郭雨, 袁昱超, 唐文勇. 基于强度分析的耐内压方形舱优化设计[J]. 中国舰船研究, 2021, 16(6): 151–158. doi: 10.19693/j.issn.1673-3185.02115
引用本文: 郭雨, 袁昱超, 唐文勇. 基于强度分析的耐内压方形舱优化设计[J]. 中国舰船研究, 2021, 16(6): 151–158. doi: 10.19693/j.issn.1673-3185.02115
GUO Y, YUAN Y C, TANG W Y. Optimal design of internal pressure resistant square cabin based on strength analysis[J]. Chinese Journal of Ship Research, 2021, 16(6): 151–158. doi: 10.19693/j.issn.1673-3185.02115
Citation: GUO Y, YUAN Y C, TANG W Y. Optimal design of internal pressure resistant square cabin based on strength analysis[J]. Chinese Journal of Ship Research, 2021, 16(6): 151–158. doi: 10.19693/j.issn.1673-3185.02115

基于强度分析的耐内压方形舱优化设计

Optimal design of internal pressure resistant square cabin based on strength analysis

  • 摘要:
      目的  为了使船用耐内压方形舱同时满足强度和轻量化的设计要求,将神经网络代理模型与多种启发式智能优化算法相结合,对耐内压方形舱室结构构件形状和尺寸进行优化分析。
      方法  选取方形舱室角隅倒角半径、板材板厚、骨材型号等作为设计变量进行三维参数化建模,根据最优拉丁超立方试验设计方法选取样本点并计算响应值,从而构建径向基(RBF)神经网络代理模型。将该代理模型分别与自适应模拟退火算法 (ASA)、多岛遗传算法 (MIGA)和粒子群算法 (PSO)这3种启发式优化算法相结合,进行全局寻优。
      结果  结果显示,3种混合优化方法均能在满足许用强度要求的基础上减轻结构重量;RBF-ASA法在全局中寻求到的最优解具有相对较好的减重效果。
      结论  所做研究可为耐内压方形舱室结构优化设计工作提供参考,对于攻克船舶运用核动力装置所面临的关键技术问题具有重要意义。

     

    Abstract:
      Objectives  In order to design a marine internal pressure resistant square cabin which meets the requirements for strength and lightweight design, the neural network surrogate model is combined with heuristic intelligent optimization algorithms and applied to the shape and size optimization of the components of such a cabin.
      Methods  The corner chamfer radius, plate thickness and beam model number are selected as design variables for conducting three-dimensional parametric modeling, and sample points are selected according to the optimal Latin hypercube experimental design method. The response values of these sample points are then calculated to build a radial basis functions (RBF) neural network surrogate model. To perform global optimization, the surrogate model is combined with three heuristic optimization algorithms respectively: an adaptive simulated annealing algorithm (ASA), multi-island genetic algorithm (MIGA) and particle swarm optimization (PSO) algorithm.
      Results  The results show that the three hybrid optimization methods can all reduce structural weight on the basis of meeting the allowable strength requirements, and the optimal solution sought by the RBF-ASA method in the overall situation has a relatively good weight reduction effect.
      Conclusions  This study can provide valuable references for the optimal design of internal pressure-resistant square cabin structures, giving it great significance for overcoming the key technical problems faced by ships using nuclear power plants.

     

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