An Efficient Optimization Method for Stacking Sequence of Composite Pressure Vessels Based on Artificial Neural Network and Genetic Algorithm

IF 2.3 4区材料科学 Q3 MATERIALS SCIENCE, COMPOSITES

Applied Composite Materials Pub Date : 2024-02-11 DOI:10.1007/s10443-024-10201-8

Jianguo Liang, Zemin Ning, Yinhui Li, Haifeng Gao, Jianglin Liu, Wang Tian, Xiaodong Zhao, Zhaotun Jia, Yuqin Xue, Chunxiang Miao

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Abstract

This paper proposes an efficient optimization method for the stacking sequence of composite pressure vessels based on the joint application of finite element analysis (FEA), artificial neural network (ANN), and genetic algorithm (GA). The composite pressure vessel has many winding layers and varied angles, and the stacking sequence of the composite pressure vessel affects its performance. It is essential to carry out the optimal design of the stacking sequence. The experimental cost for optimal design of composite pressure vessels is high, and numerical simulation is time-consuming. ANN is used to predict the fiber direction stress of composite pressure vessels, which replaces FEA in the optimization process of GA effectively. In addition, the optimization efficiency of the optimization method proposed in this paper can be improved significantly when the neural network model is employed. The optimization results show that the peak stress in the fiber direction can be reduced by 37.3% with the design burst pressure. The burst pressure of the composite pressure vessel can be increased by 13.4% by optimizing the stacking sequence of composite pressure vessels while keeping the number of plies and the winding angle unchanged. The results imply that the work undertaken in this paper is of great significance for the improvement of the safety performance of composite pressure vessels.

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基于人工神经网络和遗传算法的复合压力容器堆叠顺序高效优化方法

本文基于有限元分析（FEA）、人工神经网络（ANN）和遗传算法（GA）的联合应用，提出了一种高效的复合材料压力容器堆叠顺序优化方法。复合材料压力容器的缠绕层数多、角度变化大，复合材料压力容器的堆叠顺序会影响其性能。对堆叠顺序进行优化设计至关重要。复合材料压力容器优化设计的实验成本高，数值模拟耗时长。利用 ANN 预测复合材料压力容器的纤维方向应力，可有效取代 GA 优化过程中的有限元分析。此外，采用神经网络模型后，本文提出的优化方法的优化效率也能得到显著提高。优化结果表明，在设计爆破压力下，纤维方向的峰值应力可降低 37.3%。在保持层数和卷绕角不变的情况下，通过优化复合材料压力容器的堆叠顺序，可将复合材料压力容器的爆破压力提高 13.4%。这些结果表明，本文所做的工作对提高复合材料压力容器的安全性能具有重要意义。

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来源期刊

Applied Composite Materials 工程技术-材料科学：复合

CiteScore

4.20

自引率

4.30%

发文量

审稿时长

1.6 months

期刊介绍： Applied Composite Materials is an international journal dedicated to the publication of original full-length papers, review articles and short communications of the highest quality that advance the development and application of engineering composite materials. Its articles identify problems that limit the performance and reliability of the composite material and composite part; and propose solutions that lead to innovation in design and the successful exploitation and commercialization of composite materials across the widest spectrum of engineering uses. The main focus is on the quantitative descriptions of material systems and processing routes. Coverage includes management of time-dependent changes in microscopic and macroscopic structure and its exploitation from the material''s conception through to its eventual obsolescence.