A Cascaded Convolutional Neural Network for Two-Phase Flow PIV of an Object Entering Water
| Autor: | Xiaojun Bi, Changdong Yu, Mingjie He, Haozhe Luo, Yi-wei Fan |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Computer science
business.industry Optical flow Image segmentation Convolutional neural network Physics::Fluid Dynamics Particle image velocimetry Flow (mathematics) Cascade Calibration Computer vision Two-phase flow Artificial intelligence Electrical and Electronic Engineering business Instrumentation |
| Zdroj: | IEEE Transactions on Instrumentation and Measurement. 71:1-10 |
| ISSN: | 1557-9662 0018-9456 |
| Popis: | In the two-phase flow particle image velocimetry (PIV) experiment of an object entering water, the mask of non-computed area and the calculation of velocity field in particle images are two key stages. Due to the complexity of the edge of the object in the particle image, the mask calibration is usually performed manually and then the PIV estimation is carried out. We propose a cascaded convolutional neural network (CNN) in this paper to implement end-to-end two-phase flow fluid motion estimation. In the first stage, the image segmentation network U-Net is used to mask the non-computational area of the image and extract the liquid phase. In the second stage, we adopt the improved deep optical flow network, which is known as recurrent allpairs field transforms (RAFT) to calculate velocity field. What’s more, the corresponding datasets are generated for training model parameters. Finally, our approach is tested on synthetic and experimental images. The experimental results indicate that our approach not only reaches accurate segmentation of the calculated liquid phase region, but also achieves a high-precision velocity field calculation. Meanwhile, this cascade CNN model has high efficiency toward real-time estimation. |
| Databáze: | OpenAIRE |
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