A novel deep U-Net-LSTM framework for time-sequenced hydrodynamics prediction of the SUBOFF AFF-8-Reference-Cited by-同舟云学术

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A novel deep U-Net-LSTM framework for time-sequenced hydrodynamics prediction of the SUBOFF AFF-8

Published:2022-02-15 Issue:1 Volume:16 Page:630-645
ISSN:1994-2060
Container-title:Engineering Applications of Computational Fluid Mechanics
language:en
Short-container-title:Engineering Applications of Computational Fluid Mechanics

Author:

Hou Yuqing¹,Li Hui¹²³^ORCID,Chen Hong⁴,Wei Wei⁴,Wang Jiayue¹,Huang Yicang⁴

Affiliation:

1. The Institute of Technological Sciences, Wuhan University, Wuhan, People’s Republic of China

2. School of Power and Mechanical Engineering, Wuhan University, Wuhan, People’s Republic of China

3. Key Laboratory of Hydraulic Machinery Transients, Wuhan University, Wuhan, People’s Republic of China

4. R&D Center, Wuhan Second Ship Design and Research Institute, Wuhan, People’s Republic of China

Publisher

Informa UK Limited

Subject

Modeling and Simulation,General Computer Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/19942060.2022.2030802

Reference32 articles.

1. Study of the Resistance Test and Wall Blockage Correction Method for the Submerged Body in LCT

2. A 3D CNN-LSTM-Based Image-to-Image Foreground Segmentation

3. URANS investigation of the interaction between the free surface and a shallowly submerged underwater vehicle at steady drift

4. Coupling a hybrid CNN-LSTM deep learning model with a Boundary Corrected Maximal Overlap Discrete Wavelet Transform for multiscale Lake water level forecasting

5. Evaluation of hybrid RANS/LES models for prediction of flow around surface combatant and Suboff geometries

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