• Journal of Ocean Engineering and Technology
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• v.30 no.5
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• pp.349-355
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• 2016

Divinycell, which functions as both insulation and a supporting structure, is generally applied in the NO96-type liquefied natural gas (LNG) insulation system. Polymer-material-based Divinycell, which has a high strength and low weight, has been widely used in the offshore, transportation, wind power generation, and civil engineering fields. In particular, this type of material receives attention as an insulation material because its thermal conductivity can be lowered depending on the ambient temperature. However, it is difficult to obtain research results for Divinycell, even though the component materials of the NO96-type LNG cargo containment system, such as 36% nickel steel (invar steel), plywood, perlite, and glass wool, have been extensively studied and reported. In the present study, temperature and strain-rate dependent compressive tests on Divinycell were performed. Both the quantitative experimental data and elastic recovery are discussed. Finally, the mechanical characteristics of Divinycell were compared to the results of polyurethane foam insulation material.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.2
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• pp.193-200
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• 2011

A BOR(Boil-Off Rate) prediction model for the NO96 membrane-type LNG insulation containment filled with superlite powders during laden voyage is presented in this paper. Finite element model for the unsteady-state heat transfer analysis is constructed by considering the air and water conditions and by employing the homogenization method to simplify the complex insulation material composition. BOR is evaluated in terms of the total amount of heat invaded into LNGCC and its variation to the major variables is investigated by the parametric heat transfer analysis. Based upon the parametric results, a BOR prediction model which is in function of the LNG tank size, the insulation layer thickness and the powder thermal conductivity is derived. Through the verification experiment, the accuracy of the derived prediction model is justified such that the maximum relative difference is less than 1% when compared with the direct numerical estimation using the FEM analysis.