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The effects of imaging system resolution and laser sheet thickness on the measurement of the Batchelor scale were investigated in a single-cylinder optical engine. The Batchelor scale was determined by fitting a model spectrum to the dissipation spectrum that was obtained from fuel tracer planar laser-induced fluorescence (PLIF) images of the in-cylinder scalar field. The imaging system resolution was quantified by measuring the step-response function; the scanning knife edge technique was used to measure the 10-90% clip width of the laser sheet. In these experiments, the spatial resolution varied from a native resolution of 32.0 μm to 137.4 μm, and the laser sheet thickness ranged from 108 μm to 707 μm. Thus, the overall resolution of the imaging system was made to vary by approximately a factor of four in the in-plane dimension and a factor of six in the out-of-plane dimension.

The current work investigates the in-cylinder mixing of a fluorescent tracer species inducted into the engine through a small-diameter tube mounted along the inner port wall and the remaining inlet stream in a small-bore utility engine. Planar laser-induced fluorescence (PLIF) measurements were acquired on a single plane, parallel to and approximately 4 mm below the cylinder head deck, throughout the intake and compression strokes. The data were analyzed to qualitatively and quantitatively describe the evolution of the mixture stratification. The highest degree of stratification in the mean field was observed at a timing of 90 crank angle (CA) degrees after top dead center (aTDC) of the intake stroke, which corresponds closely to the point of maximum intake valve lift (105 CA degrees aTDC).

An analytical mechanics model was employed to predict the delamination of several thermal-barrier-coated pistons that had been previously tested in a high-output, single-cylinder diesel engine. Some of the coatings delaminated during engine operation. Results are presented for two thicknesses of the same coating material, and for two similar coatings with different levels of stiffness. All the coating thermomechanical properties such as thermal conductivity, density, volumetric heat capacity, thickness, elastic modulus, coefficient of thermal expansion, Poisson ratio and toughness, were measured prior to engine testing. Previous measurements of the piston transient heat flux, based on fast-response surface temperature data, in the same engine were used as an input to calculate the multilayer wall temperature distribution. A theoretical methodology was employed to evaluate and predict the coating durability.

Design of internal combustion (IC) engine pistons is dependent on accurate prediction of the temperature field in the component. Experimental temperature measurements can be taken but are costly and typically limited to a few select locations. High-fidelity computer simulations can be used to predict the temperature at any number of locations within the model, but the models must be calibrated for the predictions to be accurate. The largest barrier to calibration of piston thermal models is estimating the backside boundary conditions, as there is not much literature available for these boundary conditions. Bayesian model calibration is a common choice for model calibration in literature, but little research is available applying this method to piston thermal models. Neural networks have been shown in literature to be effective for calibration of piston thermal models.