In the present scenario, wherein the cost of transportation is
continuously increasing, achieving optimum fuel efficiency is key
area of focus for many Automotive OEMs. Aerodynamic drag is
prominent form of resistance any vehicle encounters while it is in
motion, and this particularly increases at higher speeds and
overtakes all other forms of resistive forces acting on vehicle.
Hence, predicting and improving aerodynamic performance of a car
forms a very important aspect in overall product design cycle.
Engineers and designers around the world try different methods for
predicting and improving the aerodynamics of a car, including
rigorous wind tunnel & test track testing. However, in our
current paper, we will be discussing a novel approach to predict
and improve the aerodynamics drag for an academic test vehicle
(Ford-Ka) model. The paper will contain two sections, first section
will showcase usage of a new meshing technology, Mosaic Mesh, and a
revolutionary breakthrough turbulence model, GEKO, to predict
aerodynamic performance and validate with the test results.
Additionally, section two will discuss the application of Ansys
Adjoint Solver, a sensitivity-based algorithm, to automatically
improve the aerodynamics of car and showcase the reduction in
overall turn-around time for design iterations. Ansys Mosaic mesh
uses a mosaic grid like approach in volume meshing to optimise cell
count and at the same time provide with a good transition mesh,
which is a Ansys patented technology. Additionally, the Generalized
K-Omega turbulence model (GEKO) model, which utilizes a free
coefficients concept for any type of CFD applications, has been
fine-tuned and used for the current study. Furthermore, we will
explore the Ansys Adjoint Solver to showcase the automatic and
intelligent way of improving the aerodynamic performance of car by
morphing the shapes of different parts of car, and showcase
reduction in overall turn-around time for the design
iterations.