Simultaneous soot parameters fields predictions accuracy improvements in laminar sooting flames from soot radiation measurements — a multi-task learning approach

Abstract

Machine learning assisted optical diagnostics could reduce conventional extensive experimental data collecting and post-processing costs and time in academic and industrial combustion measurements. In this paper, a novel Trident-Net (T-Net) architecture is designed and assisted for retrieval of high-fidelity soot temperature, volume fraction (SVF), and diameter fields simultaneously from soot radiation measurements in laminar sooting flames. Uniquely, the T-net is subtly fabricated in one branch of the encoder and three branches of decoders, which enables three adjustable cost functions and the corresponding decoder branch for soot three respective parameter fields. Contrasted with previous Back-propagation (BP) and U-net models, the T-Net is more efficient and achieves a higher entire score in terms of individual decoder manipulation. In addition, owing to the generalization performance improvement of multi-task learning, the T-Net model demonstrates decent prediction performance under few-shot learning. Thus, this proposed T-Net model showcases the great opportunities for real-time, in situ, monitoring of practical combustion pollutant emission, by embedded in a response strategy of the detection devices.