Reduction of large combustion mechanisms is usually conducted based on the detection and elimination of redundant species and reactions. Reaction elimination methods are mostly based on sensitivity analysis, which can provide insight into the kinetic system, while species elimination methods are more efficient. In this work, the species‐targeted local sensitivity analysis (STLSA) method is proposed to evaluate the importance of species and eliminate non‐crucial species and their related reactions to simplify kinetic models. This paper comprehensively evaluates the effectiveness of STLSA across various combustion scenarios, including high and low‐temperature ignition and laminar flame speed, using diverse mechanisms like USC Mech II, JetSurf 1.0, POLIMI_TOT_1412, NUIGMech1.1 and so on. Comparisons with graph‐based methods, such as DRG and DRGEP, highlight STLSA's superior efficiency and accuracy. Moreover, STLSA is compared to species‐targeted global sensitivity analysis (STGSA), demonstrating significant computation cost savings and comparable model reduction capabilities. The study concludes that STLSA is a robust and versatile tool for mechanism reduction, offering substantial improvements in computational efficiency while maintaining high accuracy in predicting key combustion properties.

中文翻译：

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基于物种靶向局部敏感性分析的化学动力学模型简化

大型燃烧机制的减少通常是基于检测和消除冗余物质和反应来进行的。反应消除方法大多基于灵敏度分析，可以深入了解动力学系统，而物种消除方法更有效。在这项工作中，提出了以物种为目标的局部敏感性分析（STLSA）方法来评估物种的重要性并消除非关键物种及其相关反应以简化动力学模型。本文使用 USC Mech II、JetSurf 1.0、POLIMI_TOT_1412、NUIGMech1.1 等多种机制，全面评估了 STLSA 在各种燃烧场景下的有效性，包括高低温点火和层流火焰速度。与基于图的方法（例如 DRG 和 DRGEP）的比较，凸显了 STLSA 卓越的效率和准确性。此外，将 STLSA 与以物种为目标的全局敏感性分析（STGSA）进行比较，证明了显着的计算成本节省和可比的模型缩减能力。研究得出的结论是，STLSA 是一种强大且多功能的机制简化工具，可显着提高计算效率，同时保持预测关键燃烧特性的高精度。