A combined SHAP-guided MARL strategy using U-net predictions of skin-friction and wall pressure achieves 34.44% drag reduction and 34.01% net energy saving with 0.43% normalized input power in turbulent channel flow.
Reynolds -number dependence of turbulent skin-friction drag reduction induced by spanwise forcing
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Sinusoidal surface grooves induce converging-diverging transverse flow in turbulent boundary layers via pressure gradients, forming a Passive Stokes Layer with an inviscid model matching experiments but yielding at most a few percent frictional drag reduction offset by pressure losses.
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Explainable deep reinforcement learning reveals energy-efficient control strategies for turbulent drag reduction
A combined SHAP-guided MARL strategy using U-net predictions of skin-friction and wall pressure achieves 34.44% drag reduction and 34.01% net energy saving with 0.43% normalized input power in turbulent channel flow.
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Passive transverse forcing of turbulent boundary-layer flow using sinusoidal surface grooves
Sinusoidal surface grooves induce converging-diverging transverse flow in turbulent boundary layers via pressure gradients, forming a Passive Stokes Layer with an inviscid model matching experiments but yielding at most a few percent frictional drag reduction offset by pressure losses.