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

机译:LNG深冷螺旋波纹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:柔性低温软管最近已成为海上液化天然气(LNG)开采的首选替代产品,其中螺旋波纹钢管是C形波纹的重要组成部分。本文基于3D壳单元,提出了LNG低温螺旋波纹管的参数化有限元模型。考虑到低温材料和较大的几何结构变形等非线性,在轴向拉力,弯曲和内压载荷下获得了机械性能特征结果。同时,通过截面尺寸分析确定用于柔性低温软管的结构形状优化的设计参数,并通过改变几何参数进行灵敏度分析。根据工作条件,制定了以最小化刚度和强度应力为目标的多目标优化方法。应用全因子实验和径向基函数(RBF)神经网络建立近似模型进行结构分析。在制造和刚度约束下,通过NSGA-n GA算法获得帕累托最优解集和参数取值范围。为液化天然气低温波纹软管的结构设计提供了一种可行的最优方法。

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