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Numerical modeling of airflow over the Ahmed body
Yunlong Liu, Alfred Moser
Air and Climate Group, Swiss Federal Institute of Technology, ETH-Zentrum WET A1, CH-8092 Zurich,
Switzerland, Tel: +41 1 6326915, Fax: +41 1 6321023
Email: liu@hbt.arch.ethz.ch, URL: http://www.airflow.ethz.ch/
ABSTRACT
Airflow over the Ahmed body is investigated by
means of transient RANS turbulence models. The
simulations have been performed using two different
differencing schemes. The performances of several
RANS turbulence models have been compared. It has
been found that Durbin’s k-ε-v2
model is more
accurate than the other turbulence models for the
wall-bounded cases with separation and
reattachment. A wall function for k-ε-v2
model has
been introduced to avoid the divergence when very
fine mesh is employed for complex geometries.
Numerical results agree well with the reported
experiments.
1. INTRODUCTION
In order to investigate the behavior of newly
developed turbulence models for complex geometry
cases, a simplified car model, known as the Ahmed
body, has been tested by Ahmed, et al[1]
in the early
1980s. The Ahmed body is made up of a round front
part, a moveable slant plane placed in the rear of the
body to study the separation phenomena at different
angles, and a rectangular box, which connects the
front part and the rear slant plane, as shown in Figure
1. All dimensions listed in figure 1 are in mm.
Several researchers have worked on the experiments
and numerical modeling of the flow over the Ahmed
body. Ahmed studied the wake structure and drag of
the Ahmed body[2-3]
, Lienhart and his colleagues[4]
conducted the experiments for two rear slant angles
(25°, 35°) at LSTM. The velocities and turbulence
kinetic energies have been measured by LDA at
several key locations. This paper will take the LSTM
test results as the validation data. Craft[5]
compared
the performance of linear and non-linear k-ε model
with two different wall functions. Basara[6]
conducted the numerical modeling of this case by
means of large eddy simulation (LES), Menter[7]
compared the
performance of the SST model and some other
turbulence models.
Figure 1: Schematic of the Ahmed body model[1]
Figure 2:Characteristic drag coefficients for the
Ahmed body for various rear slant angles ϕ
measure by Ahmed[1]
As the wake flow behind the Ahmed body is the
main contributor to the drag force, accurate
prediction of the separation process and the wake
flow are the key to the successful modeling of this
case. To simulate the wake flow accurately,
resolving the near wall region using accurate
turbulence model is highly desirable. This paper
will study the effectiveness of three different
turbulence models, including the k-ε-v2
model[8-9]
,
the k-ε model and the full stress model, for the
modeling of the flow over the Ahmed body, and
shows the behavior of different turbulence models,
as well as the effect of the grid layout and
differencing schemes on the numerical results.
2. TURBULENCE MODELS
Airflow over the Ahmed body is governed by the
Navier-Stokes equations. As turbulent flow is made
up of a spectrum of vortex scales, the turbulence
energy is distributed through the whole spectrum
based on the wavelength. Ideally, resolving all the
scales can offer the best insight into the
understanding of the turbulent flow, which can be
accomplished by direct numerical simulation (DNS).
However, it is not practical to resolve all the scales
for engineering problems such as the flow over the
Ahmed body. While TRANS, a transient Reynolds
averaged Navier-Stokes approach(RANS), offers a
very promising approach because the large scales can
be resolved while the small scales, which carry less
turbulence energy compared to the large scales, are
modeled by RANS sub-scale models. The averaged
Navier-Stokes equations take the following form:
0
x
U
i
i
=
∂
ρ∂ (1)








−
∂
∂
+
∂
∂
ν
∂
∂
+
∂
∂
ρ
−= ji
i
j
j
i
ji
i
i
uu)
x
U
x
U
(
xx
P1
g
Dt
DU (2)
According to the Boussinesq assumption, the
isotropic eddy viscosity/diffusivity formulation for
Reynolds stress reads:
)
x
U
x
U
(k
3
2
uu
i
j
j
i
tijji
∂
∂
+
∂
∂
ν−δ= (3)
In the standard k-ε turbulence model, the Boussinesq
assumption is applied together with wall functions. It
is widely used and the convergence is stable.
