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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.07144 |
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| _version_ | 1866909487939452928 |
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| author | Salvador-Jasin, David Walker, A Duncan Carrotte, Jon F |
| author_facet | Salvador-Jasin, David Walker, A Duncan Carrotte, Jon F |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_07144 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Principal Component Analysis and K-Means Clustering of Fuel-Air Mixing in Gas Turbine Combustors Salvador-Jasin, David Walker, A Duncan Carrotte, Jon F Fluid Dynamics 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. |
| title | Principal Component Analysis and K-Means Clustering of Fuel-Air Mixing in Gas Turbine Combustors |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2502.07144 |