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With the recent advances in machine learning, strategies based on data can be used to augment wall modeling in the turbulent boundary layer. Combined with the attached eddy hypothesis, the present work applies extreme gradient boosting (XGBoost) to predict the large-scale wall-attached structures at a range of wall-normal locations based on a near-wall reference position ( z R + ≈ 4) spanning a Reynolds-number range R e τ ∼ O (10 3) − O (10 5). The input and output signals are selected as the large-scale structures; here, the input signals are set as in the fixed near-wall reference position by a series of streamwise velocity ({ X − N , ... , X − 1 , X 0 , X 1 , ... , X N }), and the output signal Y 0 is set directly above X 0 . Within each dataset, the large-scale wall-attached structures are identified from the prediction modeled by XGBoost between the turbulence in the upper region and at the near-wall reference position, resulting in a successful prediction of the large-scale structures inclination angles. Along the wall-normal offset Δ z and streamwise offset L x (distance between X i and X 0 ), the slope of the feature importance (represented by contour levels) is exactly equal to the degree of inclination of large-scale structures, indicating the turbulent inner and outer connection inferred by the machine learning input and output interactions perspective. This study shows that there is a great opportunity in machine learning for wall-bounded turbulence modeling by connecting the flow interactions between near-wall and outer regions. [ABSTRACT FROM AUTHOR]

Although scaling laws and coherent structures have been well-studied in non-neutral boundary layer flows over a long period of time, a potential connection between them is still worth exploring. This work explores the structure inclination angle under non-neutral stratification conditions by way of a connection between turbulent scaling and Monin–Obukhov similarity theory. A model for theoretical structure inclination angles is built by extending the refined "turbulent eddy" model of H.-J. Huang [Phys. Rev. Fluids 4, 114702 (2019)] under stratified stability conditions. The variations in the inclination angle values with the stability parameter from the current model are found to be in good agreement with results from the literature, laboratory results, and atmospheric surface layer observations. In addition, the results show that increasing positive/negative buoyancy leads to increasing/decreasing inclination angles and steeper/moderate angles at higher wall-normal heights. This variation is parameterized and can be used to improve existing near-wall models. [ABSTRACT FROM AUTHOR]

The present work explores the spanwise logarithmic decay of the turbulence intensity for wall-attached eddies per Townsend's attached eddy hypothesis. Within the dataset spanning a friction Reynolds number range R e τ ∼ O (10 3) − O (10 6) , the coherence between the turbulence in the logarithmic region along the spanwise direction and that at a near-wall reference location is used to assess the scale-dependent coherence. Linear coherence spectrum analysis is applied as a filter to separate the coherent and incoherent portions. After this separation procedure, the turbulence intensity decay for wall-attached eddies in the spanwise direction is described in a log-linear manner, which also identifies how the scaling parameter increases with the Reynolds number. This variation is parametrized and consequently can be used to improve existing near-wall models. [ABSTRACT FROM AUTHOR]