Abstract
This paper summarizes recent progress in developing metamodels for efficiently predicting the aerodynamic loads acting on industrial aircraft configurations. We introduce a physics-based approach to reduced-order modeling based on proper orthogonal decomposition of snapshots of the full-order CFD model, and a mathematical approach to variable-fidelity modeling that aims at combining many low-fidelity CFD results with as few high-fidelity CFD results as possible using bridge functions and variants of Kriging and Cokriging. In both cases, the goal is to arrive at a model that can be used as an efficient surrogate to the original high-fidelity or full-order CFD model but with significantly less evaluation time and storage requirements. Both approaches are demonstrated on industrial aircraft configurations at subsonic and transonic flow conditions.
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Görtz, S., Zimmermann, R., Han, ZH. (2013). Variable-Fidelity and Reduced-Order Models for Aero Data for Loads Predictions. In: Kroll, N., Radespiel, R., Burg, J., Sørensen, K. (eds) Computational Flight Testing. Notes on Numerical Fluid Mechanics and Multidisciplinary Design, vol 123. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38877-4_8
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DOI: https://doi.org/10.1007/978-3-642-38877-4_8
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