Home Journals Articles Updates About
CN
image
收藏切换
Unsteady flow time history reconstruction based on deep learning
收藏切换
PDF
Qing-liang ZHAN1, 2, Chun-jin BAI1, Zhi-hu WU1, Yao-jun GE2
Journal of Ship Mechanics | 2024, 28(3) : 319 - 327
Less
收藏切换
Journal of Ship Mechanics | 2024, 28(3): 319-327
• Hydrodynamics •
Unsteady flow time history reconstruction based on deep learning
Full
Qing-liang ZHAN1, 2, Chun-jin BAI1, Zhi-hu WU1, Yao-jun GE2
Affiliations
  • 1.College of Transportation and Engineering, Dalian Maritime University, Dalian 116026, China
  • 2.Key Laboratory of Transport Industry of Wind Resistant Technology for Bridge Structures, Tongji University, Shanghai 200092, China
Published: 2024-03-20 doi: 10.3969/j.issn.1007-7294.2024.03.001
Outline
收藏切换

High-resolution flow field data are of great significance to the study of fluid mechanics. Limited by measurement methods and calculation efficiency, it is still difficult to obtain high-resolution flow fields directly in some circumstances. A low-dimensional representation model for flow time history data was poposed, and a deep learning method for reconstruction of unsteady flow time history data was developed. The proposed method extracted the time-history features contained in the samples using one-dimensional convolution directly; then, the mapping from the physical space and the encoding space was built; and finally, the decoder in the representation model was utilized to generate flow time history data at unknown positions. Unsteady laminar flow with ReD=200 was studied, and the accuracy of the method was verified. The method proposed in this paper, a new flow field data reconstruction method in an unsupervised training manner in the time dimension, can be widely used in point-based sensor data analysis.

flow reconstruction  /  flow time history  /  deep learning  /  feature extraction  /  unsupervised model
Qing-liang ZHAN, Chun-jin BAI, Zhi-hu WU, Yao-jun GE. Unsteady flow time history reconstruction based on deep learning[J]. Journal of Ship Mechanics, 2024 , 28 (3) : 319 -327 . DOI: 10.3969/j.issn.1007-7294.2024.03.001
Appendix
Less
Year 2024 volume 28 Issue 3
PDF
68
31
Cite this Article
BibTeX
Article Info
doi: 10.3969/j.issn.1007-7294.2024.03.001
  • Receive Date：2023-09-16
  • Online Date：2026-03-21
  • Published：2024-03-20
Article Data
Affiliations
History
  • Received：2023-09-16
Funding
Affiliations
    1.College of Transportation and Engineering, Dalian Maritime University, Dalian 116026, China
    2.Key Laboratory of Transport Industry of Wind Resistant Technology for Bridge Structures, Tongji University, Shanghai 200092, China
References
Share
https://castjournals.cast.org.cn/joweb/cblx/EN/10.3969/j.issn.1007-7294.2024.03.001
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House