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

小颗粒的沉积，也被称为颗粒污染，是许多传统和新兴行业（如石化行业和现代地热系统）面临的根本挑战。更具体地说，颗粒污染大大降低了效率，阻碍了这些系统的优化和充分利用。一些研究表明，颗粒物污染对全球人为排放的二氧化碳有显著贡献。迄今为止，人们对这一现象缺乏基本的理解。因此，该项目的首要目标是识别和理解沉积过程的主要潜在机制，利用最先进的技术对中/高雷诺数的湍流壁面分散流动进行高保真的数值模拟，因为目前还不可能进行详细的实验测量。该项目还包括相关教育活动，通过解决训练有素的高中教师短缺的问题，帮助提高K-12学生接受大学教育的准备程度。本研究旨在为解决目前对高雷诺数小颗粒湍流分散流动特性缺乏基本知识的问题迈出基础性的一步。提出的研究利用先进而准确的欧拉-拉格朗日方法，即通过单向耦合的点粒子直接数值模拟（PP-DNS），以获得对高湍流壁边界流动中小颗粒分散和沉积的复杂动力学的基本见解，并利用这些知识来指导第一代可靠且计算负担得起的建模框架的开发和验证。重点将放在基于点粒子的欧拉-拉格朗日框架上，该框架使用先进的湍流建模策略，即混合非定常雷诺-平均-纳维-斯托克斯/大涡模拟方法来捕捉流体相的复杂动力学。由此产生的框架为研究和理解高雷诺数湍流中的小颗粒传输和沉积动力学提供了一个独特的工具，否则这是不可能的，但对于热流体系统的优化设计至关重要。此外，目前的研究将涵盖颗粒的尺寸范围，包括在大多数新兴技术中最负责颗粒污染的典型尺寸范围。最后，关于小颗粒分散和沉积的知识有望成为结合流体动力学和病毒学的桥梁，以帮助了解COVID-19等致命病毒的传播。该奖项反映了美国国家科学基金会的法定使命，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。

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.