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

Marquette University, Milwaukee WI

Investigators

Abstract

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.