Unified scaling and shape laws for turbulent premixed methane and hydrogen jet flames
Aurora Maffei, Thomas L. Howarth, Marianna Cafiero, Florence Cameron, Michael Gauding, Joachim Beeckmann, Heinz Pitsch
Abstract
The scaling of turbulent premixed flames is typically described by correlations derived for unity-Lewis-number fuels. However, their validity for hydrogen (H$_{2}$) remains uncertain due to the thermodiffusive effects associated with its low Lewis number. In this study, turbulent premixed H$_{2}$ and methane (CH$_{4}$) jet flames are systematically compared over a wide range of operating conditions. Experiments were conducted for Reynolds numbers between 5000 and 60000 and effective Karlovitz numbers spanning 3-368. Flame structure and global flame geometry were characterized using spatially resolved OH$^{*}$ chemiluminescence imaging, allowing consistent comparison between the two fuels across different turbulence intensities. The results are interpreted via a unified framework that incorporates two thermodynamic- and fuel-dependent parameters: a flame speed factor, $α$, representing the enhancement of local burning rates, and a shape factor, $γ$, describing the scaling of mean flame geometry. Despite significant fuel-specific thermodiffusive effects associated with preferential diffusion and intrinsic reactivity, which lead H$_{2}$ flames to exhibit enhanced sensitivity to turbulence and more compact flame configurations, both H$_{2}$ and CH$_{4}$ flames are found to exhibit robust and consistent turbulent scaling behavior when analysed within the proposed unified framework. The resulting correlations provide a generalised description of turbulent burning velocity and flame structure, demonstrating that key turbulence-chemistry interactions can be captured within a common model across fuels with widely different Lewis numbers. Overall, the dataset spans multiple turbulence regimes and flame geometries for both fuels, providing a valuable experimental benchmark for the validation of turbulent combustion models across different regimes.
