Conference PaperPDF Available

Using OpenFOAM to Model Energy Taken from the Swirl behind a Car

Proceedings of OFW4 4th OpenFOAM Workshop
June 1-4, Montreal, QC, Canada
Louis Gagnon,
Marc J. Richard,
Benoît Lévesque,
Dept. of Mechanical Engineering
Laval University
Québec (Québec) Canada
Abstract. A two-dimensional numerical analysis of the Ahmed body was performed using the k-ω-SST turbulence model
from the OpenFOAM software. The analysis was then modified to include a rotating paddle wheel that captures energy
from the swirl that forms behind the vehicle. The rotating wheel is modeled using a General Grid Interface (GGI) and the
energy captured is calculated with the help of the forces library of the OpenFOAM software. Power generation reached
16.1 watts at optimal conditions. Total drag reductions up to 8.2% were found. Most computations were run in parallel
on a dual core computer.
In the perspective that a large portion of the energy consumed by ground vehicles is used to overcome pressure drag
and that the scientific community still questions whether the optimal drag reduction on a vehicle would equate to a null
pressure drag coefficient, it is attempted in this paper to show how moving parts can be added to an automobile model
to reduce the energy it consumes to overcome wind resistance. That is achieved by capturing energy from the swirls and
modifying the flow that exists behind a hatchback car. The modification is triggered by a rotating device located in the
detached flow behind the vehicle. It is suggested that the energy should be recaptured in the form of electricity because
of the current trend toward hybrid vehicles. The moving parts in question are inspired by paddle wheels and their purpose
is to recapture energy from the vortices located behind a moving vehicle. Although the simulations were performed on a
car model, tractor-trailer rigs would be ideal candidates for the type of energy capture presented in this paper, as well as
any vehicle involved in a lot of highway driving.
The Ahmed car model was chosen as the shape to analyze in the simulations because it has received widespread
attention from the scientific community since its first appearance when Ahmed, Ramm and Faltin (1984) used it in a wind
tunnel to mimic the flow found around a typical car. This model was also chosen by the European Research Community
On Flow, Turbulence, And Combustion (ERCOFTAC) to benchmark different CFD codes. The first goal was thus to create
a mesh-model combination that would reproduce the generally accepted flow characteristics of the Ahmed body and, most
important, its drag coefficient with a minimal error. Reproducing the experimental drag coefficient of the Ahmed body has
been a challenge for many researchers that studied the Ahmed body because of the difficulty to precisely locate the start
of the separation bubble on the rear slant angle of the body. However, a general idea of the appropriate drag coefficient
was grasped from the published research to be in the range of 0.25 to 0.35 when the rear slant angle is 25 degrees. It
must also be pointed out that between the two widely used angles of 25 and 35 degrees there is a drag crisis that occurs
at 30 degrees where the experimental drag value reaches 37.8 (Ahmed, Ramm and Faltin, 1984). Several attempts were
done by different authors to reproduce the transition phenomenon that occurs between those two angles where the flow
actually goes from having its longitudinal vortices start on the rear slant angle of the vehicle to having them start only
on the lower, vertical, rear end. Those vortices have a significant influence on the three-dimensional drag on the Ahmed
body because they interact with the separation bubble located on the slant angle. However, Beaudoin and Aider (2008)
have experimentally demonstrated that those vortices could be avoided by use of side wings on the slant angle wall of the
model and that removing them can also reduce drag. The goal of this project is to deal with the middle, two-dimensional,
separation bubble which exists behind the body and hosts two span-wise vortices that meet approximately in the middle
of the rear vertical wall of the car. The energy lost to the top vortex is what is recaptured by the paddle wheels used in this
Proceedings of OFW4 4th OpenFOAM Workshop
June 1-4, Montreal, QC, Canada
Figure 1. The Ahmed body, adapted with permission from (Hinterberger, García-Villalba and Rodi, 2004). Throughout
this paper, the x, y, z axes represent stream-wise, vertical, and span-wise directions, respectively.
