Beyond incompressible phenomenology: mixing in compressible turbulent flows

超越不可压缩现象学：可压缩湍流中的混合

• 批准号: 1605914
• 负责人: Diego Donzis
• 金额: $ 32万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1605914&HistoricalAwards=false
• 关键词: Beyond; incompressible; phenomenology; mixing; compressible

PI: Donzis, DiegoProposal Number: 1605914Mixing of substances or species (commonly called "scalars" in the literature) in turbulent flows is a topic of paramount importance from both fundamental and practical standpoints. The focus of this proposal is on the investigation of compressible turbulent flows and mixing. In order to achieve this goal, it is proposed to use simulations of unprecedented high fidelity, pushing the boundaries of the class of simulations known as Direct Numerical Simulations (DNS). While substantial work has been accumulated over decades on mixing in incompressible flows, there is no similar level of exploration or data on mixing in compressible turbulence in spite of its critical importance in diverse areas such as air transportation and aerodynamics, physics of solar wind, flows in stars and supernovae, chemically reacting flows in engines or the atmosphere, among many others. In applications, compressible mixing is commonly modeled using incompressible results based mainly on classical phenomenology. There is, thus, a gap in knowledge which this proposal aims to close by providing groundwork for understanding the fundamental physical processes through controlled and systematic studies in new canonical configurations and the resulting databases of high-fidelity DNS data for the community to explore. The proposed research will comprehensively investigate the effects of Reynolds number, Mach number and Schmidt number. The work proposed includes the definition of a class of canonical flows that can be used to obtain high fidelity results that could lead to advances in compressible scalar mixing. Results for these canonical flows would provide reliable data that can be used as benchmark for theories and models. Furthermore, it is proposed to develop a so-called Langley curve for compressible mixing. The high performance computing techniques to be developed as the side effect of this project and the data will be available to the community of interested scientists and engineers.

PI：Donzis，DieGopopossose编号：1605914在湍流中杂乱无章的物质或物种（文献中通常称为“标量”），这是从基本和实际观点均具有至关重要的话题。该提案的重点是研究可压缩的湍流和混合。为了实现这一目标，建议使用前所未有的高保真度的模拟，从而推动了称为直接数值模拟（DNS）的模拟类别的边界。尽管几十年来在不可压缩的流动中进行了大量工作，但尽管在空气运输和空气动力学，太阳能的物理，星星和超级诺沃的流动物理学，恒星和超级nove液的流动中，尚无类似的探索或可压缩性湍流混合数据的数据，尽管它具有至关重要的重要性。在应用中，通常使用不可压缩的结果来建模可压缩混合，主要基于经典现象学。因此，知识存在差距，该提案的目的是通过提供基础工作来通过新的规范配置中的受控和系统研究来理解基本的物理过程，以及最终的高保真DNS数据数据库，以供社区探索。拟议的研究将全面研究雷诺数，马赫数和施密特数字的影响。提出的工作包括一类规范流的定义，可用于获得高保真结果，这可能会导致可压缩标量混合的进步。这些规范流的结果将提供可靠的数据，可以用作理论和模型的基准。此外，提议开发一种所谓的Langley曲线，用于可压缩混合。高性能计算技术将作为该项目的副作用开发，并将数据提供给感兴趣的科学家和工程师社区。