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Identification of nonlinear boundary effects using nonlinear normal modes

机译：使用非线性法线模式识别非线性边界效应

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Local nonlinear effects due to micro-slip/slap introduced in boundaries of structures have dominant influence on their lower modal model. This paper studies these effects by experimentally observing the behavior of a clamped-free beam structure with local nonlinearities due to micro-slip at the clamped end. The structure is excited near one of its resonance frequencies and recorded responses are employed to identify the nonlinear effects at the boundary. The nonlinear response of structure is defined using an amplitude-dependent nonlinear normal mode identified from measured responses. A new method for reconstructing nonlinear normal mode is represented in this paper by relating the nonlinear normal mode to the clamped end displacement-dependent stiffness parameters using an eigensensitivity analysis. Solution of obtained equations results equivalent stiffness models at different vibration amplitudes and the corresponding nonlinear normal mode is identified. The approach results nonlinear modes with efficient capabilities in predicting dynamical behavior of the structure at different loading conditions. To evaluate the efficiency of the identified model, the structure is excited at higher excitation load levels than those employed in identification procedures and the observed responses are compared with the predictions of the model at the corresponding input force levels. The predictions are in good agreement with the observed behavior indicating success of identification procedure in capturing the physical merits involve in the boundary local nonlinearities.
机译：由于在结构边界引入微滑移/拍击而引起的局部非线性效应对其下部模态模型具有主要影响。本文通过实验观察受约束端的微滑移引起的具有局部非线性的无约束梁结构的行为，研究了这些影响。该结构在其共振频率之一附近被激发，并采用记录的响应来识别边界处的非线性效应。结构的非线性响应是使用从测量的响应中识别出的幅度相关的非线性法线模式来定义的。通过本征灵敏度分析，将非线性法线模式与夹紧端位移相关的刚度参数联系起来，提出了一种重构非线性法线模式的新方法。所获得方程的解可得出不同振动幅度下的等效刚度模型，并确定了相应的非线性法线模式。该方法产生具有有效能力的非线性模式，以预测结构在不同载荷条件下的动力学行为。为了评估所识别模型的效率，该结构在高于识别过程中所采用的激发载荷水平下被激发，并将观察到的响应与模型在相应输入力水平下的预测值进行比较。这些预测与观察到的行为非常吻合，表明识别过程成功地捕获了边界局部非线性所涉及的物理优点。

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