Ph.D. Thesis Defense

Shubham Karpe

(Faculty Advisor: Professor Suresh Menon)

"Modeling Soot Formation and Its Sensitivities in Reacting Flows"

Wednesday, July 22

1:00 p.m.

Montgomery Knight 317

Abstract:

Stringent government regulations on non-volatile particulate matter (nVPM) emissions have necessitated the need for predictive models of soot formation in practical combustion systems. Accurately modeling soot remains a significant challenge because it involves strongly coupled physical and chemical processes, including nucleation, surface growth, oxidation, condensation, coagulation, and particle evolution, that interact across a wide range of spatial and temporal scales with turbulence, gas-phase chemistry, differential diffusion, and radiative heat transfer. This dissertation develops a systematic computational framework for modeling soot formation and quantifying its sensitivity across a hierarchy of reacting flows, ranging from canonical flames to laboratory-scale combustors. The work begins with the development and assessment of soot modeling strategies in zero- and one-dimensional premixed and non-premixed laminar flames, identifying the dominant sources of uncertainty associated with gas-phase chemistry, soot nucleation, surface growth, and particle evolution. A reduced monodisperse soot model is then integrated into the in-house large-eddy simulation (LES) solver LESLIE for multidimensional reacting flows. Two-dimensional mixing layers are first used to assess semi-empirical and PAH-based soot models. A novel reaction-rate closure for soot transport relying on a multiscale subgrid Linear Eddy Model (LEM) is proposed and assessed in LES of canonical turbulent premixed and non-premixed ethylene flames. LES-soot methodology is further applied to investigate soot formation in a laboratory-scale Rich-Quench-Lean (RQL) combustor under two operating conditions. Finally, a computationally efficient two-stage methodology combining LES-time averaged data with a Chemical Reactor Network (CRN) is developed to examine the influence of detailed chemical kinetics and soot subprocess uncertainties on its predictions. Together, these studies establish a comprehensive computational framework and provide new insights into predictive soot modeling for practical combustion systems.

Committee:
Dr. Suresh Menon (advisor), School of Aerospace Engineering
Dr. Adam Steinberg, School of Aerospace Engineering
Dr. Joseph Oefelein, School of Aerospace Engineering
Dr. Wenting Sun, School of Aerospace Engineering
Dr. Michael Mueller, Princeton University