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- W2316290730 abstract "This paper demonstrates the feasibility of a CFD-based computational strategy aimed at on-demand predictions of aeroelastic responses of full aircraft configurations for variable flight conditions. The strategy relies on the pre-computation of a database of reduced-order bases and models for discrete flight parameters, and an interpolation method suitable for adapting in real-time the stored reduced-order information to parameter values not populated in the database. It also features a database training and reduction scheme based on concepts from machine learning to maximize both the robustness and performance of local interpolations. The application of this computational strategy to the broad aeroelastic identification of a complete F-16 fighter configuration highlights its near-real-time processing capability and demonstrates its potential for assisting flutter flight testing." @default.
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- W2316290730 date "2009-01-05" @default.
- W2316290730 modified "2023-10-14" @default.
- W2316290730 title "On-Demand CFD-Based Aeroelastic Predictions Using a Database of Reduced-Order Bases and Models" @default.
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- W2316290730 doi "https://doi.org/10.2514/6.2009-800" @default.
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