Date of Award
7-7-2023
Publication Type
Dissertation
Degree Name
Ph.D.
Department
Mechanical, Automotive, and Materials Engineering
Keywords
Carbon Nanopartilce;CFD;Combustion;Nanotechnology;Particle formation;Thermofluid
Supervisor
Nickolas Eaves
Rights
info:eu-repo/semantics/embargoedAccess
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
Particles are essential components in many natural and human-made processes. Their formation, behavior, and impact on human life and the environment are critical areas of research. One of the most crucial and prevalent examples are carbon nanoparticles, which are typically 10-300 nm in diameter and produced in many processes involving the combustion of hydrocarbons. Carbon nanoparticles are of significant interest due to both their harmful impacts on human health and the environment and their values as nanomaterials. This PhD dissertation is composed of three research studies that aim to develop and implement numerical approaches to understand the phenomena and enhance the accuracy of carbon nanoparticle formation modeling. The first study investigates the effects of hydrogen and nitrogen dilution on soot particle formation in diffusion flames. The advanced CoFlame code is used to incorporate more physical attributes into soot modeling and discuss the detailed physical and chemical effects of H2/N2 dilution on the soot formation processes. Although hydrogen may chemically boost the production of precursors, due to physical dilution, the formation and clustering of Polycyclic Aromatic Hydrocarbons (PAHs) are delayed. Results are practically useful to emission reductions from combustion sources, as well as carbon black synthesis. The second study presents a novel clustering sub-model called the fully reversible PAH clustering model (FRPC). The FRPC model shows substantial improvement in prediction of clustering behavior of PAHs, compared to common efficiency-based models used widely in the literature. The third part applies the tools used in the first two studies to implement a two-step modeling method that for the first time can predict the internal structure of carbon black nanoparticles in a flow reactor. The investigation of the reactor pressure and temperature reveals that higher temperature yields more fractal shape while higher pressure produces more compact aggregates. Overall, this dissertation provides an advanced understanding of carbon nanoparticle modeling and practical measures that can be effectively applied to industry.
Recommended Citation
Khabazipur, Arash, "Improving Carbon Nanoparticle Formation Modeling by Incorporating Physical Phenomena through Advanced Simulation Approaches" (2023). Electronic Theses and Dissertations. 9340.
https://scholar.uwindsor.ca/etd/9340