Experimental and numerical investigation of floating offshore substation dynamic responses with mooring line failures
Penulis:Â Liu, Chunlei;Â Jiang, Xin;Â Li, Hui;Â Sun, Shuzheng;Â Ren, Huilong
Informasi
JurnalOcean Engineering
PenerbitElsevier Ltd
Volume & EdisiVol. 348
Halaman -
Tahun Publikasi2026
ISSN00298018
Jenis SumberScopus
Abstrak
Among various floating offshore substations (FOSS) concepts, the barge-type platforms offer advantages such as smaller draft and simple mooring arrangement. However, the occurrence of mooring-line failure can trigger significant transient responses, posing serious risk to the safety and operability of the platform. This paper conducts both model experiment and numerical simulations to investigate the transient responses of a barge-type FOSS subjected to single line failure at one-corner. Comprehensive numerical simulations are performed to examine the effects of different peaks and failure instants of tension under both regular and regular sea states. Statistical analyses and spectrum power of dominated response parameters including platform motions, mooring tension, and accelerations are carried out. The results indicate that the instantaneous failure scenario produces substantially more severe responses than steady-state damage condition. For Line 1, the maximum tension increases by a factor of 4.3 at H = 1.0 m, but reduces to 0.61 times the reference value at Hs = 2.3 m, accompanied by large discrepancies in the standard deviation of windward-line tension. Meanwhile, a reasonable selection of the failure instants for each tension peak is critical. The remaining maximum tensions for above irregular waves differ by 16.6 % and 50 %, respectively. Additionally, highest peak tensions in windward line typically occur in phase with the maximum peaks of surge and platform acceleration. Following mooring failure, the wave-frequency energy in windward-line tensions increases markedly, leading to enhanced dynamic fluctuations in tension time histories. © 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
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