  Breakthroughs in aerodynamic optimization have made it possible to develop
efficient modes of transport with lesser exploitation of valuable resources.
This makes it crucial for technical professionals such as engineers and
scientists to understand the methodologies behind carrying out such
optimizations. A common approach towards improving the aerodynamic properties
of a vehicle is to alter its physical shape, which has concurrently been a very
strenuous process given the time consumed to remodel the vehicle for each
simulation process. This research aims to tackle this problem by using
intelligent techniques to automate the step-by-step process of remodeling the
car and arriving at a final optimized solution with a significantly lower drag
coefficient, a quantity used to measure the amount of drag force acting on a
vehicle. This is achieved by assigning particular parameters to ensure guided
improvement of the airfoil in a process known as parametrization, followed by
implementing a response surface methodology primarily to circumvent the
strenuous task of performing a large number of CFD simulations by employing
surrogate models to generate a response surface between selected independent
variables. Further, evolutionary algorithms such as Genetic Algorithm have
gained momentum in the optimization studies carried out during product design
by selecting the optimum parameters from the available design spaces on the
basis of natural evolution. The proposed method of optimization has been
successfully implemented on a prototype vehicle with an improvement of 26.6%
and 51.1% in the drag coefficient and drag area respectively.
