Jizhong Zhang | Searcharxiv

arXiv2026

Direct numerical simulation 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 over an extended actuation region with oscillation periods up to T_{sc}^+=600, scaled by the uncontrolled friction velocity u_{τ0} 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, 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 TBL develops, u_{τ0} decreases downstream, reducing the local-scaled period T^+ and thereby enhancing suppression of near-wall turbulence. Interestingly, even the results are compared at approximately fixed T^+, DR for T^+>350 still exhibits a weak positive dependence on Re_θ, consistent with recent experiments by Marusic et al. Nat. Commun., vol. 12, 2021, 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 Karman 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.

physics.flu-dyn↗

arXiv2020

Feature Super-Resolution Based Facial Expression Recognition for Multi-scale Low-Resolution Faces

Facial Expressions Recognition(FER) on low-resolution images is necessary for applications like group expression recognition in crowd scenarios(station, classroom etc.). Classifying a small size facial image into the right expression category is still a challenging task. The main cause of this problem is the loss of discriminative feature due to reduced resolution. Super-resolution method is often used to enhance low-resolution images, but the performance on FER task is limited when on images of very low resolution. In this work, inspired by feature super-resolution methods for object detection, we proposed a novel generative adversary network-based feature level super-resolution method for robust facial expression recognition(FSR-FER). In particular, a pre-trained FER model was employed as feature extractor, and a generator network G and a discriminator network D are trained with features extracted from images of low resolution and original high resolution. Generator network G tries to transform features of low-resolution images to more discriminative ones by making them closer to the ones of corresponding high-resolution images. For better classification performance, we also proposed an effective classification-aware loss re-weighting strategy based on the classification probability calculated by a fixed FER model to make our model focus more on samples that are easily misclassified. Experiment results on Real-World Affective Faces (RAF) Database demonstrate that our method achieves satisfying results on various down-sample factors with a single model and has better performance on low-resolution images compared with methods using image super-resolution and expression recognition separately.

cs.CV↗