Principal Component Analysis and K-Means Clustering of Fuel-Air Mixing in Gas Turbine Combustors
David Salvador-Jasin, A Duncan Walker, Jon F Carrotte

TL;DR
This paper introduces a statistical approach combining PCA and K-means clustering to analyze and visualize fuel-air mixing in gas turbine combustors, aiding design decisions.
Contribution
It presents a novel, computationally efficient methodology for interpreting complex flow data from LES simulations, enhancing understanding of fuel-air mixing.
Findings
Low-dimensional representation of mixing process
Unsupervised flow field discretization into similar regions
Method aids in combustor design decisions
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…
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Taxonomy
TopicsCombustion and flame dynamics · Radiative Heat Transfer Studies · Coal Combustion and Slurry Processing
