设计具有优良抗爆性能的夹层板结构对提高船舶生命力具有重要意义。本文提出一种基于神经网络与麻雀搜索算法相结合的优化方法（BP-SSA），以提高结构的抗爆性能为主要目标，对波形夹层板结构进行优化设计。通过正交试验设计建立样本点矩阵，在此基础上，利用神经网络建立设计变量与结构响应值之间的响应面模型，最后采用麻雀搜索算法对响应面模型进行多目标优化分析，得到波形夹层板结构的抗爆性能优化方案。优化后的结构抗爆性能比原结构提升了31.8%，且神经网络的预测值与有限元仿真结构一致性较好。因此，该方法在提高夹层板结构的抗爆性能的同时还减轻了结构重量，能够为工程设计提供参考。
It is of great significance to design sandwich plate structure with excellent anti-explosion performance to improve ship vitality. In this paper, an optimization method (back propagation-sparrow search algorithm, BP-SSA) based on the combination of neural network and sparrow search algorithm is proposed to optimize the design of corrugated sandwich plate structure, aiming at improving the anti-explosion performance of the structure. The sample point matrix was established through orthogonal experimental design. On this basis, the response surface model between design variables and structural response values was established by using neural network. Finally, the sparrow search algorithm was used to conduct multi-objective optimization analysis on the response surface model, and the anti-explosion performance optimization scheme of corrugated sandwich panel structure was obtained. The anti-explosion performance of the optimized structure is 31.8% higher than that of the original structure, and the predicted value of the neural network is in good agreement with the finite element simulation structure. Therefore, this method can not only improve the anti-explosion performance of sandwich panel structure, but also reduce the weight of the structure, which can provide a reference for engineering design.
2022,44(7): 25-30 收稿日期：2021-12-14
DOI：10.3404/j.issn.1672-7649.2022.07.005
分类号：U661.43
基金项目：国家自然科学基金资助项目（51779110；51809122）；江苏省自然科学基金资助项目（BK20191461）
作者简介：邱伟健(1996-),男,硕士研究生,研究方向为船舶与海洋工程结构优化设计
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