An ionic liquid (IL) trihexyltetradecylphosphonium bis(2-ethylhexyl) phosphate has been investigated as a potential antiwear lubricant additive. Unlike most other ILs that have very low solubility in nonpolar fluids, this IL is fully miscible with various hydrocarbon oils. In addition, it is thermally stable up to 347 °C, showed no corrosive attack to cast iron in an ambient environment, and has excellent wettability on solid surfaces (e.g., contact angle on cast iron <8°). Most importantly, this phosphonium-based IL has demonstrated effective antiscuffing and antiwear characteristics when blended with lubricating oils. For example, a 5 wt % addition into a synthetic base oil eliminated the scuffing failure experienced in neat oil and, as a result, reduced the friction coefficient by 60% and the wear rate by 3 orders of magnitude. A synergistic effect on wear protection was observed with the current antiwear additive when added into a fully formulated engine oil. Nanostructure examination and composition analysis revealed a tribo-boundary film and subsurface plastic deformation zone for the metallic surface lubricated by the IL-containing lubricants. This protective boundary film is believed to be responsible for the IL's antiscuffing and antiwear functionality.

In this study, a novel approach was developed to formulate surrogate fuels having characteristics that are representative of diesel fuels produced from real-world refinery streams. Because diesel fuels typically consist of hundreds of compounds, it is difficult to conclusively determine the effects of fuel composition on combustion properties. Surrogate fuels, being simpler representations of these practical fuels, are of interest because they can provide a better understanding of fundamental fuel-composition and property effects on combustion and emissions-formation processes in internal-combustion engines. In addition, the application of surrogate fuels in numerical simulations with accurate vaporization, mixing, and combustion models could revolutionize future engine designs by enabling computational optimization for evolving real fuels. Dependable computational design would not only improve engine function, it would do so at significant cost savings relative to current optimization strategies that rely on physical testing of hardware prototypes. The approach in this study utilized the stateof-the-art techniques of 13 C and 1 H nuclear magnetic resonance spectroscopy and the advanced distillation curve to characterize fuel composition and volatility, respectively. The ignition quality was quantified by the derived cetane number. Two wellcharacterized, ultra-low-sulfur #2 diesel reference fuels produced from refinery streams were used as target fuels: a 2007 emissions certification fuel and a Coordinating Research Council (CRC) Fuels for Advanced Combustion Engines (FACE) diesel fuel. A surrogate was created for each target fuel by blending eight pure compounds. The known carbon bond types within the pure compounds, as well as models for the ignition qualities and volatilities of their mixtures, were used in a multiproperty regression algorithm to determine optimal surrogate formulations. The predicted and measured surrogate-fuel properties were quantitatively compared to the measured target-fuel properties, and good agreement was found.

No abstract

The fuels used in internal-combustion engines are complex mixtures of a multitude of different types of hydrocarbon species. Attempting numerical simulations of combustion of real fuels with all of the hydrocarbon species included is highly unrealistic. Thus, a surrogate model approach is generally adopted, which involves choosing a few representative hydrocarbon species whose overall behavior mimics the characteristics of the target fuel. The present study proposes surrogate models for the nine fuels for advanced combustion engines (FACE) that have been developed for studying low-emission, high-efficiency advanced diesel engine concepts. The surrogate compositions for the fuels are arrived at by simulating their distillation profiles to within a maximum absolute error of ∼4% using a discrete multi-component (DMC) fuel model that has been incorporated in the multi-dimensional computational fluid dynamics (CFD) code, KIVA-ERC-CHEMKIN. The simulated surrogate compositions cover the range and measured concentrations of the various hydrocarbon classes present in the fuels. The fidelity of the surrogate fuel models is judged on the basis of matching their specific gravity, lower heating value, hydrogen/carbon (H/C) ratio, cetane number, and cetane index with the measured data for all nine FACE fuels.