海上风力发电
结算(财务)
抽吸
期限(时间)
岩土工程
地质学
海底管道
海洋工程
工程类
风力发电
业务
付款
机械工程
物理
电气工程
财务
量子力学
作者
Youhu Zhang,S. P. Zhao,Zhou Hong-jie,Knut H. Andersen,Bo Liu
标识
DOI:10.1016/j.apor.2024.103922
摘要
As an alternative to monopiles, suction bucket jacket foundations are gaining increasing popularity in China for supporting offshore wind turbines. One of the major design challenges and governing factors for foundation sizing is the long-term tilt caused by the differential settlement between the buckets due to a combination of prevailing wind directions and soft seabed conditions. The assessment of the long-term consolidation settlement is complicated and subjected to uncertainties, such as the load sharing between the skirt and the bucket lid, and load re-distribution with time as the soil response transits from short-term undrained behaviour to long-term drained behaviour. This paper presents an attempt to understand the short-term and long-term load sharing mechanisms for the suction bucket foundation by means of finite element analysis. Without modelling the large deformation installation process explicitly, the initial (undrained) load sharing mechanism and induced additional stress (or excess pore water pressure) in the soil body is first examined. The re-distribution of the load between the skirts and the lid as the excess pore pressures dissipate is subsequently investigated. The study examines a range of soil conditions and foundation aspect ratios. It is found that the undrained skirt wall friction capacity relative to the load level has an important impact on the initial load sharing mechanism. As consolidation takes place, significant load redistribution occurs, with the loads carried initially by the lid partially or fully transferred to the internal and external skirt wall frictions. The load sharing at completion of consolidation heavily depends on the drained skirt friction capacity relative to the load level. Guided by the numerical findings, a tentative analytical model for practical design purpose is proposed.
科研通智能强力驱动
Strongly Powered by AbleSci AI