This paper proposes a computational methodology for the aerodynamic shape design of aeronautical configurations, aiming a broad and efficient exploration of the design space. A novel adaptive sampling technique focused on the global optimization problem, the Intelligent Estimation Search with Sequential Learning (IES-SL), is presented. This approach is based on the use of Support Vector Machines (SVMs) as the surrogate model for estimating the objective function, in combination with an evolutionary algorithm (EA) to enable the discovery of global optima. The proposed methodology is applied to improve the aerodynamic performance of a two-dimensional airfoil and a three-dimensional wing and results on surrogate model validation and optimization-focused sampling criteria are discussed.

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Nowadays, one of the priorities of the European Commission is to reduce the environmental impact of aviation through the advanced design of novel aircraft configurations. This is of utmost importance in order to decrease the environmental footprint of aviation and to reduce fuel consumption and make airlines more profitable. This implies that new methods and tools for aerodynamic shape optimization will have to be developed, allowing aircraft configurations that cannot be obtained with traditional strategies. This paper focuses on the application of enhanced methods in aerodynamic shape design optimization to enable advanced aircraft configurations. In particular, this work aims to demonstrate the feasibility of the proposed strategy to reach optimal configurations that are far away from its baseline geometry. For this purpose, evolutionary algorithms are combined with support vector machines and applied to the optimization of a baseline geometry for different flow conditions. In particular, the selected application is based on the shape optimization problem of the landing gear master cylinder. Results pointed out the feasibility of the mentioned strategy to enable novel configurations within an aerodynamic shape optimization process.

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