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Calculation of Aerodynamic Drag of Human Being in Various Positions

Authors:
Abdulkareem Sh. Mahdi Al-Obaidi at Taylor's University
Abdulkareem Sh. Mahdi Al-Obaidi
Mun Hon Koo
Mun Hon Koo
Mun Hon Koo
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Abstract

This paper studies the aerodynamic drag of human being in different positions through numerical simulation using CFD with different turbulence models. The investigation considers 4 positions namely (standing, sitting, supine and squatting) which affect aerodynamic drag. Standing has the highest drag value while supine has the lowest value. The numerical simulation was carried out using ANSYS FLUENT and compared with published experimental results. Aerodynamic drag studies can be applied into sports field related applications like cycling and running where positions optimising are carried out to reduce drag and hence to perform better during the competition.
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EURECA 2013 - Calculation of Aerodynamic Drag of Human Being in Various Positions
Calculation of Aerodynamic Drag of Human Being in
Various Positions
Mun Hon Koo*, Abdulkareem Sh. Mahdi Al-Obaidi
Department of Mechanical Engineering, school of Engineering, Taylor’s University, Malaysia,
*Corresponding author: mun-hon000@hotmail.com
Abstract— This paper studies the aerodynamic drag of human
being in different positions through numerical simulation using
CFD with different turbulence models. The investigation
considers 4 positions namely (standing, sitting, supine and
squatting) which affect aerodynamic drag. Standing has the
highest drag value while supine has the lowest value. The
numerical simulation was carried out using ANSYS FLUENT
and compared with published experimental results.
Aerodynamic drag studies can be applied into sports field related
applications like cycling and running where positions optimising
are carried out to reduce drag and hence to perform better
during the competition.
Keywords— Turbulence Models, Drag Coefficient, Human Being,
Different Positions, CFD
1. Introduction
The performance of human being in various positions is strongly
affected by the resistance they experience in which the resistance
consists of aerodynamic drag. Aerodynamic drag, a resistance force
that acts upon a body moving through fluid like air and that is
opposite to the direction of motion of the body [1, 2]. In sports field
that competing with speed, aerodynamic drag is one of the important
factors. The lower the value of drag coeffi cient, the lower the
aerodynamic drag of the positions.
The main idea of this paper is to investigate the effects of
aerodynamic drag of human in different position theoretically and
numerically. The total drag is majorly formed by pressure drag and
friction drag due to the skin friction of the human body. In speed
performing sports like cycling, cyclist often optimize the positions to
reduce their drag by conducting experiment using wind tunnel. The
measurement of human body flow can be said is time consuming and
field tests are very difficult to set up. Even the experts in the related
field can only optimize the positions to reduce drag by trial and error
Therefore, computational fluid dynamic (CFD) is one of the
alternative techniques to be carried out to investigate the
aerodynamic drag. However, since the accuracy of numerical
simul ation method needs to be verified, published experimental data
are used to compare and to justify the CFD results. Usually the
maximum allowable errors in predicting the drag can range
between10-12%. The wind tunnel experiment investigation of
various positions of human was published by Schmitt [3]. The results
can be used to validate the values obtain from CFD simulation. Fig.
1 shows the 4 body positions to be studies during the research.
Fig.1 (a) Standing (b) Sitting (c) Squatting (d) Supine. [4]
2. Methods
2.1. Theoretical Analysis
According to Hoerner [4], the total aerodynamic drag of human
body can be classified into 2 components whi ch are:
=+
Where  is pressure drag coeffici ent and  is friction
drag coefficient. Pressure drag is formed from the distribution of
forces normal .to the human body surface [5]. The effects of
viscosity of the moving fluid (air) may contribute to the rising value
of pressure drag. Drag that is directly due to wall shear stress can be
knows as friction drag as it is formed due to the frictional effect. The
friction drag is the component of the wall shear force in the direction
towar d the flow, and it depends on the body surface area and the
magnitude of the wall shear stress.
2.2. Numerical Simulation
The numerical simulation in the present work is carried out using
ANSYS FLUENT 14.0. According to Chowdhurry [6], the human
body parts can be simplified due to the configuration and size of
human body is way too complex. Therefore, a simplified model of
human body with the average height and frontal area was drawn
using SolidWorks as shown in Fig. 2 (a). The model is then imported
into the virtual wind tunnel for computational studies as shown in Fig.
2 (b)
Fig. 2 (a) Simplified Human Body (b) computational domain and
boundary condition.
Steady 3D Reynolds-averaged Navier-Stokes (RANS) is used in
combination with turbulence models such as k-
ε
and k-ω with the low-
Reynolds number modeling (LRNM) together is used. The four
turbulence model to be carried out in the study is standard k-
ε
model,
realizable k-ω model, standard k-ω model and lastly shear stress
transport (SST) k-ω model. There is no universally proved that which
turbulence model is the most accurate flow for every study case [7].
