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Diesel combustion and emissions formation is spray and mixing controlled and understanding spray parameters is key to determining the impact of fuel injector operation and nozzle design on combustion and emissions. In this study, both spray visualization and computational fluid dynamics (CFD) modeling were undertaken to investigate key mechanisms for liquid length fluctuations. For the experimental portion of this study a common rail piezoelectric injector was tested in an optically accessible constant volume combustion vessel. Liquid penetration of the spray was determined via processing of images acquired from Mie back scattering under vaporizing conditions by injecting into a charge gas at elevated temperature with a 0% oxygen environment. Tests were undertaken at a gas density of 34.8 kg/m₃, 2000 bar injection pressure, and at ambient temperatures of 900, 1100, and 1300 K.

Many combustion researchers use peak pressure rise rate or ringing intensity to indicate combustion noise in lieu of microphone data or using a combustion noise meter that simulates the attenuation characteristics of the engine structure. In this paper, peak pressure rise rate and ringing intensity are compared to combustion noise using a fully documented algorithm similar to the ones used by combustion noise meters. Data from multiple engines operating under several low-temperature combustion strategies were analyzed. The results suggest that neither peak pressure rise rate nor ringing intensity provides a direct correlation to engine noise over a wide range of operating conditions. Moreover, the estimation of both metrics is often accompanied by the filtering of the pressure data, which changes the absolute value of the results.

Diesel fuels are complex mixtures of thousands of hydrocarbons. Since modeling their combustion characteristics with the inclusion of all hydrocarbon species is not feasible, a hybrid surrogate model approach is used in the present work to represent the physical and chemical properties of three different diesel fuels by using up to 13 and 4 separate hydrocarbon species, respectively. The surrogates are arrived at by matching their distillation profiles and important properties with the real fuel, while the chemistry surrogates are arrived at by using a Group Chemistry Representation (GCR) method wherein the hydrocarbon species in the physical property surrogates are grouped based on their chemical classes, and the chemistry of each class is represented by using up to two hydrocarbon species.