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，DiegoProposal Number：1605914湍流中物质或物种(文献中通常称为“标量”)的混合从基本和实际角度来看都是一个极其重要的话题。这项建议的重点是研究可压缩湍流和混合。为了实现这一目标，建议使用前所未有的高保真模拟，突破被称为直接数值模拟(DNS)的模拟类别的界限。虽然几十年来关于不可压缩流动中的混合已经积累了大量的工作，但关于可压缩湍流中的混合还没有类似水平的探索或数据，尽管它在诸如航空运输和空气动力学、太阳风的物理、恒星和超新星中的流动、发动机或大气中的化学反应流动等许多领域都具有至关重要的重要性。在应用中，可压缩混合通常使用主要基于经典现象学的不可压缩结果来建模。因此，这项提案旨在通过在新的规范配置中进行受控和系统的研究，以及由此产生的供社区探索的高保真域名数据数据库，为了解基本的物理过程提供基础，从而弥合这一知识差距。建议的研究将全面考察雷诺数、马赫数和施密特数的影响。所提出的工作包括定义一类可用于获得高保真结果的正则流，该结果可能导致可压缩标量混合的进步。这些典型流动的结果将提供可靠的数据，可用作理论和模型的基准。此外，还提出了一种可压缩混合的所谓朗利曲线。作为该项目的副作用而开发的高性能计算技术和数据将向感兴趣的科学家和工程师社区提供。