Only steady flow is performed as the studies of flow of transient can be
very complicated and hard to predict. In this study case, the velocity
inlet will be set at 10 m/s which is almost equivalent to 38 km/h as this
input boundary condition is the normal speed for a casual cycling speed.
2 (b)
2 (a)
99
EURECA 2013 - Calculation of Aerodynamic Drag of Human Being in Various Positions
3. Results
3.1 Comparison of Introductory Test
The value of drag coeffi cient is dimensionless. Fig. 3 shows the
values of drag coefficient of various positions using various
turbulence models.
Fig. 3 Drag coefficient of Various Positions with Turbulence Models.
4. Discussion
Figure 3 shows that the supine position gives the lowest drag
coefficient while the standing position gives the highest. The drag
coefficient of vari ous positions from lowest to highest value in
ascending: supine, squatting, sitting and standing. It also shows that
the lesser projected frontal area can greatly reduce the drag
coefficient. Since the input boundaries of velocity inlet, air viscosity,
air density and flow direction are the same for 4 positions; the only
factor to affect the changes in drag coefficient is the frontal area. In
others words, a less frontal area means the body expose lesser
external flow to the wind. As the standing position has the highest
projected frontal area, it exerts more forces due to the pressure and
skin friction drag on the frontal area on the human opposite the wind
direction. While the supine position has the lowest projected frontal
area because the exposed area in this position is less and thus the skin
friction drag is less as well.
In other hand, the research also carries out using different
turbulence models. For squatting and supine positions, the values
between the four turbulence models ar e very close. However, it has
huge differences between the experimental data and numerical
simulation data. This may due to the simplified human body model
as the projected frontal area is not the same as the real human sample
carry out by Schmitt [3]. The geometry of the upper body of the
simpl ified human body is too wide and therefore it has hi gher drag
coefficient. The difference of percentage errors for supine and
squatting is relatively high, from 35% to 67%. For standing and
sitting posi tions, the values between the experimental data and
numerical simulation data are very close, the maximum error
differences is only approximately 11%, which is still in the accepted
range. The difference values of each turbulence model may occur
due to the input method of the computational domain and boundary
condition at the set-up. Each turbulence model has the ir own
limitations when solving the same condition study; therefore, it may
produce the different final values.
Table 1 . Percentage Error between Published Experimental
Data and Numer ical Simulat ion Dat a
5. Conclusion and Recommendations
In conclusi on, the standing position gives the highest value of drag
coefficient while supine gives t he lowest drag coeffici ent. To
improve the accuracy, the modeling of the human body cannot be too
simpl ified as the sometimes the real body do not generate the same
projected frontal area as the simplified model. The input method and
meshing may affect the accuracy too. Further study on these options
need to be carried out more critically. However, although the
accuracy of the wind tunnel and CFD are not totally the same, CFD
still can be used to st udy in high speed appli cation sports like running,
swimming, etc. to optimize the body configuration as it gives
detailed flow for every single study. In the other hand, the results
presented in the current study are part of the ongoing study.
Investigation on apply velociti es are considered such as average of
walki ng speed, running, skiing and etc. Angle of attack is also one of
the parametric to be considered in the overall studies.
Acknowledgment
Appreciation to Taylor’s Universit y School of Engineering for the
funding and equipment support of this project.
References
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Carmeliet, J. (2010). Aerodynamic study of different cyclist
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of Biomechanics, 43(7), 1262-1268.
2. Anderson, John D. Jr. (2000). Introduction to Flight. Fourth
Edition, McGraw Hill Higher Education, Boston, Massachuset ts,
USA.
3. Schmitt, T. J. (1954). Wind Tunnel Investigation of Air Loads on
Human Being.Virginia, USA.
4. Hoerner, S. F. (1965). Fluid Dynamic Drag. Midland Park, New
Hersey, USA.
5. Cengel, Y.; Cimbala, J.; Turner, R. (2012). Fundamentals of
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6. Chowdhury, H.; Alam, F. and Subic, A. (2010). An Experimental
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Carmeliet, J. (2010). Computational fluid dynamics analysis of
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modeling and boundary layer modeling approaches. Journal of
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Positions
Percentage of Error (%)
standard
k-ϵ
realizable
k-ϵ
standard
k-ω
SST
k-ω
Standing
3.3
0.008
11.1
3.3
Sitting
5.1
2.5
3.8
0.05
Supine
44.4
40
66.7
40
Squatting
48.9
40
35.6
37.8
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  • John D Anderson
Anderson, John D. Jr. (2000). Introduction to Flight. Fourth Edition, McGraw Hill Higher Education, Boston, Massachusetts, USA.