Aerodynamic Analysis of Vehicle Using CFD

Vikas V. Chaurasiya1, Deepak B. Kushwaha2, Mohd. Raees3

1,2,3
Automobile Engineering, Theem College of Engineering
Abstract - Aerodynamic forces make its major impact on vehicles by interacting and causing drag,
lift, weight, side forces and thrust which significantly affect the fuel consumption of a vehicle. Due
to drag, the vehicle is offered near about 60% of total resistance. Thus, drag reduction is an important
parameter in vehicle design.
The purpose of this work is to simulate coefficient of drag (CD), coefficient of lift (CL), flow
separation and vortex shedding over a vehicle using commercial CFD solver and validate the
simulation with an experimental result. The analysis is done for finding out CD, CL and other flow
features with the flow velocity 22.22 m/s on Audi R8 car model.
The programs used in this work are Solidworks2015 (CAD design), GAMBIT 2.4.6 (Meshing),
ANSYS Fluent 17.0 (CFD Solver) as well as Tec plot 360 (Post Processing). Consequently, of using
these programs, this work allows us to apply, learn and link technical knowledge of aerodynamics
and computer knowledge.
Keywords are - Aerodynamics, CFD, Solidworks2015, ANSYS-Fluent 17.0, Gambit 2.4.6, and Tec
plot 360.
I. INTRODUCTION
Now-a-days, the demand of high speed cars is increasing in which vehicles’ stability, low fuel
consumption, less pollution, are of major concern. When a vehicle is moving on road, the fuel
consumption of the vehicle is affected due to aerodynamic forces like drag, lift, weight, side forces
and thrust [5]. Aerodynamic drag is the result of interaction between the vehicle shell and the
surrounding air molecules. It is caused by relative motion between the air and vehicle which results
in a net force opposing motion [4]. Aerodynamic drag increases the square of velocity [6].
Therefore, it becomes critically important at higher speed. Thus, reducing the drag coefficient in an
automobile improves the performance of vehicles as it pertains to speed and fuel efficiency [10].

In automotive industry, mainly wind tunnel and computational fluid dynamic approach are used to
estimate the drag. Due to competitiveness of the market as well as high cost, time and other
limitation, the use of CFD over traditional experimental-based analyses can be advantaged. Since
experiments have a cost directly proportional to the number of configurations desired for testing,
unlike with CFD, where large amounts of results can be produced practically with no added
expenses. CFD has increasingly provided the methodology behind an important design tool for the
automotive industry [7].
II. AERODYNAMICS
A. Aerodynamics
“Aerodynamics” is a branch of fluid dynamics concerned with studying the motion of air particularly
when it interacts with a moving object [9]. Anything that moves through air is affected by
aerodynamics. The rules of aerodynamics explain how an airplane is able to fly.

DOI : 10.23883/IJRTER.2017.3056.S0SEM 131

 International Journal of Recent Trends in Engineering & Research (IJRTER)
Volume 03, Issue 03; March - 2017 [ISSN: 2455-1457]

B. Automotive Aerodynamics
Automotive Aerodynamics is the study of the aerodynamics of road vehicles. Its main goals are
reducing drag and wind noise, minimizing noise emission and preventing undesired lift forces and
other causes of aerodynamic instability at high speeds [11].
C. Factors Contributing To Flow Field Around Vehicle
The frictional force of aerodynamic drag increases significantly with vehicle speed [8]. The major
factors, which affect the flow field around the vehicle, are the boundary layers, separation of flow
field, friction drag and lastly the pressure drag.

III. COMPUTATIONAL FLUID DYNAMICS (CFD)

According to Oleg Zikanov [1] CFD can be defined as: “CFD (Computational fluid dynamics) is a
set of numerical methods applied to obtain approximate solution of problems of fluid dynamics and
heat transfer.”

Figure 1: The different disciplines contained within computational fluid dynamics.

According to this definition, CFD is not a science by itself but a way to apply methods of one
discipline (numerical analysis) to another (heat and mass transfer). In retrospect, it is integrating not
only the disciplines of fluid mechanics with mathematics but also with computer science as
illustrated in Figure 1. The physical characteristics of the fluid motion can usually be described
through fundamental mathematical equations, usually in partial differential form, which govern a
process of interest and are often called governing equations in CFD. Jiyuan Tu, Guan Heng Yeoh
and Chaoqun Liu [2] have discussed how to solve mathematical equations with using CFD.

Figure 2: The three basic approaches to solve problems in fluid dynamics and heat transfer.

