Date of Award

Fall 2023

Document Type

Open Access Thesis


Mechanical Engineering

First Advisor

Sang Hee Won


The chemical kinetic model is one of key sub-models for computational fluid dynamics (CFD) simulation for optimizing and designing various combustors/engines. Detailed chemical kinetic models can consist of hundreds of species and thousands of elementary reaction steps. Considering the complexity in these detailed chemical kinetic models, it is important to quantify and minimize the uncertainties in the kinetic parameters. The uncertainties in the kinetic models can then be compared to uncertainties tied to experimental measurements. This approach has been frequently applied for relatively simple combustion parameters (e.g., laminar flame speed, ignition delay time), however it has not been well utilized in flow reactor experiments due to their complexity in interpreting species time-history measurements, as well as technical challenges associated with ambiguity of initial condition. The goal was to develop an approach that can incorporate flow reactor experiments in the optimization of kinetic parameters for key reactions, so that the prediction fidelity of a chemical kinetic model can be improved. An approach to define the sensitivity coefficients based on kinetic parameter perturbation and species time-histories has been developed to identify key reactions pertinent to the measured species time-history profiles. To demonstrate the applicability of this approach, it was first validated with the relatively simple chemical kinetic system for methanol oxidation. The validated method was further employed to a complicated low-temperature oxidation of n-decane to evaluate the effectiveness of constraining the kinetic uncertainties in low-temperature fuel oxidation reactions. The results suggest that the sensitivity analysis method developed can accurately determine the key reactions based on experimentally determined target species and flow reactor measurements can be used to constrain the rate parameters.


© 2024, Ana Victoria Kock