Fusing distributed aerodynamic data using Bayesian Gappy Proper Orthogonal Decomposition

Abstract

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 routinely acquired by Computational Fluid Dynamics simulations. However, due to modeling and convergence issues especially for extreme flight conditions, numerical data cannot be reliably generated throughout the entire flight envelope yet. Hence, numerical data is complemented by data from wind tunnel experiments and flight testing. However, the data from these different sources will always show some discrepancies to deal with. Data fusion methods aim at combining the individual strengths and weaknesses of data from different sources in order to provide a consistent data set for the entire parameter domain. In this work we propose an extension to the well established gappy proper orthogonal decomposition technique by interpreting the occurring least-squares problem as a regression task. A Bayesian perspective is imposed to account for uncertainties during the data fusion process. This involves a kernelized regression formulation which also leverages the problem of linearity imposed by the dimensionality reduction method. We demonstrate the performance and robustness of the approach investigating an industrial-relevant, large-scale aircraft test case fusing high quality experimental and numerical data.