Kinetic Models of Combustion
ChE Assistant Professor Richard West was awarded a $260K NSF grant for "Resolving discrepancies in detailed kinetic models of combustion via automated transition state theory calculations".
Abstract Source: NSF
In order to develop cleaner and more efficient engines, and to make better use of both petroleum-derived and alternative fuels, engineers need a better understanding of combustion. Computer models that can accurately predict how different fuels burn in different conditions will help us develop new engines and new fuels. The goal of this research is predictive, detailed kinetic modeling of combustion, in which many thousands of relevant chemical reactions are calculated accurately. As a step towards this goal, the research will detect and correct major discrepancies in the reaction rates currently used in detailed kinetic models, and in so doing create a database of reaction transition states, as well as algorithms to predict them. The proposed work will use several novel techniques to identify and correct mistakes, uncertainties, and approximations currently hidden throughout detailed kinetic models of combustion. The project will remove the human bottle-neck in performing quantum mechanical calculations of chemical kinetics, enabling effective use of High Performance Computing for accurate calculation of reaction rate expressions in the future. This high-throughput calculation of reaction kinetics has been identified as a "basic research need for clean and efficient combustion of 21st century transportation fuels", and by the Combustion Energy Frontier Research Center as an "important grand challenge".
This three year project proposes to: (1) automate the performance of quantum mechanics (QM) based Transition State Theory (TST) calculations for combustion-relevant reactions in the open-source, kinetic model building software Reaction Mechanism Generator (RMG); (2) use a newly developed kinetic model importer tool to identify every elementary reaction published in recent combustion models; (3) use the automated methods from step one to calculate the rates of reactions from step two, creating a public database of reactions, rates from the literature, rates calculated by TST, QM calculation results, and transition state geometries; and (4) identify discrepancies between reaction rates in published models and those calculated via TST, and quantify the effect these discrepancies have on the model predictions. Additionally, we will develop a related suite of Python-based teaching materials suitable for undergraduate chemical engineering curriculum, to introduce combustion science to a wider audience. A PhD student and several undergraduate students will gain valuable research experience and training whilst working on this project.