An Intelligent Recognition Method of Gas-liquid Two-phase Flow Based on Optical Image

Abstract

The invention provides an intelligent identification method of gas-liquid two-phase flow based on optical images, prepares training data sets and test data sets; constructs a fully convolutional network model FCN; identifies gas-liquid two-phase according to the trained FCN full convolutional network model Bubbles in the flow: For the FCN fully convolutional network model after training, input a picture of gas-liquid two-phase flow to be recognized for the model, and the bubbles in the picture can be identified almost accurately through the network, and the accuracy of bubble recognition can be calculated and obtained. Spend. The FCN method based on deep supervised learning and data extraction is introduced into gas-liquid two-phase flow recognition, which can automatically extract information from the pixel level through multi-layer convolution operations to extract abstract semantic concepts, using upsampling layers and multi-scale Fusion technology is used to further optimize the results, so that the high-level subnetwork can be fused with the characteristics of the low-level subnetwork many times to maintain a very high resolution, thereby improving the accuracy of bubble recognition.

Description

technical field [0001] The invention relates to an intelligent identification method of gas-liquid two-phase flow based on optical images, in particular to a gas-liquid two-phase flow identification method based on an improved full convolution network model FCN, especially for the problem of bubble identification in liquid, belonging to The field of gas-liquid two-phase flow identification and classification. Background technique [0002] In nature and industrial processes, multiphase flow problems are often involved, among which gas-liquid two-phase flow is the most common. Based on the complex hydrodynamic characteristics of bubbles in many fields such as chemical engineering, biopharmaceuticals, geophysics, and wastewater management, it is necessary to better understand the interaction between two-phase flows through experimental studies. Basic experimental research supports the development of various engineering fields by establishing closed experimental models. Furthe...

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Problems solved by technology

Due to the high complexity of underwater bubbly flow data, the robustness of the model is poor, and most industrial experiments require extremely high bubble identification accuracy. Therefore, for the identification of gas-liquid two-phase flow bubbles Identifying problems, introducing an improved deep learning model to focus on accurate identification of bubbles, has high application value in this field

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