Black-box real-time identification of sub-regime of gas-liquid flow using Ultrasound Doppler Velocimetry with deep learning
- Mao, Ning; Azman, Amirah Nabilah; Ding, Guangxin; Jin, Yubo; Kang, Can; Kim, Hyoung-Bum
- Issue Date
- PERGAMON-ELSEVIER SCIENCE LTD
- Gas-liquid flow; Flow regime; Horizontal pipe; Ultrasound Doppler velocimetry; Convolutional neural network
- ENERGY, v.239
- Journal Title
- Gas-liquid flow has a strong relationship with energy transfer, production, and transportation. A black box method based on velocity information and deep learning was proposed to identify the sub-regimes of gas-liquid flow in real time, including two parts-data acquisition and establishment of the identification model. The Ultrasound Doppler Velocimetry (UDV) method was employed to acquire the velocity information of gas-liquid flow in a horizontal pipe non-intrusively. Seven different sub-regimes are defined to perform the identification task. Poor identification accuracy was obtained with raw data; therefore, denoising was added to improve the identification accuracy. The results show that the proposed method is feasible and effective, and the Current model can achieve high accuracy similar to existing models while having the fastest identification speed. In addition, the data from untrained flow conditions were tested, and all convolutional neural network (CNN) models achieved identification accuracy higher than 91.5%. The proposed method helps in identifying two-phase flow, and its accurate and straightforward characteristics indicate broad application potential. (c) 2021 Elsevier Ltd. All rights reserved.
- Files in This Item
- There are no files associated with this item.
- Appears in
- 공학계열 > Division of Mechanical and Aerospace Engineering > Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.