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Atoom, Adam Izzeldeen

University of British Columbia

Thermoacoustic oscillations in combustors need to be monitored and mitigated as they directly impact efficiency and safety of land-based power generation systems. Monitoring the thermoacoustic oscillations requires acquisition of high-resolution flame chemiluminescence images, which is not readily available in industrial settings due to the lack of optical access to the combustor. To mitigate thermoacoustics, this thesis proposes using few sensors to produce low-resolution flame chemiluminescence images, which are then upscaled using machine learning to high-resolution flame chemiluminescence images. A multi-path convolutional upscaling neural network (NN) is proposed and used in the present study to upscale the low-resolution flame chemiluminescence images to high-resolution flame chemiluminescence images. The effectiveness of the proposed NN is validated using image similarity metrics, and comparisons are made with existing image upscaling NNs in the literature. The performance of our NN is further assessed for predicting high-resolution spatial thermoacoustic source term. The proposed NN achieved more accurate upscaling and spatial thermoacoustic source term calculation compared to previous works in the literature. As a result, the present study demonstrates the feasibility of real-time detailed monitoring of thermoacoustic oscillations in industrial applications using few sensors.

Text

2025-05-20

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/89689

Electrical Engineering

University of British Columbia

UBCO

http://creativecommons.org/licenses/by-nc-nd/4.0/