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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749454
• 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

这项研究的长期目标是开发一套新型的工具和方法，用于建模自然水域（例如湖泊，河口和海洋）中的湍流混合，其中水的密度通常随深度而增加（即液体稳定分层）。 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.我的研究将通过采用基于过程的方法来解决这个问题，在这种方法中，将建立物理系统的整体混合属性与基本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中表现为被称为相干结构（CSS）的不同结构，后者是理解自然流动过程的混合特性的关键。通过这项研究，将发现和检查与混合热，盐和污染物有关的CSS。然后，这些CS的理解将用于开发基于加工的混合模型。该研究将由分层湍流的大规模数值模拟进行驱动，并结合用于分析模拟数据和开发新模型的最新数学工具。该程序可以分为三个目标：A。以两种流配置进行模拟，即分层动量唤醒和分层剪切框； B.从AIM A中获得的分层湍流数据库的提取相干结构（CSS），使用两个新型的数学工具，即动态模式分解和流动复发分析；和C.诊断已识别的CSS的混合特性并将这些特性合成为适用于自然流量系统的预测混合模型。该程序将（i）开发新一代的定量工具来推断数值的湍流混合速率