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- W4379780293 abstract "In the frame of the MONNALISA Project of the Clean Sky 2 Joint Undertaking (CSJU), we develop a calibration framework for improving the prediction accuracy of a low-order aerodynamic model. The enhancement is sought with respect to a reference application case, an unconventional “Advanced Rear End” component for the forthcoming generation of ultra-efficient aircraft, which is a specific focus of the MONNALISA Project. The calibration framework develops upon a classical Bayesian inference approach providing not only optimal values for the model parameters, but also a probabilistic characterization in terms of their peculiar probability distribution within well defined bounds. The reference data include both numerical predictions from computational fluid dynamics simulations and experimental measurements collected at the large wind-tunnel facility (GVPM) at Politecnico di Milano." @default.
- W4379780293 created "2023-06-09" @default.
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- W4379780293 date "2023-06-08" @default.
- W4379780293 modified "2023-09-25" @default.
- W4379780293 title "Bayesian calibration of a low order aerodynamic model for the design of unconventional tail empennages" @default.
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- W4379780293 doi "https://doi.org/10.2514/6.2023-4422" @default.
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