A bottom-up approach to assemble reduced combustion kinetics mechanisms is proposed as an alternative to conventional reduction techniques. Rather than relying on simulations using detailed mechanisms to identify the set of species and reactions to include in the reduced mechanism, the proposed “building” algorithm follows an add-as-needed approach, in which reduced mechanisms are progressively augmented with individual reactions carefully selected among a restricted list in order to properly capture combustion dynamics in increasingly varied operating conditions. The algorithm is first described in details, and its characteristics and performance are explored through several examples. In a first example, reduced mechanisms able to capture methane/air auto-ignition in a constant volume homogeneous reactor are built, and compared to those generated with a conventional graph-based reduction technique. In the second example, the selection behavior of the algorithm is explored at the medium (methane) and large (heptane) mechanism scale, showing some computational advantage in using a building, bottom-up approach. Finally, the flexibility of the algorithm to add, onto a reduced mechanism, kinetic pathways that were initially not considered in the reduction is demonstrated, using the addition of a reduced representation of NO${}_x$ pathways on a previously obtained methane oxidation reduced mechanism as example. The algorithm is found to yield similar results compared to reduction techniques informed by detailed mechanisms, while providing increased efficiency and flexibility to the end-user.

提出了一种自下而上的方法来组装减少的燃烧动力学机制，作为传统减少技术的替代方法。所提出的“构建”算法不是依赖于使用详细机制的模拟来识别要包含在简化机制中的一组物种和反应，而是遵循按需添加的方法，其中简化机制通过精心选择的个体反应逐渐增强为了在日益变化的运行条件下正确捕捉燃烧动态，在一个受限列表中。首先详细描述该算法，并通过几个例子探讨其特点和性能。在第一个示例中，构建了能够在恒定体积均质反应器中捕获甲烷/空气自燃的简化机制，并与使用传统的基于图的缩减技术生成的那些进行比较。在第二个示例中，算法的选择行为在中（甲烷）和大型（庚烷）机制尺度上进行了探索，显示了使用自下而上的构建方法的一些计算优势。最后，使用减少的 NO 表示，证明了该算法将最初未在减少中考虑的动力学途径添加到减少的机制上的灵活性${}_{\times }$例如，先前获得的甲烷氧化还原机制的途径。发现该算法与由详细机制通知的减少技术相比产生相似的结果，同时为最终用户提供更高的效率和灵活性。