Rheological damping of slender rods

Efficient and accurate modelling of mooring and cable systems for Floating Offshore Wind Turbines are a key factor in the coupled dynamic analysis for the realistic assessment of such floating structures. In order to improve the modelling of the mooring and cable systems, a new extension of the slen...

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Detalhes bibliográficos
Autores: Trubat Casal, Pau|||0000-0001-8292-8272, Molins i Borrell, Climent|||0000-0001-8292-0473
Formato: artículo
Fecha de publicación:2019
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/166616
Acesso em linha:https://hdl.handle.net/2117/166616
https://dx.doi.org/10.1016/j.marstruc.2019.102639
Access Level:acceso abierto
Palavra-chave:Deep-sea moorings--Mathematical models
Offshore structures--Design and construction--Mathematical models
Finite element model
Slender rod
Mooring system
Cable
Material damping
Axial damping
Bending damping
Rheological damping
Offshore
Estructures marines -- Disseny i construcció
Amarratges
Àrees temàtiques de la UPC::Enginyeria civil::Materials i estructures
Descrição
Resumo:Efficient and accurate modelling of mooring and cable systems for Floating Offshore Wind Turbines are a key factor in the coupled dynamic analysis for the realistic assessment of such floating structures. In order to improve the modelling of the mooring and cable systems, a new extension of the slender rod finite element model proposed by Garret with the inclusion of rheological damping is presented. The model takes into account the damping produced for the rod material in both the axial and the bending forces. Derivation of the axial and bending damping forces is presented and assessed in terms of the critical damping. The implementation of the model is presented and tested through three verification simulations and two validation examples, which show a good agreement when comparing with experimental results and prove the capacity and robustness of the model.