Numerical Investigation of Burial Depth Effects on Tension of Submarine Power Cables

Created by W.Langdon from gp-bibliography.bib Revision:1.8576

@Article{shen:2024:JMSE,

• author = "Jiayi Shen and Yingjie Liang and Huabin Hong and Jiawang Chen",
• title = "Numerical Investigation of Burial Depth Effects on Tension of Submarine Power Cables",
• journal = "Journal of Marine Science and Engineering",
• year = "2024",
• volume = "12",
• number = "11",
• pages = "Article No. 1972",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2077-1312",
• URL = "https://www.mdpi.com/2077-1312/12/11/1972",
• DOI = "doi:10.3390/jmse12111972",
• abstract = "To protect submarine power cables from damage caused by anchoring and fishing, submarine power cables in shallow water areas are buried to a certain depth through a cable laying machine. However, limited attention has been paid to studying the stress behaviour of submarine power cables while considering the effects of burial depth. In this research, static and dynamic analyses are carried out using three-dimensional numerical models performed by the OrcaFlex v11.0 to investigate the effects of burial depths on cable tension during the cable installation under various conditions. Numerical simulation results show that the peak tension of the submarine power cable increases linearly with the increase in burial depth. In addition, the burial depth can also change the tension state at the endpoint of the submarine power cable. The endpoint of the cable is in a compressed state when h < 2 m and the cable turns into a tensile state when h >= 2 m. Finally, genetic programming (GP) is used to analyse numerical simulation results to propose a prediction model that can be used to estimate the peak tension of the submarine power cable during cable installation under various burial depths in shallow sea areas. It should be noted that the proposed GP model is based on the analyses of numerical results; therefore, the GP model is open for further improvements as more experimental data become available.",
• notes = "also known as \cite{jmse12111972}",

}

Genetic Programming entries for Jiayi Shen Yingjie Liang Huabin Hong Jiawang Chen

Citations