(Drs. Pandeli Durbetaki and Stuart Daw co-advisors)
"Application of Deterministic Chaos Theory to Cyclic Variability in
The purpose of this research is to use concepts from deterministic chaos theory to develop an improved understanding of cycle-to-cycle variations in spark-ignited (SI) internal combustion engines. It is conjectured that prior-cycle effects such as residual unburned fuel are important deterministic causes of cyclic variability in engines. These prior-cycle effects are expected to be more evident during lean fueling and when high levels of exhaust gas residual (EGR) are present. Chaotic time-series analysis methods such as Poincaré sectioning, mutual information, data symbolization, symbol sequence histograms, time irreversibility, modified Shannon entropy, and return maps are used to analyze cycle-resolved combustion data from multi- and single-cylinder research engines at idle conditions. Traditional statistical measures are also compared with the results yielded by nonlinear time-series analysis. The overall results suggest that cyclic variability has at least some nonlinear deterministic structure. Based upon the experimental results and consideration of the physical mechanisms involved, an improved nonlinear model is proposed for cyclic variability. Confirmation of deterministic structure also implies that it may be possible to diagnose and predict future undesirable combustion events (such as misfire). Approaches are suggested to extend the EGR tolerance and/or lean operating limit of SI engines utilizing closed-loop control to reduce cyclic variability.