The U.S. Energy Department (DOE) has announced up to $7 million for eight universities to accelerate the introduction of affordable, scalable and sustainable high-performance fuels for use in high-efficiency, low-emission engines.
Under the Co-Optimization of Fuels and Engines (Co-Optima) initiative, the DOE’s Bioenergy Technologies Office and Vehicle Technologies Office are collaborating to maximize energy savings and on-road vehicle performance, while dramatically reducing transportation-related petroleum consumption and harmful emissions.
The DOE selected eight universities under the Co-Optima funding opportunity:
- Cornell University, Ithaca, N.Y. – Cornell University, in partnership with the University of California, San Diego, will examine the combustion characteristics of several diesel/biofuel blends. This will provide the information needed to understand how these blends burn compared with traditional petroleum-based fuels to help design cleaner, more efficient combustion engines.
- The University of Michigan, Ann Arbor, Mich. – The University of Michigan will develop an engine combustion model using software that is capable of simulating a range of different parameters that could occur in a combustion chamber, such as combustion duration, flame speed and pressure development. The system will be designed to maximize ease of use, reliability and accuracy, as well as to reduce the expense of a full engine cycle simulation by 80% relative to the current option. The data gained from the model can help maximize alternative fuel performance and will be used to guide engine manufacturers.
- The University of Michigan-Dearborn, Dearborn, Mich. – The University of Michigan-Dearborn, with partner Oakland University, will use a miniature ignition screening rapid compression machine, an experimental apparatus used to study ignition properties, to gain a better understanding of the ignition and combustion characteristics (e.g., ignition delay) of alternative fuels. This novel method streamlines the evaluation of auto-ignition performance, without the need for more extensive and costly engine testing.
- The University of Alabama, Tuscaloosa, Ala. – The University of Alabama will examine the combustion properties of biofuels and blends using advanced diagnostic techniques under realistic advanced compression ignition (ACI) engine conditions. ACI engines can deliver both high efficiencies and low emissions. The goal is to create a model to predict combustion properties of various fuel blends to help optimize its use in ACI engines.
- Louisiana State University, Baton Rouge, La. – Louisiana State University, along with partners Texas A&M and the University of Connecticut, will develop a method that efficiently characterizes alternative fuel candidates, along with associated models and metrics, for predicted engine performance.
- The Massachusetts Institute of Technology, Cambridge, Mass. – The Massachusetts Institute of Technology, in partnership with the University of Central Florida, will develop detailed kinetic models for several biofuels using an advanced computational approach. The project will construct computer models to predict the combustion chemistry of proposed biofuels, which can then be used to determine which of the proposed fuels will have high performance in advanced engines.
- Yale University, New Haven, Conn. – Yale University, along with Pennsylvania State University, will measure sooting tendencies of various biofuels and develop emission indices relevant to real engines. This will enable the selection of biomass-derived fuels that minimize soot emissions in next-generation engines.
- The University of Central Florida, Orlando, Fla. – The University of Central Florida will generate fuel characterization data by measuring and evaluating important performance metrics such as fuel spray atomization, flame topology, volatility, viscosity, soot/coking and compatibility for prioritized fuels. The research will characterize and predict combustion properties of biomass‐based, low-emission fuels and blends in engine‐relevant conditions.
