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Flame blowout: Transition to an absorbing phase
Vishnu R Unni1, Swetaprovo Chaudhuri2, R I Sujith1
1Department of Aerospace Engineering, Indian Institute of Technology Madras, Chennai 600036, India.
Flame blowout in gas turbines, driven by lean fuel conditions, is modeled using flamelet population dynamics. This approach reveals blowout as a transition to flame extinction, offering insights for reduced-order models.
Area of Science:
- Combustion science
- Turbulent flows
- Nonlinear dynamics
Background:
- Turbulent flames in gas turbines are prone to extinction at lean fuel conditions, a phenomenon known as flame blowout.
- Traditional views consider blowout as a loss of combustor stability, but pre-blowout dynamics suggest a need for more comprehensive models.
- Understanding these dynamics is crucial for developing reduced-order models for flame behavior.
Purpose of the Study:
- To model the emergent dynamics of flamelet populations in turbulent flames near blowout conditions.
- To investigate flame blowout as a population dynamics process analogous to a contact process.
- To capture the complex dynamics observed in turbulent flames approaching blowout.
Main Methods:
- A cellular automata based model was employed to simulate the population dynamics of flamelets.
- The model treats the turbulent flame as a collection of interacting flamelets.
- Flame blowout is represented as the extinction of the flamelet population.
Main Results:
- The cellular automata model qualitatively captures the dynamics of turbulent flames approaching blowout.
- Flame blowout is demonstrated to be analogous to a threshold-like transition to an absorbing phase.
- The population dynamics of flamelets provide a novel perspective on flame extinguishment.
Conclusions:
- Modeling flamelet population dynamics offers a new framework for understanding turbulent flame blowout.
- The cellular automata approach successfully replicates key pre-blowout flame dynamics.
- Flame blowout exhibits characteristics of a phase transition, suggesting potential for simplified modeling.
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