Multidimensional Aerodynamic Variable-Fidelity Surrogate Model for Conceptual Aircraft Design with UNICADO

In this contribution, surrogate models of variable fidelity based on Gaussian Process Regression, applied to the aerodynamic analysis within the context of conceptual aircraft design are presented. The approximated functional relations are $C_D = f(C_L)$ for the one-dimensional case and $C_D = f(C_L, h, \text{Ma})$ for the three-dimensional case. For the low-fidelity aerodynamic analysis semi-empirical methods combined with a multi-lifting-line method are used. The higher-fidelity model is based on Reynolds-averaged Navier-Stokes computational fluid dynamics simulations. The surrogate model is constructed by combining the low-fidelity and higher-fidelity models. Furthermore a mission-informed sampling strategy is presented, which reduces the model uncertainty at flight conditions of interest. Finally, the impact of the utilization of enhanced polars is evaluated by comparing the resulting fuel burn on different off-design missions with those simulated missions performed with the low-fidelity polars.