Retrieving soot volume fraction fields for laminar axisymmetric diffusion flames using convolutional neural networks

[Display omitted] •Numerical framework for simulating experimental measurements for axisymmetric flames.•The framework enables generation of rich datasets for reference and measured signals.•A Convolutional Neural Network (CNN) is used to retrieve soot volume fraction fields.•CNN outperforms traditi...

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Bibliographic Details
Published in:Fuel (Guildford) 2021-02, Vol.285, p.119011, Article 119011
Main Authors: Rodríguez, A., Escudero, F., Cruz, J.J., Carvajal, G., Fuentes, A.
Format: Article
Language:English
Subjects:
Artificial neural networks
Attenuation
CoFlame code
Deconvolution
Diffusion flames
Estimation
Ill-posed problem
Image reconstruction
Inverse problems
Line of sight
LOSA technique
Neural networks
Noise sensitivity
Parameter estimation
Parameter sensitivity
Regularization
Soot
Soot diagnostics
Synthetic images
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Summary:[Display omitted] •Numerical framework for simulating experimental measurements for axisymmetric flames.•The framework enables generation of rich datasets for reference and measured signals.•A Convolutional Neural Network (CNN) is used to retrieve soot volume fraction fields.•CNN outperforms traditional inversion methods based on numerical deconvolution.•CNN trained with synthetic data can retrieve the fields from real experimental data. Typical procedures for estimating soot volume fraction distribution in laboratory flames require solving ill-posed inverse problems to recover the fields from convoluted signals that integrate light extinction from soot particles along the line-of-sight of a photo-detector. Classical deconvolution methods are highly sensitive to noise and the choice of tunable regularization parameters, which prevents obtaining consistent estimations even for the same reference flame settings. This paper presents a novel approach based on Convolutional Neural Networks (CNNs) for estimating the soot volume fraction fields from 2D images of line-of-sight attenuation (LOSA) measurements in coflow laminar axisymmetric diffusion flames. Using a set of reference synthetic soot volume fraction fields of canonical flames and their corresponding projected LOSA images, we trained a CNN for reconstructing soot fields from images representing the data captured by a camera. Experimental results show that the proposed CNN approach outperforms classical deconvolution methods when reconstructing the flame spatial soot distribution from noisy images of LOSA.
ISSN:0016-2361
1873-7153
DOI:10.1016/j.fuel.2020.119011