Advancing ammonia-hydrogen combustion reaction kinetics: Informative experimental datasets constructed through global-sensitivity-based clustering

Ammonia has emerged as a highly promising zero-carbon energy carrier in recent years. To address its combustion challenges, blending with hydrogen has been proposed as an effective solution. This approach necessitates a comprehensive understanding of the underlying reaction kinetics for practical implementation. The extensive experimental data available on NH₃/H₂ combustion offers the potential for advancing chemical kinetics understanding through model optimization. However, effectively utilizing the extensive dataset poses significant challenges, primarily due to prohibitively high computational costs when processing large volumes of data for model optimization. Moreover, indiscriminate use of all available data without proper quality assessment is inadvisable, given the potential issues of data inconsistency. To comprehensively extract kinetic information from existing experimental dataset and facilitate efficient model development, this study implements a global-sensitivity-based clustering approach. The 2786 NH₃/H₂ experimental data are categorized into 40 distinct clusters. Representative exemplars from each cluster are selected to construct a streamlined yet information-dense dataset, which is subsequently employed for model optimization, resulting in a substantial enhancement of the model's predictive performance. Although information redundancy exists in current datasets, certain aspects of reaction kinetics may remain unexplored due to limitations in existing experimental data. Within the range of experimental conditions that current experimental equipment can cover, there are still many unexplored condition regions. To investigate the potential impact of conducting experiments under these new conditions on advancing NH₃/H₂ combustion modeling, we systematically extend the dataset to incorporate 6695 theoretically feasible experimental conditions. Notably, eight previously insensitive reactions demonstrate significant sensitivity within this expanded framework. Furthermore, eleven additional prospective experimental conditions exhibiting high sensitivity to these eight parameters are identified and incorporated into the existing informative dataset. Subsequent model optimization utilizing this enhanced dataset of 51 elements yields significantly improved performance. This study demonstrates that carefully-designed future experiments can provide novel insights into the reaction kinetics of ammonia-hydrogen combustion kinetics, as well as offering valuable guidance for future experimental investigations.