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MULTI-OBJECTIVE SHAPE OPTIMIZATION DESIGN FOR LNG CRYOGENIC HELICAL CORRUGATED STEEL PIPE

机译:LNG低温螺旋瓦楞钢管的多目标形状优化设计

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The flexible cryogenic hose has been a favored alternative for offshore liquefied natural gas (LNG) exploitation recently, of which helical corrugated steel pipe is the crucial component with C shaped corrugation. Parametric finite-element models of LNG cryogenic helical corrugated pipe are presented based on 3D shell element in this paper. Taking account of nonlinearity such as cryogenic material and large geometric structural deformation, mechanical behavior characteristics results are obtained under axial tensional, bending and inner pressure loads. Meanwhile, the design parameters are determined for the shape optimization of structures of the flexible cryogenic hose through sectional dimension analysis, and sensitivity analysis is performed with changing geometric parameters. A multi-objective optimization with the object of minimizing stiffness and strength stress is formulated based on operation condition. Full factorial experiment and radial basis function (RBF) neural network are applied to establish the approximated model for the analysis of the structure. The Pareto optimal solution set and value range of parameters are obtained through NSGA-n GA algorithm under manufacturing and stiffness constraints. It provides a feasible optimal approach for the structural design of LNG cryogenic corrugated hose.
机译:柔性低温软管最近是海上液化天然气(LNG)开采的青睐替代品,其中螺旋瓦楞钢管是具有C形状波纹的关键部件。本文基于3D壳元素提出了LNG低温螺旋波纹管的参数有限元模型。考虑到非线性,例如低温材料和大的几何结构变形,机械行为特性结果在轴向张力,弯曲和内压负荷下获得。同时,确定设计参数通过截面尺寸分析来确定柔性低温软管的结构的形状优化,并且通过改变几何参数进行灵敏度分析。基于操作条件,配制了利用最小化刚度和强度应力的物体的多目标优化。完整的因子实验和径向基函数(RBF)神经网络被应用于建立近似模型的结构分析。通过制造和刚度约束下通过NSGA-N GA算法获得Pareto最佳解决方案集和值范围。它为LNG低温瓦楞软管的结构设计提供了一种可行的最佳方法。

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