Publication: Characterization of soot properties in coflow laminar axisymmetric diffusion flames using image processing and machine learning
Date
2022
Authors
Rodríguez Barreda, Alonso Antonio
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Abstract
Traditional non-invasive optical techniques for estimating soot volume fraction and soot temperature
in coflow laminar axisymmetric diffusion flames require solving ill-posed inverse problems from
convoluted signals. Applying these techniques using inversion and regularization makes them highly
susceptible to experimental noise and the choice of adjustable regularization parameters, which can
prevent obtaining consistent and accurate estimations.
This thesis proposes replacing these techniques with a machine learning model that implements
the inversion step in the estimation of these soot properties. We develop a framework to generate
physically-grounded simulated signals of reference soot fields and their corresponding convoluted
projections in the camera plane, thus enabling a supervised learning approach to train our machine
learning model. We focus on line-of-sight attenuation measurements for the characterization of the
soot volume fraction field and Broadband Emission measurements for the characterization of the soot
temperature field, as these techniques represent a promising approach to low-cost characterization
of their respective soot property in flames.
Using our framework, we generate datasets for each of the two soot properties to train a model
based on U-Net, a fully convolutional neural network previously used in similar inversion problems.
We then compare the performance of our models over the synthetic dataset versus traditional
techniques and perform a final validation over data obtained during experimental campaigns. These
results show that our machine learning models outperform traditional inversion techniques when
processing noisy measurements, especially in areas of interest for soot formation, such as the flame
center and its path of maximum soot concentration along the flame. The resilience to noise shown by
machine learning models makes them attractive for implementing low-cost techniques to characterize
soot properties in flames using experimental equipment of different quality, representing a promising
research avenue.
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Keywords
HOLLÍN , FRACCIÓN DE VOLUMEN DE HOLLÍN , PIROMETRÍA DE HOLLÍN