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Dynamic mode decomposition of syngas (H2/CO) flame during transition to high-frequency instability in turbulent combustor

Author

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  • Baraiya, Nikhil A.
  • Ramanan, Vikram
  • Nagarajan, Baladandayuthapani
  • Vegad, Chetankumar S.
  • Chakravarthy, S.R.

Abstract

The present experimental study investigates the transition in syngas combustion dynamics from low-to high-frequency thermo-acoustic instability obtained by continuously varying the Reynolds number (Re). Simultaneous OH* and CO2* chemiluminescence images were acquired to understand the role of flame dynamics in underscoring the transition. Further analysis is performed with two objectives— to identify the most driving regions of the two chemiluminescence maps by means of a spatial Rayleigh index map and to secure a correlation between the most driving region(s) and flame regions most receptive to positive coupling with acoustics. In order to realize the second objective, we perform segmented temporal Dynamic Mode Decomposition (DMD) of the flame chemiluminescence, which provides a spatial perspective for the growth rate. The growth rate variation of the OH* chemiluminescence map during the transition to high-frequency instability is observed to be convex shape, while CO2* chemiluminescence displays a bi-modal variation. These features corroborate the physical mechanism underscoring the high-frequency oscillations in the presence of highly receptive and spatially staggered flame regions. Further, the most receptive regions also map to the most driving regions, thereby establishing a one-to-one mapping between the source term distribution of the system acoustic variables and that of acoustic energy.

Suggested Citation

  • Baraiya, Nikhil A. & Ramanan, Vikram & Nagarajan, Baladandayuthapani & Vegad, Chetankumar S. & Chakravarthy, S.R., 2023. "Dynamic mode decomposition of syngas (H2/CO) flame during transition to high-frequency instability in turbulent combustor," Energy, Elsevier, vol. 263(PD).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pd:s0360544222028845
    DOI: 10.1016/j.energy.2022.125998
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    1. Zhang, Long & Zhou, Hua & Ren, Zhuyin, 2024. "Physics-guided fuel-switching neural networks for stable combustion of low calorific industrial gas," Energy, Elsevier, vol. 303(C).

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