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Time-resolved continuous images of wrinkling flame front cross-sections were acquired by a laser-light sheet technique in an optically accessible spark ignition engine. The test engine was operated at various engine speeds and compression ratios. The fractal dimension of the curve, D2, was measured in a time series for each cycle. Analysis of the data shows that as the flame propagates the fractal dimension, D2, is close to unity a short time after spark ignition and then increases. Examination of the relationship between the growth rate of the fractal dimension, ΔD2/Δt, and D2 reveals that the higher D2 is, the lower ΔD2/Δt becomes. An Empirical equation for ΔD2/Δt was derived as a function of the ratio of the turbulence intensity to the laminar burning velocity and pressure. This model was tested in an SI engine combustion simulation, and results compared favorably with experimental data.

We have developed a model-based control for the air intake system in a variable valve engine, employing total engine simulation, the response surface method and multi-objective optimization scheme. In our technique, we performed the simulation model tuning and validation, followed by the creation of a dataset for the polynomial regression analysis of the charging efficiency. A D-optimal design, robust least squares method, and likelihood-ratio test were demonstrated to yield a robust and accurate control model. Coupling the total engine simulator with a genetic algorithm, model based calibration for optimal valve timing stored in lookup table was carried out under multiple objectives and restrictions. The reliability of the implementation control model, which considers the effect of gas dynamics in the intake system, was confirmed using a model-in-the-loop simulation.

The purpose of this study is to develop model-based methodologies which employ thermo-fluid dynamic engine simulation and multiple-objective optimization schemes for engine control and calibration, and to validate the reliability of the method using a dynamometer test. In our technique, creating a total engine system model begins by first entirely capturing the characteristics of the components affecting the engine system's behavior, then using experimental data to strictly adjust the tuning parameters in physical models. Engine outputs over the full range of engine operation conditions as determined by design of experiment (DOE) are simulated, followed by fitting the provided dataset using a nonlinear response surface model (RSM) to express the causal relationship among engine operational parameters, environmental factors and engine output. The RSM is applied to an L-jetronic® air-intake system control logic for a turbocharged engine.