Flickering Buoyant Diffusion Flames in Weakly Rotatory Flows
arXiv:2208.09278 · doi:10.1007/s00162-023-00671-0
Abstract
Flickering buoyant diffusion methane flames in weakly rotatory flows were computationally and theoretically investigated. The prominent computational finding is that the flicker frequency nonlinearly increases with the rotational intensity number R (up to 0.24), which measures the relative importance of the rotational speed compared with the methane jet speed. This finding is consistent with the previous experimental observations that flame flicker is enhanced by rotatory flows within a certain extent. Based on the vortex-dynamical understanding of flickering flames that the flame flicker is caused by the periodic shedding of buoyancy-induced toroidal vortices, we formulated a scaling theory for flickering buoyant diffusion flames in weakly rotatory flows. The theory predicts that, with respect to the flicker frequency f_0 at R=0, the increase of the flicker frequency f at nonzero R obeys the scaling relation f-f_0~R^2, which agrees very well with the present computational results. In physics, the externally rotatory flow enhances the radial pressure gradient around the flame, and the significant baroclinic effect contributes an additional source for the growth of toroidal vortices so that their periodic shedding is faster.
24 pages, 9 figures, research paper
References in corpus (6)
- Diffusion-flame flickering as a hydrodynamic global mode
- Observed dependence of characteristics of liquid-pool fires on swirl magnitude
- Velocity Reconstruction in Puffing Pool Fires with Physics-Informed Neural Networks
- Dynamical Mode Recognition of Triple Flickering Buoyant Diffusion Flames: from Physical Space to Phase Space and to Wasserstein Space
- Vortex Interaction in Triple Flickering Buoyant Diffusion Flames
- Oscillation and collective behavior in convective flows
Cited by in corpus (3)
- Dynamical Mode Recognition of Coupled Flame Oscillators by Supervised and Unsupervised Learning Approaches
- Computational Identification and Stuart-Landau Modeling of Collective Dynamical Behaviors of Octuple Laminar Diffusion Flame Oscillators
- Dimensionality Reduction and Dynamical Mode Recognition of Circular Arrays of Flame Oscillators Using Deep Neural Network