Recurrence networks to study dynamical transitions in a turbulent combustor
V Godavarthi1, V R Unni1, E A Gopalakrishnan2
1Department of Aerospace Engineering, IIT Madras, Chennai 600036, India.
Chaos (Woodbury, N.Y.)
|July 7, 2017
Summary
Recurrence network analysis reveals distinct topological patterns during gas turbine combustor transitions. This method can predict thermoacoustic instability and lean blowout, crucial for efficient fuel lean operation.
Area of Science:
- Combustion science and engineering
- Nonlinear dynamics and complex systems
- Acoustic-combustion interactions
Background:
- Gas turbine combustors face challenges with thermoacoustic instability and lean blowout under fuel lean conditions.
- These phenomena arise from complex nonlinear interactions between turbulent reactive flow and the combustor's acoustic field.
- Understanding transitions between dynamic regimes is critical for operational stability.
Purpose of the Study:
- To investigate the use of recurrence networks for analyzing dynamic transitions in gas turbine combustors.
- To identify early warning indicators for thermoacoustic instability and lean blowout.
- To correlate network topology with different combustion regimes.
Main Methods:
- Time series data from a dynamic variable in the thermoacoustic system were transformed into ε-recurrence networks.
- Topological characteristics of these networks were analyzed.
- The presence or absence of power law degree distribution was examined across different combustion states.
Main Results:
- Recurrence networks exhibited power law degree distribution during combustion noise and pre-blowout oscillations.
- Power law degree distribution was absent during thermoacoustic instability and intermittency.
- Network topological measures effectively captured transitions in the turbulent combustor.
Conclusions:
- Recurrence network analysis provides valuable tools for monitoring gas turbine combustor dynamics.
- Topological features of recurrence networks can serve as early warning signals for impending instability and blowout.
- This approach enhances the understanding and prediction of complex combustion phenomena.
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