发表机构
Saint Anthony Falls Laboratory, University of Minnesota; Department of Civil, Environmental, and Geo-Engineering, University of Minnesota（明尼苏达大学圣安东尼瀑布实验室; 明尼苏达大学土木、环境与地球工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种二维随机建模框架，利用少量关键参数生成高雷诺数粗糙壁湍流边界层，无需UMZ数据集，再现实验测量结果并验证代码。
AI 中文摘要
大气表面层流动的计算具有挑战性，主要由于表面粗糙度和高雷诺数，这两者都要求在近表面区域具有极高的空间分辨率。我们开发了一个基于二维随机的模型，用于生成和流向拼接瞬时的、阶梯状速度剖面，这些剖面具有壁湍流的关键要素，即均匀动量区（UMZ）和剪切层（Ehsani et al. 2024a,b），以及涡旋（Ehsani et al. 2026）。该模型在此扩展到对数层顶部，以再现Saddoughi和Veeravalli（2000）测量的高雷诺数、粗糙壁、湍流边界层，无需UMZ数据集的支持，仅使用少量关键流动参数：泰勒微尺度{\lambda}T、边界层高度{\delta}、摩擦速度u{\tau}和气动粗糙度长度z0。主要挑战在于将随机模型与UMZ和涡旋特征的缩放分布进行整合。所得的统计矩、能谱和结构函数与实验结果进行了比较。验证后的代码已在GitHub仓库中提供。
英文摘要
Atmospheric surface layer flows are computationally challenging, predominantly due to surface roughness and the high Reynolds number, both of which demand exceptionally high spatial resolution in the near-surface region. We have developed a 2-D stochastic-based model for the generation and the streamwise concatenation of instantaneous, step-like velocity profiles featuring key elements of wall turbulent flows, i.e., uniform momentum zones (UMZ) and shear layers (Ehsani et al. 2024a,b), and vortices (Ehsani et al. 2026). The model is extended herein to the top of the logarithmic layer to reproduce the high-Reynolds-number, rough-wall, turbulent boundary layer measured by Saddoughi and Veeravalli(2000), without the support of a UMZ dataset, using only a handful of critical flow parameters: the Taylor microscale λT, the boundary layer height δ, the friction velocity uτ, and the aerodynamic roughness length z0. The primary challenge lies in the integration of the stochastic model with the scaled distributions of the UMZ and vortex characteristics. The resulting statistical moments, energy spectra, and structure functions are compared against the experimental results. The validated code is made available in a GitHub repository.
Comments29 pages, 7 figures
