Data Fusion of Wing Pressure Distributions Using Scalable Gaussian Processes

Abstract

Aircraft design requires a large volume of aerodynamic data to characterize various flight conditions throughout the aircraft’s flight envelope, and the data are typically obtained through wind tunnel testing and numerical analysis. Data acquisition can be costly and inevitably entails multiple sources of uncertainty. Data fusion techniques aim to bring together the strengths and mitigate the limitations of data from various information sources. A multifidelity data fusion framework employing Gaussian process regression is adopted herein and applied to the surface pressure data of a large aircraft wing model. The modeling approach is non-hierarchical in that there is no established hierarchy of accuracy among the information sources. Scalability issues arising from the large volume of data required for the study of pressure distributions are overcome by using an approximate Gaussian process regression based on stochastic variational inference, enabling the data fusion framework to be applied effectively to industry-relevant analysis and design challenges. The approach is demonstrated for a high-dimensional data set generated through wind tunnel testing in an industrial setting and numerical analysis.