Identification method for flow pattern in upward pipe based on wavelet transform and neural network

In order to realize the intelligent identification of gas-liquid two-phase flow patterns in upward pipes, the flow pattern identification method based on wavelet transform and probabilistic neural network was proposed. The gas-liquid two-phase flow test was conducted using the small indoor loop experimental device of China University of Petroleum (East China), and the flow pattern in upward pipes and the corresponding liquid holdup signals were acquired. Wavelet transform was used to decompose the liquid holdup signal into five levels, and the standard deviation of the decomposed signal was extracted as the input parameter of probabilistic neural network to identify the stratified flow, bubble flow, slug flow and severe slug flow patterns obtained in the experiment. The results show a good recognition effect of the method on four flow patterns. The overall recognition rate is 96.5%, and the recognition rate for stratified flow and severe slug flow can be up to 98%. The identification method for flow pattern in upward pipe based on wavelet transform and probabilistic neural network can effectively overcome the influence of subjective factors in traditional recognition methods, significantly improve the accuracy of flow pattern recognition, and also facilitate a more intelligent recognition process.