Integrated Fiber-Optic Sensing and Deep Learning for Supersonic Inlet Flow Reconstruction and Buzz Diagnosis

Abstract

Monitoring the internal flow stability of supersonic inlets is critical for flight safety but faces dual challenges: acquiring high-fidelity data under harsh environments and inferring global flow states from sparse measurements. To address these challenges, this article proposes a “sensor-to-algorithm” closed-loop framework for precise shock wave monitoring and buzz diagnosis. At the sensing level, a temperature-decoupled fiber Bragg grating (FBG) pressure sensor array is designed to overcome cross sensitivity, ensuring high-fidelity and interference-immune data acquisition in supersonic flows. At the algorithm level, advanced deep learning models are employed to process the sparse optical signals. Specifically, a mask-guided convolutional neural network (MG-CNN) reconstructs global flow field visualizations for terminal shock localization, while a time–frequency-aware CNN (TF-CNN) identifies complex dynamic buzz regimes. Experimental results demonstrate that this integrated framework achieves a shock localization error of 3.8 mm and a 99.04% accuracy in buzz diagnosis, providing a highly reliable solution for supersonic inlet health monitoring.