Abstract

Flows over complex surfaces exhibit rich physics that offer opportunities for control—such as drag reduction or heat transfer enhancement—but can also lead to detrimental effects like increased drag from roughness or contamination by bacterial biofilms. In some systems, surfaces (such as rough or porous materials) passively modify momentum transfer without being significantly affected by the flow. In others, including bacterial biofilms or lubricated walls, the surface evolves dynamically under flow, resulting in strong coupling and feedback between surface and flow. This talk presents recent progress toward reduced-order and data-driven models that efficiently capture these interactions. For passive surfaces, homogenization and machine-learning approaches are used to represent their effective influence on the flow. For responsive surfaces, models describe and eventually enable control of the coupled surface–flow evolution. Together, these advances provide new ways for predicting and controlling flow–surface interactions across physical and biological systems.