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A Foundational Step Towards the Development of a Predictive Framework for Particle Deposition in Wall-Bounded Turbulent Flows

开发壁面湍流中颗粒沉积预测框架的基础性步骤

基本信息

批准号：
2219446
负责人：
Amirfarhang Mehdizadeh
金额：
$ 33.37万
依托单位：
University of Missouri-Kansas City
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-05-01 至 2025-04-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2219446&HistoricalAwards=false
关键词：
Foundational Step Towards Development Predictive

项目摘要

Deposition of small particles, also denoted as particulate fouling, is a fundamental challenge in many traditional and emerging industries such as petrochemical industries and modern geothermal systems. More specifically, particulate fouling significantly reduces the efficiency and prevents optimization and full utilization of such systems. Several studies have shown that particulate fouling significantly contributes to global CO2 anthropogenic emissions. To date, there is a lack of fundamental understanding of this phenomenon. Therefore, the overriding aim of this project is to identify and understand major underlying mechanisms of the deposition process using high-fidelity numerical simulations using state-of-the-art techniques for turbulent wall-bounded dispersed flows at moderate/high Reynolds numbers, as detailed experimental measurements are not currently possible. The project also includes relevant educational activities that help increase the readiness of K-12 students for college education by addressing the shortage of adequately trained high school teachers. The research aims to take a foundational step to address the existing lack of fundamental knowledge on characteristics of turbulent dispersed flows of small particles at high Reynolds numbers. The proposed research leverages an advanced and accurate Euler-Lagrangian approach, i.e., Point-Particle Direct Numerical Simulation (PP-DNS) via one-way coupling, to gain fundamental insights into the complex dynamics of small particles dispersion and deposition inside highly turbulent wall-bounded flows and to use this knowledge to guide the development and validation of first generation of reliable and computationally affordable modelling framework. Particular focus will be on a Point-Particle based Euler-Lagrangian framework that uses an advanced turbulence modeling strategy i.e., hybrid Unsteady Reynolds-averaged-Navier-Stokes/Large Eddy Simulation methodology to capture complex dynamics of the fluid phase. The resulting framework provides a unique tool to study and understand small particle transport and deposition dynamics in high Reynolds number turbulent flows that otherwise would not be possible and yet, essential for an optimized design of Thermo-Fluid systems. Further, the current study will cover particles in a size range that encompasses typical size range most responsible for particulate fouling in majority of emerging technologies. Finally, the knowledge on dispersion and deposition of small particles is expected to serve as a bridge to combine fluid dynamics and virology to help understand transmission of deadly viruses such as COVID-19.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.

小颗粒的沉积，也被称为颗粒结垢，是许多传统和新兴行业（如石油化工行业和现代地热系统）的根本挑战。更具体地，颗粒结垢显著地降低了效率，并且妨碍了这种系统的优化和充分利用。一些研究表明，颗粒污染对全球人为CO2排放有很大贡献。迄今为止，人们对这一现象缺乏基本的了解。因此，本项目的首要目标是识别和理解的沉积过程中使用高保真数值模拟使用国家的最先进的技术在中等/高雷诺数的湍流壁边界分散流的主要潜在机制，详细的实验测量是目前不可能的。该项目还包括相关的教育活动，通过解决训练有素的高中教师短缺问题，帮助K-12学生为接受大学教育做好准备。该研究的目的是采取一个基础性的步骤，以解决现有的缺乏基本知识的小颗粒在高雷诺数的湍流分散流的特性。拟议的研究利用了先进和准确的欧拉-拉格朗日方法，即，通过单向耦合进行点粒子直接数值模拟（PP-DNS），以获得对高度湍流壁面流动内部小颗粒分散和沉积的复杂动力学的基本见解，并利用这些知识指导第一代可靠且计算上负担得起的建模框架的开发和验证。特别关注基于点粒子的欧拉-拉格朗日框架，该框架使用先进的湍流建模策略，即，混合非定常雷诺平均Navier-Stokes/大涡模拟方法来捕捉流体相的复杂动力学。由此产生的框架提供了一个独特的工具，研究和理解小颗粒的运输和沉积动力学在高雷诺数湍流，否则将是不可能的，但，热流体系统的优化设计必不可少的。此外，目前的研究将涵盖的颗粒尺寸范围，包括典型的尺寸范围最负责的微粒污垢在大多数新兴技术。最后，关于小颗粒的分散和沉积的知识有望成为联合收割机流体动力学和病毒学的桥梁，以帮助了解致命病毒（如COVID-19）的传播。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。