Metamaterial-free, zero-drag, spherical hydrodynamic cloaks enabled by machine learning

Le résumé fourni par la source

Hydrodynamic cloaks, a type of metamaterials possessing zero-drag properties, show fascinating potential for aerospace, marine engineering, and high-speed transportation. However, achieving zero drag with hydrodynamic cloaks in viscous flows is challenged by the complexity of the Navier–Stokes equations. This study designs spherical hydrodynamic cloaks based on machine learning, which allow objects to move in a viscous fluid without disturbing the flow fields. These cloaks merely require the supply of uniform external forces, unaided by metamaterials, allowing objects wrapped in the cloak to move unimpeded through viscous flow fields. Numerical simulations show that these cloaks provide significant drag reduction efficiency (up to 96.26%) and enhance flow stability by eliminating lift fluctuations. These findings provide new insights into flow control and expand the applicability of hydrodynamic metamaterials to high Reynolds number environments, with promising applications in multiphysics fields such as thermal-hydrodynamic coupling and acoustic-hydrodynamic coupling.

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