The Department of Energy’s (DOE) Co-Optimization of Fuels & Engines (Co-Optima) initiative recently highlighted the work that Lawrence Livermore National Laboratory (LLNL) scientists have performed on models of high-performance fuels to see how they would perform in advanced internal combustion engines.
The Co-Optima initiative aims to simultaneously transform transportation fuels and vehicles to maximize performance and energy efficiency, minimize environmental impact and accelerate widespread adoption of innovative combustion strategies. The research and development collaboration between DOE, nine national laboratories and industry is a first-of-its-kind effort to combine biofuels and combustion R&D, building on decades of advances in both fuels and engines.
In the 2017 Co-Optima year in review, LLNL’s work in combustion kinetics models were featured for their role in predicting how molecular structure affects research octane numbers and octane sensitivity, the fuel properties with the greatest impact on engine efficiency for boosted spark ignition engines.
Co-Optima research reveals why using exhaust gas recirculation to prevent knock loses its effectiveness under boosted conditions in spark ignition engines. Engine knock limits the efficiency of boosted spark ignition (SI) engines. Exhaust gas recirculation (EGR) is used to attenuate knock in SI engines. LLNL kinetic models were used to understand the influence of EGR on the chemical reactions that lead to knock at different engine operating conditions.
Accurately predicting the octane blending behavior of blendstocks in gasoline from fundamental kinetic principles is essential for conducting accurate simulations of engine performance, yet this ability has remained an elusive challenge for half a century. LLNL kinetic models were developed and used to predict fuel octane properties when high-performance fuels were blended into a base gasoline. These predictions agree quite well with measured octane properties at various blend levels.
Advanced compression ignition (ACI) engines have the potential to provide high efficiency and lower emissions. However, the reactivity in the engine is difficult to control. The LLNL Combustion Simulation team developed a sensitivity metric and an automated computer search using LLNL kinetic models to identify fuel blendstocks that have the potential to control the reactivity in ACI engines.
For the first time, LLNL developed a detailed chemical kinetic model that represents all the major chemical classes in diesel fuel.