期刊
JOURNAL OF THE ENERGY INSTITUTE
卷 109, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.joei.2023.101258
关键词
Soot pyrometry; Artificial neural networks; Broadband EMI
This paper presents a low-cost approach based on Artificial Neural Networks (ANNs) for retrieving fields of soot temperature in laminar flames from broadband soot emission signals captured with a color camera. The approach generates numerical simulations of soot temperature fields and their corresponding projections to the camera plane, and uses large datasets to design and train different ANN models. The experiments show that properly trained ANNs outperform traditional techniques in retrieving soot temperature and can provide accurate temperature fields from emission measurements taken in real experimental campaigns.
This paper presents a low-cost approach based on Artificial Neural Networks (ANNs) for retrieving fields of soot temperature in laminar flames from broadband soot emission signals captured with a color camera. Using a framework to generate numerical simulations of soot temperature fields in laminar flames and their corre-sponding projections to the camera plane, we generated large datasets for designing and training different ANN models to infer the relationships between the reference temperature fields and the emission measurements captured with the camera.Experiments over simulated datasets show that properly trained ANNs outperform traditional onion-peeling deconvolution techniques used for retrieving soot temperature from emission signals, delivering accurate tem-perature estimations that are close to the ones obtained with the more sophisticated modulated absorption/ emission techniques that require a much more complex experimental setup. We also show that ANNs trained with simulated data can provide consistent and accurate temperature fields from emission measurements taken in real experimental campaigns using both commercial and industrial-grade color cameras.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据