Charles E. Newman

The intensity of knock in an internal combustion engine: An experimental and modeling study

Experimental data have been obtained that characterize knock occurrence times and knock intensities in a spark ignition engine operating on indolene and 91 primary reference fuel, as spark timing and inlet temperature were varied. Individual, in-cylinder pressure histories measured under knocking conditions were conditioned and averaged to obtain representative pressure traces. These averaged pressure histories were used as input to a reduced and detailed chemical kinetic model. The time derivative of CO concentration and temperature were correlated with the measured knock intensity and percent cycles knocking. The goal was to evaluate the potential of using homogeneous, chemical kinetic models as predictive tools for knock intensity.

Application of heuristic and machine-learning approach to engine model calibration

Automation of engine model calibration procedures is a very challenging task because (1) the calibration process searches for a goal state in a huge, continuous state space, (2) calibration is often a lengthy and frustrating task because of complicated mutual interference among the target parameters, and (3) the calibration problem is heuristic by nature, and often heuristic knowledge for constraining a search cannot be easily acquired from domain experts. A combined heuristic and machine learning approach has, therefore, been adopted to improve the efficiency of model calibration. We developed an intelligent calibration program called ICALIB. It has been used on a daily basis for engine model applications, and has reduced the time required for model calibrations from many hours to a few minutes on average. In this paper, we describe the heuristic control strategies employed in ICALIB such as a hill-climbing search based on a state distance estimation function, incremental problem solution refinement by using a dynamic tolerance window, and calibration target parameter ordering for guiding the search. In addition, we present the application of a machine learning program called GID3* for automatic acquisition of heuristic rules for ordering target parameters.