| The main objectives of this work are is to determine the minimum number of relevant parameters that must be controlled to control cycle-to-cycle variations through CCV modeling, sensitivity analyses and non-dimensional analysis, and to search for multi-cylinder history dependence. That would help to control CCVs and to introduce helpful tools that assist engine designers.; A mathematical model that predict the cyclic variability in IMEP was introduced in four steps A, B, C, and D using different assumptions. The lowest errors (1.9%) were obtained by equating the cylinder pressure-volume-curve area for both the measured data and the model as followed in step D. This model shows that IMEP is a function of 14 characterizing parameters (parameters which are driven from engine pressure-volume curve). Moreover, a correlation study for the characterizing parameters has been conducted in order to determine the independent ones. To determine the most dominant parameters, a sensitivity study was conducted for the previously determined independent parameters. Correlation and sensitivity study show that maximum cylinder pressure, compression and expansion polytropic indexes, and suction and exhaust pressures are the five relevant independent parameters.; Conducting non-dimensional analysis of independent parameters in order to reduce the number of variables shows that dimensionless groups predict variations in IMEP with lower error than dimensional parameters.; The CCVs were evaluated simultaneously in two different engine cylinders at different engine operating conditions. The speed, with which the electronic ignition system reacts to the oxygen availability in the exhaust gases, seems to be the reason behind the positive correlation between cylinders 1 and 3, and negative correlations between cylinders 1 and 4. |
