Principal Component Analysis and K-Means Clustering of Fuel-Air Mixing in Gas Turbine Combustors

Abstract

As a direct consequence of liquid kerosene injection, aeroengine combustors may be categorized as non-premixed combustion systems, characterized by a swirl-stabilized and highly complex flow field. In addition to the flow of air through the fuel injector, there are a large number of other features through which oxidizer can enter the heat release region. These can have an impact on local fuel-air mixing, inducing strong spatial and temporal variations in stoichiometry, thereby affecting emissions and combustion system performance. This paper discusses a novel statistical methodology, based on Principal Component Analysis (PCA) and K-means clustering, that aims to improve understanding of fuel-air mixing in realistic aeroengine combustors. The method is applied in a postprocessing step to data sampled from a Large Eddy Simulation (LES), where every chamber inflow has been tagged with a unique passive scalar, which allows it to be traced across space and time. PCA is used to construct a low-dimensional, visually interpretable representation of a spatially localized fuel-air mixing process, while K-means clustering is employed to produce an unsupervised discretization of the flow field into regions of similar fuel-air mixing characteristics. The proposed methodology is computationally inexpensive, and the easily interpretable outputs can help the combustion engineer make better informed decisions about combustor design.