Integrity Assessment of a Free-Fall Lifeboat Launched From a FPSO

Abstract

The paper addresses the structural integrity assessment of lifeboat launched from floating production, storage and offloading (FPSO) vessels. The study is based on long-term drop lifeboat simulations accounting for more than 50 years of hindcast data of metocean conditions and corresponding FPSO motions. Selection of the load cases and strength analyses with high computational time is a challenge. The load cases analyzed are those corresponding to the 99th percentile of long term distribution of indicators for large slamming loads (CARXZ) or large submergence (Imaxsub). For six selected cases, the time-varying pressure distribution on the lifeboat hull during and after water impact is calculated by CFD simulations using StarCCM+. The finite element model (FEM) of the composite structure of the lifeboat is modelled by ABAQUS. Quasi-static finite element (FE) analyses are performed for the selected load cases. The structural integrity is assessed by the maximum stress and Tsai-Wu failure measure. In the present study, the load and resistance factors are combined and applied to the response. A sensitivity study is performed to investigate the non-linear load/response effects when the load factor is applied to the load. In addition, dynamic analysis is performed with the time-varying pressure distribution for selected case and the dynamic effect is investigated.

Performance on the drop impact test of the cone capsule shaped portable tsunami lifeboat using penalty method contact analysis

“…Zakki, [15] presented fl uid structure interaction analysis to develop the hull form of free-fall lifeboat for the evacuation system on offshore platform.In the other study, Zakki investigate the launching parameter on the motion pattern of freefall lifeboat using FSI Analysis, [16]. Guomin Ji, presented the structural integrity assessment of free fall lifeboat launched on the FPSO, [17]. Ringsberg,[18], has investigated free-fall lifeboat structural response using quasi response prediction methods.…”