Chemical Kinetic Mechanism Reduction based on Complex Network Analysis
The proposed doctoral project endeavours to overcome current limitations in the understanding and reduction of chemical kinetic mechanisms by conducting an extensive analysis based on complex networks. The overarching research question is:
How and to what extent can analytical methods from complex network theory be used for analysis and model reduction of reaction mechanisms in combustion kinetics?
Enhancing combustion modelling methods and developing better predictive capabilities at lower computational costs are pressing issues. A key factor for success in these areas is the improved understanding of combustion kinetics models, particularly with respect to understanding the structure and interaction of the highly coupled and interconnected mechanisms necessary to capture the relevant flame physics. Regarding the necessary decrease of computational effort, a central issue is the reduction of kinetic mechanisms. For large mechanisms of complex fuels, however, no method has yet been found that efficiently achieves high reduction levels without seriously limiting the prediction accuracy of the model. But, the underlying structure of kinetic mechanisms suggests that flame chemistry could also be represented as a complex network. In recent years, complex network analysis has been successful in unravelling equally complex systems, such as social interconnections, or international transport systems. Therefore this doctoral project proposes the following work program with its three sequential work areas to connect the studies of complex networks to combustion kinetics and to ultimately further knowledge of modelled combustion chemistry and reduce kinetic mechanisms:
1. Prepare investigations by developing an algorithm that converts the chemical kinetics into complex networks and implementing functions for selected network measures and visualization
2. Analyse networks extracted from a set of zero-dimensional homogeneous reactors with measures such as PageRank or Betweenness Centrality as indicators of their relevance in mechanisms
3. Use obtained knowledge to develop novel mechanism reduction strategies and investigate the evolution of networks in one-dimensional flames