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

Abstract

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 N2 diluted ethylene diffusion flames. Furthermore, the model capability tests were carried out as well for CO2 diluted ethylene diffusion flames. Eventually, the model performance subjected to the Modulated Absorption/Emission (MAE) technique measurement uncertainties were detailed.