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Computer Vision-Based Classification of Flow Regime and Vapor Quality in Vertical Two-Phase Flow.
- Source :
- Sensors (14248220); Feb2022, Vol. 22 Issue 3, p996, 1p
- Publication Year :
- 2022
-
Abstract
- This paper presents a method to classify flow regime and vapor quality in vertical two-phase (vapor-liquid) flow, using a video of the flow as the input; this represents the first high-performing and entirely camera image-based method for the classification of a vertical flow regime (which is effective across a wide range of regimes) and the first image-based tool for estimating vapor quality. The approach makes use of computer vision techniques and deep learning to train a convolutional neural network (CNN), which is used for individual frame classification and image feature extraction, and a deep long short-term memory (LSTM) network, used to capture temporal information present in a sequence of image feature sets and to make a final vapor quality or flow regime classification. This novel architecture for two-phase flow studies achieves accurate flow regime and vapor quality classifications in a practical application to two-phase CO<subscript>2</subscript> flow in vertical tubes, based on offline data and an online prototype implementation, developed as a proof of concept for the use of these models within a feedback control loop. The use of automatically selected image features, produced by a CNN architecture in three distinct tasks comprising flow-image classification, flow-regime classification, and vapor quality prediction, confirms that these features are robust and useful, and offer a viable alternative to manually extracting image features for image-based flow studies. The successful application of the LSTM network reveals the significance of temporal information for image-based studies of two-phase flow. [ABSTRACT FROM AUTHOR]
- Subjects :
- TWO-phase flow
DEEP learning
GASES
ONLINE databases
VAPORS
COMPUTER vision
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 22
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- Sensors (14248220)
- Publication Type :
- Academic Journal
- Accession number :
- 155265281
- Full Text :
- https://doi.org/10.3390/s22030996