Date of Award
Summer 2019
Document Type
Open Access Thesis
Department
Mechanical Engineering
First Advisor
Sang Hee Won
Abstract
Chemical kinetic characteristics of real fuels exhibit high-dimensional complexity due to the excessive number of molecules and molecular classes. As a method of projecting this high-dimensional complexity, which is pertinent to real fuels, to the low-dimensional description, either a surrogate approach or detailed experiments have been utilized, can guide the construction of chemical kinetic models for real fuel. Although the validity of the surrogate approach has been extensively demonstrated by a wide range of canonical experiments for whole fuel/air mixtures, the use of empirical fuel property indicators still worries whether or not the surrogate mixture truly captures the more complex chemical behaviors coupled with fuel physical properties (e.g. distillation). Particularly, a recent experiment has shown that the near-limit combustion behaviors (e.g. lean blow off in gas turbine combustor) are strongly governed by the chemical characteristics of the front (light) end in fuel boiling characteristics. Thus, it is of importance to develop an alternative approach, which can fundamentally characterize fuel chemical properties along the fuel distillation curve. Using Nuclear Magnetic Resonance (NMR) spectra, it is possible to quantify the specific chemical functional groups present in a sample. To demonstrate the applicability of chemical functional group approach in conjunction with NMR spectra interpretation, a surrogate formulation approach based on NMR spectra is demonstrated by using a 12-component model fuel and a known fuel and comparing the synthetic NMR spectra between target fuels and surrogate mixtures.
Rights
© 2019, Stuart Nates
Recommended Citation
Nates, S.(2019). Surrogate Formulation Based on Chemical Functional Group Analysis. (Master's thesis). Retrieved from https://scholarcommons.sc.edu/etd/5501