【作者】 王国华；

【导师】 郑晓静；

【作者基本信息】 兰州大学 ， 工程力学， 2014， 博士

【摘要】 我们在甘肃青土湖地区干涸平坦的湖床上建立了高雷诺数流动观测列阵,对大气表面层三维风速、温度、沙尘浓度等进行了空间多点梯度同步测量,获得了大量高雷诺数大气表面层流动数据。通过对观测数据的严格筛选及预处理,得到了ReιO(106)的与规范湍流边界层可比的中性平稳流动数据,并利用这些数据对边界层流动的统计特征、谱以及湍流结构进行了分析。本文的主要结果和创新点有：发现对数标度的平均速度廓线的卡门常数随着雷诺数的增加而增大,在雷诺数达到Reι～O(106)的大气表面层中卡门常数约为0.43,高于实验室低雷诺数边界层结果。在证实大气表面层中存在大尺度与超大尺度湍流结构的基础上,发现超大尺度结构尺度随高度增加而增大,表面层中上部超大尺度结构仍显著存在,其流向尺度超过10倍表面层厚度；而且超大尺度结构的能量贡献随雷诺数增大,在表面层中上部超过70%。流向预乘谱分析发现中低波数区域谱线随高度的变化存在“翻转”现象,这一现象揭示了大气表面层中上部的VLSMs起源与实验室流动中"Bottom-up"的壁面小涡自组织机制不同,其来源于表面层外大涡"top-down"的破碎过程。进一步的,通过对流向功率谱特征及变化规律进行分析提取,本文给出了一个新的湍流谱表征公式,可以准确刻画功率谱中-1次方谱区并反映其随高度的变化。在此基础上,建立了一个高雷诺数流动的风速预测模型,预测结果与实测数据的比较证明该模型具有较高的精度。另外,表面层中展向湍动能的垂向分布也近似满足对数线性标度的规律,而且展向功率谱均存在符合-1次方标度的区域,分析两者密切相关。更多还原

【Abstract】 Observations using the observation array of high Reynolds number turbulence, which was arranged on the dry flats of Qingtu Lake, were performed and large amounts of data of high Reynolds number surface flow were obtained. After rigorous screening and pretreatment to the measured data, the steady wind data in the near neutral flow were selected and used to analyze the mean velocity profile, distributions of turbulence intensity and Reynolds shear stress, spectra and turbulence structures. The results show that the statistics characters at Reτ-O(106) agree with their variation with Reynolds number suggested by previous laboratory works and it indicate the near neutral atmospheric surface layer (ASL) behaves like a canonical turbulent boundary layer during the selected hours of data. The main works and innovations of this thesis are concluded as follows:The von Karman constant which is a key parameter in logarithm-law representation of mean velocity profile increase with Reynolds number and is about0.43at Reτ～O(106) in the atmospheric surface layer, higher than its magnitude in low Reynolds number flows. Similar as the vertical distribution of streamwise turbulence intensity, the spanwise turbulence intensity varies log-linearly with height.The large scale motions (LSMs) and very large scale motions (VLSMs) are confirmed in ASL by different ways. The VLSMs are detected clearly in the upper of ASL and their streamwise length scale of VLSMs increase with wall normal distance and their streamwise length scale reach up to10δ. The contribution of VLSMs to the turbulence kinetic energy is more than70%, which is higher than the results of low and moderate Reynolds number flow. Particularly, the pre-multiplied spectrum curves of different heights reverse at low wave-number range. It suggests that the VLSMs in upper ASL come from the outer "large eddy" moving downwards and sheared by surface, i.e., the "Top-Down" mechanism. It is different from the "Bottom-Up" mechanism which believes the VLSMs originate with self-organization of small streaks near the surface.Further, a new streamwise velocity power spectrum representation formula is proposed through analyzing the measured power spectra and their variation. It can characterize the "-1" spectral law range accurately. Based on the analysis results of spectra and the turbulence structures, a velocity prediction model for high Reynolds number flow is established. With this model, the wind velocity at a higher height can be predicted using the measured wind velocity at a lower height, and the statistics characteristics and spectra of predicted velocities agree well with the measured results.Spectrum analysis shows "-1" spectral law region in spanwise velocity power spectra, and the spanwise turbulent kinetic energy varies log-linearly with wall-normal distance.更多还原