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

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

Abstract

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.

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

Abstract

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.
Paper Structure (17 sections, 5 equations, 9 figures, 2 tables)

This paper contains 17 sections, 5 equations, 9 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Experimental Setup and Instrumentation
  3. Methodology
  4. Signal Preprocessing
  5. Recurrence Quantification Analysis
  6. Statistical-Spectral Analysis
  7. Stacking Ensemble Framework
  8. Results
  9. Flame Regime Dynamics Characterization
  10. Stable Flame (SF)
  11. Flames with Repetitive Extinction and Ignition (FREI)
  12. Propagating Flame (PF)
  13. Flame Regime Classification
  14. Prediction based on Recurrence Quantification Features
  15. Prediction based on Statistical-Spectral Features
  16. ...and 2 more sections

Figures (9)

  • Figure 1: (a) Schematic of the experimental setup with flow lines, diagnostic tools, and the external heater aravind2023dynamics. (b) Inner wall temperature profile plotted along the combustor axis.
  • Figure 2: The Flame regime map for Re versus $\Phi$. The blue, red, and green colors on the map denote Stable Flame (SF), Flames with Repetition of Extinction and Ignition (FREI), and Propagating Flame (PF) regimes, respectively.
  • Figure 3: The workflow for Recurrence Quantification Analysis.
  • Figure 4: Stacking ensemble Framework for Mesoscale Flame regime Classification, describing the usage of a comprehensive suite of four classifiers followed by a meta-learner.
  • Figure 5: Plots depicting the Dynamical signal characteristics of the Stable Flame (SF) regime observed at $\Phi=1.0$ and $Re=224$. The left and the right columns display the signal segment and the plots corresponding to OH* chemiluminescence and Acoustic pressure signals, respectively.
  • ...and 4 more figures