Identification method of gas–liquid two-phase flow pattern based on dual-domain multi-scale feature fusion framework

Abstract Accurate identification of the flow pattern of gas–liquid two-phase flow is of great significance to ensure the transportation safety of industrial pipelines. We propose a flow pattern identification method based on dual-domain and multi-scale feature fusion. Firstly, the gas–liquid two-phase flow pattern information in the vertical pipeline is collected by array resistance sensors, and the measured data is processed by dimensionality reduction. Firstly, the gas–liquid two-phase flow pattern information in the vertical pipeline is acquired using array resistance sensors, and the measured data undergoes dimensionality reduction for preprocessing. Subsequently, the reduced-dimensional data is segmented through a sliding window approach. The segmented data is analyzed using Choi-Williams Distribution and Discrete Fourier Transform to extract the energy signal and power spectrum signal, respectively. These signals are then encoded into two-dimensional grayscale images by pseudo-image encoding, providing a basis for investigating the flow pattern transition mechanisms in two-phase flow. Finally, the two grayscale images are fed into the dual-domain multi-scale feature fusion network for flow pattern recognition. The results show that this method can reveal the evolution behavior of gas–liquid two-phase flow from both the frequency domain and the time–frequency domain. The average recognition accuracy reaches 97.78%, achieving accurate identification of flow patterns.