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Mixing controlled compression ignition, i.e., diesel engines are efficient and are likely to continue to be the primary means for movement of goods for many years. Low-net-carbon biofuels have the potential to significantly reduce the carbon footprint of diesel combustion and could have advantageous properties for combustion, such as high cetane number and reduced engine-out particle and NOx emissions. We developed a list of over 400 potential biomass-derived diesel blendstocks and populated a database with the properties and characteristics of these materials. Fuel properties were determined by measurement, model prediction, or literature review. Screening criteria were developed to determine if a blendstock met the basic requirements for handling in the diesel distribution system and use as a blend with conventional diesel. Criteria included cetane number ≥40, flashpoint ≥52°C, and boiling point or T90 ≤338°C.

This study presents estimates for measurement uncertainties for a Homogenous Charge Compression Ignition (HCCI)/Low-Temperature Gasoline Combustion (LTGC) engine testing facility. A previously presented framework for quantifying those uncertainties developed uncertainty estimates based on the transducers manufacturers’ published tolerances. The present work utilizes the framework with improved uncertainty estimates in order to more accurately represent the actual uncertainties of the data acquired in the HCCI/LTGC laboratory, which ultimately results in a reduction in the uncertainty from 30 to less than 1 kPa during the intake and exhaust strokes. Details of laboratory calibration techniques and commissioning runs are used to constrain the sensitivities of the transducers relative to manufacturer supplied values.

In this paper, a framework for estimating experimental measurement uncertainties for a Homogenous Charge Compression Ignition (HCCI)/Low-Temperature Gasoline Combustion (LTGC) engine testing facility is presented. Detailed uncertainty quantification is first carried out for the measurement of the in-cylinder pressure, whose variations during the cycle provide most of the information for performance evaluation. Standard uncertainties of other measured quantities, such as the engine geometry and speed, the air and fuel flow rate and the intake/exhaust dry molar fractions are also estimated. Propagating those uncertainties using a Monte Carlo simulation and Bayesian inference methods then allows for estimation of uncertainties of the mass-average temperature and composition at IVC and throughout the cycle; and also of the engine performances such as gross Integrated Mean Effective Pressure, Heat Release and Ringing Intensity.

Injection spray dynamics is known to be of great importance when modeling turbulent multi-phase flows in diesel engines. Two key aspects of spray dynamics are liquid breakup and penetration, both of which are affected by the initial sizes of the injected droplets. In the current study, injection of liquid n-heptane is characterized with initial droplet sizes with diameters on the order of 0.10 - 0.25 nozzle diameters. This is done for a Reynolds Averaged Navier-Stokes (RANS) RNG k-ε turbulence model with a minimum grid size of 125 μm and for a Large Eddy Simulations (LES) viscosity turbulence model with a minimum grid size of 62.5 μm. The results of both turbulence models are validated against non-reacting experimental data from the Engine Combustion Network (ECN). The results show that the injected droplet sizes have a significant impact on both liquid and vapor penetration lengths.