Estimation of the fuel mixing of annular extruded fuel multi-jets in cavity flame holder at the supersonic combustion chamber via predictive surrogate model

Cavity flame holder is a well-organized method for fuel mixing inside the combustion chamber of supersonic vehicles. In this study, the combined machine learning technique of proper orthogonal decomposition (POD) combined with Long Short-Term Memory network (LSTM) is used for prediction of fuel jet penetration inside the cavity flame holder at free stream Mach=2.2 is investigated. Computational Fluid Dynamic is applied for the three-dimensional model of a cavity flame holder with extruded multi-nozzles. A comparison of single and extruded multi-nozzles for the fuel mixing at the combustor with cavity configuration is also done. The presented results show that the use of multi-jet efficiently increases the fuel mixing inside the cavity since the vortex structure is enhanced in this configuration. The usage of a shock creator pushes the shear layer into the cavity and limits the fuel penetration inside the cavity. Therefore, the shock creator is not recommended for the fuel mixing of extruded multi-jet injection systems.