Figure 2. The two-dimensional representation of the Ahmed body with the paddle wheel located behind its rear vertical
3.1 Turbulence model
The turbulence model that yielded the most appropriate result is the k-ω-SST model; it is also known for its strong
capability to model attached and detached flows as well as flows subjected to adverse pressure gradients. LES models
were considered but were not deemed appropriate for two-dimensional modeling even though some authors have reported
successful use of LES in two dimensions (Bouris and Bergeles, 1999). Many reports of RANS models being used with
a reasonable error to model the Ahmed body are available in the literature. The k-ω-SST model features an automatic
wall treatment and uses a k-ωtype of model within the boundary layer and a k-type of model in the free-stream flow.
Considering that Bayraktar found good results using the RNG k-model on the Ahmed body (Bayraktar, Landman and
Baysal, 2001), use of k-equations in the free-stream flow should give reliable results. For more information on the k-
ω-SST model the reader is encouraged to see the paper by Menter which mathematically describes the turbulence model
(Menter and Esch, 2001).
Proceedings of OFW4 4th OpenFOAM Workshop
June 1-4, Montreal, QC, Canada
3.2 Parameters and boundary conditions
The air velocity used in the analysis is U = 60 m/s and kinematic viscosity is ν= 14.75 ×106m2/s. The Reynolds
number of this analysis, based on model length is thus Re = 4.25×106. Boundary conditions based on a 0.5% turbulence
intensity (Ahmed, Ramm and Faltin, 1984) and turbulent length scale of 0.05 m were used to estimate k and ωinlet
boundary conditions.
3.3 Mesh
A Reynolds number of 4.25 ×106requires a very fine mesh. It was initially attempted to make use of a structured
boundary layer mesh around the whole vehicle. However, due to the diverse behavior of the flow near the body, a structured
boundary layer mesh was only used at locations of very small longitudinal variations of the flow properties. Thus, only
two small zones of the mesh are structured and use about 500 less cells than an equivalent unstructured mesh. Using
a boundary layer mesh on the front was not appropriate because the pressure gradient along the tangent of the surface
is as strong as along the normal. On the rear slant-angle and vertical surfaces a structured mesh was also inappropriate
due to sudden changes in pressure at specific locations and a velocity distribution typical of detached flows. It was also
necessary to have a well resolved mesh behind the car to properly simulate the wake. The mesh on the wall of the vehicle
also required a fine resolution because of its influence on the drag coefficient. The zone just upstream of the vehicle was
meshed slightly coarser than the wake because there are no vortices in front of the car: only a saddle point that affects the
upstream flow. Considering that it is generally recommended to have at least 15 nodes in the boundary layer and that the
flat plate boundary layer is estimated to be 2 cm thick (Cousteix, 1989), it is not possible to precisely resolve the said layer
and the simulations rely mostly on wall models from the solver. In his three-dimensional numerical analysis of the Ahmed
body, Bayraktar settled for a 4.4M cells unstructured mesh because it yielded a fine correlation between experimental and
numerical drag coefficients (Bayraktar, Landman and Baysal, 2001). From that number of cells and the assumption that
the relative numbers of cells in longitudinal, lateral, and vertical directions were a, 0.25a, and 0.2a, respectively it was
found using Eq. (1) that an equivalent 2D mesh would have 40K cells. Ncells,2Dis the number of cells of the equivalent
two-dimensional mesh. The mesh that was used for the present analysis is slightly rougher and has 27.5K cells.
