深層学習に基づく気泡検出技術を用いたロッドバンドル流路内3次元可視化計測
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| Published in | 混相流 Vol. 39; no. 1; pp. 61 - 71 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | Japanese |
| Published |
日本混相流学会
15.03.2025
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0914-2843 1881-5790 |
| DOI | 10.3811/jjmf.2025.004 |
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| Author | 上澤, 伸一郎 小野, 綾子 吉田, 啓之 |
|---|---|
| Author_xml | – sequence: 1 fullname: 小野, 綾子 organization: 日本原子力研究開発機構 炉物理・熱流動研究グループ – sequence: 1 fullname: 上澤, 伸一郎 organization: Corresponding author – sequence: 1 fullname: 吉田, 啓之 organization: 日本原子力研究開発機構 炉物理・熱流動研究グループ |
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| References | [2] Wang, B. and Socolofsky, S. A., A Deep-sea, High-speed, Stereoscopic Imaging System for in situ Measurement of Natural Seep Bubble and Droplet Characteristics., Deep Sea Res. Part I Oceanogr. Res. Pap., Vol. 104, 134-148 (2015). [9] She, W., Gao, Q., Zuo, Z., Liao, X., Zhao, L., Zhang, L., Nie, D. and Shao, X., Experimental Study on a Zigzagging Bubble Using Tomographic Particle Image Velocimetry with Shadow Image Reconstruction, Phys. Fluids, Vol. 33(8), 083313 (2021). [7] Ui, A., Furuya, M., Arai, T. and Shirakawa, K., Measurement of Forced Convection Subcooled Boiling Flow Through a Vertical Annular Channel with High-speed Video Cameras and Image Reconstruction, J. Nucl. Sci. Technol., Vol. 59(2), 148-162 (2022). [3] Fu, Y. and Liu, Y., 3D Bubble Reconstruction Using Multiple Cameras and Space Carving Method, Meas. Sci. Technol., Vol. 29(7), 075206 (2018). [8] Zhang, T., Qian, Y., Yin, J., Zhang, B. and Wang, D., Experimental Study on 3D Bubble Shape Evolution in Swirl Flow, Exp. Therm. Fluid Sci., Vol. 102, 368-375 (2019). [10] Chang, Y., Müller, C., Kováts, P., Guo, L. and Zähringer, K., Hydrodynamics and Shape Reconstruction of Single Rising Air Bubbles in Water Using High-speed Tomographic Particle Tracking Velocimetry and 3D Geometric Reconstruction, Exp. Fluids, Vol. 65(1), 6 (2023). [15] Nagatake, T. and Yoshida, H., Measurement of the Water-vapor Void Fraction in a 4×4 Unheated Rod Bundle, J. Nucl. Sci. Technol., Vol. 60(11), 1417-1430 (2023). [5] Wang, H., Yang, Y., Dou, G., Lou, J., Zhu, X., Song, L. and Dong, F., A 3D Reconstruction Method of Bubble Flow Field Based on Multi-View Images by Bi-direction Filtering Maximum Likelihood Expectation Maximization Algorithm, Int. J. Multiph. Flow, Vol. 165, 104480 (2023). [13] Uesawa, S. and Yoshida, H., Deep Learning-Based Bubble Detection with Swin Transformer, J. Nucl. Sci. Technol., Vol. 61(11), 1438-1452 (2024). [12] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S. and Guo, B., Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows, Proc. 2021 IEEE/CVF Int. Conf. Comput. Vision, 9992-10002 (2021). [1] Bian, Y., Dong, F., Zhang, W., Wang, H. and Tan, C., 3D Reconstruction of Single Rising Bubble in Water Using Digital Image Processing and Characteristic Matrix, Particuology, Vol. 11(2), 170-183 (2013). [6] Wang, H., Xu, Y., Li, S. and Wang, J., Effects of Gas Flow Rate on Rising Bubble Chains and Induced Flow Fields: An Experimental Study, Int. J. Multiph. Flow Vol. 170, 104623 (2024). [11] He, K., Gkioxari, G., Doll?r, P. and Girshick, R., Mask R-CNN, Proc. 2017 IEEE Int. Conf. Comput. Vision, 17467816 (2017). [4] Chen, L., Xu, C., Li, J. and Zhang, B., A 3D Measurement Method of Bubbles Based on Edge Gradient Segmentation of Light Field Images, Chem. Eng. J., Vol. 452, 139590 (2023). [14] Zhang, Y., Sun, P., Jiang, Y., Yu, D., Yuan, Z., Luo, P., Liu, W. and Wang, X., ByteTrack: Multi-Object Tracking by Associating Every Detection Box, European Conf. Comput. Vision (2021). [16] Prasser, H. and Häfeli, R., Signal Response of Wire-mesh Sensors to an Idealized Bubbly Flow, Nucl. Eng. Des. Vol. 336, 3-14 (2018). |
