Towards predictive combustion kinetic models: Progress in model analysis and informative experiments

One of the key tasks of combustion chemistry research is to develop accurate and robust combustion kinetic models for practical fuels. An accurate and robust kinetic model yields predictions that are highly consistent with experimental measurements over a wide range of operating conditions, with prediction uncertainties that are acceptable. Reliable experimental data generated by various powerful diagnostic techniques continue to play an essential role in the development of such models. This review focuses on the contributions of synchrotron-based species measurements in combustion systems, on model validation, model structure development, and model parameter optimization. Special emphasis is placed on recently reported strategies for informative and reliable experimental data generation, including combustion kinetic model input parameter evaluation, computational cost reduction for model analysis, model-analysis-based experimental design, experimental data treatment and error reduction. Particularly, the active-subspace-based method (ASSM) can reduce the dimensionality of combustion kinetic models and the aritificial-neural-network-based surrogates (ANN-HDMR and ANN-MCMC) can reduce the computational cost significantly. Global-sensitivity based experimental design methods including sensitivity entropy and surrogate model similarity (SMS) can guide kinetics-information-enriched experimental data generation. Model-analysis-based calibration for experimental errors and feature extraction of experimental targets can improve the experimental data quality. A computational framework (OptEx) enabling the integration of experimental data with mechanism development, experimental design and model optimization, provides a new means to develop reliable kinetic models more efficiently and effectively. (c) 2020 The Combustion Institute. Published by Elsevier Inc. All rights reserved.

Automated summary

The accurate and robust kinetic models are crucial in combustion chemistry research. Reliable experimental data and advanced diagnostic techniques play essential roles in model development. New strategies like ASSM and ANN-HDMR can reduce model dimensionality and computational cost significantly, while global-sensitivity based experimental design methods can guide kinetics-information-enriched data generation. The computational framework OptEx provides a new means for integrating experimental data with mechanism development, design, and optimization to develop reliable kinetic models more efficiently and effectively.