(Georgia Institute of Technology. School of Aerospace Engineering, 2023-06)
Sampaio Felix, Barbara; Perron, Christian; Ahuja, Jai; Mavris, Dimitri N.
The present work proposes an efficient parametric approximation of mission drag polars by combining multi-fidelity surrogate models with parametric reduced order modeling techniques. Traditionally, semi-empirical aerodynamic analyses are used to provide drag polars needed for mission analysis during the conceptual design of aircraft. The database needed for these methods is unavailable for unconventional vehicles, and for this reason, many studies rely on higher-fidelity models typical of preliminary design to perform design space exploration for novel vehicle geometries. Due to the high computational cost and evaluation time of these higher-fidelity models, researchers constrain the design space exploration of vehicles by either relying on single discipline optimization or obtaining mission drag polars for a few vehicle geometries within their design loop. The present work demonstrates the application of Hierarchical Kriging surrogate models to obtain mission drag polars for fixed vehicle geometries. Then, the proper orthogonal decomposition reduced order model with Kriging interpolation is used to approximate the coherent structure of mission drag polars. The proposed method is demonstrated on a supersonic commercial aircraft. Experiments showed that both the multi-fidelity surrogate model and the reduced order model are able to emulate vehicle drag polars well for fixed and varying vehicle geometries, respectively.