CNN-based tomographic reconstruction of laser absorption in a gas turbine model combustor

Zhiyao Yin; Patrick Nau; Hannah Scheffold

doi:10.1364/LACSEA.2022.LF1C.5

Optical Sensors and Sensing Congress 2022 (AIS, LACSEA, Sensors, ES)
Technical Digest Series (Optica Publishing Group, 2022),
paper LF1C.5
•https://doi.org/10.1364/LACSEA.2022.LF1C.5

CNN-based tomographic reconstruction of laser absorption in a gas turbine model combustor

Zhiyao Yin, Patrick Nau, and Hannah Scheffold

Laser Applications to Chemical, Security and Environmental Analysis 2022

Vancouver, British Columbia Canada
11–15 July 2022
ISBN: 978-1-957171-10-4

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Machine Learning and Computational Sensing (Joint Theme COSI and LACSEA on Machine Learning) (LF1C)

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Z. Yin, P. Nau, and H. Scheffold, "CNN-based tomographic reconstruction of laser absorption in a gas turbine model combustor," in Optical Sensors and Sensing Congress 2022 (AIS, LACSEA, Sensors, ES), Technical Digest Series (Optica Publishing Group, 2022), paper LF1C.5.

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Abstract

Tomographic reconstruction of laser absorption of H₂O is demonstrated in a swirl-stabilized gas turbine model combustor. Superior reconstruction performance is achieved against conventional methods using a nonlinear regression technique based primarily on convolutional neural networks.

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