Process-based modelling of turbulent mixing in stratified natural waters

分层天然水中湍流混合的基于过程的建模

• 批准号: RGPIN-2018-04329
• 负责人: Zhou, Qi
• 金额: $ 1.89万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=669847
• 关键词: Process; based; modelling; turbulent; mixing

The long-term objective of this research is to develop a novel set of tools and approaches for modelling turbulent mixing in natural waters, such as lakes, estuaries and oceans, in which the density of water typically increases with depth (i.e. the fluid is stably stratified). Understanding stratified turbulent mixing is crucial for a range of problems, from modelling the ecological health of the Great Lakes to predicting oceanic heat uptake in a changing climate.******Current methods to predict mixing rely heavily on empirical relations, and a major shortcoming of these relations is that they are largely ‘unaware' of the processes that are responsible for mixing, thus introducing great uncertainties in the predictions. My research will address this issue by adopting a process-based approach, where the critical links between the overall mixing properties of the physical systems and the underlying flow processes will be established. These generic flow processes can manifest themselves in environmental flows as distinct structures known as coherent structures (CSs), the latter serving as the key to understanding the mixing properties of natural flow processes. Through this research, CSs that are relevant to mixing heat, salt and pollutant in lakes and oceans will be identified and examined. The understandings of these CSs will then be utilized to develop processed-based models for mixing.******The research will be driven by large-scale numerical simulations of stratified turbulent flows, combined with state-of-the-art mathematical tools for analyzing simulation data and developing new models. The program can be divided into three aims: A. Perform simulations in two flow configurations, i.e. stratified momentum wake and stratified shear box; B. Extract coherent structures (CSs) from the numerical database of stratified turbulence obtained in aim A, using two novel mathematical tools, i.e. dynamic mode decomposition and flow recurrence analysis; and C. Diagnose the mixing properties of the CSs identified and synthesize these properties into predictive mixing models appropriate for natural flow systems.******This program will (i) develop a new generation of quantitative tools to infer turbulent mixing rates for numerical modellers and field scientists, (ii) generate an unprecedented stratified turbulence dataset for fluid dynamicists, and (iii) discover a new ‘zoo' of coherent structures for mathematicians and theorists. An improved understanding of the mixing and transport of heat, dissolved oxygen and nutrients etc. in natural waters will facilitate robust decision making by the public and private entities with regard to Canada's vital resources such as freshwater and fisheries. The proposed program will train 3 undergraduate, 2 MSc and 2 PhD students, who will develop expertise in environmental modelling and acquire highly transferable skills, e.g. high-performance computing and data analytics.

这项研究的长期目标是开发一套新的工具和方法，用于模拟自然沃茨中的湍流混合，例如湖泊，河口和海洋，其中水的密度通常随深度增加（即流体稳定分层）。了解分层湍流混合对于一系列问题至关重要，从模拟五大湖的生态健康到预测气候变化中的海洋热吸收。目前的方法来预测混合严重依赖于经验关系，这些关系的一个主要缺点是，他们在很大程度上是“不知道”的过程，负责混合，从而引入了很大的不确定性的预测。我的研究将通过采用基于过程的方法来解决这个问题，其中将建立物理系统的整体混合特性与底层流动过程之间的关键联系。这些通用的流动过程可以在环境流动中表现为称为相干结构（CS）的独特结构，后者是理解自然流动过程混合特性的关键。通过这项研究，将确定和检查与湖泊和海洋中的热，盐和污染物混合有关的CS。对这些CS的理解将用于开发基于过程的混合模型。**该研究将通过分层湍流的大规模数值模拟，结合最先进的数学工具来分析模拟数据和开发新模型。该计划可以分为三个目标：A。在两种流动配置中进行模拟，即分层动量尾流和分层剪切盒; B。利用动态模态分解和流动递推分析两种新的数学工具，从目标A中获得的分层湍流数值数据库中提取相干结构;诊断所识别的CS的混合特性，并将这些特性合成为适用于自然流动系统的预测混合模型。**该计划将（i）开发新一代的定量工具，以推断湍流混合率的数值模拟和现场科学家，（ii）生成一个前所未有的分层湍流数据集的流体动力学家，和（iii）发现一个新的“动物园”的相干结构的数学家和理论家。更好地了解自然沃茨中热量、溶解氧和营养物质等的混合和运输，将有助于公共和私营实体就加拿大的淡水和渔业等重要资源作出强有力的决策。该计划将培养3名本科生，2名硕士生和2名博士生，他们将发展环境建模方面的专业知识，并获得高度可转移的技能，例如高性能计算和数据分析。