Abstract
A fast algorithm is developed for ranking the species in a chemistry set according to their importance to the modeled densities of user-specified species of interest. The species ranking can be constructed for any set of user-specified plasma conditions, but here we focus predominantly on low-temperature plasmas, with gas temperatures between 300 and 1500 K covering the typical range of ICP and CCP plasma sources. This ranking scheme can be used to acquire insight into complex chemistry sets for modeling plasma phenomena or for a species-oriented reduction of the given chemistry set. The species-ranking method presented is based on a graph-theoretical representation of the detailed chemistry set and establishing indirect asymmetric coupling coefficients between pairs of species by the means of widely used graph search algorithms. Several alternative species-ranking schemes are proposed, all building on the theory behind different flavors of the directed relation graph method. The best-performing ranking method is identified statistically, by performing and evaluating a species-oriented iterative skeletal reduction on six, previously available, test chemistry sets (including O2–He and N2–H2) with varying plasma conditions. The species-ranking method presented leads to reductions of between 10 and 75% in the number of species compared to the original detailed chemistry set, depending on the specific test chemistry set and plasma conditions.
Highlights
Utilizing the unique properties of the low-temperature plasma has become an integral part of almost every industry sector, spanning over a wide range of applications such as medicine, biotechnology, surface modification, microfabrication, har-vesting energy, thrusters, ozone generation or abatement systems, to name just a few
The species-ranking method presented leads to reductions of between 10 and 75% in the number of species compared to the original detailed chemistry set, depending on the specific test chemistry set and plasma conditions
A method of ranking the species in a chemistry set according to their importance for modeling densities of a pre-defined set of species of interest is a crucial component of the presented species-oriented method for skeletal reduction of chemistry sets
Summary
Utilizing the unique properties of the low-temperature plasma has become an integral part of almost every industry sector, spanning over a wide range of applications such as medicine, biotechnology, surface modification, microfabrication, har-. While one might come across published chemistry sets which have been reduced to smaller sizes to accurately model only a particular application with a particular set of plasma conditions, such as the chemistry set for plasma in air reduced by Bak and Cappelli [12] from the detailed chemistry set by Kossyi et al [13], these published reduced sets are, by design, only valid for a narrow domain of plasma conditions and parameters described in the publications and cannot be readily adopted for a more general case It follows that methods for identifying and eliminating redundant species (and reactions) from very large detailed chemistry sets, or more generally, methods reducing the dimensionality of the underlying systems of partial differential equations are of great importance for more computationally costly plasma models, capable of generating more insight than simple 0D models. M Hanicinec et al reduction cases which are used to identify the statistically bestperforming ranking scheme in section 5; the last section provides conclusions and outlook
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