LAUSR

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.