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

被引：0

作者：

Zhan Q.-L. ^{[1
]}

Bai C.-J. ^{[1
]}

Ge Y.-J. ^{[2
]}

机构：

[1] College of Transportation and Engineering, Dalian Maritime University, Liaoning, Dalian

[2] State Key Laboratory for Disaster Reduction in Civil Engineering, Tongji University, Shanghai

来源：

Gongcheng Lixue/Engineering Mechanics | 2023年 / 40卷 / 09期

关键词：

deep learning; feature extraction; flow reconstruction; flow time history; unsupervised model;

D O I：

10.6052/j.issn.1000-4750.2021.12.0005

中图分类号：

学科分类号：

摘要：

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. © 2023 Tsinghua University. All rights reserved.

引用

页码：13 / 19

页数：6

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