Multi Objective Optimization of a Car's Side View Mirror Design

Neural Concept's predictive models and powerful multi-objective optimization features can be used to find highly innovative and performant designs. Our Korean partners and resellers from CAD-IT used Neural Concept to run a multi-objective optimization of a car's side view mirror design, on behalf of an automotive customer. The platform minimized both lift and drag forces while ensuring that the mirror's surface stays unchanged.

At the beginning of the project we had at our disposal a pre-existing dataset of Star CCM + CFD simulations of different mirror designs, that we used to train a model to predict the surface pressure field at a given speed.

This dataset was created in a previous phase of the project, and it was made with an old parametrization that was inevitably changing the mirror's surface. However, thanks to the uniqueness of Neural Concept's predictive models, the free morphing used by Neural Concept's optimization algorithm can be totally unrelated to the parametrizations used to generate the training dataset of the predictive model. This made it possible for us to explore totally new, disruptive design spaces, in a matter of a few minutes, without the need of launching any extra simulation.

Once the predictive model was trained, it was used by a genetic algorithm for the design's optimization to obtain the pressure fields of each geometry, which was then integrated to obtain the final objectives: the drag and lift forces. The results are shown in the following figures: