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

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.