a×0.2a×0.25a= 4.4×106=a= 444.80 Ncells,2D=a×0.2a40000 (1)
3.4 Rotating interface
Figure 3. Mesh on the rear of the vehicle and paddle wheel
The General Grid Interface (GGI) of the OpenFOAM software was used to allow the paddle wheel to rotate with
respect to the car. This is accomplished by having the solver interpolate the values at a virtual interface which is indicated
Proceedings of OFW4 4th OpenFOAM Workshop
June 1-4, Montreal, QC, Canada
to the solver by the definition of two coincident circles that delimit the inner (rotating part) and outer (fixed car) parts of
the mesh. As seen in Fig. 2, the mesh resolution at the interface is increased in order to make the interface as close as
possible to a perfect circle and reduce to a minimum the empty zones that occur between the inner and outer parts of the
3.5 Validation
The results from the unaltered two-dimensional Ahmed body simulations ran were used to calculate the drag coefficient
differences between the energy-recapturing and the reference model. It was thus necessary to have a certain level of
confidence towards the solution of the flow around that particular body. This level of assurance was gained by running
a mesh refinement test with a mesh that contained twice as many nodes in each direction. The refined mesh has 111.5K
cells. The results from the two meshes matched, within a reasonable error, for all of the flow characteristics. An accurate
enough correlation between the two mesh’s drag coefficients was also found. The simulation with the finer mesh yielded
a slightly higher drag coefficient which interestingly makes the calculation of total drag reduction by use of the paddle
wheel conservative. The refined mesh gives a finer view of the flow properties but both clearly share the same general
flow characteristics.
As mentioned in the introduction, the goal is to have an added part that captures energy from the flow. For results
to be interesting the total drag with the added piece cannot be higher than that of the unaltered Ahmed body; or, in case
that the total drag is increased then the energy captured from the flow would have to surpass the energy lost to drag. To
calculate how much power is extracted from the flow, the moment around the z-axis (Mz) is taken from the analysis and
equated into ecapture according to Eq. (2). Also, Eq. (3) serves as a measure of how much power is required to overcome
a specific drag coefficient at the traveling velocity of the car.
ecapture =Mz×R×2π
60 ×w
edrag = ( 1
A= 0.288 ×0.389 (4)
U is the velocity of the car and the free-stream flow. ρis the density of the ambient air, taken as 1.2kg/m3. R is the
rotational velocity of the paddle wheel. A is the frontal area of the car and CDis the drag coefficient considered for
conversion into equivalent energy. wis the width of the three-dimensional Ahmed body and Tis the thickness of the two-
dimensional model used in the analysis. In a practical application, the power captured from the flow would be converted
into electricity. This assumption is made for two reasons, which are the current trend towards electric and hybrid vehicles
and the ability of an electric generator to regulate the forces applied on the paddle wheel while harvesting electrical power
from it. Old fashion paddle wheels are used to keep the analysis of energy capture from the swirl simple. A constant
rotating velocity of the wheel is imposed and power generated is calculated from the average of moments acting on the
paddle wheel. To apply this to practical implementation one could use a paddle wheel with a relatively large moment
of inertia but that creates a problem of added weight to the vehicle. The other approach, more realistic, is to have the
generator programmed to "damp" velocity fluctuations by making it respond to fluctuations of Mzby giving or taking
power to the wheel. It is probably preferable to have a steady rotation velocity for design considerations.
In the results, CD,body and CD,part represent the drag coefficients on the Ahmed body and on the added part, respec-
tively. CD ,power is the energy saved by the avoided drag when vehicle travels at 60 m/s. ecaptur e is energy in watts
captured by the rotating paddle wheel. The reference case gives a CD,body of 0.300 and that value is used when quanti-
fying the amount of drag saved by the various configurations. The reference CD,body = 0.3requires 4.36kW when the
three-dimensional Ahmed car is moving at 60m/s. CD,saved is the coefficient of the avoided drag and is defined in Eq.
CD,saved = 0.3CD,body CD ,part (5)
5.1 Selected cases
5.1.1 Fixed rotational velocity
In Tab. 1, results from cases of rotating paddle wheels are reported. The wheel’s center of rotation y-position is 0.19
m below the top edge of the body. All wheels have 4 blades.
Proceedings of OFW4 4th OpenFOAM Workshop
June 1-4, Montreal, QC, Canada
Table 1. Results from selected cases with fixed rotational velocity.
