Experimental characterization of wingtip vortices using smoke flow visualizations

In order to predict the axial development of the wingtip vortices strength an accurate theoretical model is required. Several experimental techniques have been used to that end, e.g. PIV or hotwire anemometry, but they imply a significant cost and effort. For this reason, we have carried out experiments using the smoke-wire technique to visualize smoke streaks in six planes perpendicular to the main stream flow direction. Using this visualization technique, we obtained quantitative information regarding the vortex velocity field by means of Batchelor's model~\cite{batchelor}, which only depends on two free parameters, i.e. the vortex strength, $S$, and the virtual origin, $z_0$. Results for two chord based Reynolds numbers have been compared with those provided by del Pino et at. (2011), finding good agreement.

Combination of Evolutionary Algorithms with Experimental Design, Traditional Optimization and Machine Learning

Evolutionary algorithms alone cannot solve optimization problems very efficiently since there are many random (not very rational) decisions in these algorithms. Combination of evolutionary algorithms and other techniques have been proven to be an efficient optimization methodology. In this talk, I will explain the basic ideas of our three algorithms along this line (1): Orthogonal genetic algorithm which treats crossover/mutation as an experimental design problem, (2) Multiobjective evolutionary algorithm based on decomposition (MOEA/D) which uses decomposition techniques from traditional mathematical programming in multiobjective optimization evolutionary algorithm, and (3) Regular model based multiobjective estimation of distribution algorithms (RM-MEDA) which uses the regular property and machine learning methods for improving multiobjective evolutionary algorithms.