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.

燃烧化学研究的关键任务之一是为实际燃料开发准确而可靠的燃烧动力学模型。准确而稳健的动力学模型所得出的预测与在宽范围的工作条件下的实验测量高度一致，并且预测不确定性是可以接受的。由各种强大的诊断技术生成的可靠实验数据继续在此类模型的开发中发挥重要作用。这篇综述着重于燃烧系统中基于同步加速器的物质测量，模型验证，模型结构开发和模型参数优化方面的贡献。特别着重于最近报道的用于提供信息量可靠的实验数据的策略，包括燃烧动力学模型输入参数评估，模型分析的计算成本降低，基于模型分析的实验设计，实验数据处理和误差减少。特别地，基于活动子空间的方法（ASSM）可以减少燃烧动力学模型的维数，基于人工神经网络的替代方法（ANN-HDMR和ANN-MCMC）可以显着降低计算成本。基于全局灵敏度的实验设计方法（包括灵敏度熵和替代模型相似性（SMS））可以指导动力学信息丰富的实验数据生成。基于模型分析的实验误差标定和实验目标特征提取可以提高实验数据质量。