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.

中文翻译：

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通过预测代理模型估计超音速燃烧室腔体火焰稳定器中环形挤压燃料多喷嘴的燃料混合

腔体火焰稳定器是超音速飞行器燃烧室内燃料混合的一种组织良好的方法。在本研究中，采用适当正交分解（POD）与长短期记忆网络（LSTM）相结合的组合机器学习技术来预测自由流马赫=2.2时腔体火焰稳定器内的燃料射流穿透力。计算流体动力学应用于挤压多喷嘴腔体火焰稳定器的三维模型。还对具有空腔结构的燃烧室中燃料混合的单喷嘴和挤压多喷嘴进行了比较。结果表明，多喷嘴的使用有效地增加了腔内的燃料混合，因为在这种配置中涡流结构得到了增强。使用冲击发生器将剪切层推入空腔并限制燃料在空腔内的渗透。因此，不建议将冲击发生器用于挤压式多喷嘴喷射系统的燃料混合。