HHT与神经网络在油气两相流流型识别中的应用

For acquiring the flow regime information of two-phase flow,a flow regime identification method using the Hilbert-Huang Transform (HHT) combined with Radial Basis Function neural networks was put forward.In this study,oil-gas two-phase flow in horizontal pipe was taken as the experimental object, differential pressure signals coming from Venturi tube were handled by Hilbert-Huang Transform,and characteristic vector closely associated with the flow regime were obtained.Flow regime was identified by using Radial Basis Function neural networks.While oil flux was in the range of 4.2 to 7.0 m3·h -1 and gas flux was 0 to 30 m3·h -1, this method showed high identification precision for bubble flow, slug flow, churn flow and annular flow et al.The experimental study showed that this method could precisely identify the flow regime and could be used easily.