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首页> 外文期刊>Mechanical systems and signal processing >Frequency-domain nonlinear model updating based on analytical sensitivity and the Multi-Harmonic balance method
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Frequency-domain nonlinear model updating based on analytical sensitivity and the Multi-Harmonic balance method

机译：基于分析灵敏度和多谐波平衡法的频域非线性模型更新

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摘要

The predicted dynamic response of the structure with nonlinearity can be in some difference from the experimental one due to the discrepancies of nonlinear parameters between the constructed model and the real structure. The gradient-based nonlinear model updating procedures is an effective tool to reduce the difference. However, the computational costs for the sensitivity matrix could be too expensive. In this paper, a novel approach of the frequency-domain nonlinear model updating using the analytical sensitivity and the Multi-Harmonic Balance Method (MHBM) is proposed. The analytical sensitivity, directly derived from the frequency domain algebraic function obtained by the MHBM and Gamma matrix-based DFT-AFT methods, can significantly reduce the iteration time in model updating process. The proposed nonlinear model updating procedure is easily carried out and also considered as the FRF-based model updating framework. To illustrate the method, a simulated 3DOF nonlinear structure is utilized and differences between the updated nonlinear parameters and the original ones are reduced below 0.4% after updating. Besides, the proposed updating procedure has the best performance compared with the numerical method and the Nelder-Mead optimization. The simulation shows that the proposed analytical sensitivity calculation is generally above 4 times faster than the numerical one. Finally, the updating process of a real 3DOF nonlinear lumped parameter structure is described in detail. After the updating, the overlay of the prediction responses and the experimental ones is in a good agreement. Results indicate the efficiency and superiority of the proposed method to update nonlinear structures.
机译：由于构造模型与实际结构之间的非线性参数的差异，具有非线性的结构的预测动态响应可能与实验中的某些差异不同。基于梯度的非线性模型更新程序是减少差异的有效工具。但是，灵敏度矩阵的计算成本可能太昂贵。本文提出了一种使用分析灵敏度和多谐波平衡法（MHBM）的频域非线性模型更新的新方法。通过MHBM和基于伽马基矩阵的DFT-AFT方法获得的频域代数函数直接导出的分析灵敏度，可以显着降低模型更新过程中的迭代时间。所提出的非线性模型更新程序很容易进行，也被视为基于FRF的模型更新框架。为了说明该方法，在更新之后使用模拟非线性结构的非线性结构，并且更新的非线性参数和原始非线性参数和原始值之间的差异降低了0.4％。此外，与数值方法和Nelder-Mead优化相比，所提出的更新程序具有最佳性能。模拟表明，所提出的分析敏感性计算通常比数值速度快4倍。最后，详细描述了非线性集总参数结构的真实3DOF的更新过程。在更新之后，预测响应的叠加和实验性的叠加是良好的一致性。结果表明提出了更新非线性结构的方法的效率和优越性。

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