Intelligent reconstruction of the flow field in a supersonic combustor based on deep learning

The data-driven intelligent reconstruction of a flow field in a supersonic combustor aids the real-time monitoring of wave system evolution in a scramjet flow field structure, allowing the determination of the combustion state for active flow control. In this paper, a deep learning architecture based on a multi-branch fusion convolutional neural network (MBFCNN) is proposed to reconstruct the flow field in a supersonic combustor. Experiments on hydrogen-fueled scramjets with different equivalence ratios were carried out in a direct-connected supersonic pulse combustion wind tunnel with an inflow Mach number of 2.5 to establish a dataset for MBFCNN network training and testing. The trained model successfully reconstructed the flow field structure from measured wall pressure data. The flow field reconstruction model provided a rich information source for the evolution of the wave system structure under the self-ignition conditions of the hydrogen-fueled scramjet, greatly improving the detection accuracy. The proposed deep learning architecture method was compared with basic convolutional neural network and symmetric convolutional neural network methods. The three methods all accurately reconstructed the flow field of the supersonic combustor. However, the proposed MBFCNN provided the best reconstruction results, and its average linear correlation coefficient in the test set was 0.952. The proposed MBFCNN had a lower mean square error and higher peak signal-to-noise ratio than the other two methods, which verified that the proposed model is eminently able to reconstruct and predict the flow field of a supersonic combustor. Published under an exclusive license by AIP Publishing.

Automated summary

This paper proposes a deep learning architecture based on a multi-branch fusion convolutional neural network for the reconstruction of the flow field in a supersonic combustor. Experimental results show that this method has advantages in improving the accuracy of monitoring the structure of supersonic flow fields.