基于优化阶跃激励的索穹顶密集模态测试方法

针对索穹顶模态频率分布密集的特点，以经典的稀疏时域法（STD）为例，解释此类常规方法进行结构密集模态识别精度低的原因.为了提高识别精度，配合模态识别的时域法，提出基于优化阶跃激励的密集模态测试方法.该方法通过优化激励的布置和大小，增强结构自由振动中待识别模态的贡献且同时抑制邻近模态的贡献，采用遗传算法来寻求最优激励模式.利用Geiger索穹顶算例来考察方法的有效性.分析结果表明，优化阶跃激励可以使密集模态识别问题转化成为孤立模态识别问题，采用该方法可以有效提高索穹顶密集模态的识别精度.

Method for identifying modal parameters of closely spaced modes of cable domes by optimizing step excitations

The classic sparse time domain (STD) method was employed to explain why the conventional methods easily produced inaccurate identification of closely spaced modes for cable domes presented intensive frequencies. A modal testing method based on the cooperation of optimal step excitations and conventional time domain methods was proposed to improve the identification accuracy. The contribution of the target mode to the structural free vibration was enhanced by means of optimizing the locations and the amplitudes of step excitations. Meanwhile, the contributions of its adjacent modes were suppressed. The genetic algorithm was utilized to optimize the step excitations. A Geiger cable dome was investigated for the capability of the proposed method. The numerical results show that the identification of closely spaced modes can be transformed to that of sparse ones by optimizing step excitations. The proposed method can effectively improve the identification accuracy of intensive modes for cable domes.