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Machine learning-based super-resolution reconstruction of turbulent flow simulations over superhydrophobic surfaces
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DOI：10.1016/j.ijheatfluidflow.2025.110032.png)
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Abstract
En 中文
• Super-resolution model reconstructs turbulence over pattern-resolved superhydrophobic surfaces. • Paired DNS training reconstructs under-resolved flow fields. • Model accuracy is higher in log-layer than in near-wall viscous sublayer. • Error metrics validate SR in reducing cost of multiscale flow simulations.
Keywords:
super-resolution
turbulence modeling
superhydrophobic surfaces
direct numerical simulation
multiscale flow simulations
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5.1
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3.3K
Citations:
5.7K
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