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arXiv 2602.19871physics.flu-dynnlin.CD
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基于线性和非线性动态分析见解的机器学习集成火焰状态分类用于mesoscale燃烧器

Machine Learning based Ensemble Flame Regime Classification for Mesoscale Combustors based on Insights from Linear and Nonlinear Dynamic Analysis

Department of Mechanical Engineering, Indian Institute of Science(印度科学学院机械工程系)

M Ashwin Ganesh, Akhil Aravind, Balasundaram Mohan, Saptarshi Basu

AI总结:

本研究通过机器学习集成方法,利用动态和统计分析对mesoscale燃烧器中的火焰状态进行分类,结合化学发光和声压信号分析,揭示火焰状态的动态特征。

摘要:

深入了解小尺度火焰行为可以提高微反应器、紧凑型发电系统、消防安全技术等各种应用的效率,这些应用中燃烧被限制在微或mesoscale尺度。在mesoscale燃烧器中观察到的火焰状态,即稳定火焰、具有重复熄灭和点燃的火焰,以及传播火焰,表现出独特的动态特性,这些特性使其彼此不同。在本研究中,我们从动态和统计的角度系统地检查了mesoscale燃烧器中观察到的各种火焰状态。我们的实验方法涉及在石英管(一个光学可访问的mesoscale燃烧器)中稳定火焰,其内径为5毫米。使用预混甲烷-空气混合物作为燃料,其等效比和雷诺数作为输入参数。获取燃烧动态表征的瞬时OH*化学发光、声压信号以及高速火焰成像。本研究的目标是分析这些观察到的火焰状态所关联的独特的动态特征。为此,基于实验获得的OH*化学发光和声压时间序列信号,进行了递归量度分析,随后进行了统计-频谱分析。随后,基于从上述两项分析中提取的特征,实施了基于堆叠集成的机器学习框架,用于mesoscale火焰状态分类。此外,还绘制了连续小波变换(CWT)标量图和三维相位图,以直观地阐明系统动态的演变以及这些火焰状态中竞争时间尺度的复杂相互作用。

英文摘要:

Gaining insights into flame behaviour at small scales can lead to improvements in the efficiency of micro-reactors, compact power generation systems, fire safety technologies, and various other applications where combustion is confined to micro or mesoscales. Flame regimes observed in mesoscale combustors, namely Stable flame, Flames with repetitive extinction and ignition, and Propagating flame, exhibit unique dynamic characteristics that differentiate them from one another. In this study, we systematically examine the various flame regimes observed in mesoscale combustors from both dynamical and statistical standpoints. Our experimental methodology involves stabilizing a flame inside a quartz tube (an optically accessible mesoscale combustor) with an inner diameter of 5 mm. A premixed methane-air mixture is used as fuel, with its equivalence ratio and Reynolds number being the input parameters. Instantaneous OH* chemiluminescence and Acoustic pressure signals, along with high-speed flame imaging, were acquired for combustion dynamics characterization. The objective of this study is to analyze the distinct dynamical signatures associated with these observed flame regimes. For this purpose, Recurrence Quantification Analysis, followed by a Statistical-Spectral analysis, has been performed based on the experimentally acquired OH* Chemiluminescence and Acoustic pressure time-series signals. Subsequently, a stacking ensemble-based machine learning framework has been implemented for mesoscale flame regime classification based on the features extracted from the two aforementioned analyses. In addition, Continuous wavelet transform (CWT) scalograms and three-dimensional phase plots have been graphed to visually elucidate the evolution of system dynamics and the complex interaction of competing time scales in these flame regimes.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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