Abstract
Complex kinetic representation involving thousands of reacting species and tens of thousands of reactions are currently required for the rational analysis of modern combustion systems. In order to represent, analyze and visualize effectively the ignition processes advanced computational techniques will be required. Recently, we introduced a novel concept that captured the principal elemental transformations in complex kinetic environments in the form of graphs. In order to arrive at a compact representation of the information content of these graphs novel concepts from machine learning, such as feature selection in time series and hashing, are implemented. These approaches allow the projection of the totality of the information contained in the graph describing the chemical transformations onto a single scalar. The temporally evolving graphs are treated as streaming data and locality-preserving hashing allows the unique assignment of a scalar “motif” value to each such graph. Analysis of those motifs allows the quick identification of “clusters” of identical reaction graphs that correspond to regimes with similar kinetic histories. The approach is illustrated with highly complex kinetic mechanisms describing pentane autoignition. It is demonstrated how this novel representation allows the quick identification of regions where similar temporal history of the chemical transformations is experienced.