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• W3023448064 abstract "Summary A multi‐fidelity reduced‐order model (ROM), which incorporates low‐fidelity data to improve the prediction of high‐fidelity results, is proposed for the reconstruction of steady flow field at different conditions. The spatial basis functions of low‐fidelity and high‐fidelity data, which are generated for all training sets are extracted separately by proper orthogonal decomposition. Then a surrogate model is used to construct mappings between the mode coefficients obtained from low‐fidelity and the high‐fidelity data. In the online stage, both the low‐fidelity flow at the predicted state and the surrogate model are needed to predict the mode coefficients of the high‐fidelity flow, and the high‐fidelity flow field is subsequently reconstructed. This method differs from existing surrogate‐based reduced‐order modeling method because it allows the use of partial physical information for flow estimation, which is coming from the low‐fidelity data instead of adopting a black‐box mapping between system state and the projection coefficients. Numerical studies are presented for a lid‐driven cavity problem and transonic flow past a NACA0012 airfoil. Two low‐fidelity models, with either a coarse mesh or a lower numerical order, are considered respectively. Results show that the proposed multi‐fidelity ROM predicts the flow field accurately and outperforms the traditional methods in both interpolated and extrapolated conditions." @default.
• W3023448064 created "2020-05-13" @default.
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• W3023448064 date "2020-05-20" @default.
• W3023448064 modified "2023-10-17" @default.
• W3023448064 title "Multi‐fidelity surrogate reduced‐order modeling of steady flow estimation" @default.
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• W3023448064 doi "https://doi.org/10.1002/fld.4850" @default.
• W3023448064 hasPublicationYear "2020" @default.
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