CFD has also become one of the three basic methods or approaches that can be employed to solve
problems in fluid dynamics and heat transfer. As demonstrated in Figure 2, each approach is
strongly interlinked and does not lie in isolation.

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 International Journal of Recent Trends in Engineering & Research (IJRTER)
Volume 03, Issue 03; March - 2017 [ISSN: 2455-1457]

A. How does CFD code work?

CFD codes are structured around the numerical algorithms that can be engaged in fluid problems. In
order to provide easy access to their solving power, all commercial CFD packages include difficult
user interfaces, input problem parameters and to examine the results. Hence all codes contain three
main elements:
 Pre-Processor
 Solver
 Post-processor
B. Pre Processor

A pre-processor is used to define the geometry for the computational domain of interest and generate
the mesh of control volumes (for calculations). Generally, the finer the mesh in the areas of large
changes is the more accurate solution. Fineness of the grid also determines the computer hardware
and calculation time needed [3].
C. Solver

The solver makes the calculations using a numerical solution technique, which can use finite
difference, finite element, or spectral methods. Most CFD codes use finite volumes, which is a
special finite difference method. First the fluid flow equations are integrated over the control
volumes (resulting in the exact conservation of relevant properties for each finite volume), then these
integral equations are discretized (producing algebraic equations through converting of the integral
fluid flow equations), and finally an iterative method is used to solve the algebraic equations [3].
D. Post Processor

The post-processor provides for visualization of the results, and includes the capability to display the
geometry/mesh, create vector, contour, and 2D and 3D surface plots. Particles can be tracked
throughout a simulation, and the model can be manipulated (i.e. changed by scaling, rotating, etc.),
and all in full colour animated graphics [3].
E. Problem Solving with CFD

There are many decisions to be made before setting up the problem in the CFD code. Some of the
decisions to be made can include: whether the problem should be 2D or 3D, which type of boundary
conditions to use, whether or not to calculate pressure/temperature variations based on the air flow
density, which turbulence model to use, etc. The assumptions made should be reduced to a level as
simple as possible, yet still retaining the most important features of the problem to be solved in order
to reach an accurate solution. After the above decisions are made, the geometry and mesh can be
created. The grid should be made as fine as required to make the simulation grid independent.

IV. VEHICLE GENERIC MODEL AND DIMENSIONS

Geometric models were modeled by using Solidworks2015 modeling software. For the present
analysis only 2-D profile of Audi R8 car model was used. The modeling process involved importing
the vehicle blueprints into Solidworks with the help of which 3D curves were projected. These
curves then acted as boundaries to generate surfaces. The final surface model was converted into
IGS file format before importing it to Ansys. The figure 3 shows 2D profile of Audi R8 car model
and the figure 4 shows the final surface model of the car.

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Volume 03, Issue 03; March - 2017 [ISSN: 2455-1457]

Figure 3: Solidworks profile of Audi R8.

Figure 4: Surface to export to Ansys.

V. CREATING FLUID ENCLOSURE

In order to simulate the air flow around the vehicle, a fluid volume needs to be created which will
encompass the vehicle. This was done by creating an enclosure around the vehicle and subtracting
the vehicle body. This enclosure acts as the air domain. To reduce the overall computational cost
and time, the vehicle was considered symmetric laterally. Dimensions of analysis domain are
presented in Figure: 5, where L = 4431mm.

Figure 5: Computational domain.

VI. MESH GENERATION

While generating the mesh, sizing functions were used wherever necessary in order to obtain
accurate lift/drag parameters. Two bodies of refinements were added to properly capture the flow in
the region closest to the vehicle and also capture the flow in the wake. Since boundary layer

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Volume 03, Issue 03; March - 2017 [ISSN: 2455-1457]

separation has a significant effect on drag. After meshing problem in GAMBIT, the mesh consists of
quads and triangular. The total number of elements obtained was 58.102 thousand. Where Figure. 6
& 7 shows the final mesh.

Figure 6: Mesh of Computational Domain. Figure 7: Mesh around Car Profile.

VII. BOUNDARY CONDITIONS

Velocity of the air at the inlet boundary condition is set in FLUENT with a value of 22.22 m/s. The
outlet boundary condition is set to pressure outlet with the gauge pressure of 0 Pa. The car contour,
the top and the bottom of the virtual wind tunnel are set as symmetry. The density of air is set as
1.225 kg/m3 and the viscosity of air is 1.7894 x 10-5 kg/ms.

