The goal of this project is to develop an automatic reaction mechanism generation scheme using exascale computations to construct high fidelity chemical models for the simulation of RCCI combustion including multi-component fuel chemistry at low-temperature, high pressure, and sooting flames.
This project aims to automatically predict organic synthesis routes for arbitrary specified molecules from feed-stocks and execute these syntheses with minimal human intervention.
This project is on developing and refining chemical kinetic models for candidate biofuels and improving model construction and refinement processes.
Particulate emissions from aviation are a growing area of interest, particularly with regards to the potential contribution to climate change via radiative forcing and the health risks associated with high levels of particular matter. We are interested in determining the degree to which naphthalene contributes to soot emissions in jet engines.
The MIT Mobility of the Future project is a multidisciplinary effort to project how global ground transportation will work in 2050. It is funded by an industrial consortium, and Prof. Green is the Faculty Chair of the effort. Our group is in charge of the technoeconomic parameters around Fuels and Vehicles, and we are collaborating closely with the EPPA team. Other participants in the project are focused on consumer preferences, transportation mode options (particularly in urban areas), and the system dynamics that control the timescale of transitions in the transportation system (e.g. from combustion vehicles to battery-powered vehicles).
Natural gas is an increasingly plentiful and economical fuel. However, widespread adoption in multiple applications requires a strong fundamental understanding of its combustion. While models that describe natural gas combustion at atmospheric conditions exist, we aim to build on these models to accurately simulate combustion at the more extreme conditions present in an internal combustion engine. In our study, Extinction Strain Rate (ESR) prediction is used for validation and mechanism development.
This project is developing robust and efficient computational methods to automatically and systematically search for new and important chemical reactions.
We are developing new processes to recover hydrogen gas from hydrogen sulfide. Hydrogen sulfide is produced on a large scale during the hydrodesulfurization of hydrocarbons.
For most technologically important systems, including combustion, pyrolysis, and atmospheric oxidation of organic compounds, it is very difficult to construct a reliable kinetic model. There are typically hundreds of reaction intermediates, and only a small fraction of the rate parameters are known experimentally. However advances in computational chemistry, numerical methods, and computer hardware have now made it possible to construct and solve kinetic models which can reliably modeling these complicated systems.
For more information, download the slides from Prof. Green’s keynote talk at the 8th International Conference on Chemical Kinetics.
We are interested in understanding the fundamental mechanisms for upgrading Arabian Heavy Crude using supercritical water and developing a model to measure, predict, and quantify the upgrading process.
Several strains of endophytic fungi have been shown to directly convert lignocellulosic biomass to a wide range of compounds, including ketones, ethers, and terpenoids, that could serve as potential biofuels. Understanding the combustion chemistry of these compounds is key to providing fuel target recommendations for synthetic biologists to optimize and scale-up these fungi metabolic pathways.
In the Chemical Dynamics Laboratory (CDL) at MIT, we have designed and built a novel apparatus to measure the temperature- and pressure-dependent chemical kinetics and the product-branching ratios of gas-phase radical reactions that are important in combustion.
The open-source software Reaction Mechanism Generator (RMG), developed by our group in Python and Java, explores networks of reaction mechanisms and discovers the subset of species and reactions that are able to represent the macroscopic properties (e.g. ignition delay) of the complete reaction network. We are extending RMG to account for heteroatoms, such as sulfur and nitrogen, to extend RMG’s range of chemistry.
Catalysts are crucial to transform biomass derived bio-oils into intermediates for liquid transportation fuels. We are studying the reaction kinetics and mechanisms during hydro-deoxygenation of such oxygenates derived from biomass using large scale Density Functional Theory (DFT) calculations. The discovered kinetics and mechanisms allow us to generate leads for experimental catalysis studies and to computationally design new catalyst materials.
Oxyfuel combustion may facilitate carbon capture techniques, leading to a possible carbon-neutral combustion; however, it also affects sulfur chemistry during combustion of sulfur-containing fuels. Sulfur oxides (SOx) have adverse effects on human health and the environment. We aim to understand SOx formation in oxyfuel combustion using computational tools.
JP-10 (exo-tetrahydrodicyclopentadiene) is an important fuel in military applications, particularly in the development of air-breathing propulsion systems. One the main advantages of JP-10 as a fuel is its high volumetric energy density; it also offers low freezing point and good heat transfer properties. We use RMG to generate a detailed combustion model of the complex chemical kinetics of JP-10.
Integrated gasification combined cycle with carbon capture and sequestration (IGCC/CSS) is one of the most promising routes for capturing CO2 in a plant. However, the current solvent-based CO2 capture process needs to operate at low temperature, significantly decreasing the overall energy efficiency of the plant since the gas stream must be cooled, captured, then reheated. We are working on finding a solid material to capture CO2 at the preexisting process temperatures.
Homogeneous charge compression ignition (HCCI) engines have the potential for high efficiencies and low pollutant emissions. By comparison with spark ignition (SI) engines, for example, HCCI engines can yield a 15-20% increase in fuel economy while emitting lower levels of oxides of nitrogen (NOx). Despite these advantages, however, a number of technical issues must be resolved before HCCI engines become mainstream. Many of the complications stem from the fact that HCCI engines are more sensitive to the details of the combustion chemistry than SI and diesel engines. Hence, a solid understanding of the physical and chemical processes is required to develop practical, efficient, and robust engines.