基于GA−BPSO算法的水下航行器艉部结构模态测点优化布置

Optimization sensor placement for underwater vehicle 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,提出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,开展艉部结构测点均匀布置和优化布置的模态对比实验。
    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0.033 3,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 complex vibration modes and excessive number of modal test points in underwater vehicle stern structures, this paper proposes an optimal sensor placement method.
    Method First, a finite-element model of a typical stern structure is constructed using S3 and S4R plate elements to simulate the structure's shell and rib plates. Subsequently, the structural parameters, such as nodes, elements, and the stiffness and mass matrices are extracted from the model file. To optimize sensor placement, a composite objective function is constructed by integrating a three-dimensional redundancy elimination index and the modal assurance criterion (MAC). The three-dimensional redundancy elimination index ensures adequate spatial separation between sensors in each direction and from the center of the stern structure. The MAC helps maintain the linear independence of modal shapes. Then, the GA−BPSO algorithm is employed to optimize sensor placement using binary coding. Each particle in the particle-swarm algorithm represents a placement plan, with its dimensionality corresponding to the number of candidate degrees of freedom. Each degree of freedom can either as 1 (sensor placed) or 0 (no sensor). The algorithm updates particle velocities and positions based on an inertia weight, learning factors, and random numbers. Positions are then discretized to 0 or 1 through a threshold. Genetic algorithm operators such as replication, crossover, and mutation are introduced to improve the performance of the binary discrete particle-swarm algorithm. The parameters of the GA−BPSO algorithm are carefully set. For example, the population size is set to 600, the number of iterations to 2 000, and the inertia weight decreases from 0.9 to 0.4 over the course of the iterations.
    Results The optimized sensor layout reduces the number of required sensor locations from 840 (uniform layout) to 200. The maximum off-diagonal value in the MAC matrix in the optimized layout drops to 0.0333, the frequency deviation remains below 1%, and the modal shapes show a high degree of consistency.
    Conclusion The proposed method effectively achieves a balance between the linear independence and visualization of modal shapes, demonstrating its applicability for modal testing of underwater stern structures.

     

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