A flow pattern recognition method for gas-liquid two-phase flow based on dilated convolutional channel attention mechanism
Tallennettuna:
| Julkaisussa: | PLoS One vol. 20, no. 6 (Jun 2025), p. e0325784 |
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| Päätekijä: | |
| Muut tekijät: | |
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Public Library of Science
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| Linkit: | Citation/Abstract Full Text Full Text - PDF |
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| 024 | 7 | |a 10.1371/journal.pone.0325784 |2 doi | |
| 035 | |a 3223867926 | ||
| 045 | 2 | |b d20250601 |b d20250630 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Liu, Jie | |
| 245 | 1 | |a A flow pattern recognition method for gas-liquid two-phase flow based on dilated convolutional channel attention mechanism | |
| 260 | |b Public Library of Science |c Jun 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Addressing the issue of insufficient key feature extraction leading to low recognition rates in existing deep learning-based flow pattern identification methods, this paper proposes a novel flow pattern image recognition model, Enhanced DenseNet with transfer learning (ED-DenseNet). The model enhances the deep feature extraction capability by introducing a multi-branch structure, incorporating an ECA attention mechanism into Dense Blocks and dilated convolutions into Transition Layers to achieve multi-scale feature extraction and refined channel information processing. Considering the limited scale of the experimental dataset, pretrained DenseNet121 weights on ImageNet were transferred to ED-DenseNet using transfer learning. On a gas-liquid two-phase flow image dataset containing Annular, Bubbly, Churn, Dispersed, and Slug flow patterns, ED-DenseNet achieved an overall recognition accuracy of 97.82%, outperforming state-of-the-art models such as Flow-Hilbert–CNN, especially in complex and transitional flow scenarios. Additionally, the model’s generalization and robustness were further validated on a nitrogen condensation two-phase flow dataset, demonstrating superior adaptability compared to other methods. | |
| 653 | |a Flow distribution | ||
| 653 | |a Feature extraction | ||
| 653 | |a Pattern recognition | ||
| 653 | |a Flow pattern | ||
| 653 | |a Datasets | ||
| 653 | |a Data processing | ||
| 653 | |a Nuclear reactors | ||
| 653 | |a Identification methods | ||
| 653 | |a Neural networks | ||
| 653 | |a Signal processing | ||
| 653 | |a Two phase flow | ||
| 653 | |a Information processing | ||
| 653 | |a Slug flow | ||
| 653 | |a Deep learning | ||
| 653 | |a Machine learning | ||
| 653 | |a Multiphase flow | ||
| 653 | |a Transfer learning | ||
| 653 | |a Transition layers | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Wu, Yang | |
| 773 | 0 | |t PLoS One |g vol. 20, no. 6 (Jun 2025), p. e0325784 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3223867926/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3223867926/fulltext/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3223867926/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |