REPRESENTATION AND RECONSTRUCTION OF FLOW AROUND BRIDGE DECK USING TIME HISTORY DEEP LEARNING

ZHAN Qing-liang; BAI Chun-jin; GE Yao-jun

doi:10.6052/j.issn.1000-4750.2021.12.0005

Volume 40 Issue 9

Sep. 2023

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Article Navigation > Engineering Mechanics > 2023 > 40(9): 13-19

ZHAN Qing-liang, BAI Chun-jin, GE Yao-jun. REPRESENTATION AND RECONSTRUCTION OF FLOW AROUND BRIDGE DECK USING TIME HISTORY DEEP LEARNING[J]. Engineering Mechanics, 2023, 40(9): 13-19. doi: 10.6052/j.issn.1000-4750.2021.12.0005

Citation:

ZHAN Qing-liang, BAI Chun-jin, GE Yao-jun. REPRESENTATION AND RECONSTRUCTION OF FLOW AROUND BRIDGE DECK USING TIME HISTORY DEEP LEARNING[J]. Engineering Mechanics, 2023, 40(9): 13-19. doi: 10.6052/j.issn.1000-4750.2021.12.0005

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REPRESENTATION AND RECONSTRUCTION OF FLOW AROUND BRIDGE DECK USING TIME HISTORY DEEP LEARNING

doi: 10.6052/j.issn.1000-4750.2021.12.0005

1.
College of Transportation and Engineering, Dalian Maritime University, Dalian, Liaoning 116026, China
2.
State Key Laboratory for Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China

Received Date: 2021-12-31
Rev Recd Date: 2022-04-14

Available Online: 2022-06-25

Publish Date: 2023-09-06

Abstract

Abstract

High-resolution flow field data has a great significance to the study of fluid induced vibration and vortex induced vibration mechanism. Limited by measurement methods and calculation efficiency, it is still difficult to obtain high-resolution flow fields. Thusly, the low-dimensional representation model of flow time history data is adopted, and a deep learning method is proposed for the reconstruction of unsteady flow time history data. A low-dimensional representation model is established for the unsteady flow field based on the one-dimensional convolution method; The mapping relationship is developed between the physical space and the encoding space; The decoder in the representation model is utilized to generate the flow field time history data at any position. The problem of unsteady flow around bridge deck is verified, and the accuracy of the method is proved. The method proposed is a high-precision flow field data reconstruction method in the time dimension, and it is an unsupervised training method. It is a brand-new method that can be widely used in point-based sensor data processing.
- flow reconstruction,
- flow time history,
- deep learning,
- feature extraction,
- unsupervised model

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References(21)

References

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