Case 1 2 3 4 5 6
Center x 1.077 1.11 1.077 1.11 1.077 1.077
Wheel radius 0.05 0.04 0.05 0.04 0.04 0.05
RPM 2500 2000 2000 4000 2000 2300
CD,body 0.3084 0.2939 0.3122 0.2954 0.3130 0.3122
CD,part -0.0323 -0.0148 -0.0335 -0.0207 -0.0353 -0.0334
CD,saved 0.0240 0.0209 0.0213 0.0252 0.0227 0.0213
CD,power 348 304 309 366 324 309
ecapture 0.9 8.2 12.8 -4.6 6.0 10.38
1.45 1.455 1.46 1.465 1.47 1.475 1.48
ecapture (W)
Time (s)
Figure 4. Plot of ecaptur e vs Time for one full revolution of the paddle wheel from case 3
1.45 1.455 1.46 1.465 1.47 1.475 1.48
Time (s)
Figure 5. Plot of CD ,saved vs Time for one full revolution of the paddle wheel from case 3
Figures (4) and (5) illustrate the energy captured and the drag coefficient avoided, respectively, for a full revolution of
the paddle wheel in case 3. The data is taken from t= 1.45sto t= 1.48s. To illustrate how the flow stabilizes in the
first tenths of second of the simulation, the output power from case 6 is plotted against time in Fig. (6). Each calculation
is done on one full revolution of the paddle wheel, which is equivalent to 4 geometric cycles. After time t= 0.26sthe
power output stabilizes at ecapture = 10.38 ±0.01Wthoughout the simulation.
5.1.2 Variable rotational velocity
The OpenFOAM GGI code was modified to allow the wheel to rotate according to a sinusoidal function. Two results
are reported here and they are both based on the geometry of case 3, reported in the fixed rotational velocity cases. Case
7 and case 8 have rotational velocity functions, Rvar, defined in Eq. (6) and Eq. (7), respectively. Ris the base rotational
velocity, which is 2000 RPM for both cases, and t is time in seconds. The only difference between the two cases is the
phase angle of the sinusoidal function. Rvar was designed so that the paddle wheel moves at maximum rotational velocity
when the maximum energy, and thus maximum Mz, are seen from the fixed rotational velocity cases. This was done in an
attempt to reduce fluctuations in the energy generated. Since the energy production has a period exactly equal to a fourth
of the period of oscillation of the wheel the frequency of the sinusoidal function is also chosen as a fourth of the period
of the wheel. The phase of the sinusoidal function comes from a graphical approximation to match the peaks of power
Proceedings of OFW4 4th OpenFOAM Workshop
June 1-4, Montreal, QC, Canada
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
Time (s)
Power output (W)
Figure 6. Plot of power output vs time calculated per paddle-wheel revolution starting at t= 0. Each cycle is 0.0261
seconds long based on a rotational velocity of 2300 RPM. Data taken from case 6.
generation and variable rotational velocity.
Rvar,7=R×(1.0+0.2×sin(2.9249 + R×π
Rvar,8=R×(1.0+0.2×sin(2.3 + R×π
Table 2. Results from selected cases with variable rotational velocity.
Case 7 8
CD,body 0.3095 0.3104
CD,part -0.0342 -0.0335
CD,saved 0.0248 0.0231
CD,power 359 336
ecapture 12.0 16.1
1.8 1.9 2 2.1 2.2 2.3 2.4
Time (s)
Power output (W)
Figure 7. Plot of power output vs time calculated per paddle-wheel revolution starting at t= 0. Each cycle is 0.03 seconds
long based on a rotational velocity of 2000 RPM. Data taken from case 7.
5.2 Comparison cases
Some cases of non-rotating paddle wheels and modified wheels were run to test whether the drag reduction from the
rotating paddle wheels was exclusive to their rotation or could be achieved by a similar but non-rotating object. Results
of these cases are summarized in Tab. 3. The added parts have center positions of (1.077, -0.19), radii of 0.05 m, and
Proceedings of OFW4 4th OpenFOAM Workshop
June 1-4, Montreal, QC, Canada
2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2
Time (s)
Power output (W)
Figure 8. Plot of power output vs time calculated per paddle-wheel revolution starting at t= 0. Each cycle is 0.03 seconds
long based on a rotational velocity of 2000 RPM. Data taken from case 8.
