Abstract
This paper presents a study to design, analyze and optimize an airfoil trailing edge, i.e., shape morphing of the airfoil trailing-edge topology. The primary idea behind morphing is to improve the wing performance for different flight conditions. Modern aircrafts are designed for unique operating conditions. In order to obtain the best configuration, a dynamic optimization algorithm has been developed based on a Multi-swarm Particle Swarm Optimization algorithm (MPSO), a population-based stochastic optimization algorithm inspired by the social interaction among insects or animals. However, with respect to aircraft design and in the context of computational fluid dynamics (CFD), function evaluations are computationally expensive; typically requiring large computational grids to obtain a reasonable representation of the flow-field. In this paper, the developed MPSO algorithm is combined with a Kriging surrogate representation of the objective space, to alleviate the computational effort. The topology of the trailing edge is defined and characterized by four control points. Two different hypothetical mission profiles are analyzed. The results exhibit an improvement of around 2% with respect to the original airfoil for every flight condition treated.
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Fico, F., Urbino, F., Carrese, R., Marzocca, P., Li, X. (2017). Surrogate-Assisted Multi-swarm Particle Swarm Optimization of Morphing Airfoils. In: Wagner, M., Li, X., Hendtlass, T. (eds) Artificial Life and Computational Intelligence. ACALCI 2017. Lecture Notes in Computer Science(), vol 10142. Springer, Cham. https://doi.org/10.1007/978-3-319-51691-2_11
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