Date of Award

10-1-2021

Publication Type

Thesis

Degree Name

M.A.Sc.

Department

Mechanical, Automotive, and Materials Engineering

First Advisor

N.Eaves

Second Advisor

M. Zheng

Third Advisor

J. Tjong

Keywords

Gas turbines, Monte carlo, Partially stirred reactor network, PaSR Network, Reactor network, SRM network

Rights

info:eu-repo/semantics/openAccess

Abstract

Due to their negative health implications, the reduction of emissions from combustion processes is imperative. In order to reduce emissions, predicting and understanding their thermo-chemical formation in different types of combustion systems are necessary. Modern combustion systems, such as internal combustion engines and gas turbine engines, require innovative numerical methodologies that aid in the understanding of underlying chemical kinetics and mixing time-scales, all while being computationally inexpensive. However, current inclusive methodologies for predicting emissions and ignition events rely on computationally expensive computational fluid dynamics. The main objective of this thesis is to present a newly developed stochastic reactor model network that employs detailed physics to explain thermo-chemical phenomena in continuously flowing devices. The numerical methodology behind the model is explored and the SRM network is compared to existing 0-D perfectly stirred reactor network model. The results of the SRM network match the PSR network within 0.1% error. Finally, the ability of stochastic reactor model networks to predict rare events is examined. It is seen that at an epsilon of 0.1 the model predicts rare events.

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