A graph-based approach to developing adaptive representations of complex reaction mechanisms
- Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 (United States)
An effective adaptive mechanism reduction approach based on flux graph clustering is proposed in this paper. The instantaneous element flux is quantified and considered as a proxy for describing the reactive propensities of the system. Our underlying hypothesis is that even though particular conditions may be characterized by a multitude of combinations of species mass fraction, T, and P, the essential chemistry, and hence the reaction propensity of the mixture that is active under this family of conditions, is the same. Therefore, we opt to use the instantaneous fluxes through the active reactions as an intrinsic property of the system. Flux graphs are first constructed for the chemical reaction system under numerous conditions aiming at capturing the attainable region. Similarity between flux graphs is quantified through the distances between corresponding vectors, using the cosine coefficient and a novel graph-distance metric taking into account the magnitude of each flux and the activity distribution of different fluxes. A hierarchical clustering algorithm is implemented to group similar instantaneous flux graphs into clusters, and consequently a reduced mechanism is generated for each cluster. A search algorithm is defined afterward to assign a new query point to a particular flux graph cluster, and subsequently the reduced mechanism associated with this cluster is used to describe the system at this time point. Finally, the methodology is demonstrated using n-pentane combustion in an adiabatic plug flow reactor model and a pairwise mixing stirred reactor model. (author)
- OSTI ID:
- 21125484
- Journal Information:
- Combustion and Flame, Vol. 155, Issue 4; Other Information: Elsevier Ltd. All rights reserved; ISSN 0010-2180
- Country of Publication:
- United States
- Language:
- English
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