4 blades for cases with blades. Cases 0 and 30 model a fixed paddle wheel rotated by 0 and 30 from the horizontal
position, respectively. Case A models a paddle wheel whose inner radius was enlarged to 0.045 m which makes it almost
a cylinder. Case B is a copy of case A but the paddle wheel revolves at 2500 RPM. Power consumed by the paddle wheel
in case B is 1.28 watts. Case C is a 0.05 m radius cylinder that does not rotate. Case D is a rough attempt at making
the rear of the Ahmed body more streamline in order to compare the drag reduced by this streamline rear with the drag
reduced by various wheel configurations. Graphical representation of the comparison cases are given in Tab. 3 to simplify
Table 3. Results from comparison cases.
Case 030A B C D
CD,body 0.3111 0.3063 0.3099 0.3288 0.3107 0.2151
CD,part -0.0046 -0.0069 -0.0392 -0.0351 -0.0551 -
CD,saved -0.0065 0.0006 0.0292 0.0061 0.0445 0.0849
CD,power -94.5 8.8 445 87.9 645 1233
Several configurations of the paddle wheel were tested. The most power captured from the flow is from the case
with paddle wheels rotating at 2000 RPM and having a radius of 0.05 m and the power generated is 12.8 watts. The
configuration that reduces the drag coefficient the most is a paddle wheel rotating at roughly 4000 RPM, located slightly
upwind of the maximum turbulent kinetic energy location, and having a radius of 0.04 m. A full center serves to avoid
incoming flow from being diverted between the paddles and wasting some of its kinetic energy in doing so. Cases that
do not have a full center paddle wheel do not yield as much energy and are not documented in this paper. It was also
noticed that the paddles should not interfere, or at least the least possible, with the flow outside the separation bubble
since doing so significantly increases drag without increasing the amount of power generated. It was not expected that
the drag would be reduced by the rotating paddle wheels. Therefore, it is questioned whether the reduced drag might
be due to the effective body of the modified Ahmed model being more streamline. Thus, tests were run to see if a non-
rotating paddle wheel also reduces the drag coefficient. Tests were also run with a non-rotating cylinder fixed behind the
vehicle. Non-rotating paddle wheel tests show that the beneficial effect of the rotating paddle wheels is stronger. The
test with the non-rotating cylinder of 0.05 m radius (comparison case C) drastically reduces the total drag coefficient of
the vehicle-cylinder assembly; however, when looking at Fig. 8 one can question whether the results are valid since the
CD,saved strongly fluctuates. The result from the non-rotating cylinder is somewhat troubling because it shows a positive
Proceedings of OFW4 4th OpenFOAM Workshop
June 1-4, Montreal, QC, Canada
pressure downstream of the body; usually the pressure downstream of a body subjected to a moving flow is negative.
Also, by comparing the velocity streamlines of the reference case with those of the paddle wheel cases on Fig. 9 and 10,
respectively, one notices how the upper vortex is much smaller when the rotating paddle wheel is present; this is part of
the explanation why the drag coefficient is reduced. Also, the vortex is still present and creates a suction on the paddle
wheel thus further increasing CD,saved . The streamlines also show that the flow circulates around the paddle wheel and
from the moments one sees that some of the energy of those streamlines is transferred to the wheel. Finally, those two
figures also show that the maximum turbulent kinetic energy is lower in the case with the paddle wheel, which very likely
indicates that less energy is lost by viscous dissipation. From, the analysis, it seems clear that RPM is highly influent on
the output energy of the system. The best results are obtained at 2000 RPM. As expected, when the RPM of the paddle
wheel reaches a certain value, the power generated turns into power that has to be fed to the wheel. On the other hand, the
problem associated with a too low RPM is that the forces on the paddle wheel do not increase enough to compensate for
the lower velocity of the wheel and thus the power generated falls. The fluctuations in energy captured and drag respond
to the rotation of the paddle wheel as seen on Fig. 7. Four force cycles are noticeable for each revolution of the paddle
wheel. Consider that a full revolution of the paddle wheel corresponds to four quarter cycles which are each geometrically
identical. Also, in the published literature, most of all Ahmed body analyses are ran with a fixed floor, and that is different
from real-world situations where the floor has a relative velocity with respect to the car equal to the velocity of the car
itself. This should not greatly modify the results but it still was reported to have a 8% influence on the drag coefficient
(Krajnovic and Davidson, 2005). Krajnovic reported that a moving floor actually influences the flow around the rear slant
angle. That zone is where this paper is focused; however, the purpose of this paper is to show how flow structures found
on a typical car can be used to generate energy. Specific car models are not analyzed yet and the Ahmed body is only used
as a reference to create typical car flows and validate the calculations. Finally, it should be noted that more tests have to be
run in order to figure out the best combination of energy capture and drag reduction. The authors believe that it is possible
to get a positive energy capture to accompany results of large drag reduction given buy the non-moving cylinder; the good
combination of rotational velocity and blade geometry only has to be found. So far the best results come from a variable
rotational speed case which shows that the energy capture can be adapted to the flow and motivates the implementation of
a GGI code that has a rotating velocity dependent on the flow properties or forces acting upon it.
