纤维复合海洋立管铺层设计复杂，且变量众多。借助粒子群智能算法，通过Matlab与Ansys联合仿真，完成内压作用下纤维复合海洋立管的铺层优化设计。首先，在Ansys中建立参数化模型进行有限元分析，并与已有结果对比验证其正确性；其次，针对传统粒子群算法在优化高维问题时容易陷入局部最优解的缺陷，引入拉丁超立方采样、信息熵以及“靠拢、变异及重分布”机制对算法进行改进；最后，在Matlab中编写改进算法的主程序并调用Ansys辅助分析进行纤维复合海洋立管的局部优化设计。结果表明，改进粒子群算法性能显著提高；优化后的设计能给出一组重量更轻、可供设计人员参考的纤维复合海洋立管结构参数。
The design of fiber composite marine risers is complex and involves numerous variables. Utilizing the particle swarm optimization algorithm, combined with Matlab and Ansys co-simulation, the optimization design of the fiber composite marine risers under internal pressure was completed. First, a parametric model was established in Ansys for finite element analysis, and its correctness was verified by comparison with existing results. Next, to address the drawback of traditional particle swarm optimization algorithms, which tend to fall into local optima when solving high-dimensional problems, improvements were made by introducing Latin hypercube sampling, information entropy, and a 'convergence, mutation, and redistribution' mechanism. Finally, the main program of the improved algorithm was written in Matlab, and Ansys was called to assist with the local optimization design of fiber composite marine risers. The results show that the performance of the improved particle swarm algorithm is significantly enhanced, and the optimized design provides a set of lighter weight fiber composite riser structure parameters that can be referenced by designers.
2025,47(17): 183-189 收稿日期：2024-9-23
DOI：10.3404/j.issn.1672-7649.2025.17.029
分类号：P751
基金项目：国家自然科学基金资助项目（51968018）；海南大学科研启动基金（kyqd1632）
作者简介：李安征(1997-),男,硕士研究生,研究方向为纤维复合材料海洋立管优化设计
参考文献：
[1] CHIEMELA V A, NATHANIEL G, AGBOMERIE C O, et al. Composite risers for deep waters using a numerical modelling approach[J]. Composite Structures, 2019, 210: 486-499.
[2] 余杨, 刘成, 余建星, 等. 基于遗传算法的缓波型立管多目标集成优化[J]. 海洋工程, 2023, 41(2): 31-41.
[3] 黄松, 黄宇杰, 何杨烨. 基于神经网络优化算法的海洋动态缓波型立管设计[J]. 海洋工程, 2024, 42(3): 83-94.
[4] 刘昊, 杨和振, 姜豪. 深海复合材料立管拉伸外压承载能力多目标优化设计[J]. 中国海洋平台, 2013, 28(2): 45-52.
[5] 刘昊. 深海复合材料立管力学特性分析与优化设计研究[D]. 上海: 上海交通大学, 2013.
[6] 刘昊, 杨和振. 基于多岛遗传算法的深海复合材料悬链线立管优化设计[J]. 哈尔滨工程大学学报, 2013, 34(7): 819-825.
[7] 王竹. 复合材料悬链线立管的数值模拟和优化设计[D]. 武汉: 华中科技大学, 2017.
[8] SILVA R F D, TEÓFILO F A F, EVANDRO P, et al. Optimization of composite catenary risers[J]. Marine Structures, 2013, 33: 1-20.
[9] 王春光, 冯阅. 深水纤维复合材料海洋立管的优化设计[M]. 北京: 地质出版社, 2021.
[10] ABS(American Bureau of Shipping). Guide for Building and Classing Subsea Riser Systems, third ed[S]. Houston: American Bureau of Shipping, 2017.
[11] SATHIYA V, CHINNADURAI M, RAMABALAN S. Mobile robot path planning using fuzzy enhanced improved Multi-Objective particle swarm optimization (FIMOPSO)[J]. Expert Systems with Applications, 2022, 198: 116875.
[12] SENGATHIR J, PRIYA D M. Hybrid grey wolf and improved particle swarm optimization with adaptive intertial weight-based multi-dimensional learning strategy for load balancing in cloud environments[J]. Sustainable Computing: Informatics and Systems, 2023, 38: 100875.
[13] LONG X, CAI W, YANG L, et al. Improved particle swarm optimization with reverse learning and neighbor adjustment for space surveillance network task scheduling[J]. Swarm and Evolutionary Computation, 2024, 85: 101482.
[14] 门茂琛, 赵睿, 张金帅, 等. 基于改进模拟退火-粒子群的配电网分布式光伏承载力评估[J]. 浙江大学学报(工学版), 2024, 58(6): 1255-1265.
[15] 杨帆, 张佳宁, 刁峰, 等. 基于自适应粒子群算法的船舶机舱双层布局优化研究[J]. 舰船科学技术, 2024, 46(17): 69-76.
YANG F, ZHANG J N, DIAO F et al. Research on double-layer layout optimization of ship engine room based on adaptive particle swarm algorithm[J]. Ship Science and Technology, 2024, 46(17): 69-76.
[16] 冯阅, 罗立胜, 韩建刚. 基于智能优化方法的斜拉桥索力优化[J]. 中外公路, 2021, 41(6): 174-177.
