Estimation of the Global Equivalence Ratio of a Swirl Combustor from a Small Number of Absorption Spectra Using Machine Learning.

IF 2.2 3区 化学 Q2 INSTRUMENTS & INSTRUMENTATION Applied Spectroscopy Pub Date : 2024-10-01 Epub Date: 2024-08-22 DOI:10.1177/00037028241268279
Cheolwoo Bong, Seong-Kyun Im, Hyungrok Do, Moon Soo Bak
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Abstract

A new optical diagnostic method that predicts the global fuel-air equivalence ratio of a swirl combustor using absorption spectra from only three optical paths is proposed here. Under normal operation, the global equivalence ratio and total flow rate determine the temperature and concentration fields of the combustor, which subsequently determine the absorption spectra of any combustion species. Therefore, spectra, as the fingerprint for a produced combustion field, were employed to predict the global equivalence ratio, one of the key operational parameters, in this study. Specifically, absorption spectra of water vapor at wavenumbers around 7444.36, 7185.6, and 6805.6 cm-1 measured at three different downstream locations of the combustor were used to predict the global equivalence ratio. As it is difficult to find analytical relationships between the spectra and produced combustion fields, a predictive model was a data-driven acquisition. The absorption spectra as an input were first feature-extracted through stacked convolutional autoencoders and then a dense neural network was used for regression prediction between the feature scores and the global equivalence ratio. The model could predict the equivalence ratio with an absolute error of ±0.025 with a probability of 96%, and a gradient-weighted regression activation mapping analysis revealed that the model leverages not only the peak intensities but also the variations in the shape of absorption lines for its predictions.

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快讯:利用机器学习从少量吸收光谱估算漩涡燃烧器的全局等效比。
本文提出了一种新的光学诊断方法,该方法仅利用三条光路的吸收光谱来预测漩涡燃烧器的整体燃料-空气等效比。在正常运行情况下,全局等效比和总流量决定了燃烧器的温度场和浓度场,随后决定了任何燃烧物的吸收光谱。因此,在本研究中,光谱作为产生的燃烧场的指纹,被用来预测全局当量比(关键运行参数之一)。具体来说,在燃烧器下游三个不同位置测量到的波长分别为 7444.36、7185.6 和 6805.6 cm-1 左右的水蒸气吸收光谱被用来预测全局当量比。由于很难找到光谱和产生的燃烧场之间的分析关系,因此预测模型是一种数据驱动的采集。作为输入的吸收光谱首先通过堆叠卷积自动编码器(CAE)进行特征提取,然后使用密集神经网络(DNN)对特征得分和全局当量比进行回归预测。梯度加权回归激活映射分析表明,该模型不仅能利用峰强度,还能利用吸收线形状的变化进行预测。
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来源期刊
Applied Spectroscopy
Applied Spectroscopy 工程技术-光谱学
CiteScore
6.60
自引率
5.70%
发文量
139
审稿时长
3.5 months
期刊介绍: Applied Spectroscopy is one of the world''s leading spectroscopy journals, publishing high-quality peer-reviewed articles, both fundamental and applied, covering all aspects of spectroscopy. Established in 1951, the journal is owned by the Society for Applied Spectroscopy and is published monthly. The journal is dedicated to fulfilling the mission of the Society to “…advance and disseminate knowledge and information concerning the art and science of spectroscopy and other allied sciences.”
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