It was found that the rotating paddle wheel can generate 16.1 watts while reducing drag by 7.7%. It can also reduce
drag by 8.4% if power is supplied to the paddle wheel. This reduction is calculated from the extrapolation of two-
dimensional simulations. This extrapolation should hold as long as a device such as the one that Beaudoin and Aider
(2008) experimented with is used to eliminate the influence of the longitudinal vortices on the span-wise vortices. As
for future plans, finer meshes, three-dimensional analyses, different blade shapes, flow driven RPM, and the effects of
friction, are considered for study.
The authors would like to thank all those who contributed to the development of the OpenFOAM software which made
the simulations reported in this paper possible.
Ahmed, S. R., Ramm, G. and Faltin, G., 1984, "Some salient features of the time averaged ground vehicle wake", Tech-
nical Report TP-840300, Society of Automotive Engineers, Warrendale, Pa., 31 p.
Bayraktar, I., Landman, D. and Baysal, O., 2001, "Experimental and computational Investigation of Ahmed body for
ground vehicle aerodynamics", SAE Transactions: Journal of Commercial Vehicles, Vol.110, No. 2, pp. 613-626.
Bouris, D. and Bergeles, G., 1999, "2D LES of vortex shedding from a square cylinder", Journal of Wind Engineering
and Industrial Aerodynamics, Vol.80, pp.31-46.
Beaudoin, J.-F. and Aider, J.-L., 2008, "Drag and lift reduction of a 3d bluff body using flaps", Experiments in Fluids,
Vol.44, No. 4, pp. 491-501.
Cousteix, J., 1989, "Turbulence et couche limite", Cépaduès Éditions, PARIS?? pp. XX166.
Hinterberger, C., García-Villalba, M. and Rodi, W., 2004, "Large Eddy Simulation of flow around the Ahmed body",
Lecture Notes in Applied and Computational Mechanics, The Aerodynamics of Heavy Vehicles: Trucks, Buses, and
Trains, R. McCallen, F. Browand, J. Ross (Eds.), Springer Verlag, pp..
Krajnovic, S. and Davidson, L., 2005, "Influence of floor motions in wind tunnels on the aerodynamics of road vehicles",
Journal of Wind Engineering and Industrial Aerodynamics, Vol.93, pp. 677-696.
Menter, F. and Esch, T., 2001, "Elements of industrial heat transfer predictions", Proceedings of the 16th Brazilian
Congress of Mechanical Engineering, Vol.20, pp. 117-127.
Recently, several open source codes for computational fluid dynamics (CFD) have been introduced and are spreading fast. Our group has chosen the OpenFOAM as a platform to develop our own in-house code. In this brief review, we would like to share the information on the codes and what we have experienced so far. We introduce several features of OpenFOAM, which include the performance compared with commercial packages, estimation for current user population, and our own prospect for future improvement in performance and growth in user population. In addition, we briefly introduce our experience gained in embedding the level set method into the OpenFOAM.
