z-logo
open-access-imgOpen Access
Machine learning-assisted soot temperature and volume fraction fields predictions in the ethylene laminar diffusion flames
Author(s) -
Tao Ren,
YongWu Zhou,
Qianlong Wang,
Haifeng Liu,
Zhen Li,
C.Y. Zhao
Publication year - 2021
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.413100
Subject(s) - soot , volume fraction , materials science , combustion , diffusion flame , adiabatic flame temperature , thermodynamics , biological system , computer science , analytical chemistry (journal) , physics , chemistry , organic chemistry , combustor , biology
Inferring local soot temperature and volume fraction distributions from radiation emission measurements of sooting flames may involve solving nonlinear, ill-posed and high-dimensional problems, which are typically conducted by solving ill-posed problems with big matrices with regularization methods. Due to the high data throughput, they are usually inefficient and tedious. Machine learning approaches allow solving such problems, offering an alternative way to deal with complex and dynamic systems with good flexibility. In this study, we present an original and efficient machine learning approach for retrieving soot temperature and volume fraction fields simultaneously from single-color near-infrared emission measurements of dilute ethylene diffusion flames. The machine learning model gathers information from existing data and builds connections between combustion scalars (soot temperature and volume fraction) and emission measurements of flames. Numerical studies were conducted first to show the feasibility and robustness of the method. The experimental Multi-Layer Perceptron (MLP) neural network model was fostered and validated by the N 2 diluted ethylene diffusion flames. Furthermore, the model capability tests were carried out as well for CO 2 diluted ethylene diffusion flames. Eventually, the model performance subjected to the Modulated Absorption/Emission (MAE) technique measurement uncertainties were detailed.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here