Studying turbulent scale and space interactions using active grid wind tunnel and DNS database experiments

使用主动网格风洞和 DNS 数据库实验研究湍流尺度和空间相互作用

• 批准号: 1033942
• 负责人: Charles Meneveau
• 金额: $ 29.02万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1033942&HistoricalAwards=false
• 关键词: Studying; turbulent; scale; space; interactions

The proposed work will use novel approaches in both experimental and cyber-based fluid dynamics to address fundamental issues related to space and scale interactions in turbulent flows. The study of such interactions will impact predictive capabilities and help educate a new generation of students with know-how in integrated fluid dynamics experimentation and data-intensive science. To address multi-scale interactions in turbulence, a new type of active grid with fractal winglets will be built that injects kinetic energy over a broad range of scales but without generating a mean shear. To address spatial interactions in turbulence, another new active grid that produces a uniform turbulent kinetic energy gradient without mean velocity shear will be constructed. Data will be acquired in a wind-tunnel using thermal and particle image velocimetry. Results from these two new basic flows will shed light on the cascade, on the role of initial distribution of energy across scales, and on spatial diffusion rates. The project will also study the evolution of smaller-scale sub-portions of turbulent fluid. In order to follow in 3D the time-evolution of such fluid patches, we will use the recently developed Web-Services accessible public turbulence database that contains a 1024^4 space-time history of forced isotropic turbulence. Similarities and differences with the global evolution as known from the literature and the new experimental results will be studied providing the intellectual merit of the project, and also implications on subgrid-scale models for LES will be established. In terms of broader impacts, turbulence research as proposed here is part of a larger push to deal with systems with nonlinear complex behavior and many degrees of freedom. Large-scale numerical simulation of such systems is, today, at center stage of scientific discussions with important societal ramifications (global change, energy, etc.). Better integrating physical understanding, experimentation, and novel web-based analysis of large databases has the potential to generate new knowledge in the context of the emerging new paradigm of data-intensive science. Graduate education and training will stress the interplay between physical experimentation, simulation and the use of new cyber-based research tools.

拟议的工作将使用实验和基于网络的流体动力学中的新方法来解决与湍流中的空间和规模相互作用有关的基本问题。对此类互动的研究将影响预测能力，并帮助教育新一代的学生在集成的流体动力学实验和数据密集型科学中的专业知识。为了解决湍流中的多尺度相互作用，将构建一种与分形小翼的新型活性网格，该网格将在广泛的尺度上注入动能，但不会产生平均剪切。为了解决湍流中的空间相互作用，将构建另一个新的活性网格，该网格将构建一个均匀的湍流动能梯度而无需平均速度剪切。数据将使用热和粒子图像速度法在风能中获取。这两个新的基本流的结果将揭示级联反应，跨尺度的初始分布以及空间扩散速率的作用。该项目还将研究湍流较小规模的子部分的演变。为了遵循此类流体补丁的时间进化，我们将使用最近开发的Web服务访问的公共湍流数据库，该数据库包含1024^4的强制各向同性湍流的时空历史。从文献和新的实验结果中知道的，将研究项目的智力优点，并且还将确定对LES的智力优点的相似之处和新实验结果的相似之处。在更广泛的影响方面，此处提出的湍流研究是应对具有非线性复杂行为和许多自由度的系统的更大努力的一部分。如今，对这种系统的大规模数值模拟是在科学讨论的中心阶段，具有重要的社会影响（全球变化，能源等）。更好地整合物理理解，实验和基于新的大型数据库的新型网络分析，有可能在新兴的数据密集型科学范式的背景下生成新知识。研究生教育和培训将强调物理实验，模拟与使用新的基于网络的研究工具之间的相互作用。