However, the above-mentioned assumption is not
always true because of the an-isotropic nature of the
flow in specific cases, such as the cases that involve
the flows in the near wall region.
According to Launder[10]
, the normal stress
2
v ,
perpendicular to the wall, plays the most important
role to the eddy viscosity. Motivated by this idea,
Durbin[8-9]
devised a three-equation model, known as
the k-ε-v2
model, or v2f model. The idea is to resolve
the normal stress
2
v , along with solving the
modified k and ε equations. The near wall region is
resolved exactly and the wall-reflection is considered
by means of elliptic relaxation in the model. It has
been reported[8-9, 11-13]
that this model is a
significant improvement over the two-equation
model for several test cases, such as channel flow,
backward facing step, etc. Governing equations
read:








∂
∂
σ
ν
+ν
∂
∂
+ε−=
jk
t
j
k
x
k
)(
x
P
Dt
Dk
(4)








∂
ε∂
σ
ν
+ν
∂
∂
+
ε−
=
ε
ε
εε
j
t
j
2k1
x
)(
xT
CPC
Dt
D
(5)
]
x
v
)[(
xk
vkf
Dt
vD
j
2
k
t
j
2
22
2
∂
∂
σ
ν
+ν
∂
∂
+
ε
−= (6)
k
P
C
T
]k/v3/2[
)C1(f
x
f
x
k
2
2
122
j
22
j
−
−
−=−








∂
∂
∂
∂ (7)
22f is a quotient of the pressure strain Φ by the
turbulent kinetic energy k
22
TvC 2
t µ=ν
19.0C =µ , C , C ,
, σ
44.11 =ε
3.1=ε
9.12 =ε
0.1k =σ
i
j
j
i
i
j
tk
x
U
)
x
U
x
U
(P
∂
∂
∂
∂
+
∂
∂
ν=
The time scale T and length scale L can be obtained
from the following:
})(6,
k
max{T 2/1
ε
ν
ε
=
})(C,
k
max{3.0L 4/1
32/3
ε
ν
ε
= η ,
p4
2
w
2
w22 )
y
v
(
20
f
ε
ν
−= , where index w and p
denote the values at the wall and that in the first
cell above the wall, respectively.
To study the performance of the k-ε-v2
turbulence
model for application in complex geometries,
several different turbulence models, including the
k-ε model with wall function, the k-ε-v2
model and
full Reynolds stress model with wall function have
been investigated in this paper. In this study, a wall
function for f is introduced in the near wall region
for k-ε-v
22
2
model.
Based on the numerical results for the channel flow
DNS data of Kim[14]
et al (1987), in the near the wall
region, we can get the non-dimensional value of f22
as a function of y+, so the f22 value for the first near
wall cell can be obtained. The following formula is
used to calculate the f22 value for the first near wall
element:
ν+−+= ++
µ
+
/))0.13y/(44.4)0.13y/(65.0(kC)y(f 58.15.0
22
( y+
≥20)
ν−×+= +−
µ
+
/)]20y(10511.200199.0[kC)y(f 45.0
22
( y+
<20)
It should be pointed out that the difference from
Durbin’s original model is that a value is given on
the first point near the wall for f22 equation, along
with the wall functions for the other equations, just
like the case for the k-ε model. The purpose of this
boundary condition for f22 equation is to improve the
robustness of the original model.
3. PHYSICAL CASE AND NUMERICAL
METHOD
The size of the computation domain is 7.044m long,
2.0m wide and 1.05m high, with the down stream
portion extended five meters behind the rear of the
Ahmed body. The Ahmed body is 1.044m long,
0.288m high and 0.389m wide, with a rear slant
angle of 35°, as shown in figure 1. The projected
area of the Ahmed body in the mainstream
direction is 0.112m
xA
2
, which corresponds to a
blockage ratio of 5.33%. The bottom surface of the
Ahmed body is located at 0.05m above the ground.
