Due to the specific prediction requirements in combustion simulations, the reduced chemical mechanism developed focusing on particular applications is usually needed. A systematic method for chemical mechanism reduction orientated toward specific applications is proposed in this work. The reduction process is divided into two parts for large-molecule fuels, including the reduction of the fuel-specific sub-mechanism and the reduction of the C0–C4 sub-mechanisms. In the first part, path sensitivity analysis and global sensitivity analysis are used to identify the key reaction classes in the fuel-specific sub-mechanism, and then the rate of production (ROP) analysis and genetic algorithm are utilised to recognise the representative reactions with the maximum flux in the key reaction classes and optimise the reaction rate coefficients within their uncertainties. For the second part, the directed relation graph with error propagation and sensitivity analysis (DRGEPSA) is employed to reduce the C0–C4 sub-mechanisms. And then, the genetic algorithm with binary variables is applied to further reduce the pathways of the small-molecule reactions targeted at ignition delay times in shock tubes, major species (fuel, O₂, CO, and CO₂) profiles in jet-stirred reactors, and laminar flame speeds, respectively. Finally, reaction lumping is conducted by identifying the quasi-steady-state (QSS) species. Based on a detailed mechanism of primary reference fuel (PRF) consisting of 2,870 species and 9,233 reactions, three reduced models orientated toward specific applications, including Model_ST with 42 species and 82 reactions targeted at the ignition timings in shock tubes (ST), Model_JSR with 43 species and 79 reactions targeted at the major species concentrations in jet-stirred reactors (JSR), and Model_LPF with 41 species and 69 reactions targeted at laminar flame speeds in laminar premixed flames (LPF) are constructed, respectively. The validation results indicate that all the three reduced mechanisms can achieve good predictions in their featured specific applications.

由于燃烧模拟中的特定预测要求，通常需要针对特定​​应用开发的简化化学机制。在这项工作中，提出了一种面向特定应用的化学机理还原的系统方法。大分子燃料的还原过程分为两个部分，包括燃料特定子机制的还原和C0-C4子机制的还原。在第一部分，路径敏感性分析和全局敏感性分析用于识别燃料特定子机制中的关键反应类别，然后利用产率（ROP）分析和遗传算法识别关键反应类别中通量最大的代表性反应，并在其不确定性范围内优化反应速率系数。对于第二部分，使用带有误差传播和灵敏度分析的有向关系图 (DRGEPSA) 来减少 C0-C4 子机制。然后，应用具有二元变量的遗传算法，进一步减少针对激波管、主要物种（燃料、O_图2、CO 和 CO ₂ ) 分别在喷射搅拌反应器中的分布和层流火焰速度。最后，通过识别准稳态 (QSS) 物质进行反应集总。基于包含 2,870 种和 9,233 种反应的初级参考燃料 (PRF) 的详细机制，三个面向特定应用的简化模型，包括具有 42 种和 82 种反应的模型_ST，针对激波管 (ST) 中的点火正时，模型具有 43 种物质和 79 种反应的_JSR，针对喷射搅拌反应器 (JSR) 和_{LPF模型中的主要物质浓度}分别构建了针对层流预混合火焰 (LPF) 中层流火焰速度的 41 种和 69 个反应。验证结果表明，所有三种简化机制都可以在其特色特定应用中实现良好的预测。