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This study summarizes the peer-reviewed literature regarding the use of raw pyrolysis liquids (PLs) created from woody biomass as fuels for compression-ignition (CI) engines. First, a brief overview is presented of fast pyrolysis and the potential advantages of PLs as fuels for CI engines. Second, a discussion of the general composition and properties of PLs relative to conventional, petroleum-derived diesel fuels is provided, with emphasis on the differences that are most likely to affect PL performance in CI-engine applications. Next, a synopsis is given of the peer-reviewed literature describing experimental studies of CI engines operated using neat PLs and PLs combined in various ways with other fuels. This literature conclusively indicates that raw PLs and PL blends cannot be used as “drop-in replacements” for diesel fuel in CI engines, which is reflected in part by none of the cited studies reporting successful operation on PL fuels for more than twelve consecutive hours.

Computational fluid dynamic (CFD) simulations that include realistic combustion/emissions chemistry hold the promise of significantly shortening the development time for advanced high-efficiency, low-emission engines. However, significant challenges must be overcome to realize this potential. This paper discusses these challenges in the context of diesel combustion and outlines a technical program based on the use of surrogate fuels that sufficiently emulate the chemical complexity inherent in conventional diesel fuel.

Ducted Fuel Injection (DFI) engines have emerged as a promising technology in the pursuit of a clean and efficient combustion process. This article aims at elucidating the effect of piston geometry on the engine performance and emissions of a metal DFI engine. Three different types of pistons were investigated and the main piston design features including the piston bowl diameter, piston bowl slope angle, duct angle and the injection nozzle position were examined. To achieve the target, computational fluid dynamics (CFD) simulations were conducted coupled to a reduced chemical kinetics mechanism. Extensive validations were performed against the measured data from a conventional diesel engine. To calibrate the soot model, genetic algorithm and machine learning methods were utilized. The simulation results highlight the pivotal role played by piston bowl diameter and fuel injection angle in controlling soot emissions of a DFI engine.