Burning Phases Separation by Application of Clustering Algorithm to Hybrid Rocket Combustion Data

Abstract

K-means is a clustering algorithm that allows the classification of data into specific groups or clusters. Data points belonging to the same cluster are more similar (same properties and/or features) to each other than those in other groups. In this study, Kmeans++ clustering, an improved variant of the K-means clustering, was applied to hybrid rocket combustion data, in order to get insights into the complex flow phenomena. Hybrid rocket propulsion is a promising technology for many applications, because it allows for cost reductions, still being able to deliver similar performance as solid and liquid propulsion. However, the complex fluid dynamic and combustion phenomena taking place in the hybrid rocket combustion chamber are not fully understood yet and still a matter of ongoing research. In the last years, many combustion tests with different paraffin-based fuels were performed at the German Aerospace Center (DLR) with an optically accessible combustion chamber. For gaining a better insight into the combustion process, the tests were captured with a high-speed video camera. This led to a huge amount of data images for each test, which needed to be analysed in detail. In the framework of this study, the combustion data set was clustered with a K-means++ algorithm. From the results it is possible to observe that different clusters, corresponding to different combustion phases, were identified by the algorithm. A dependency of the combustion flame behaviour on the oxidizer mass flow and fuel composition was also found.