Fatigue damage analysis for a floating offshore wind turbine mooring line using the artificial neural network approach-Reference-Cited by-同舟云学术

Fatigue damage analysis for a floating offshore wind turbine mooring line using the artificial neural network approach

Published:2016-11-29 Issue:sup1 Volume:12 Page:S288-S295
ISSN:1744-5302
Container-title:Ships and Offshore Structures
language:en
Short-container-title:Ships and Offshore Structures

Author:

Li Chun Bao¹,Choung Joonmo¹^ORCID

Affiliation:

1. Department of Naval Architecture and Ocean Engineering, Inha University, Incheon, Republic of Korea

Publisher

Informa UK Limited

Subject

Mechanical Engineering,Ocean Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/17445302.2016.1254522

Reference18 articles.

1. [ASTM] American Society of Testing Materials. 2005. ASTM standard practices for cycle counting in fatigue analysis. Pennsylvania: Annual book of ASTM standards, Vol. 3. p. 836–884. ASTM/E1049-1134.

2. Spectral methods for lifetime prediction under wide-band stationary random processes

3. Design of Mooring Lines of a Floating Offshore Wind Turbine in South Offshore Area of Jeju

Cited by 30 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Efficient fatigue damage estimation of offshore wind turbine foundation under wind-wave actions;Journal of Constructional Steel Research;2024-10

2. A deep learning approach based on domain generalization for mooring tension prediction in floating structures;Ocean Engineering;2024-08

3. Low-supervised domain-adversarial neural network for cross-domain mooring line failure detection;Applied Ocean Research;2024-08

4. A fully coupled dynamic water-mooring line system: Numerical implementation and applications;Ocean Engineering;2024-02

5. Study on Mooring Design of 15 MW Floating Wind Turbines in South China Sea;Journal of Marine Science and Engineering;2023-12-22