Honors College Thesis


Comparing Chemical Kinetic Model Reduction Techniques Using pyMARS Public Deposited

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  • Accurately modeling the chemistry of conventional and alternative liquid transportation fuels in combustion technology is vital to predicting important quantities such as the burning rate, heat release, and pollutant emissions. However, incorporating detailed chemical kinetic models into reactive-flow simulations poses a significant challenge due to the associated high computational expense resulting from chemical stiffness and model size. Methods for reducing the size and complexity of kinetic models are needed to incorporate them into detailed multidimensional simulations. While numerous methods have been (and continue to be) developed, few objective comparisons have been made between methods; furthermore, few software implementations of reduction methods are openly available. To address both issues, I have developed the open-source software pyMARS (Python-based Automatic Reduction Software), which implements established reduction methods including directed relation graph (DRG), DRG with error propagation (DRGEP), path flux analysis, and sensitivity analysis. I will introduce and describe the design of pyMARS, and use it to compare reduction methods using a wide range of hydrocarbon kinetic models. By running the different reduction methods with various inputs, I hope to gain insight on which algorithms tend to perform better under certain conditions. Key Words: Chemical kinetic models, Model reduction, Reaction kinetics
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  • This material is based upon work supported by the National Science Foundation under grant ACI-1535065, and the URSA Engage program at Oregon State University.
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