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The field of diesel combustion research is producing numerous reports on studies of premixed combustion, which promises simultaneous reduction of both NOx and soot, in order to meet increasingly stringent regulations on harmful emissions from automobiles. However, although premixed combustion can simultaneously reduce both NOx and soot, certain issues have been pointed out, including the fact that it emits greater quantities of unburned HC and CO gases and the fact that it limits the operating range. Furthermore, this combustion method sets the ignition delay longer with the aim of promoting the mixing of fuel and air. This raises issues with the product due to the combustion instability and sensitivity to the uneven fuel properties that are found on the market, the capability of the engine response under transient conditions, the deterioration in combustion noise, and so on.

This study focuses on improvement of the predictive accuracy of empirical engine models using the Model Base Calibration (MBC) method. This research discusses the effects of the number of measurement points on the accuracy of models for different Design of Experiments (DoE) by using a direct-injection 4-cylinder diesel engine. The results show that the predictive accuracy of the models converges on fixed values when the number of measurement points is increased in Latin Hypercube Sampling (LHS) and D-Optimal Design. This is because the probability density distribution of the measurement data has little variation as the number of measurement points increases. Comparing LHS and D-Optimal indicates that D-Optimal displays a higher level of accuracy, it is able to extend the boundary model because of its greater number of measurement points at the boundaries of the boundary model.

Formula One engines, which are the pursuit of the ultimate in performance, tend to be comparatively vulnerable to durability issues. These engines sometimes run under a state of unstable combustion as compensation for improved fuel economy. To cope with these issues, there have been strong demands in the racing field for a technology that will allow constant monitoring and prompt action to be carried out on system malfunctions and failures, as well as unstable combustion. The research program described in this paper deals with an onboard technology for monitoring combustion under all the operational conditions using ionic current measurement. The technology will possibly be applied to engine management and car-to-pit communications via telemetering. The scope of the control it offers includes; detection of misfire and hesitation, detection and management of detonation, and management of lean-burn combustion.