The incoming flow, located at one meter upstream of
the front surface, is at 40 m/s with 0.2% free stream
turbulence level, the corresponding Mach number is
about 0.115, which gives a vehicle height based
Reynolds number of 7.68×105
. Airflow is assumed to
be incompressible, heat transfer is not considered in
this study. Outflow is assumed fully developed and
the zero-gradient velocity boundary condition is
imposed. The ground and the body surfaces are
treated as no-slip smooth walls. All the other
boundaries are symmetry, because the size of the
wind tunnel is much larger than the computation
domain. Figure 3 shows part of the grid distribution
near the Ahmed body symmetrical plane. Because of
the complexity of this Ahmed body model, the
computation domain has been split into 46 blocks, so
that each processor is responsible for the
computation of several blocks. 16 processors are
employed for parallel computation. To limit the
total number of element cells, a non-uniform
structured grid is constructed, with the near wall
region using smaller grid size to control the first y+
at around 20 to 50. Total element number is
460,000.
It has been found that the grid quality is crucial for
convergence, and smaller time step is required in
the initial stage to ensure the computation not to
diverge. The differencing scheme can affect the
accuracy of the solution, in the initial stage, the
upwind differencing scheme is employed in the
initial computation stage because it is robust, but it
is not as accurate as second order central difference
scheme. At time=0.6s it is switched to the second
order central difference scheme to get a good final
solution.
Figure 3: Grid distribution near the Ahmed body
4. RESULTS AND DISCUSSION
The results shown below are the averaged value
over several cycles of the transient flow field,
unless it is specially stated.
At upstream air velocity of 40m/s, an unsteady
wake in the downstream is generated downstream.
The unsteady wake comprises two vortices behind
the rear with the larger one in the higher part, and
the smaller one in the lower part, as shown in the
streamline in figure 4. The background color in
figure 4 shows the contour of the turbulent kinetic
energy k. It is found that the peak value of k is
located in the center of the small vortex
downstream of the body, as observed in the
experiment[4]
.
Figure 5 shows the mean velocity and flow field in
the symmetry plane of the Ahmed body. Vortical
structures extend more than 0.5m beyond the end
the body rear. The reverse flow climbs up to the
rear slant, as observed in the experiment[4]
.
Figure 4: unsteady wake behind the body predicted
using the k-ε-v2
model
Figure 5: Flow field in the symmetry section colored
by the stream-wise u-velocity predicted by the k-ε-v2
model
Figure 6: Comparison of velocity profile of
numerical results and experiment
Figure 6 gives a mean velocity profile comparison of
the numerical results with the experiments for the
separation zone. Geometrical parameters are
normalized by the height of the Ahmed body, L
(0.288m). Compared with the standard k-ε model
and full Reynolds stress model, the k-ε-v2
model gets
better results for velocities above the rear slant and
behind the Ahmed body, because the velocities
predicted by the k-ε-v2
model fit well with the
experimental data[4]
. The numerical results of the full
stress model and the k-ε model predicted a wake
being recovered too soon at the downstream, and
predicted velocities have larger discrepancies when
compared to the experiment data.
Figures 7a-d show the downstream development of
the counter-rotating vortex system and contours of
the predicted turbulent kinetic energy k at four
different sections: 80mm, 200mm, 500mm and
1500mm downstream of the body, respectively. It
confirms that two counter-rotating trailing vortices
are generated downstream of the Ahmed body, and
the vortices effect, though very small, still remains
at more than 1.5m away from the rear, and the
velocity deficit still visible at more than 4m behind
the Ahmed body.
Figures 8 shows the drag coefficients predicted by
the k-ε-v2
model. The time averaged drag force FD
is found by integration of surface pressure and the
shear stress, the drag coefficient is defined as
following:
x
2
0
D
D
Au5.0
F
C
ρ
=
Where ρ is the air density, is the upstream bulk
velocity, is the projected area of the Ahmed
body in x direction.
0u
xA
It should be pointed out that before time=0.6s, the
upwind differencing scheme is used. At time=0.6s,
the differencing scheme is switched to the second
order central differencing scheme. Its averaged
value of the drag force coefficient is 0.262, which
shows that the numerical result agrees quite well
with the experiment data of Ahmed[1]
, which is
about 0.26, if the second order central differencing
scheme is employed. This verified that, not only the
turbulence model and the near wall grid resolution
are affecting the accuracy of the predicted drag
coefficient, the proper selection of the differencing
scheme also plays an important role to make CFD
more accurate.
Table 1 gives a list of the measurement data and the
predicted drag coefficient and its components,
where Ck, CB, CS, CD represents the drag coefficient
at the nose, back, the rear slop and the total.
