Wing profile evolution driven by computational fluid dynamics

In the domain of fluid dynamics, the problem of shape optimization is relevant because is essential to increase lift and reduce drag forces on a body immersed in a fluid. The current state of the art in this aspect consists of two variants: (1) evolution from an initial guess, using optimization to achieve a very specific effect, (2) creation and genetic breeding of random individuals. These approaches achieve optimal shapes and evidence of response under parameter variation. Their disadvantages are the need of an approximated solution and/or the trial - and - error generation of individuals. In response to this situation, this manuscript presents a method which uses Fluid Mechanics indicators (e.g. streamline curvature, pressure difference, zero velocity neighborhoods) to directly drive the evolution of the individual (in this case a wing profile). This pragmatic strategy mimics what an artisan (knowledgeable in a specific technical domain) effects to improve the shape. Our approach is not general, and it is not fully automated. However, it shows to efficiently reach wing profiles with the desired performance. Our approach shows the advantage of application domain - specific rules to drive the optimization, in contrast with generic administration of the evolution.