摘要
参数概率灵敏度分析是可靠性设计中非常重要的一项工作,它可以提供基本变量分布参数的变化引起可靠性的变化信息,为判断系统参数的重要性提供依据。本文将商用有限元计算-神经网络方法-Monte Carlo法相结合,基于这种快速响应模型的复杂结构可靠性分析方法,针对结构随机参数的概率灵敏度分析,提出一个考虑随机变量全局分散性的新的度量参数,作为一种工程实用的快速近似方法,相对于传统的确定性及局部灵敏度具有更清晰的意义和指导作用,通过一个算例实现了该法在工程上的应用,并利用ANSYS的概率分析模块验证了该法的有效性。
The analysis of parametric probabilistic sensitivity analysis is important for reliability-based design, which shows changes of system reliability caused by the change of basic variances. In this paper, the commerical FE simulation-neural network-Monte Carlo methods were used together, based on the quick-response model, the parametric probabilistic sensitivity was analyzed too, and a new scaling parameter was presented here considering the global disperisty of stochastic parameters. The sensitivity indices can be computed by the simple and approximate formula in engineering. A numerical example is presented to verify the feasibility and the effectivity by comparing with the analysis of ANSYS.
出处
《计算力学学报》
EI
CAS
CSCD
北大核心
2011年第B04期29-32,共4页
Chinese Journal of Computational Mechanics
基金
国家基础研究计划(2010CB832700)
NSAF基金(10876100)
中物院科学技术发展基金(2010B203025)资助项目
