Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Simultaneous soot multi-parameter fields predictions in laminar sooting flames from neural network-based flame luminosity measurement I: methodology

Not Accessible

Your library or personal account may give you access

Abstract

We originally report the use of a neural network-based method for diagnosing multiple key parameters in axis-symmetric laminar sooting flames. A Bayesian optimized back propagation neural network (BPNN) is developed and applied to flame luminosity to predict the planar distribution of soot volume fraction, temperature, and primary particle diameter. The feasibility and robustness of this approach are firstly assessed using numerical modeling results and then further validated with experimental results of a series of laminar diffusion sooting flames. This proposed BPNN model-based flame luminosity approach shows high prediction accuracies, typically up to 114 K, 0.25 ppm, and 2.56 nm for soot temperature, volume fraction, and primary particle diameter, respectively. We believe that the present machine learning-assisted optical diagnostics paves a more efficient, lower costing, and high-fidelity way for multi-parameters simultaneous diagnosis in combustion and reacting flows.

© 2021 Optical Society of America

Full Article  |  PDF Article
More Like This
Predicting simultaneously fields of soot temperature and volume fraction in laminar sooting flames from soot radiation measurements - a convolutional neural networks approach

Yi Wu, Zhen Li, Qianlong Wang, Guillaume Legros, Chaomin Li, and Zhiwen Yan
Opt. Express 30(12) 21230-21240 (2022)

Machine learning-assisted soot temperature and volume fraction fields predictions in the ethylene laminar diffusion flames

Tao Ren, Ya Zhou, Qianlong Wang, Haifeng Liu, Zhen Li, and Changying Zhao
Opt. Express 29(2) 1678-1693 (2021)

Effect of soot self-absorption on color-ratio pyrometry in laminar coflow diffusion flames

Nathan J. Kempema and Marshall B. Long
Opt. Lett. 43(5) 1103-1106 (2018)

Data Availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Figures (5)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Tables (3)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Equations (4)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved