Super-resolution reconstruction of flow field of hydrogen-fueled scramjet under self-ignition conditions

This paper reports experiments on a hydrogen-fueled scramjet performed at different equivalence ratios in a ground pulse combustion wind tunnel with a Mach-2.5 incoming flow. In the non-chemical reaction flow before the fuel was ignited, the flow field was oscillatory, and from the pressure monitor, the oscillation period was 0.07 s and the oscillation amplitude was 0.035 MPa. Schlieren and flame self-luminescence images of the combustor flow were obtained synchronously, and the flow-field structure was stable with the flame concentrated in the shear layer. Deep learning was used to subject the low-resolution combustion flow field to super-resolution analysis to reconstruct a high-resolution flow field. To improve the spatial resolution of the flow field during self-ignition of the hydrogen-fueled scramjet and study the flow mechanism and coupling rule of turbulent fluctuations in the ignition process, a multipath asymmetric residual network (MARN) is proposed based on a single-path super-resolution convolutional neural network (SRCNN) and a residual network model (ResNet_16). The experimental results show that compared with SRCNN and ResNet_16, MARN has the best precision and performance regarding the super-resolution flow field in the self-ignition of hydrogen fuel in terms of the mean peak signal-to-noise ratio, mean structural similarity, and average correlation coefficient as well as being the least complicated. The proposed method offers the possibility of developing lightweight super-resolution models for the flow fields in supersonic combustors; it shows enormous potential for revealing the physical flow of the fuel and air mixture, and it offers accurate forecasts of self-ignition times.