Sparse identification of time-space coupled distributed dynamic load
Sparse identification of time-space coupled distributed dynamic load
复制标题
时空耦合分布动载荷的稀疏辨识
DOI：
10.1016/j.ymssp.2020.107177
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发表时间：
2021-02-01
影响因子：
8.4
通讯作者：
Li, Kun
中科院分区：
文献类型：
--
作者：
Liu, Jie;Li, Kun
提出了一种基于盲源分离和正交匹配追踪的时-空耦合分布动载荷识别方法。通过适当的正交分解，时空耦合分布动力荷载可以分解成一系列独立的空间分布函数和时程函数。考虑到荷载分布函数和时程函数对结构响应的影响不同，分析了分布动力荷载识别的非唯一性。将模态载荷视为未知独立时程函数的加权混合，采用绿色核函数法和正则化方法识别模态载荷。然后，引入盲源分离技术实现负荷时程函数的辨识，采用正交匹配追踪算法实现负荷分布函数的唯一稀疏解。最后，稀疏识别的分布动态载荷和等效表示为几个集中的动态载荷作用在适当的位置。该方法能够处理复杂结构上的时空耦合分布动荷载，并能分别实现其空间分布表示和时程重构。数值算例的结果表明了该方法的合理性和有效性。(C)2020爱思唯尔有限公司保留所有权利。
A novel and efficient method utilizing blind source separation and orthogonal matching pursuit is proposed in this paper to identify the time-space coupled distributed dynamic loads. By using proper orthogonal decomposition, the time-space coupled distributed dynamic load can be decomposed into the form of series of independent spatial distribution functions and time history functions. Considering the influences of the load distribution functions and time history functions on the structural responses are different, the nonuniqueness of distributed dynamic load identification is analyzed. The modal loads can be regarded as the weighted mixtures of the unknown independent time history functions and are identified by Green's kernel function method and regularization. Then, the blind source separation technique is introduced to realize the identification of load time history functions, and the orthogonal matching pursuit algorithm is adopted to achieve the unique sparse solutions for load distribution functions. Finally, the distributed dynamic load is sparsely identified and equivalently represented as several concentrated dynamic loads acting on the appropriate positions. This proposed method is capable to deal with the time-space coupled distributed dynamic load on complicated structures, and to realize its spatial distribution representation and time history reconstruction separately. The results of the numerical examples demonstrate the reasonability and effectiveness of the method. (C) 2020 Elsevier Ltd. All rights reserved.