A Predictive Model for the Streamwise Velocity in the Near‐Neutral Atmospheric Surface Layer

Abstract

For the reconstruction of the streamwise velocity field in the high‐Reynolds‐number (Reτ ≈ Ο(106)) near‐neutral atmospheric surface layer (ASL), a predictive model was proposed in our work. In our model, the streamwise velocity time series at each height are predicted by the superposition of mean velocity, small‐scale turbulent signals, and large‐scale turbulent signals. All the parameters used by our model have clear physical meaning. In addition, the parameters can be directly determined by the experiment that is easy to be achieved in the ASL. The model can predict the streamwise velocity between 0.9 and 30 m in the near‐neutral ASL based only on single‐point‐measured large‐scale streamwise velocity signal. For example, mean velocity, turbulence intensity, and power spectrum of streamwise velocity can be predicted by the new model. Furthermore, the streamwise velocity time series predicted by the new model have a good correlation with the directly measured results, especially for large‐scale structures. It indicates that our model can be used to reconstruct the streamwise velocity field in the logarithmic region of the near‐neutral ASL, and our model is an important complement to the Marusic‐Mathis‐Hutchins model.

Large-scale structures of wall-bounded turbulence in single- and two-phase flows: advancing understanding of the atmospheric surface layer during sandstorms

“…The previously documented maximum Reynolds number is = 647 (Ji, Munjiza, Avital, Xu & Williams, 2014). Even if the Lagrangian point-particle method is employed (Sardina et al, 2012;Vreman, 2007;Zhao, Andersson, & Gillissen, 2013), the highest Reynolds number currently achievable is also limited to (10 3 ) (Richter, 2015; Wang & Richter, E5-4 H. Liu and X. Zheng 2019). To improve the Reynolds number, the LES may be a feasible approach, but currently, there is no general model for describing particle-laden turbulence at high Reynolds numbers.…”

“…Figure 16. Comparison between the model prediction results and measured results at (a) 0.9 m; (b) 1.71 m; (c) 7.15 m; and (d) 30 m, taken fromHan, Liu, et al (2019).…”

Three‐Dimensional Representation of Large‐Scale Structures Based on Observations in Atmospheric Surface Layers

“…(), and Han et al. (). Data validations by Wang and Zheng () confirmed that the selected ASL data exhibit properties that are typical of canonical TBL flows in general and thus can be used in studies of high‐Reynolds‐number wall turbulence.…”

Robust relation of streamwise velocity autocorrelation in atmospheric surface layers based on an autoregressive moving average model

“…Extended AR models for multivariate situations combined with data clustering methods were used to investigate interactions between motions with distinct characteristic scales (Vercauteren & Klein 2015;Vercauteren, Mahrt & Klein 2016;Vercauteren et al 2019). Moreover, the ARMA model and its combination with other statistical models can be used to analyse and predict non-stationary ASL turbulence (Zhang et al 2023).…”