首页> 外文期刊>Reliability Engineering & System Safety >A global surrogate model technique based on principal component analysis and Kriging for uncertainty propagation of dynamic systems
【24h】

A global surrogate model technique based on principal component analysis and Kriging for uncertainty propagation of dynamic systems

机译:一种基于主成分分析和动态系统不确定性传播的全球代理模型技术

获取原文
获取原文并翻译 | 示例
       

摘要

Dynamic systems modeled by computationally intensive numerical models with time-dependent output are common in engineering. Efficient uncertainty propagation of such dynamic models remains a challenging task, which requires accurate prediction of time-dependent output over the entire time domain. When the output is high-dimensional, the size and multivariate nature of the data will cause new computational challenges. In this case, principal component analysis (PCA) can be used to reduce the dimension of output, which retains several principle components (PCs) that account for nearly all the uncertainty of the dynamic output. Then the Kriging model can be constructed based on these PCs instead of the entire dynamic output, which is named as PCA-K method. Based on this idea, this paper, develops a global surrogate model technique called PCA-AK for efficient uncertainty propagation of dynamic systems in the considered time interval, and further improves the reliability analysis ability of PCA-K. An adaptive sampling method is used in PCA-AK, which selects more samples near the limit state function as the training samples. In order to test the applicability of PCA-K and PCA-AK for unknown problems, a more direct pre-judgment method is also proposed in the paper to determine the reconstruction error of the PCA first. Results show that both the PCA-K and PCA-AK can dramatically improve the efficiency of the uncertainty propagation of the dynamic systems with acceptable accuracy, while PCA-AK exhibits more advantages in reliability analysis.
机译:由具有时间依赖输出的计算密集型数值模型建模的动态系统在工程中是常见的。这种动态模型的有效不确定性传播仍然是一个具有挑战性的任务,这需要准确地预测整个时域上的时间依赖性输出。当输出为高维时,数据的大小和多变量性质将导致新的计算挑战。在这种情况下,主成分分析(PCA)可用于降低输出的尺寸,该尺寸保留了几个原理组件(PCS),该组件(PC)占动态输出的几乎所有不确定性。然后可以基于这些PC而不是整个动态输出来构造Kriging模型,而不是整个动态输出,该动态输出被命名为PCA-K方法。基于这个想法,本文开发了一种称为PCA-AK的全球代理模型技术,以便在考虑的时间间隔中提供动态系统的有效不确定性传播,并进一步提高了PCA-K的可靠性分析能力。在PCA-AK中使用自适应采样方法,该方法在限制状态函数附近选择更多的样本作为训练样本。为了测试PCA-K和PCA-AK的适用性,对于未知问题,还提出了一种更直接的预判断方法,以确定PCA的重建误差。结果表明,PCA-K和PCA-AK都可以通过可接受的精度显着提高动态系统的不确定性传播的效率,而PCA-AK在可靠性分析中表现出更多优点。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号