通过构建径向基（Radial Basis Function，RBF）神经网络近似模型，分别采用自适应模拟退火算法（Adaptive Simulated Annealing，ASA）、多岛遗传算法（Multi-Island Genetic Algorithm，MIGA）、粒子群法（Particle Swarm Optimization，PSO）3种优化算法，以重量最轻为目标，对油船整体舱段进行基于CCS规范的优化设计；之后利用Abaqus非线性有限元法计算了轻量化舱段的极限强度，并通过建立极限状态方程，进一步研究优化算法对船体极限强度可靠性的影响；最后，为直观、量化地反映船舶的风险变化程度，通过提出失效概率折减率μ，发现PSO-RBF算法下减重单位舱段重量，船体梁失效概率增加最少。
In order to evaluate the effect of optimization algorithm on the ultimate strength reliability of the oil tanker, this paper combines iSIGHT optimization software and MSC finite element software, using parametric finite element modeling technology, optimized Latin experimental design method, radial basis function neural network theory, adaptive simulated annealing algorithm and other technical methods to optimize the design of the oil tanker section. Then, the paper uses the non-linear finite element method to assess the ultimate strength of the lightweight oil tanker section. According to the principle of the reliability, the first-order second-moment method is adopted to calculate the reliability and the failure probability of different optimization algorithms. Finally, the results indicate that if per unit weight reduced, the hull girder failure probability would increase the least under the PSO-RBF algorithm.
2019,41(6): 32-36 收稿日期：2018-06-12
DOI：10.3404/j.issn.1672-7649.2019.06.007
分类号：U661
基金项目：高技术船舶科研经费资助项目（工信部联装函[2016]548号）
作者简介：卓思雨(1993-),女,硕士研究生,研究方向为船舶与海洋工程结构物设计制造
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