| References_xml | – reference: [16] Prasser, H. and Häfeli, R., Signal Response of Wire-mesh Sensors to an Idealized Bubbly Flow, Nucl. Eng. Des. Vol. 336, 3-14 (2018). – reference: [11] He, K., Gkioxari, G., Doll?r, P. and Girshick, R., Mask R-CNN, Proc. 2017 IEEE Int. Conf. Comput. Vision, 17467816 (2017). – reference: [7] Ui, A., Furuya, M., Arai, T. and Shirakawa, K., Measurement of Forced Convection Subcooled Boiling Flow Through a Vertical Annular Channel with High-speed Video Cameras and Image Reconstruction, J. Nucl. Sci. Technol., Vol. 59(2), 148-162 (2022). – reference: [1] Bian, Y., Dong, F., Zhang, W., Wang, H. and Tan, C., 3D Reconstruction of Single Rising Bubble in Water Using Digital Image Processing and Characteristic Matrix, Particuology, Vol. 11(2), 170-183 (2013). – reference: [2] Wang, B. and Socolofsky, S. A., A Deep-sea, High-speed, Stereoscopic Imaging System for in situ Measurement of Natural Seep Bubble and Droplet Characteristics., Deep Sea Res. Part I Oceanogr. Res. Pap., Vol. 104, 134-148 (2015). – reference: [3] Fu, Y. and Liu, Y., 3D Bubble Reconstruction Using Multiple Cameras and Space Carving Method, Meas. Sci. Technol., Vol. 29(7), 075206 (2018). – reference: [6] Wang, H., Xu, Y., Li, S. and Wang, J., Effects of Gas Flow Rate on Rising Bubble Chains and Induced Flow Fields: An Experimental Study, Int. J. Multiph. Flow Vol. 170, 104623 (2024). – reference: [14] Zhang, Y., Sun, P., Jiang, Y., Yu, D., Yuan, Z., Luo, P., Liu, W. and Wang, X., ByteTrack: Multi-Object Tracking by Associating Every Detection Box, European Conf. Comput. Vision (2021). – reference: [4] Chen, L., Xu, C., Li, J. and Zhang, B., A 3D Measurement Method of Bubbles Based on Edge Gradient Segmentation of Light Field Images, Chem. Eng. J., Vol. 452, 139590 (2023). – reference: [8] Zhang, T., Qian, Y., Yin, J., Zhang, B. and Wang, D., Experimental Study on 3D Bubble Shape Evolution in Swirl Flow, Exp. Therm. Fluid Sci., Vol. 102, 368-375 (2019). – reference: [12] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S. and Guo, B., Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows, Proc. 2021 IEEE/CVF Int. Conf. Comput. Vision, 9992-10002 (2021). – reference: [10] Chang, Y., Müller, C., Kováts, P., Guo, L. and Zähringer, K., Hydrodynamics and Shape Reconstruction of Single Rising Air Bubbles in Water Using High-speed Tomographic Particle Tracking Velocimetry and 3D Geometric Reconstruction, Exp. Fluids, Vol. 65(1), 6 (2023). – reference: [13] Uesawa, S. and Yoshida, H., Deep Learning-Based Bubble Detection with Swin Transformer, J. Nucl. Sci. Technol., Vol. 61(11), 1438-1452 (2024). – reference: [9] She, W., Gao, Q., Zuo, Z., Liao, X., Zhao, L., Zhang, L., Nie, D. and Shao, X., Experimental Study on a Zigzagging Bubble Using Tomographic Particle Image Velocimetry with Shadow Image Reconstruction, Phys. Fluids, Vol. 33(8), 083313 (2021). – reference: [15] Nagatake, T. and Yoshida, H., Measurement of the Water-vapor Void Fraction in a 4×4 Unheated Rod Bundle, J. Nucl. Sci. Technol., Vol. 60(11), 1417-1430 (2023). – reference: [5] Wang, H., Yang, Y., Dou, G., Lou, J., Zhu, X., Song, L. and Dong, F., A 3D Reconstruction Method of Bubble Flow Field Based on Multi-View Images by Bi-direction Filtering Maximum Likelihood Expectation Maximization Algorithm, Int. J. Multiph. Flow, Vol. 165, 104480 (2023). |
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| SubjectTerms | 3D Visualization Bubble Detection Bubbly Flow Deep Learning Rod Bundle |
| Title | 深層学習に基づく気泡検出技術を用いたロッドバンドル流路内3次元可視化計測 |
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