Pref is the reference pressure which is set to 0 Pa,
when comparing the drag coefficients, the force
components are sensitive to its location. In this
paper the reference pressure is located at the outlet.
Table 1: Validation of drag force and force
components
Ck CB CS CD Error%
Ahmed[1]
0.020 0.095 0.090 0.260 -
LSTM[4]
- 0.129 0.121 - -
k-ε+WF 0.026 0.105 0.111 0.242 -6.8
k-ε-v2
0.020 0.124 0.120 0.264 +1.5
SSG 0.010 0.102 0.098 0.210 -19.2
RSM 0.013 0.093 0.178 0.282 +8.5
SST 0.026 0.107 0.108 0.241 -7.3
Based on the comparison of drag coefficients with
LSTM experiment data, it can be concluded that
Durbin’s k-ε-v2
models gives the best result,
followed by k-ε, SST and RSM model.
7a: 80mm
7b: 200mm
7c:500mm
7d: 1500mm
Figure 7a-d: Velocities and contours of the
predicted turbulent kinetic energy k at 80mm,
200mm, 500mm and 1500mm behind the body
predicted by the k-ε-v2
model
Figure 8: Drag force coefficient predicted by
the k-ε-v2
model
5. CONCLUSIONS
The flow field and drag force of the flow over the
Ahmed body can be simulated by computational
approach. Compared with the standard k-ε model
and the full stress model, the k-ε-v2
model performs
better, and the second-order central differencing
scheme is more accurate than upwind scheme.
ACKNOWLEDGEMENTS
This study is supported by MOVA, a project
undertaken by thermo-fluids section directed by
professor K. Hanjalic. Part of this work is finished at
TU Delft under the supervision of professor K.
Hanjalic. Academic discussion with W. Khier, O.
Ouhlous and M. Hadziabdic at TU Delft, The
Netherlands, is gratefully acknowledged. Special
thanks are extended to the Cray T3E computer center
at Delft University of Technology, The Netherlands.
REFERENCES
[1] Ahmed, S.R., Ramm G.: Some Salient Features
of the Time-Averaged Ground Vehicle Wake. SAE
Technical Paper 840300, 1984.
[2] Ahmed, S.R.,Wake Structure of typical
automobile shapes. Transactions of the ASME,
Journal of Fluids Engineering V103, P162-169, 1981
[3] Ahmed, S.R., Influence of base slant on the wake
structure and drag of road vehicles. Transactions of
the ASME, Journal of Fluids Engineering V105,
P429-434, 1983
[4] Lienhart H., Stoots C.,Becker S., Flow and
turbulence structures on the wake of a similified car
model(Ahmed model), DGLR Fach. Symp. der AG
ATAB, Stuttgart University, 2000.
[5] T. J. Craft, S. E. Gant, H. Iacovides, B.E.
Launder, Computational study of flow around the
Ahmed car body, 9th
ERCOFTAC workshop on
refined turbulence modeling, Darmstadt University
of Technology, Germany, 2001
[6] B. Basara, Numerical simulation of turbulent
wakes around a vehicle, FEDSM 99-7324, 1999
[7] L. Durand, M.Kuntz, F. Menter, Validation of
CFX-5 for the Ahmed Car Body, 10th joint
ERCOFTAC (SIG-15) -IAHR-QNET/CFD
Workshop on Refined Turbulence Modelling,
October 10-11, 2002
[8] Durbin P.A.: Near Wall turbulence closure
modeling without “damping functions”. Theoretical
and Computational Fluid Dynamics 3, P1-13, 1991
[9] Durbin P.A.: Separated Flow Computations
with the k-ε-v2
Model, AIAA Journal, V33, N4,
PP659-664, 1995
[10] Launder, B. E., Low Reynolds Number
Turbulence Near Walls, UMIST Mechanical
Engineering Dept, Rept.TFD/86/4, University of
Manchester, England, UK, 1986.
[11] M. Behnia, S. Parneix and P. A. Durbin,
Prediction of heat transfer in an axisymmetric
turbulent jet impinging on a flat plate. International
journal of heat and mass transfer, V41, N12,
PP1845-1855, 1998
[12] Georgi Kalitzin, Towards a robust and
efficient v2f implementation with application to
transonic bump flow, CTR Annual Research Briefs,
PP291-299, 2000
[13] Lien F S, Durbin, P.A., Non-linear k-ε-v2
modeling with application to high lift. Proc. of the
summer program, CTR, NASA Ames/Stanford
Univ. 5-22, 1996
[14] Robert D. Moser, John Kim and Nagi N.
Mansour, DNS of turbulent channel flow up to
=590, Physics of Fluids, V111, N4, PP943-
945, 1999
τRe

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Numerical modeling of airflow over the Ahmed body using RANS turbulence models

  • 1. Numerical modeling of airflow over the Ahmed body Yunlong Liu, Alfred Moser Air and Climate Group, Swiss Federal Institute of Technology, ETH-Zentrum WET A1, CH-8092 Zurich, Switzerland, Tel: +41 1 6326915, Fax: +41 1 6321023 Email: liu@hbt.arch.ethz.ch, URL: http://www.airflow.ethz.ch/ ABSTRACT Airflow over the Ahmed body is investigated by means of transient RANS turbulence models. The simulations have been performed using two different differencing schemes. The performances of several RANS turbulence models have been compared. It has been found that Durbin’s k-ε-v2 model is more accurate than the other turbulence models for the wall-bounded cases with separation and reattachment. A wall function for k-ε-v2 model has been introduced to avoid the divergence when very fine mesh is employed for complex geometries. Numerical results agree well with the reported experiments. 1. INTRODUCTION In order to investigate the behavior of newly developed turbulence models for complex geometry cases, a simplified car model, known as the Ahmed body, has been tested by Ahmed, et al[1] in the early 1980s. The Ahmed body is made up of a round front part, a moveable slant plane placed in the rear of the body to study the separation phenomena at different angles, and a rectangular box, which connects the front part and the rear slant plane, as shown in Figure 1. All dimensions listed in figure 1 are in mm. Several researchers have worked on the experiments and numerical modeling of the flow over the Ahmed body. Ahmed studied the wake structure and drag of the Ahmed body[2-3] , Lienhart and his colleagues[4] conducted the experiments for two rear slant angles (25°, 35°) at LSTM. The velocities and turbulence kinetic energies have been measured by LDA at several key locations. This paper will take the LSTM test results as the validation data. Craft[5] compared the performance of linear and non-linear k-ε model with two different wall functions. Basara[6] conducted the numerical modeling of this case by means of large eddy simulation (LES), Menter[7] compared the performance of the SST model and some other turbulence models. Figure 1: Schematic of the Ahmed body model[1] Figure 2:Characteristic drag coefficients for the Ahmed body for various rear slant angles ϕ measure by Ahmed[1] As the wake flow behind the Ahmed body is the main contributor to the drag force, accurate prediction of the separation process and the wake flow are the key to the successful modeling of this case. To simulate the wake flow accurately, resolving the near wall region using accurate turbulence model is highly desirable. This paper will study the effectiveness of three different turbulence models, including the k-ε-v2 model[8-9] ,
  • 2. the k-ε model and the full stress model, for the modeling of the flow over the Ahmed body, and shows the behavior of different turbulence models, as well as the effect of the grid layout and differencing schemes on the numerical results. 2. TURBULENCE MODELS Airflow over the Ahmed body is governed by the Navier-Stokes equations. As turbulent flow is made up of a spectrum of vortex scales, the turbulence energy is distributed through the whole spectrum based on the wavelength. Ideally, resolving all the scales can offer the best insight into the understanding of the turbulent flow, which can be accomplished by direct numerical simulation (DNS). However, it is not practical to resolve all the scales for engineering problems such as the flow over the Ahmed body. While TRANS, a transient Reynolds averaged Navier-Stokes approach(RANS), offers a very promising approach because the large scales can be resolved while the small scales, which carry less turbulence energy compared to the large scales, are modeled by RANS sub-scale models. The averaged Navier-Stokes equations take the following form: 0 x U i i = ∂ ρ∂ (1)         − ∂ ∂ + ∂ ∂ ν ∂ ∂ + ∂ ∂ ρ −= ji i j j i ji i i uu) x U x U ( xx P1 g Dt DU (2) According to the Boussinesq assumption, the isotropic eddy viscosity/diffusivity formulation for Reynolds stress reads: ) x U x U (k 3 2 uu i j j i tijji ∂ ∂ + ∂ ∂ ν−δ= (3) In the standard k-ε turbulence model, the Boussinesq assumption is applied together with wall functions. It is widely used and the convergence is stable. However, the above-mentioned assumption is not always true because of the an-isotropic nature of the flow in specific cases, such as the cases that involve the flows in the near wall region. According to Launder[10] , the normal stress 2 v , perpendicular to the wall, plays the most important role to the eddy viscosity. Motivated by this idea, Durbin[8-9] devised a three-equation model, known as the k-ε-v2 model, or v2f model. The idea is to resolve the normal stress 2 v , along with solving the modified k and ε equations. The near wall region is resolved exactly and the wall-reflection is considered by means of elliptic relaxation in the model. It has been reported[8-9, 11-13] that this model is a significant improvement over the two-equation model for several test cases, such as channel flow, backward facing step, etc. Governing equations read:         ∂ ∂ σ ν +ν ∂ ∂ +ε−= jk t j k x k )( x P Dt Dk (4)         ∂ ε∂ σ ν +ν ∂ ∂ + ε− = ε ε εε j t j 2k1 x )( xT CPC Dt D (5) ] x v )[( xk vkf Dt vD j 2 k t j 2 22 2 ∂ ∂ σ ν +ν ∂ ∂ + ε −= (6) k P C T ]k/v3/2[ )C1(f x f x k 2 2 122 j 22 j − − −=−         ∂ ∂ ∂ ∂ (7) 22f is a quotient of the pressure strain Φ by the turbulent kinetic energy k 22 TvC 2 t µ=ν 19.0C =µ , C , C , , σ 44.11 =ε 3.1=ε 9.12 =ε 0.1k =σ i j j i i j tk x U ) x U x U (P ∂ ∂ ∂ ∂ + ∂ ∂ ν= The time scale T and length scale L can be obtained from the following: })(6, k max{T 2/1 ε ν ε = })(C, k max{3.0L 4/1 32/3 ε ν ε = η , p4 2 w 2 w22 ) y v ( 20 f ε ν −= , where index w and p denote the values at the wall and that in the first cell above the wall, respectively. To study the performance of the k-ε-v2 turbulence model for application in complex geometries, several different turbulence models, including the k-ε model with wall function, the k-ε-v2 model and full Reynolds stress model with wall function have been investigated in this paper. In this study, a wall
  • 3. function for f is introduced in the near wall region for k-ε-v 22 2 model. Based on the numerical results for the channel flow DNS data of Kim[14] et al (1987), in the near the wall region, we can get the non-dimensional value of f22 as a function of y+, so the f22 value for the first near wall cell can be obtained. The following formula is used to calculate the f22 value for the first near wall element: ν+−+= ++ µ + /))0.13y/(44.4)0.13y/(65.0(kC)y(f 58.15.0 22 ( y+ ≥20) ν−×+= +− µ + /)]20y(10511.200199.0[kC)y(f 45.0 22 ( y+ <20) It should be pointed out that the difference from Durbin’s original model is that a value is given on the first point near the wall for f22 equation, along with the wall functions for the other equations, just like the case for the k-ε model. The purpose of this boundary condition for f22 equation is to improve the robustness of the original model. 3. PHYSICAL CASE AND NUMERICAL METHOD The size of the computation domain is 7.044m long, 2.0m wide and 1.05m high, with the down stream portion extended five meters behind the rear of the Ahmed body. The Ahmed body is 1.044m long, 0.288m high and 0.389m wide, with a rear slant angle of 35°, as shown in figure 1. The projected area of the Ahmed body in the mainstream direction is 0.112m xA 2 , which corresponds to a blockage ratio of 5.33%. The bottom surface of the Ahmed body is located at 0.05m above the ground. The incoming flow, located at one meter upstream of the front surface, is at 40 m/s with 0.2% free stream turbulence level, the corresponding Mach number is about 0.115, which gives a vehicle height based Reynolds number of 7.68×105 . Airflow is assumed to be incompressible, heat transfer is not considered in this study. Outflow is assumed fully developed and the zero-gradient velocity boundary condition is imposed. The ground and the body surfaces are treated as no-slip smooth walls. All the other boundaries are symmetry, because the size of the wind tunnel is much larger than the computation domain. Figure 3 shows part of the grid distribution near the Ahmed body symmetrical plane. Because of the complexity of this Ahmed body model, the computation domain has been split into 46 blocks, so that each processor is responsible for the computation of several blocks. 16 processors are employed for parallel computation. To limit the total number of element cells, a non-uniform structured grid is constructed, with the near wall region using smaller grid size to control the first y+ at around 20 to 50. Total element number is 460,000. It has been found that the grid quality is crucial for convergence, and smaller time step is required in the initial stage to ensure the computation not to diverge. The differencing scheme can affect the accuracy of the solution, in the initial stage, the upwind differencing scheme is employed in the initial computation stage because it is robust, but it is not as accurate as second order central difference scheme. At time=0.6s it is switched to the second order central difference scheme to get a good final solution. Figure 3: Grid distribution near the Ahmed body 4. RESULTS AND DISCUSSION The results shown below are the averaged value over several cycles of the transient flow field, unless it is specially stated. At upstream air velocity of 40m/s, an unsteady wake in the downstream is generated downstream. The unsteady wake comprises two vortices behind the rear with the larger one in the higher part, and the smaller one in the lower part, as shown in the streamline in figure 4. The background color in figure 4 shows the contour of the turbulent kinetic energy k. It is found that the peak value of k is located in the center of the small vortex downstream of the body, as observed in the experiment[4] . Figure 5 shows the mean velocity and flow field in the symmetry plane of the Ahmed body. Vortical structures extend more than 0.5m beyond the end the body rear. The reverse flow climbs up to the rear slant, as observed in the experiment[4] .
  • 4. Figure 4: unsteady wake behind the body predicted using the k-ε-v2 model Figure 5: Flow field in the symmetry section colored by the stream-wise u-velocity predicted by the k-ε-v2 model Figure 6: Comparison of velocity profile of numerical results and experiment Figure 6 gives a mean velocity profile comparison of the numerical results with the experiments for the separation zone. Geometrical parameters are normalized by the height of the Ahmed body, L (0.288m). Compared with the standard k-ε model and full Reynolds stress model, the k-ε-v2 model gets better results for velocities above the rear slant and behind the Ahmed body, because the velocities predicted by the k-ε-v2 model fit well with the experimental data[4] . The numerical results of the full stress model and the k-ε model predicted a wake being recovered too soon at the downstream, and predicted velocities have larger discrepancies when compared to the experiment data. Figures 7a-d show the downstream development of the counter-rotating vortex system and contours of the predicted turbulent kinetic energy k at four different sections: 80mm, 200mm, 500mm and 1500mm downstream of the body, respectively. It confirms that two counter-rotating trailing vortices are generated downstream of the Ahmed body, and the vortices effect, though very small, still remains at more than 1.5m away from the rear, and the velocity deficit still visible at more than 4m behind the Ahmed body. Figures 8 shows the drag coefficients predicted by the k-ε-v2 model. The time averaged drag force FD is found by integration of surface pressure and the shear stress, the drag coefficient is defined as following: x 2 0 D D Au5.0 F C ρ = Where ρ is the air density, is the upstream bulk velocity, is the projected area of the Ahmed body in x direction. 0u xA It should be pointed out that before time=0.6s, the upwind differencing scheme is used. At time=0.6s, the differencing scheme is switched to the second order central differencing scheme. Its averaged value of the drag force coefficient is 0.262, which shows that the numerical result agrees quite well with the experiment data of Ahmed[1] , which is about 0.26, if the second order central differencing scheme is employed. This verified that, not only the turbulence model and the near wall grid resolution are affecting the accuracy of the predicted drag coefficient, the proper selection of the differencing scheme also plays an important role to make CFD more accurate. Table 1 gives a list of the measurement data and the predicted drag coefficient and its components, where Ck, CB, CS, CD represents the drag coefficient at the nose, back, the rear slop and the total. Pref is the reference pressure which is set to 0 Pa, when comparing the drag coefficients, the force components are sensitive to its location. In this paper the reference pressure is located at the outlet. Table 1: Validation of drag force and force components Ck CB CS CD Error% Ahmed[1] 0.020 0.095 0.090 0.260 - LSTM[4] - 0.129 0.121 - - k-ε+WF 0.026 0.105 0.111 0.242 -6.8 k-ε-v2 0.020 0.124 0.120 0.264 +1.5 SSG 0.010 0.102 0.098 0.210 -19.2 RSM 0.013 0.093 0.178 0.282 +8.5 SST 0.026 0.107 0.108 0.241 -7.3
  • 5. Based on the comparison of drag coefficients with LSTM experiment data, it can be concluded that Durbin’s k-ε-v2 models gives the best result, followed by k-ε, SST and RSM model. 7a: 80mm 7b: 200mm 7c:500mm 7d: 1500mm Figure 7a-d: Velocities and contours of the predicted turbulent kinetic energy k at 80mm, 200mm, 500mm and 1500mm behind the body predicted by the k-ε-v2 model Figure 8: Drag force coefficient predicted by the k-ε-v2 model
  • 6. 5. CONCLUSIONS The flow field and drag force of the flow over the Ahmed body can be simulated by computational approach. Compared with the standard k-ε model and the full stress model, the k-ε-v2 model performs better, and the second-order central differencing scheme is more accurate than upwind scheme. ACKNOWLEDGEMENTS This study is supported by MOVA, a project undertaken by thermo-fluids section directed by professor K. Hanjalic. Part of this work is finished at TU Delft under the supervision of professor K. Hanjalic. Academic discussion with W. Khier, O. Ouhlous and M. Hadziabdic at TU Delft, The Netherlands, is gratefully acknowledged. Special thanks are extended to the Cray T3E computer center at Delft University of Technology, The Netherlands. REFERENCES [1] Ahmed, S.R., Ramm G.: Some Salient Features of the Time-Averaged Ground Vehicle Wake. SAE Technical Paper 840300, 1984. [2] Ahmed, S.R.,Wake Structure of typical automobile shapes. Transactions of the ASME, Journal of Fluids Engineering V103, P162-169, 1981 [3] Ahmed, S.R., Influence of base slant on the wake structure and drag of road vehicles. Transactions of the ASME, Journal of Fluids Engineering V105, P429-434, 1983 [4] Lienhart H., Stoots C.,Becker S., Flow and turbulence structures on the wake of a similified car model(Ahmed model), DGLR Fach. Symp. der AG ATAB, Stuttgart University, 2000. [5] T. J. Craft, S. E. Gant, H. Iacovides, B.E. Launder, Computational study of flow around the Ahmed car body, 9th ERCOFTAC workshop on refined turbulence modeling, Darmstadt University of Technology, Germany, 2001 [6] B. Basara, Numerical simulation of turbulent wakes around a vehicle, FEDSM 99-7324, 1999 [7] L. Durand, M.Kuntz, F. Menter, Validation of CFX-5 for the Ahmed Car Body, 10th joint ERCOFTAC (SIG-15) -IAHR-QNET/CFD Workshop on Refined Turbulence Modelling, October 10-11, 2002 [8] Durbin P.A.: Near Wall turbulence closure modeling without “damping functions”. Theoretical and Computational Fluid Dynamics 3, P1-13, 1991 [9] Durbin P.A.: Separated Flow Computations with the k-ε-v2 Model, AIAA Journal, V33, N4, PP659-664, 1995 [10] Launder, B. E., Low Reynolds Number Turbulence Near Walls, UMIST Mechanical Engineering Dept, Rept.TFD/86/4, University of Manchester, England, UK, 1986. [11] M. Behnia, S. Parneix and P. A. Durbin, Prediction of heat transfer in an axisymmetric turbulent jet impinging on a flat plate. International journal of heat and mass transfer, V41, N12, PP1845-1855, 1998 [12] Georgi Kalitzin, Towards a robust and efficient v2f implementation with application to transonic bump flow, CTR Annual Research Briefs, PP291-299, 2000 [13] Lien F S, Durbin, P.A., Non-linear k-ε-v2 modeling with application to high lift. Proc. of the summer program, CTR, NASA Ames/Stanford Univ. 5-22, 1996 [14] Robert D. Moser, John Kim and Nagi N. Mansour, DNS of turbulent channel flow up to =590, Physics of Fluids, V111, N4, PP943- 945, 1999 τRe