Particle trajectory and pulsation flame of magnesium combustion

Green recyclability, high-energy density and zero-carbon nature are among the pivotal benefits of magnesium sustainable fuel. In current study, two crucial issues corresponding to the premixed magnesium combustion comprising the trajectories of magnesium and magnesia and pulsation flame are assessed. Regarding the detailed multi-zone flame structure and consideration of thermal and dynamical non-equilibrium, the governing equations and boundary conditions are derived. These equations involve the impacts of decisive items, namely semivariable thermophysical properties, thermophoretic motion, chamber heat loss and flame radiation. The results reveal that with decreasing particle diameter, the importance of the items considered in current modeling increases and its outputs become closer to experimental data. The heat loss of combustion chamber containing magnesium particles with 15 mu m diameter has the greatest impact on flame velocity prediction. While for 34 and 70 mu m particles, the semi-variable properties and flame radiation are the main influential items in the flame velocity calculation, respectively. Eventually, the magnesium pulsation combustion is scrutinized under the external perturbation in flow velocity. It is found that using helium as neutral gas instead of nitrogen lessens the maximum amplitude of flame oscillation by 16.75 times.

Automated summary

Green recyclability, high-energy density, and zero-carbon nature are the key advantages of magnesium sustainable fuel. This study evaluates two crucial issues related to premixed magnesium combustion, namely the trajectories of magnesium and magnesia, and pulsation flame. By considering detailed multi-zone flame structure and various factors, the governing equations and boundary conditions are derived. The results show that the importance of considered factors increases with decreasing particle diameter, and the heat loss of the combustion chamber has the greatest impact on flame velocity prediction.