Turbulence Generation in Common Near-Calm Conditions

常见的近平静条件下湍流的产生

• 批准号: 1115011
• 负责人: Larry Mahrt
• 金额: $ 85.37万
• 依托单位: NorthWest Research Associates, Incorporated
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-10-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1115011&HistoricalAwards=false
• 关键词: Turbulence; Generation; Common; Near; Calm

Turbulence near the earth's surface under low wind speed conditions (which are common to nighttime and other stably stratified conditions) is primarily generated by gravity waves and other small-scale non-turbulent motions. Although such processes have received little attention and are poorly understood, the nocturnal near-calm regime is common, particularly with high-pressure systems over flat terrain and under a wider variety of synoptic conditions in small basins and valleys with weak down-valley slope. The time-averaged surface flux is then dominated by infrequent mixing events of unknown origin. Existing methods of data analysis have generally failed to identify the basic physics of generation of mixing events under such conditions. Proper attribution of the root causes of the mixing events requires more emphasis on spatial variation than provided in existing analysis strategies. This project will provide such emphasis by initially focusing on dense networks of sonic anemometers and auxiliary data to more comprehensively characterize the temporal and spatial variability of the turbulence as well as mean turbulence quantities. Obtained representations will include scale-dependent probability distributions and stochastic models with extreme-value capabilities. Such approaches require very large data sets, which have been recently acquired. Since the non-turbulent motions and event mixing can be site dependent, a more limited version of the analysis will be applied to ~20 different field programs representing a wide range of surface conditions. To obtain more even finer spatial information, the investigator plans to deploy fiber-optic cables capable of providing high density information on temperature fluctuations and video based semi-Lagrangian tracking of machine-generated fog elements, all embedded within a new network of sonic anemometers and pressure sensors. These measurements will be integrated with tower profiles of turbulence quantities and deeper profiling from sodars to examine the generation of mixing events in a quasi-three dimensional framework.The intellectual merit of this effort centers on pioneering work to understand and stochastically describe those complex flow features involving superposition of waves, microfronts and other complex non-deterministic signatures thought to be common under near-calm conditions via application of new observational tools and non-standard analysis techniques. Broader impacts will include contributions toward improved model parameterizations of surface fluxes and dispersion under weak winds conditions and improved ability to anticipate localized surface cooling associated with dense fog formation or frost damage to crops. An existing instructional video of near-surface motions based on machine-generated fog, currently used by a number of universities in boundary-layer course work, will be expanded into a set of more professional instructional videos on local atmospheric flows for broader distribution and use. A joint effort with a local high school will provide media suitable secondary school settings and public demonstrations.

在低风速条件下（夜间和其他稳定分层条件通常），地球表面附近的湍流主要由重力波和其他小规模的非扰动运动产生。 尽管此类过程很少受到关注，并且知之甚少，但夜间近校友政权很常见，尤其是在平坦的地形上高压系统，在小盆地和山谷较弱的小盆地中的多种概要条件下。 然后，时间平均的表面通量由未知来源的不经常混合事件主导。 现有的数据分析方法通常未能识别在这种情况下混合事件的产生的基本物理。 与现有分析策略相比，混合事件的根本原因的适当归因需要更加强调空间变化。 该项目将通过最初专注于声音传感器计和辅助数据的密集网络来提供这种重点，以更全面地表征湍流的时间和空间可变性以及平均湍流数量。 获得的表示形式将包括具有极高功能的规模依赖性概率分布和随机模型。 这种方法需要非常大的数据集，这些数据集已被最近获取。 由于非扰动的动作和事件混合可以取决于站点，因此分析的更有限版本将应用于约有20个不同的场程序，代表了广泛的表面条件。 为了获取更多更精细的空间信息，研究人员计划部署能够提供有关温度波动和基于视频的机器生成雾元素的半拉格朗日跟踪的高密度信息的光纤电缆，这些信息都嵌入了新的声音散热器和压力传感器的网络中。 这些测量结果将与湍流数量的塔式概况和从SODAR的较深的分析进行整合，以检查在三维框架中的混合事件的产生。这项工作的智力优点集中在开创性工作上，集中于理解和随机地描述涉及浪潮，微观范围和其他复杂范围的复杂范围的复杂流程，并描述了这些复杂的范围，并描述了涉及新的复杂范围，并涉及其他复杂的范围，并涉及其他复杂的范围，并涉及其他复杂的范围，并涉及其他复杂的范围，并描述了范围的不合格，并描述了涉及的范围，并涉及其他复杂的范围，并描述了涉及的范围，并描述了涉及浪潮，微观的复杂性符号，并涉及其他复杂范围的范围。观察工具和非标准分析技术。 更广泛的影响将包括在弱风条件下改善表面通量和色散的模型参数的贡献，以及提高预测与密集雾形成或对农作物的霜冻损害相关的局部表面冷却的能力。 现有的基于机器生成的雾的近表面动作的教学视频，目前在边界层课程中使用了许多大学，将扩展为一组有关当地大气流的更专业的教学视频，以进行更广泛的分布和使用。 与当地高中的联合努力将为媒体提供合适的中学环境和公开示威。