Computations involving air plasma chemistry are often confronted with the necessity to deal with a large number of chemical species and reactions. In this article, an algorithm is demonstrated, which efficiently identifies and eliminates unimportant species and reactions, which can lead to a computational speedup of up to an order of magnitude. The proposed reduction method is independent of and can be combined with other time-saving algorithms, such as adaptive time stepping. The method was applied to two test cases: relaxation of plasma with hot neutrals (initial $T_{g} \gg T_{e}$ ), which may occur due to shock-induced heating during meteor reentry at high altitude and the relaxation of atmospheric pressure plasma with hot electrons (initial $T_{e} \gg T_{g}$ ), which may result from a laser-induced plasma breakdown. In the first scenario, the full set of 54 species and 1010 reactions was reduced to ten species and 106 reactions while achieving less than 3% error in the electron density. For the second case, a set containing 16 species and 195 reactions was sufficient. The test cases demonstrate that the directed relational graph method can reduce significantly the number of chemical species and reactions, thus streamlining computations. The algorithm is expected to have wider application and a strong impact on the reduction of extremely large data sets, e.g., hydrocarbons, chemistry with large dynamic time scales, and computations involving plasma chemistry in multidimensional simulations.

中文翻译：

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有向关系图在等离子体弛豫过程中空气等离子体化学中的应用


涉及空气等离子体化学的计算经常需要处理大量的化学物质和反应。本文演示了一种算法，该算法可以有效识别并消除不重要的物质和反应，从而使计算速度提高一个数量级。所提出的缩减方法独立于其他节省时间的算法，例如自适应时间步进，并且可以与其他节省时间的算法结合使用。该方法应用于两个测试案例：具有热中性粒子的等离子体弛豫（初始 $T_{g} \gg T_{e}$ ），这可能是由于流星在高空再入期间冲击引起的加热而发生的，以及等离子体的弛豫具有热电子的大气压等离子体（初始 $T_{e} \gg T_{g}$ ），这可能是激光诱导等离子体击穿的结果。在第一种情况下，全套 54 个物质和 1010 个反应减少到 10 个物质和 106 个反应，同时电子密度误差小于 3%。对于第二种情况，包含 16 个物种和 195 个反应的集合就足够了。测试用例表明，有向关系图方法可以显着减少化学物质和反应的数量，从而简化计算。该算法预计将具有更广泛的应用，并对超大数据集的减少产生重大影响，例如碳氢化合物、具有大动态时间尺度的化学以及多维模拟中涉及等离子体化学的计算。