Structural Safety Assessment for FLNG-LNGC System During Offloading Operation Scenario

The crashworthiness of the cargo containment systems （CCSs） of a floating liquid natural gas （FLNG） and the side structures in side-by-side offioading operations scenario are studied in this paper. An FLNG vessel is exposed to potential threats from collisions with a liquid natural gas carrier （LNGC） during the offioading operations, which has been confirmed by a model test of FLNG-LNGC side-by-side offioading operations. A nonlinear finite element code LS-DYNA is used to simulate the collision scenarios during the offioading operations. Finite element models of an FLNG vessel and an LNGC are established for the purpose of this study, including a detailed LNG cargo containment system in the FLNG side model. Based on the parameters obtained from the model test and potential dangerous accidents, typical collision scenarios are defined to conduct a comprehensive study. To evaluate the safety of the FLNG vessel, a limit state is proposed based on the structural responses of the LNG CCS. The different characteristics of the structural responses for the primary structural components, energy dissipation and collision forces are obtained for various scenarios. Deformation of the inner hull is found to have a great effect on the responses of the LNG CCS, with approximately 160 mm deformation corresponding to the limit state. Densely arranged web frames can absorb over 35% of the collision energy and be proved to greatly enhance the crashwo- rthiness of the FLNG side structures.

Material discrimination using cosmic ray muon scattering tomography with an artificial neural network

Introduction Muon scattering tomography(MST)can be employed to scan cargo containers and vehicles for special nuclear materials by using cosmic muons.However,the flux of cosmic ray muons is relatively low for direct detection.Thus,the detection has to be done in a short timescale with small numbers of muons to satisfy the demands of practical applications.Method In this paper,we propose an artificial neural network(ANN)algorithm for material discrimination using MST.The muon scattering angles were simulated using Geant4 to formulate the training set,and the muon scatter angles were measured by Micromegas detection system to create the test set.Results The ANN-based algorithm presented here ensures a discrimination accuracy of 98.0%between aluminum,copper and tungsten in a 5 min measurement of 4×4×4 cm^(3)blocks.

Large-scale liquefied natural gas ships

Design Unit and Construction Shipyard:Hudong-Zhonghua Shipbuilding(Group)Co.,Ltd.Ship Classification Society:China Classification Society,Lloyd's Register of Shipping,American Bureau of Shipping,etc.1 Overview of liquefied natural gas(LNG)ships Compared with other fossil fuels,such as coal and oil,natural gas emits less carbon dioxide and no sulfide and dust;therefore,it is considered cleaner and an important direction for China’s implementation of its energy strategy adjustment.Natural gas accounts for over 20%of primary energy worldwide.