Supersonic combustion flow field reconstruction based on multi-view domain adaptation generative network in scramjet combustor

The efficient and precise reconstruction of supersonic combustion flow fields enables real-time sensing and control of hypersonic vehicles. However, current flow field reconstruction methodologies often suffer from limited prediction accuracy, poor generalization capabilities, and high model energy consumption. In this research, a robust and efficient multi-source data fusion framework for combustion flow field reconstruction based on a multi-view domain adaptation generative network (MV-DAGN) is developed and evaluated. In order to utilize multivariate flow field data, this study adopts a multi-view learning approach to thoroughly integrate various physical field data. It introduces an MV-DAGN framework for training models on multi-source data from supersonic combustor with a Mach 2.5 low equivalence ratio derived from ground-based pulse combustion wind tunnels. The concept of transfer learning is incorporated, and the fusion of wall pressure and flame field data is utilized to validate the flow field reconstruction by including a limited set of high equivalence ratio data. Subsequently, to diminish the model's training duration and enhance the prediction speed of the combustion flow field, a lightweight MV-DAGN model is established.