CAREER: A scalable multiscale modeling framework to explore soot formation in reacting flows

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。烟尘是不完全燃烧过程中形成的含碳颗粒物，对公众健康和福利有重大不利影响，是气候变化中的重要强迫因子。为了准确地理解和减轻烟尘的影响，我们需要了解与烟尘形成和生长相关的所有过程-从原子水平（又名原子尺度）的烟尘开始到设备水平（又名设备尺度）的真实燃烧系统中的成熟。不幸的是，这种详细的多尺度建模仍然是一项艰巨的任务。这导致在预测和控制烟尘排放及其对气候和公众健康的影响方面存在巨大的知识差距和重大的不确定性。该项目将创建一个模型框架，将燃烧系统的小规模原子建模与大规模工程建模相结合。该项目将能够更好地预测燃烧产生的烟尘排放，从而实现更清洁的燃烧系统。该项目还将在原子水平上详细了解烟尘的性质，从而更好地了解烟尘对地球的影响。这样做，该项目将服务于NSF的使命，以促进科学的进步和促进国家的健康，繁荣和福利。该项目所做技术工作的直接影响是双重的。首先，它将导致更完整的理解煤烟的物理学和详细的洞察煤烟在真实的世界中的演变。第二，多物理场和多尺度模拟框架的发展将开辟一个新的视野，在燃烧碳烟的理论探索。在这个项目中开发的多尺度桥接策略可以适用于其他需要多尺度和多物理场探索的问题。沿着技术发展，项目还将与艺术博物馆合作开展外展活动，鼓励社区就煤烟过程的复杂性、煤烟对社会的影响、环境政策和环境正义等问题进行基于事实和数据的讨论。将有高中生参与的活动，这将有助于促进科学计算，并鼓励学生从事STEM研究。 该项目将通过利用新的计算方法在不同尺度上连接不同的物理学领域。在原子尺度上，该项目将使用分子动力学等技术来揭示烟尘开始的物理和化学过程。这些模型的结果沿着实际烟尘颗粒的高分辨率电子显微镜图像将使用机器学习技术进行分析，以创建一个新的随机烟尘建模框架。这种碳烟建模框架将保留从原子尺度模型中获得的详细知识，同时在连续尺度模拟中有效地操作，例如在燃烧装置的反应计算流体动力学（CFD）模拟中。随机烟尘模型将结合使用一种新的混合欧拉-拉格朗日方法详细和准确的湍流化学和辐射模型。这种混合欧拉-拉格朗日方法将提供一个独特的混合数据任务并行性和自动负载平衡，从而为多尺度、多物理场反应流求解器提供一个高效、可扩展的框架，用于详细探索烟尘过程。该奖项反映了NSF的法定使命，并被认为值得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来支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Soot, a carbonaceous particulate matter formed during incomplete combustion, has significant adverse effects on public health and welfare and is an important forcing agent in climate change. To accurately understand and mitigate the effects of soot, we need to understand all the processes related to soot formation and growth - from the inception of soot at an atomic level (aka atomic scale) to its maturation in real-world combustion systems at the device level (aka device scale). Unfortunately, such detailed multiscale modeling remains a daunting task. This leads to a significant gap of knowledge and significant uncertainty in the prediction and control of the emission of soot and its effects on the climate and public health. This project will create a framework of models to combine small-scale atomistic modeling with larger-scale engineering modeling of combustion systems. The project will enable a better predictive capability for modeling soot emission from combustion which will lead to cleaner combustion systems. The project will also provide a detailed insight into the properties of soot at an atomic level enabling a better understanding of the effects of soot on the planet. In so doing, the project will serve NSF's mission to promote the progress of science and to advance the national health, prosperity, and welfare. The direct impacts of the technical work done in this project are two-fold. First, it will lead to a more complete understanding of the physics of soot inception and a detailed insight into the evolution of soot in the real world. Second, the developed multiphysics and multiscale modeling framework will open up a new horizon in the theoretical exploration of soot in combustion. The multiscale bridging strategies developed in this project can be adapted to other problems that require multiscale and multiphysics explorations. Along with the technical development, the project will also conduct outreach activities in collaboration with an art museum to encourage the community in fact- and data-based discourse on issues such as complexities of soot processes, the effect of soot on the society, environmental policies, and environmental justice, etc. Additionally, there will be activities involving high school students that will help promote scientific computing and encourage students to pursue STEM research. This project will bridge different domains of physics across different scales by utilizing novel computational approaches. At the atomic scale, this project will use techniques such as molecular dynamics to unravel the physics and chemistry of the soot inception. The results from these models along with high-resolution electron microscopic images of actual soot particles will be analyzed using machine learning techniques to create a novel stochastic soot modeling framework. This soot modeling framework will retain the detailed knowledge gained from atomic-scale models while efficiently operating at continuum-scale simulations such as in reacting computational fluid dynamics (CFD) simulations of combustion devices. The stochastic soot model will be combined with detailed and accurate turbulent chemistry and radiation models using a novel hybrid Eulerian-Lagrangian approach. This hybrid Eulerian-Lagrangian approach will provide a unique hybrid data-task parallelism and automatic load balancing leading to an efficient and scalable framework for multiscale, multiphysics reacting flow solver for detailed exploration of soot processes.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.