多项式混沌
数学
随机过程
系统标识
应用数学
数学优化
正交性
随机优化
参数辨识问题
卡鲁宁-洛夫定理
高斯分布
计算机科学
算法
蒙特卡罗方法
统计
数据库
几何学
量子力学
物理
度量(数据仓库)
模型参数
作者
Shaoqing Wu,Yanwei Sun,Yanbin Li,Qingguo Fei
出处
期刊:Journal of Vibration and Acoustics
日期:2019-04-05
卷期号:141 (4)
被引量:15
摘要
Abstract A stochastic dynamic load identification algorithm is proposed for an uncertain dynamic system with correlated random system parameters. The stochastic Green's function is adopted to establish the relationship between the Gaussian excitation and the response. The Green's function is approximated by the second-order perturbation method, and orthogonal polynomial chaos bases are adopted to replace the corresponding bases in the Tayler series. The stochastic system responses and the stochastic forces are then represented by the polynomial chaos expansion (PCE) and the Karhunen–Loève expansion (KLE), respectively. A unified probabilistic framework for the stochastic dynamic problem is formulated based on the PCE. The stochastic load identification problem of an uncertain dynamic system is then transformed into a stochastic load identification problem of an equivalent deterministic system with the orthogonality of the PCE. Numerical simulations and experimental studies with a cantilever beam under a concentrate stochastic force are conducted to estimate the statistical characteristics of the stochastic load from the stochastic structural response samples. Results show that the proposed method has good accuracy in the identification of force's statistics when the level of uncertainty in the system parameters is not small. Large errors in the identified statistics may occur when the correlation in the random system parameters is neglected. Different correlation lengths for the random system parameters are investigated to show the effectiveness and accuracy of the proposed method.
科研通智能强力驱动
Strongly Powered by AbleSci AI