Challenges and perspectives of combustion chemistry research

In the past decades, combustion chemistry research grew rapidly due to the development of combustion diagnostic methods,quantum chemistry methods, kinetic theory, and computational techniques. A lot of kinetic models have been developed for fuels from hydrogen to transportation fuel surrogates. Besides, multi-scale research method has been widely adopted to develop comprehensive models, which are expected to cover combustion conditions in real combustion devices. However, critical gaps still remain between the laboratory research and real engine application due to the insufficient research work on high pressure and low temperature combustion chemistry. Besides, there is also a great need of predictive pollutant formation model. Further development of combustion chemistry research depends on a closer interaction of combustion diagnostics, theoretical calculation and kinetic model development. This paper summarizes the recent progress in combustion chemistry research briefly and outlines the challenges and perspectives.

Machine learning for integrating combustion chemistry in numerical simulations

A strategy based on machine learning is discussed to close the gap between the detailed description of combustion chemistry and the numerical simulation of combustion systems.Indeed,the partial differential equations describ-ing chemical kinetics are stiffand involve many degrees of freedom,making their solving in three-dimensional unsteady simulations very challenging.It is discussed in this work how a reduction of the computing cost by an order of magnitude can be achieved using a set of neural networks trained for solving chemistry.The ther-mochemical database used for training is composed of time evolutions of stochastic particles carrying chemical species mass fractions and temperature according to a turbulent micro-mixing problem coupled with complex chemistry.The novelty of the work lies in the decomposition of the thermochemical hyperspace into clusters to facilitate the training of neural networks.This decomposition is performed with the Kmeans algorithm,a local principal component analysis is then applied to every cluster.This new methodology for combustion chemistry reduction is tested under conditions representative of a non-premixed syngas oxy-flame.

Mechanisms and Kinetics of Reaction CHCIBr＋NO2

The reaction CHClBr＋NO2 was investigated via quantum chemical methods and kinetic calculations. The reaction mechanism on the singlet potential energy surface（PES） was considered by B3LYP method, and the energies were calculated at the CCSD（T） and CASPT2 levels of theory. The rate constants and the ratios of products were obtained by utilizing VTST and RRKM methods over wide temperature and pressure ranges. Our results indicate that carbon-to-nitrogen approach via a barrierless process is preferred in the initial association of CHClBr and NO2. The dominant product is BrNO＋CHCIO（PI）, which agrees well with the experimental observation. P2（ClNO＋CHBrO） and P3（HNO＋CBrClO） may also have minor contributions to the reaction. The calculated overall rate constants are independent of pressure and consistent with the experimental data, which can be fitted with the following equation over the temperature range of 200--1500 K： k（T）=2.31 × 10^-15T^0.99exp（771/T）. Compared with reaction CH2Br＋NO2, reaction CHCIBr＋NO2 has decreased the overall rate constants.