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Data-driven modeling of pressure and wall shear stress distributions on marine propellers via deep learning
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DOI：10.1016/j.oceaneng.2026.127446.png)
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Abstract
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• A data-driven model predicts pressure and wall shear stress on propeller blades. • The preprocessing technique transforms unstructured CFD data into structured input. • Attention-enhanced U-Net captures critical blade flow features effectively. • Computational cost is reduced by two orders of magnitude versus CFD.
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