VIII. SOLVER
For this analysis, a pressure based transient state solver was used. The solution methods, equations
used along with the input data are listed below:
 Pressure based transient state solver.
 Shear stress transport (SST k-ɷ ) model.
 Air velocity at inlet is 22.22 m/s.
A. Transient Flow Analysis
Transient flow is the flow, wherein the flow velocity and pressure are changing with time. When
changes occur to a fluid system such as during starting or stopping, in such a situation transient flow
conditions exists. Otherwise the system is in steady state. Often, transient flow conditions persist as
oscillating pressure and velocity waves for some time after the initial event that caused it. Time step
size (∆t) must be small enough to resolve time-dependent features observed in transient flow and to
make sure convergence is reached within the number of Max Iterations per time step. The setting
selected were:
 Time step value: 0.057969 second
 Max iterations per time step: 20
 Number of time steps: 18000.

IX. RESULT AND DISCUSSION

The figures shown below are the various flow features and parameter of Audi R8 2D car model.

Table 1: Graphical representation of result and their value

Figure Characteristics value

Figure 8 Represent the value of coefficient of lift ( CL) of analyzed model 1.894

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Volume 03, Issue 03; March - 2017 [ISSN: 2455-1457]

Figure 8: coefficient of lift (CL) Figure 9: coefficient of drag (CD)

Figure 10: velocity magnitude. Figure 11: flow separation

Figure 12: static pressure. Figure 13: coefficient of pressure.

Figure 14: vorticity Figure 15: turbulent kinetic energy.

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Volume 03, Issue 03; March - 2017 [ISSN: 2455-1457]

Figure 9 Represent the value of coefficient of drag ( CD) of analyzed model 0.243

Figure 10 The streamline and magnitude of velocity around the vehicle (5 - 30)
profile. m/s
Figure 11 Flow separation at rear end of vehicle profile. -------------

Figure 12 The static pressure around vehicle profile. (-400 - 200)

Pa
Figure 13 Coefficient of pressure. (-0.8 – 0.82)

Figure 14 Vorticity around vehicle profile. (20 - 340)

1/s
Figure 15 Turbulent kinetic energy around vehicle profile. (1 – 30)
m2/s2

Experimental result of coefficient of drag (CD) of Audi car since 1991 to 2017 ranges from 0.23 to
0.33 [12]. The coefficient of drag (CD) obtained from current analysis of Audi model using CFD is
0.243 which is within the range of experimental result. Thus the obtained result is reliable and
validate the experimental result.

X. CONCLUSION

On the basis of obtained result, it can be concluded that CFD can provide near about accurate result
in comparison with experimental result. In this work 2D analysis is done which is very helpful and
usually proceeded by a 3D analysis, because they can provide some basic guidelines that could be
redesigned on the product in order to that the resulting 3D analysis provide better and more
acceptable results. This approach can significantly shorten the time of analyzing a problem, because
the 2D analysis in relation to 3D is of course much simpler and the time for obtaining a solution is
much shorter. So, the 2D analysis is a good indicator of the real state, however it is necessary to note
that the results could significantly change when the same problem is considered in 3D.

REFERENCES
[1] Oleg Zikanov, “Essential Computational Fluid Dynamics”, John Wiley & Sons, Inc. Hoboken, New Jersey, March
2010.
[2] Jiyuan Tu, Guan Heng Yeoh and Chaoqun Liu, “Computational Fluid Dynamics: A Practical Approach”,
Butterworth-Heinemann; 1st edition, Burlington, MA, November 2007.
[3] Versteeg H., Malalasekra W., “An Introduction to Computational Fluid Dynamics: The Finite Volume Method”,
Second Edition, Pearson Education Limited, Essex, England (2007).
[4] Anderson, John D. Jr., Introduction to Flight.
[5] Thomas D. Gillespie, Fundamentals of Vehicle Dynamics, SAE.
[6] Joseph Katz, Automotive Aerodynamics, John Wiley & Sons, Ltd, 2106.
[7] N. Ashton, A. Revell And R, Poletto, “Grey-Area Mitigation For The Ahmed Car Body Using Embedded DDES”,
Progress In Hybrid Rans-Les Modelling, Springer International Publishing Switzerland 2015.
[8] Tuncer Cebeci, Jian P. Shao, Fassi Kafyeke, Eric Laurendeau, Computational Fluid Dynamics for Engineers: From
Panel to Navier-Stokes, Springer, 2005, ISBN 3-540-24451-4.
[9] https://www.grc.nasa.gov/WWW/K-12/airplane/bga.html
[10] http://nextbigfuture.com/2009/03/reducing-drag-on-cars-and-trucks-by-15.html
[11] https://en.wikipedia.org/wiki/Automotive_aerodynamics
[12] https://en.wikipedia.org/wiki/Automobile_drag_coefficient

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