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• W4206621237 abstract "Accurate and computationally cheap forecasts using Reduced Order Models (ROMs) can facilitate active predictive control strategies to stabilize unsteady flow over airfoils. Galerkin projection based Reduced Order Models (GP-ROMs), derived by projecting the Navier Stokes equations on a truncated Proper Orthogonal Decomposition (POD) basis, have been widely adopted because of their low computational costs and theoretical formulation based on the governing fluid flow equations. However, the traditional GP-ROMs suffer from instabilities and inaccuracies for predictions over long time horizons, in spite of calibration. To address these issues we implement the recently proposed Neural Galerkin Projection (NeuralGP)procedure –a differentiable programming-based approach that blends the strengths of the physics driven GP-ROM technique to the purely data driven neural network based techniques previously suggested in literature. The predictive capabilities of the two methods are tested for a transonic flow over a buffeting NACA0012 airfoil. While both techniques give accurate forecasts over short time prediction horizons, only the NeuralGP ROM is able to accurately track the reference POD coefficients over longer prediction horizons with the calibrated GP-ROM diverging in amplitude and frequency. The results highlight the increased accuracy of the NeuralGP technique compared to the calibrated GP-ROM method while being less computationally expensive and more interpretable compared to the traditional physics-agnostic neural networks." @default.
• W4206621237 created "2022-01-25" @default.
• W4206621237 creator A5021314035 @default.
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• W4206621237 date "2022-01-03" @default.
• W4206621237 modified "2023-10-07" @default.
• W4206621237 title "Galerkin Reduced Order Models for Compressible Flows with Differentiable Programming" @default.
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• W4206621237 doi "https://doi.org/10.2514/6.2022-0373" @default.
• W4206621237 hasPublicationYear "2022" @default.
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