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The increasing number of controllable parameters in modern engine systems has led to increasingly complicated and enlarged engine control software. This in turn has created dramatic increases in software development time and cost. Model-based control design seems to be an effective way to reduce development time and costs and also to enable engineers to understand the complex relationship between the many controllable parameters and engine performance. In the present study, we have developed model-based methodologies for the engine calibration process, employing engine cycle simulation and regression analysis. The reliability of the proposed method was investigated by validating the regression model predictions with measured data.

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.