Hydrodynamic cloaking offers a transformative approach to drag reduction by manipulating flow without disturbing the surrounding field. However, existing designs for laminar flows require either complex metamaterials with anisotropic properties or multilayer force distributions that are experimentally impractical. This study presents a novel single-layer hydrodynamic cloak for incompressible laminar flows at Re = 100, achieved through a synergistic combination of uniform volume forces and sliding wall boundaries. We employ a machine learning-driven optimization framework using Interior Point Optimizer algorithm to determine optimal control parameters that minimize flow disturbances at the cloak boundary. The objective function, defined as the mean squared error of velocity deviations at monitoring points, quantifies cloaking performance. Comparative simulations demonstrate the superior performance of synergistic control: drag reduction efficiency reaches 99.4%, compared to 89.9% for sliding wall-only and 40.7% for volume force-only control. Flow field analysis confirms the complete elimination of vortex shedding and flow wakes, with all disturbances confined within the cloaking region while maintaining an undisturbed background flow. Additionally, lift fluctuations are entirely suppressed, effectively preventing vortex-induced vibrations. This work establishes the theoretical necessity of dual-mechanism control for achieving zero drag in viscous environments and provides a feasible pathway toward experimental validation, with potential applications in transportation, aerospace, and marine engineering.
Wang et al. (Thu,) studied this question.