Strategies for Aerodynamic Development discusses computational techniques for aerodynamic shape optimization to reduce drag. Adjoint methods efficiently compute gradients with respect to design variables, allowing optimization of hundreds or thousands of variables at once. Free-form deformation uses control points to parameterize geometry changes, making complex geometry manipulation easier. Direct optimization of the Common Research Model wing minimized drag subject to constraints.

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Strategies for Aerodynamic Development
PROFESSIONAL ELECTIVE
(Automotive Aerodynamics)
Name : Vamsi.K
Reg.no : 15104047
Dept : Auto-7A

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Strategies for Aerodynamic Development.
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. Air is also
considered a fluid in this case.
Aerodynamic shape optimization based on high-
fidelity models is a computational intensive endeavour. The majority
of the computational time is spent in the flow solver, and in the
gradient calculation.The techniques are tested using the Common
Research Model wing benchmark defined by the Aerodynamic Design
Optimization Discussion Group (ADODG). The aerodynamic model
solves the Reynolds-averaged Naiver–Stokes equations with a
Spalart–Allmaras turbulence model. A gradient based optimization
algorithm is used in conjunction with an adjoint method that
computes the required derivatives. The drag coefficient is minimized
subject to lift, pitching moment, and geometric constraints.
To reduce the overall computational cost is to
reduce the gradient computational time, which was pioneered by
Jameson through the development of adjoint method, which
efficiently compute gradients with respect to large numbers of shape
design variables. With an efficient adjoint implementation, the cost of
computing the gradient of a single function of interest with respect to
hundreds or thousands of shape design variables is roughly of the
same order of the cost of one flow solution. Those methods have been
successfully applied in recent aerodynamic shape optimizations.
The FFD (free-form deformation) volume
parametrizes the geometry changes rather than the geometry itself,
resulting in a more efficient and compact set of geometry design
variables, thus making it easier to manipulate complex geometries.
Any geometry may be embedded inside the volume by performing a

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Newton search to map the parameter space to the physical space. All
the geometric changes are performed on the outer boundary of the
FFD volume. Any modification of this outer boundary indirectly
modifies the embedded objects. The key assumption of the FFD
approach is that the geometry has constant topology throughout the
optimization process, which is usually the case in wing design. In
addition, since FFD volumes are trivariate B-spline volumes, the
derivatives of any point inside the volume can be easily computed.
Figure 1 shows the FFD volume and the geometric control points used
in the aerodynamic shape optimization. The shape design variables
are the displacement of all FFD control points in the vertical (z)
direction.
Fig1

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Direct Aerodynamic Shape Optimization ;
If We present the direct aerodynamic design optimization for the
CRM wing benchmark problem .We use the L1 grid (3.6 M cells)
directly for the optimization. This is currently the most common way
to perform aerodynamic shape optimization due to its simplicity. The
optimization is computed with 64 processors. Figure 2,3 shows a
detailed comparison of the baseline wing and the optimized wing
using direct optimization.
To increase drag, the plane can have many flaps it can lift
vertically, which will help it increase the drag on the airplane, which is
useful to help the plane decelerate or roll. To decrease drag,
the plane needs to be streamlined, in a tear drop shape, but not so
much so as to create too much friction drag.
Fig-2

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The strategies presented is to, open a new door to aerodynamic
shape optimization. Further development of the techniques at the
optimization level, have the potential to make future large-scale
optimization more efficient and effective.
No matter for car also, how slowly a car is going, it takes some energy to
move the car through the air. This energy is used to overcome a force called
Drag. Drag, in vehicle aerodynamics, is comprised primarily of three forces:
Frontal pressure, or the effect created by a vehicle body pushing air out of the
way
Effect of Reynolds number ;
Fig3

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The experiments conducted by Ahmed et al. (1984) were
performed at a wind speed of 60 m/s. This corresponds to a Reynolds
number of 4.29 million based on model length. Bayraktar (2001)
studied the effect of Reynolds number on lift and drag coefficients.
The experiments were performed at Reynolds number in the range of
2.2 to 13.2 million. It was observed that over this wide range of
Reynolds number, the drag coefficient only altered by 3.5 percent
while the lift coefficient altered by 2 percent. Thus it was concluded
that the drag coefficient is insensitive at high Reynolds numbers (of
the order of 106 ).
Top 10 Most Aerodynamic Cars
 Mercedes-Benz S-Class.
 Hyundai Elantra Eco.
 Infiniti Q50.
 BMW i8.
 Mazda Mazda3 Sedan.
 Nissan GT-R.
 Hyundai Sonata Hybrid.
 Tesla Model S.
If your car has an effective air dam or diffuser, side skirts will
help reduce the leakage of air into that low-pressure region
under the car. Side skirts can increase downforce and decrease
drag at the same time. Radiator airflow is a huge air leak that
reduces the effectiveness of your air dam and/or splitter.