关键词: 分形, 关联维数, 径向基函数, 神经网络, 多相流, 流型, 流型识别

Abstract: There exist no accurate evaluation indexes in traditional identification methods of flow regime.Usually,researchers can only give ambiguous description of the characteristics of each working condition.This brings out great dependence on judgement of researchers and makes automatic and on line identification of flow regime impossible.In order to overcome the shortcomings of traditional identification methods,mineral oil,air and water are used as working fluids to simulate the crude oil,natural gas and water multiphase flow in horizontal transportation pipeline,a piezo-resistance differential pressure transducer is adopted to measure the differential pressure of the flow.Thus the differential pressure signals which reflect the characteristics of fluctuation of oil-gas-water multiphase flow are obtained.By using the reconstruction of pesudo-phase space algorithm (Grassberger-Procaccia algorithm),the correlation dimensions of the denoised differential pressure signals are obtained.Based on the correlation dimensions of different flow regimes and working conditions,the characteristic vectors of different flow regimes and different working conditions are constituted.Thus the quantification of characteristics of flow regime of oil-gas-water multiphase flow is realized.The characteristic vectors are then input into the Radius Basis Function (RBF) neural network.Through training with study samples,the trained RBF neural network can automatically identify the flow regime of test samples (differential pressure signals of unknown flow regimes) on line.Results showed that the method discussed in this paper had the merits such as high accuracy,fast response and no artificial intervention.It has a promising prospect for application in petroleum and nuclear industries.

Key words: fractal, correlation dimension, Radius Basis Function, neural network, multiphase flow, flow regime, flow regime identification