基于GA-BPSO算法的艉部结构模态测点优化布置

Optimization Sensor Placement for Stern Structure Modal Analysis Based on GA-BPSO Algorithm

  • 摘要: 目的针对水下航行器艉部结构振型复杂、模态测试测点多问题,本文提出了一种基于遗传算法和二进制离散粒子群混合算法(GA-BPSO)的测点优化布置方法。方法首先建立典型艉部结构有限元模型并提取结构参数,构建三维消冗指标和模态置信准则的组合目标函数,然后基于GA-BPSO算法对艉部结构进行模态测点优化布置。为验证优化方法的有效性,开展了艉部结构测点均匀布置和优化布置的模态实验。结果结果表明:优化后测点数量由均匀布置方案的840个减少至200个,优化布置方案模态置信矩阵最大非对角元素降低至0.0333,频率误差控制在1%以内,且振型吻合度较高。结论本文方法有效兼顾了模态振型的线性独立性和可视化效果,可用于水下艉部结构模态测试。

     

    Abstract: Objectives Aiming at the problem of complex vibration modes and multiple modal test points of the stern structure of underwater vehicles, a sensor optimization layout method based on genetic algorithm and binary discrete particle swarm hybrid algorithm (GA-BPSO) is proposed in this paper. Methods Firstly, a finite element model of a typical stern structure is established and structural parameters are extracted, and a combined objective function of three-dimensional redundancy elimination index and modal confidence criterion is constructed. Then, the modal measurement points of the stern structure are optimally arranged based on the GA-BPSO algorithm. To verify the effectiveness of the optimization method, modal experiments with uniform and optimal arrangement of measurement points of the stern structure are carried out. Results The results show that, the optimized placement reduces the number of test points from 840 to 200. The maximum off-diagonal element in the modal confidence matrix of the optimized placement is reduced to 0.0333, with frequency errors controlled within 1% and high modal shape correlation. Conclusions The proposed method effectively balances the linear independence of modal shapes and the visualization quality, making it suitable for modal testing of underwater stern structure.

     

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