
doi: 10.25967/570233
During the development of an aircraft, a multitude of aerodynamic data is required for different flight conditions throughout the flight envelope. Nowadays, a large portion of this data is acquired through Computational Fluid Dynamics (CFD) simulations. However, due to modeling and convergence issues especially for extreme flight conditions, numerical data cannot be reliably generated throughout the entire flight envelope. Numerical data is therefore complemented by data from wind tunnel experiments and flight testing. Because of errors mainly introduced by the physical modeling and the discretization of the problem on the one hand and experimental limitations on the other hand, the data from these different sources will always show some discrepancies to deal with. Data fusion methods aim at combining the individual strengths of data from different sources in order to provide a consistent data set throughout the entire parameter domain. For the past four years, DLR and Airbus have been working together in the field of aerodynamic data fusion and have jointly funded a dedicated research position. This work provides an overview on the advances made within this collaboration.
DGLR, Data-driven Modelling, DLRK, Gappy POD, Daten Fusion, 2022
DGLR, Data-driven Modelling, DLRK, Gappy POD, Daten Fusion, 2022
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