Full-text available
We present an experimental study on flow control over a classic 3D bluff-body used in automotive aerodynamics (Ahmed 1983). Flow control is achieved through moving flaps fixed on every edge around the two rear flat surfaces of the model. Different pairs of flaps, with variable angle compared to the walls, are tested. Parametric studies show that the most efficient configuration for the flaps is along the side edges of the rear slant, i.e. in the region where longitudinal vortices are created, and also at the junction between the roof and the rear slant, where the flow separates. We also explore the combinations of different flap configurations. We find interesting results showing cumulative effects between some configurations leading to −25% drag reduction and −105% lift reduction. Finally, particle image velocimetry measurements show that one of the key effects is the control of the longitudinal vortices created at the side edges of the rear slant.
The effect of a moving floor on the flow around a simplified car with a typical fastback geometry is investigated. Two large-eddy simulations of the flows with stationary and moving floors are made and both instantaneous and time-averaged results are compared. It is found that the floor motion reduces drag by 8%8% and lift by 16%16%. Changes in the flow are found to be global but are largest close to the floor and on the rear slanted surface of the vehicle. The wake flow is found to be relatively insensitive to the floor movement, in agreement with previous experimental observations. The periodicity of the flow events is found to be dependent on whether the floor is moving. Power spectral density of both the lift and the drag contain only one dominant frequency peak when the moving floor is adopted as compared to scattered spectra in the stationary floor case. Changes in the qualitative picture of the flow are limited to the flow near the floor and on the slanted surface of the body. However, changes in the surface pressure on the body and the history of the flow show the need of a moving floor in experimental and numerical simulations.
In the high Reynolds number flow past a square cylinder, turbulent fluctuations are superimposed on the periodic vortex shedding motion making numerical calculation of the flow a difficult task. In the present work a two-dimensional large eddy simulation is performed with no-slip boundary conditions at the solid walls. A filtering procedure is introduced in frequency space to separate the periodic from the turbulent fluctuations and the kinetic energy of both is calculated along the centerline behind the rod. The drag coefficient, vortex shedding frequency and spacing of the vortices in the wake are also calculated and the results are validated against experimental measurements. It is found that the two-dimensional large eddy simulation using a fine grid resolution, especially in the near wall region, gives a good representation of the quasi-two-dimensional mechanisms of the flow since they are directly simulated instead of being modeled as with statistical turbulence models.
For a basic ground vehicle type of bluff body, the time averaged wake structure is analysed. At a model length based reynolds number of 4.29 million, detailed pressure measurements, wake survey and force measurements were done in a wind tunnel. Some flow visualisation results were also obtained. Geometric parameter varied was base slant angle. A drag breakdown revealed that almost 85% of body drag is pressure drag. Most of this drag is generated at the rear end. Wake flow exhibits a triple deck system of horseshoe vortices. Strength, existence and merging of these vortices depend upon the base slant angle. Characteristic features of the wake flow for the low drag and high drag configurations is described. Relevance of these phenomena to real ground vehicle flow is addressed.
Elements of industrial heat transfer predictions
  • F Menter
  • T Esch
Menter, F. and Esch, T., 2001, "Elements of industrial heat transfer predictions", Proceedings of the 16th Brazilian Congress of Mechanical Engineering, Vol.20, pp. 117-127.
Experimental and computational Investigation of Ahmed body for ground vehicle aerodynamics
  • I Bayraktar
  • D Landman
  • O Baysal
Bayraktar, I., Landman, D. and Baysal, O., 2001, "Experimental and computational Investigation of Ahmed body for ground vehicle aerodynamics", SAE Transactions: Journal of Commercial Vehicles, Vol.110, No. 2, pp. 613-626.
Large Eddy Simulation of flow around the Ahmed body
  • C Hinterberger
  • M García-Villalba
  • W Rodi
Hinterberger, C., García-Villalba, M. and Rodi, W., 2004, "Large Eddy Simulation of flow around the Ahmed body", Lecture Notes in Applied and Computational Mechanics, The Aerodynamics of Heavy Vehicles: Trucks, Buses, and Trains, R. McCallen, F. Browand, J. Ross (Eds.), Springer Verlag, pp..