基于RBF的油气水段塞流流型超声识别方法OA北大核心CSTPCD

Flow pattern recognition plays a crucial role in the efficient operation and management of oil pipelines.However,the existing methods primarily focus on gas-liquid and oil-water two-phase flow,with limited accuracy in identifying flow patterns within the oil-gas-water slug flow segment.To address this limitation,this study proposed an ultrasound-based method for identifying flow patterns in the oil-gas-water slug flow segment using a radial basis function(RBF)neural network.The proposed method utilized the unique characteristics of phase distribution within the oil-gas-water slug flow segment and establishes a comprehensive set of 350 ultrasound test simulation models.By employing ultrasound transmission attenuation and reflection echo techniques,the response characteristics of the oil-gas-water slug flow segment within the pipeline were investigated.The transmission attenuation signals were then extracted to differentiate between the liquid film region,bubble entrainment region,and stable liquid slug region.To classify the flow patterns,the statistical features,such as the energy of reflected signal time series data,were extracted and utilized as inputs for the RBF neural network.The experimental results demonstrated that the proposed method achieves a high flow pattern recognition rate of 95.7％ based on the ultrasound propagation mechanism and RBF neural network.This research provided a theoretical foundation for implementing flow pattern recognition of oil-gas-water slug flow in horizontal pipelines using ultrasound technology.The application of the RBF neural network-based recognition algorithm significantly enhanced the accuracy and efficiency of flow pattern identification,offering valuable insights for the effective operation and control of oil pipeline systems.