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To address the growing need for detailed chemistry in engine simulations, new software tools and validated data sets are being developed under an industry-funded consortium involving members from the automotive and fuels industry. The results described here include systematic comparison and validation of detailed chemistry models using a wide range of fundamental experimental data, and the development of software tools that support the use of detailed mechanisms in engineering simulations. Such tools include the automated reduction of reaction mechanisms for targeted simulation conditions. Selected results are presented and discussed.

The combustion behavior in a modem 4-valve engine using a broad range of methanol/gasoline fuel mixtures was studied. The initial flame development was examined by using a spark plug fiber optics probe. Approximately, the kernel expansion speed, Sg, is relatively unchanged from M0 to M40; jumps by ∼30% from M40 to M60; and then remains roughly constant from M60 to M100. Statistics of the IMEP indicate that at a lean idle condition the combustion rate and robustness correlate with Sg: a higher value of Sg gives better combustion. Thus M60-M100 fuels give better idle combustion behavior than the M0-M40 fuels.

A method for determining the flame contour based on the flame arrival time at the fiber optic (FO) spark plug and at the head gasket ionization probe (IP) locations has been developed. The experimental data were generated in a single-cylinder Ricardo Hydra spark-ignition engine. The head gasket IP, constructed from a double-sided copper-clad circuit board, detects the flame arrival time at eight equally spaced locations at the top of the cylinder liner. Three other IP's were also installed in the cylinder head to provide additional intermediate data on flame location and arrival time. The FO spark plug consists of a standard spark plug with eight symmetrically spaced optical fibers located in the ground casing of the plug. The cylinder pressure was recorded simultaneously with the eleven IP signals and the eight optical signals using a high-speed PC-based data acquisition system.

With the recent advancement in sensing and controller technologies architecture design of an autonomous driving system becomes an important issue. Researchers have been developing different sensors and data processing technologies to solve the issues associated with fast processing, diverse weather, reliability, long distance recognition performance, etc. Necessary considerations of diverse traffic situations and safety factors of autonomous driving have also increased the complexity of embedded software as well as architecture of autonomous driving. In these circumstances, there are almost countless numbers of possible architecture designs. However, these design considerations have significant impacts on cost, controllability, and system reliability. Thus, it is crucial for the designers to make a challenging and critical design decision under several uncertainties during the conceptual design phase.

With the increasing complexity of automotive systems and the related increasing use of software in them, new approaches are needed to ensure safety. In these new types of automotive systems, safety and reliability are different and require different engineering approaches. Accidents are increasingly due to design errors and to dysfunctional interactions among components rather than component failure. In addition, safety must be engineered and built into the design from the beginning; it is not possible to effectively and affordably add safety devices onto a finished design. This paper describes the need for new approaches to automotive safety and describes an alternative to the traditional reliability-based approaches to safety engineering. The new approach is based on systems theory and views accidents in terms of lack of control or enforcement of the behavioral constraints required to ensure safety.