半潜浮式风机的动力响应分析

OC4半潜浮式风机综合性能较好，但其浮式基础结构质量和结构复杂性使其建造成本高昂，而WindFloat半潜浮式风机浮式基础具有结构简单、建造成本低和减摇效果好等优点，但是适应水深较小且只适合特定海域。结合OC4和WindFloat半潜浮式风机浮式基础的结构特点，针对200 m水深环境设计OC4-WindFloat半潜浮式风机基础。基于叶素理论、莫里森公式和势流理论，通过有限元软件对OC4-WindFloat半潜浮式风机的固有周期及风浪联合作用下的动态响应进行耦合分析，并与OC4半潜浮式风机结果进行对比研究。结果显示，OC4-WindFloat半潜浮式风机固有周期及动态响应均满足相关规定，且具有比OC4更低的建造成本，相比WindFloat可适用更深的海域。研究结果对于浮式基础型式研究有一定的指导意义。     

Cargo ship aft panel stresses prediction by deconvolution

This article introduces novel extreme value prediction method that can be used for a variety of offshore engineering applications. First, to demonstrate the novel method, fictitious data from a non-linear Duffing oscillator and measured wave heights were used as examples. The second incident included a container ship that experienced significant deck panel strains while traveling across the Atlantic Ocean in bad weather. The main concern for cargo ship transportation is potential loss of container owing to violent movements. It is challenging to model such a situation because waves and ship motions are both non-stationary and complicatedly nonlinear. Extreme motions greatly increase the role of nonlinearities, activating effects of second and higher order.Furthermore, due to the scaling and the choice of sea state, laboratory testing may also be called into doubt. Therefore, data collected from actual ships during difficult weather voyages offers a special perspective on the statistics of ship motions.This paper aims to highlight an alternative method of extrapolation that is based on intrinsic properties of the data set itself and does not assume any extrapolation functional class. Extreme value predictions typically originate from certain statistical distribution functional classes to fit the data and then extrapolate. Engineering design can make use of the unique extrapolation method that has been proposed. The proposed method's forecast accuracy has been verified in comparison to the Averaged Conditional Exceedance Rate (ACER) extrapolation method.