The slotted-orifice is a new type of flow sensor for gas-liquid two-phase flow measurement,and there is no correlation of two-phase multipliers for the slotted-orifice available.Based on the air-water two-phase flow experimental data of slotted-orifice,five typical correlations of the standard throttle device were applied to the data,and the application range and the reason for error involved from these correlations were analyzed.Based on different modeling ideas,three new correlations,which include the Lockhart-Martinelli parameter,gas pressure and gas Froude number,were proposed and used in the study of metering technology of wet gas flow.The accuracy of new correlations can meet the gas industry demands for production based metering.
A slotted orifice has many superiorities over a standard orifice.For single-phase flow measurement,its flow coefficient is insensitive to the upstream velocity profile.For two phase flow measurement,various character-istics of its differential pressure(DP)are stable and closely correlated with the mass flow rate of gas and liquid.The complex relationships between the signal features and the two-phase flow rate are established through the use of a back propagation(BP)neural network.Experiments were carried out in the horizontal tubes with 50mm inner di-ameter,operated with water flow rate in the range of 0.2m3·h-1 to 4m3·h-1,gas flow rate in the range of 100m3·h-1 to 1000m3·h-1,and pressure at 400kPa and 850kPa respectively,where the temperature is ambient temperature.This article includes the principle of wet gas meter development,the experimental matrix,the signal processing techniques and the achieved results.On the basis of the results it is suggested that the slotted orifice couple with a trained neural network may provide a simple but efficient solution to the wet gas meter development.
A novel noninvasive approach, based on flow-induced vibration, to the online flow regime identification for wet gas flow in a horizontal pipeline is proposed. Research into the flow-induced vibration response for the wet gas flow was conducted under the conditions of pipe diameter 50 mm, pressure from 0.25 MPa to 0.35 MPa, Lockhart-Martinelli parameter from 0.02 to 0.6, and gas Froude Number from 0.5 to 2.7. The flow-induced vibration signals were measured by a transducer installed on outside wall of pipe, and then the normalized energy features from different frequency bands in the vibration signals were extracted through 4-scale wavelet package transform. A "binary tree" multi-class support vector machine(MCSVM) classifier, with the normalized feature vector as inputs, and Gaussian radial basis function as kernel function, was developed to identify the three typical flow regimes including stratified wavy flow, annular mist flow, and slug flow for wet gas flow. The results show that the method can identify effectively flow regimes and its identification accuracy is about 93.3%. Comparing with the other classifiers, the MCSVM classifier has higher accuracy, especially under the case of small samples. The noninvasive measurement approach has great application prospect in online flow regime identification.
