Adaptive Sampling Strategies for Surrogate-Based Aerodynamic Optimization
The chapter proposes the application of surrogate-based optimization to the efficient design of aeronautical configurations. The surrogate model consists of the Proper Orthogonal Decomposition of computed aerodynamic flow fields and Radial Basis Functions interpolation to reconstruct the aerodynamic flow at any unknown design vector. The surrogate model is coupled to an evolutionary algorithm to globally explore the design space. Several adaptive sampling strategies are proposed, either objective-driven (i.e. aimed at improving the fitness function) or error-driven (i.e. aimed at reducing the prediction error of the surrogate model globally). The proposed methodology is applied to the design optimization of a two-dimensional airfoil in multi-point transonic conditions. The results of different training strategies are critically discussed and compared.
KeywordsDesign Space Proper Orthogonal Decomposition Proper Orthogonal Decomposition Mode Kriging Model Adaptive Sampling
The author thanks Dr. Esther Andres Perez and Dr. Mario J. Martin-Burgos from Instituto Nacional de Tcnica Aeroespacial (INTA), Spain, for providing the NURBS parameterization tool and supporting its integration. The development and experiments presented in the paper have been partially achieved within the AG52 GARTEUR action group (http://ag52.blogspot.it/, www.garteur.org) that has been established to explore these surrogate-based global approaches. The main objective of the action group is, by means of a European collaborative research, to make a deep evaluation and assessment of surrogate-based global optimization methods for aerodynamic shape design, dealing with the main challenges as the curse of dimensionality, reduction of the design space and error metrics for validation, amongst others.
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