol

Occup Physiol, 67, 144-9.

Davies, C. T. 1980. Effect of air resistance on the

metabolic cost and performance of cycling. Eur J Appl

Physiol Occup Physiol, 45, 245-54.

Debraux, P., Grappe, F., Manolova, A. V. & Bertucci, W.

2011. Aerodynamic drag in cycling: methods of

assessment. Sports Biomechanics, 10, 197-218.

Defraeye, T., Blocken, B., Koninckx, E., Hespel, P. &

Carmeliet, J. 2010a. Aerodynamic study of different

cyclist positions: CFD analysis and full-scale wind-

tunnel tests. J Biomech, 43, 1262-8.

Defraeye, T., Blocken, B., Koninckx, E., Hespel, P. &

Carmeliet, J. 2010b. Computational fluid dynamics

analysis of cyclist aerodynamics: performance of

different turbulence-modelling and boundary-layer

modelling approaches. J Biomech, 43, 2281-7.

Defraeye, T., Blocken, B., Koninckx, E., Hespel, P. &

Carmeliet, J. 2011. Computational fluid dynamics

analysis of drag and convective heat transfer of

individual body segments for different cyclist

positions. J Biomech, 44, 1695-701.

Di Prampero, P. E., Cortili, G., Mognoni, P. & Saibene, F.

1979. Equation of motion of a cyclist. J Appl Physiol

Respir Environ Exerc Physiol, 47, 201-6.

Garcia-Lopez, J., Ogueta-Alday, A., Larrazabal, J. &

Rodriguez-Marroyo, J. A. 2014. The use of velodrome

tests to evaluate aerodynamic drag in professional

cyclists. Int J Sports Med, 35, 451-5.

Garcia-Lopez, J., Rodriguez-Marroyo, J. A., Juneau, C. E.,

Peleteiro, J., Martinez, A. C. & Villa, J. G. 2008.

Reference values and improvement of aerodynamic

drag in professional cyclists. J Sports Sci, 26, 277-86.

Grappe, F., Candau, R., Belli, A. & Rouillon, J.-D. 1997.

Aerodynamic drag in field cycling with special

reference to the Obree's position. Ergonomics, 40,

1299-1311.

Hoogeveen, A. R. & Schep, G. 1997. The plasma lactate

response to exercise and endurance performance:

relationships in elite triathletes. Int J Sports Med, 18,

526-30.

Martin, J. C., Milliken, D. L., Cobb, J. E., Mcfadden, K. L.

& Coggan, A. R. 1998. Validation of a mathematical

model for road cycling power. Journal of applied

biomechanics, 14, 276-291.

Oggiano, L., Leirdal, S., Saetran, L. & Ettema, G.

Aerodynamic optimization and energy saving of

cycling postures for international elite level cyclists.

ISEA Conference, 2008 Biarritz.

Oggiano, L., Spurkland, L., Bardal, L. M. & Sætran, L.

Aerodynamical Resistance in Cycling - CFD

Simulations and Comparison with Experiments.

Proceedings of the 3rd International Congress on Sport

Sciences Research and Technology Support, 2015

Lisbon, Portugal. 183-189.

Preliminary Study: A New Method to Assess the Effective Frontal Area of Cyclists