Identification of the energy contributions associated with wall-attached eddies and very-large-scale motions in the near-neutral atmospheric surface layer through wind LiDAR measurements

Abstract

Recent works on wall-bounded flows have corroborated the coexistence of wall-attached eddies, whose statistical features are predicted through Townsend's attached-eddy hypothesis (AEH), and very-large-scale motions (VLSMs). Furthermore, it has been shown that the presence of wall-attached eddies within the logarithmic layer is linked to the appearance of an inverse-power-law region in the streamwise velocity energy spectra, upon significant separation between outer and viscous scales. In this work, a near-neutral atmospheric surface layer is probed with wind light detection and ranging to investigate the contributions to the streamwise velocity energy associated with wall-attached eddies and VLSMs for a very-high-Reynolds-number boundary layer. Energy and linear coherence spectra (LCS) of the streamwise velocity are interrogated to identify the spectral boundaries associated with eddies of different typologies. Inspired by the AEH, an analytical model for the LCS associated with wall-attached eddies is formulated. The experimental results show that the identification of the wall-attached-eddy energy contribution through the analysis of the energy spectra leads to an underestimate of the associated spectral range, maximum height attained and turbulence intensity. This feature is due to the overlap of the energy associated with VLSMs obscuring the inverse-power-law region. The LCS analysis estimates wall-attached eddies with a streamwise/wall-normal ratio of about 14.3 attaining a height of about 30 % of the outer scale of turbulence.

Stochastic modelling of the instantaneous velocity profile in rough-wall turbulent boundary layers

“…However, the convergence of the mean velocity still suffers from large-scale unsteady motions that are known to characterise the ASL (Hutchins & Marusic 2007;Guala, Metzger & McKeon 2011;Hutchins et al 2012;Puccioni et al 2023), and it is thus intrinsically related to the choice of the averaging time and possible filtering strategies separating the turbulence from a slowly varying mean flow. When downsampled in time by approximately the integral time scale, the statistically independent experimental profiles U(i) better resemble the sequence of the numerically generated profiles; their convergence is seemingly achieved after about O(10 2 ) realisations, comparably with the stochastic procedures.…”

“…2020) and in the ASL under different thermal stability conditions (Puccioni et al. 2023; Salesky 2023). The UMZs and the associated shear layers can be thus framed as the representative attached eddy, coherent structure responsible for the momentum transport and the establishment of the logarithmic velocity profile over a wide range of surface roughness conditions and Reynolds numbers (Prandtl 1925; Bautista et al.…”

The effect of turbulent motions on particle spatial distribution in high-Reynolds-number particle-laden flows

“…(2012) and Puccioni et al. (2023). Due to the complexity and uncontrollability of the field observation conditions, to ensure that the obtained ASL observation data can truly and reliably reflect the nature of high-Reynolds-number particle-laden two-phase wall turbulence, it is necessary to conduct specific selection and preprocessing, including wind direction correction (Wilczak, Oncley & Stage 2001), steady wind selection (Foken et al.…”

“…In addition, ogive analysis of the velocity signals indicates that there is good collapse in the cumulative frequency distribution when time length is more than 50 min. Therefore, to ensure the statistical convergence, the measured data were divided into multiple hourly time series for subsequent analysis, which is consistent with the previous standard practice in ASL studies, such as Hutchins et al (2012) and Puccioni et al (2023). Due to the complexity and uncontrollability of the field observation conditions, to ensure that the obtained ASL observation data can truly and reliably reflect the nature of high-Reynolds-number particle-laden two-phase wall turbulence, it is necessary to conduct specific selection and preprocessing, including wind direction correction (Wilczak, Oncley & Stage 2001), steady wind selection (Foken et al 2004), thermal stability judgment (Högström 1988;Högström, Hunt & Smedman 2002;Metzger et al 2007) and detrending manipulation (Hutchins et al 2012), which is consistent with Hutchins et al (2012) and Wang & Zheng (2016).…”

“…(2012) () and Puccioni et al. (2023) (). Moreover, it is consistent with results from experiments and numerical simulations (Zhang, Wang & Lee 2008; Li & McKenna Neuman 2012; Wang et al.…”