Upscaling very low-resolution chemiluminescence images for prediction of thermoacoustic source term using machine learning
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Atoom, Adam Izzeldeen
issued: 2025-05-20T07:00:00Z
issued: 2024
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: 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.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/89689
note: Upscaling Very Low-ResolutionChemiluminescence Images forPrediction of Thermoacoustic SourceTerm Using Machine LearningbyAdam Izzeldeen AtoomB.Sc. Hons., The University of Jordan, 2021A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF APPLIED SCIENCEinThe College of Graduate Studies(Electrical Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)November 2024© Adam Izzeldeen Atoom, 2024The following individuals certify that they have read, and recommend to the Collegeof Graduate Studies for acceptance, a thesis entitled:Upscaling Very Low-Resolution Chemiluminescence Images for Predic-tion of Thermoacoustic Source Term Using Machine Learningsubmitted by Adam Izzeldeen Atoom in partial fulfilment of the requirements of thedegree of Master of Applied ScienceDr. Anas Chaaban, School of EngineeringCo-supervisorDr. Sina Kheirkhah, School of EngineeringCo-supervisorDr. Ahmad Al-Dabbagh, School of EngineeringSupervisory Committee MemberDr. Zheng Liu, School of EngineeringSupervisory Committee MemberDr. Shahria Alam, School of EngineeringUniversity ExamineriiAbstractThermoacoustic oscillations in combustors need to be monitored and mitigated as theydirectly impact efficiency and safety of land-based power generation systems. Monitoringthe thermoacoustic oscillations requires acquisition of high-resolution flame chemilumines-cence images, which is not readily available in industrial settings due to the lack of opticalaccess to the combustor. To mitigate thermoacoustics, this thesis proposes using few sen-sors to produce low-resolution flame chemiluminescence images, which are then upscaledusing machine learning to high-resolution flame chemiluminescence images. A multi-pathconvolutional upscaling neural network (NN) is proposed and used in the present study toupscale the low-resolution flame chemiluminescence images to high-resolution flame chemi-luminescence images. The effectiveness of the proposed NN is validated using image similar…
has type: Thesis/Dissertation
date issued: 2025-02
is shown at: 10.14288/1.0447295
language: eng
degree discipline: Electrical Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Chaaban, Anas
contributor: Kheirkhah, Sina
type: Text
identifier URI: http://hdl.handle.net/2429/89689