Direct numerical simulations of out-scale-actuated spanwise wall oscillation in turbulent boundary layers

Spanwise wall oscillation (SWO) of turbulent boundary layers (TBLs) is investigated via direct numerical simulations (DNS) over an extended actuation region (momentum Reynolds number 344 < Re_θ < 2340) with oscillation periods up to T⁺_sc = 600, scaled by the uncontrolled friction velocity uτ0 at the onset of SWO (i.e. Re_θ = 344). For low periods (T⁺_sc < 200), drag reduction (DR) decreases with increasing Re_θ , consistent with conventional inner-scaled control strategies targeting near-wall turbulence. In sharp contrast, for large periods (T⁺_sc > 200), DR increases with Re_θ . For example, at T ⁺_sc = 600, DR rises from 1.3 % at Re_θ = 713 to 7.0 % at Re_θ = 2340. This unexpected growth is partly explained by the streamwise evolution of the effective oscillation parameter: as a TBL develops, u_τ0 decreases downstream, reducing the local-scaled period T ⁺ and thereby enhancing suppression of near-wall turbulence. Interestingly, if the results are compared at approximately fixed T⁺, then DR for T⁺ > 350 still exhibits a weak positive dependence on Re_θ , consistent with recent experiments by Marusic et al. (2021, Nat. Commun., vol. 12, 5805). We further develop a new analytical relationship that links DR to the upward shift of mean velocity in the wake region. Unlike previous formulations, the relationship avoids logarithmic-region fitting and does not rely on an invariant Kármán constant under SWO, while maintaining good agreement with DNS data. Flow diagnostics – including Reynolds stresses, skin-friction decomposition, and energy spectra – demonstrate that the observed variation of DR with Reynolds number (Re) arises from period-dependent modulation of near-wall turbulence. Overall, these findings challenge the conventional view that DR inevitably deteriorates with Re, and demonstrate that out-scaled actuation can instead enhance DR performance – offering new physical insights for high-Re control strategies.