Experimental study of the effect of CO2 on temperature and soot volume fraction in C2H4/air co-flow laminar diffusion flame

This paper originally proposes a physics informed neural networks (PINNs) model for the simultaneous prediction of soot temperature and volume fraction fields in laminar flames from experimental soot integral radiation. Contrasted with the previous data-driven models, the PINNs model incorporates the line-of-sight soot radiation integral equation into the model architecture. Doing so, the superiority of the physics informed neural networks model is displayed in terms of prediction accuracy under limited training data size and training efficiency. Due to the significant reduction of training experimental data dependence, such gray-box physics informed methodology sheds light on practical combustion devices combustion monitoring, i.e., limited optical window endoscope engines.

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Experimental study of the effect of CO₂ on temperature and soot volume fraction in C₂H₄/air co-flow laminar diffusion flame

Abstract: The soot volume fraction distributions of CO2-diluted C2H4/air co-flow laminar diffusion flame were measured by a CMOS camera and inverse Abel transform. The soot formation was suppressed by the density, transport and chemical effects of CO2.

Cited by 3 publications

References 36 publications

Effects of N2 and CO2 addition to oxidizer on soot formation in n-heptane laminar diffusion flame at preheating temperatures

Effects of N2 and CO2 addition to oxidizer on soot formation in n-heptane laminar diffusion flame at preheating temperatures

Study on the microstructure and soot formation mechanism of hydrogen addition ethylene inverse diffusion flame

Soot temperature and volume fraction field predictions via line-of-sight soot integral radiation equation informed neural networks in laminar sooting flames

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