结合SVR响应面与粒子群优化的有限元模型修正

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摘要提出了一种模型修正方法，可以在不依赖模型灵敏度的前提下，利用较少的计算量实现对结构有限元模型的参数修正。该方法首先构建代理模型替代结构有限元模型，通过计算少量样本点，训练支持向量回归机预测参数所对应的响应；其次，以结构固有频率的残差为目标函数，利用粒子群优化算法实现全局寻优求解，得到修正后的有限元模型参数；进一步，以带孔平板为试验研究对象，基于实测数据验证了所提方法的有效性，并讨论不同参数、样本点数等对模型修正精度的影响；最后，用某卫星结构模型修正算例证明了本文方法相对基于灵敏度分析的方法在计算耗时上的优势。本研究旨在为具有复杂参数-响应特征的结构模型修正提供技术支持。

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	何子豪1
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	吴邵庆 1
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关键词 ：有限元模型修正, 支持向量回归机, SVR响应面, 粒子群优化, 试验研究

Abstract：A model updating method independent of model sensitivity is proposed in which the parameter of the structural finite element model can be updated with less computational effort. Surrogate model is firstly established to replace the structural finite element model, and the support vector regression can be trained to predict the response corresponding to the parameters via the calculation on a small number of sample points; Secondly, taking the residual of natural frequency as objective function, the particle swarm algorithm is used to achieve the global optimization, which leads to the updated parameters of the finite element model; Then, a plate with holes is taken as the experimental research object, and the effectiveness of the proposed method is verified based on the measurement data. The influence of different parameters and different number of sample points, etc. on the model updating accuracy are further discussed. Finally, a satellite structural model updating example is given to prove the advantages of the proposed method on computational efficiency over the method based on sensitivity analysis. This research work aims to provide technical support for the modification of structural models with complex parameter-response characteristics.

Key words： finite element model updating support vector regression SVR response surface particle swarm optimization experimental research

收稿日期: 2022-04-19 出版日期: 2023-08-15

引用本文:

何子豪1,2，吴邵庆 1,2,3. 结合SVR响应面与粒子群优化的有限元模型修正[J]. 振动与冲击, 2023, 42(15): 163-172.
HE Zihao1,2, WU Shaoqing1,2,3. Finite element model modification combining SVR response surface and PSO. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(15): 163-172.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I15/163

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