CAREER: High fidelity numerical simulations of turbulent flow separation at high Reynolds numbers with passive scalar transport.

职业：采用被动标量传输的高雷诺数湍流分离的高保真度数值模拟。

基本信息

批准号：
2314303
负责人：
Guillermo Araya
金额：
$ 50万
依托单位：
University of Texas at San Antonio
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-01-01 至 2025-02-28
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2314303&HistoricalAwards=false
关键词：
CAREER fidelity numerical simulations turbulent

项目摘要

The proposed research will provide foundational knowledge on an issue in fluid dynamics that is not well understood: flow separation and its association with heat and contaminant transport. Flow separation occurs when fluid flow past an object (e.g., wing of an aircraft) abruptly "detaches" from the surface. A swirling motion, called wakes or vortices, is then formed in the separated region which result in increasing the fluid drag force on the object. Flow separation is unwanted and can seriously reduce performance of engineered devices such as aircraft and turbines. The use of high-resolution numerical simulations will permit the advancement of more efficient turbulence models. Such models will facilitate the development of flow, heat, and contaminant transport control tools to mitigate unwanted flow separation effects. The proposed research and educational objectives will be carried out at a Hispanic Serving Institution, located in the Commonwealth of Puerto Rico. The educational component of this project promotes the use of high-performance computing, computational thinking, and modeling. Such concepts will be introduced to undergraduate and graduate courses using flipped classroom concept. The post-processed data and resulting codes will be made accessible to the public for analysis and validation. Additionally, a computing and visualization facility will be developed as a tool for research, learning, and outreach in departmental Summer Camps for K-12 students and teachers. The principal aim of this study is to numerically elucidate the details behind passive scalar phenomena during boundary layer separation, resulting from the two most frequent causes of flow detachment: streamwise and wall-curvature driven pressure gradient, in combination and isolated forms. The study will cover nearly the entire spectrum, particularly in the near wall region, where experimental techniques and turbulence models exhibit severe limitations. By identifying and tracking Lagrangian Coherent Structures (LCS) and exploring the relationship between LCS kinematics and passive scalar fields, it may be possible to accurately explain the separation process. These research efforts will apply the use of tremendous computational resources, requiring state-of-the-art petascale, parallel and Graphics Processing Unit (GPU) programming. Specific objectives include i) perform Direct Numerical Simulation (DNS) of spatially-developing turbulent boundary layers under strong deceleration at experimental Reynolds numbers, ii) understand the transport phenomena in the separation process by means of low/high order statistics analysis and create an LCS data bank along with iOS/Android apps for augmented/virtual reality, iii) develop a unique data-driven modeling of sub-grid scale (SGS) in Large Eddy Simulation (LES), focusing on the uncharted area of passive scalar. This project is jointly funded by the Fluid Dynamics Program and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

拟议的研究将为流体动力学中尚未被充分理解的问题提供基础知识：流动分离及其与热量和污染物传输的关联。当流体流过物体（例如飞机机翼）时突然从表面“分离”，就会发生流动分离。然后在分离的区域中形成称为尾流或涡流的旋转运动，这导致物体上的流体阻力增加。流动分离是不必要的，并且会严重降低飞机和涡轮机等工程设备的性能。高分辨率数值模拟的使用将允许开发更有效的湍流模型。此类模型将促进流量、热量和污染物传输控制工具的开发，以减轻不必要的流量分离效应。拟议的研究和教育目标将在位于波多黎各联邦的西班牙裔服务机构进行。该项目的教育部分促进高性能计算、计算思维和建模的使用。这些概念将使用翻转课堂概念引入本科生和研究生课程。后处理的数据和生成的代码将向公众开放以进行分析和验证。此外，还将开发计算和可视化设施，作为 K-12 学生和教师在院系夏令营中进行研究、学习和推广的工具。本研究的主要目的是从数值上阐明边界层分离过程中被动标量现象背后的细节，这种现象是由两种最常见的流动分离原因引起的：流向和壁曲率驱动的压力梯度，以组合形式和孤立形式。该研究将覆盖几乎整个光谱，特别是在近壁区域，那里的实验技术和湍流模型表现出严重的局限性。通过识别和跟踪拉格朗日相干结构（LCS）并探索LCS运动学与被动标量场之间的关系，或许可以准确地解释分离过程。这些研究工作将使用大量的计算资源，需要最先进的千万亿级、并行和图形处理单元 (GPU) 编程。具体目标包括 i) 在实验雷诺数强减速下对空间发展的湍流边界层进行直接数值模拟 (DNS)，ii) 通过低/高阶统计分析了解分离过程中的输运现象并创建 LCS数据银行以及用于增强/虚拟现实的 iOS/Android 应用程序，iii) 在大涡模拟 (LES) 中开发独特的数据驱动的次网格尺度 (SGS) 建模，重点关注未知领域被动标量。该项目由流体动力学计划和刺激竞争研究既定计划 (EPSCoR) 共同资助。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。