Comparison of Adaptive Sampling Methods for Generation of Surrogate Aerodynamic Models

A surrogate modeling strategy, using effective interpolation and sampling methods, facilitates a reduction in the number of computational fluid dynamics simulations required to construct an aerodynamic model to a specified accuracy. In this paper, two adaptive sampling strategies are compared for generating surrogate models, based on Kriging and radial basis function interpolation, respectively. The relationships between the two model formulations are discussed, and three test cases are considered, including analytic functions and recovery of aerodynamic coefficients for two example applications: longitudinal flight mechanics analysis for the DLR-F12 aircraft and structural loads analysis of an RAE2822 airfoil. For the airfoil example, models of C-L, C-D, and C-M were constructed with the two sampling strategies using Euler/boundary-layer-coupled computational fluid dynamics and a three-dimensional flight envelope of incidence, Mach, and Reynolds number. The two sampling approaches direct some samples toward exploration of the domain by minimizing model uncertainty and some toward refinement of local nonlinearities, by adapting to model curvature or extrema. The results provide some evidence that, for certain functions, in certain scenarios, each update scheme could be useful. Both methods were at least better than traditional space-filling sampling for all the test cases presented.