“Quantitative modeling plays a central role in the optimization of fuel consumption and pollutant minimization.”

It deals with a central issue of combustion research, i.e. combustion chemistry, which forms the basis for understanding and modeling practical combustion systems—their performance and particularly their pollutant emissions.

It was intended to provide the state-of-the-art of the quantitative understanding of key reactions in combustion. It is a synthesis of our collective understanding of four general areas of research: hydrogen combustion, the formation and control of NOx, the formation of polycyclic aromatic hydrocarbons (PAH) and soot, and low-temperature chemistry and "cool flames." The latter topic is an important aspect of understanding "knock" in internal combustion engines.

Understanding the mechanism and the rates of combustion reactions forms the basis for quantitative modeling of practical combustion systems, such as combustion in car engines, turbines, household burners, forest fires, etc. Quantitative modeling plays a central role in the optimization of fuel consumption and pollutant minimization.

This paper was written by invitation as part of the 50th birthday celebration of The Combustion Institute in 2004. An oral presentation of the paper was given as a plenary lecture at the 30th biennial International Symposium on Combustion in that year. The three authors—James A. Miller, Michael J. Pilling, and Jürgen Troe—are all from different countries and from different scientific backgrounds, thus giving the paper perspective. No problems were encountered in putting our ideas together.

The social, political, and economic implications of research of this type are enormous. Fundamental science like that reported here forms the most rational basis for an optimization of the combustion-based use of energy resources. Since combustion technology will continue to play, quantitatively, the most important role in energy economy, at least for the next few decades, the significance of such research cannot be overstated.