Aerodynamic shape optimization of ground vehicles is always a multi-objective optimization problem. Several aerodynamic goal functions such as drag, crosswind stability, aeroaccoustics (wind noise) etc. have to be optimized in the same time. Furthermore, a design that leads to improvement of one aerodynamics property can lead to deterioration of another property.
We use several different optimization strategies to find the optimal aerodynamic shape of vehicles. One strategy is based on surrogate-based models (SBM) of aerodynamic goal functions. The models are constructed based on either steady or unsteady CFD simulations in a choice of design of experiments (DOE). Ones the SBMs are constructed, a search algorithm (e.g a genetic algorithm) is used to obtain a set of optimal designs of vehicles containing the best trade-offs of the objective functions (so called Pareto optimal front).