Identification and quantification of spatial interval uncertainty in numerical models
Identification and quantification of spatial interval uncertainty in numerical models
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DOI：
10.1016/j.compstruc.2017.07.006
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发表时间：
2017-11
影响因子：
4.7
通讯作者：
M. Faes;D. Moens
中科院分区：
文献类型：
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作者：
M. Faes;D. Moens
本文提出了一种识别和量化空间不确定性的新方法，建模为区间场。为了对模型参数的空间不确定性进行现实评估，必须确定区间场的维数及其构成基函数和区间标量。为此，本文介绍了一种基于客观测量数据的识别方法。在这种情况下的具体挑战在于，必须基于在分析模型的结果域中获得的可能的高维测量数据集，在所考虑的结构的高分辨率离散模型上识别连续空间输入参数。在该方法中，基于测量数据有效维数的概念来量化场维数。通过最小化分别限制测量数据和区间场的实现的半空间的梯度之间的差异来识别区间场的基函数。该方法通过两个案例研究进行说明：悬臂梁的动态模型和铸造压力容器的准静态模型。结果表明，所提出的方法能够准确识别模型参数上存在的区间场不确定性，并且这种识别对于测量数据集的大小是稳健的。
This paper presents a novel methodology for the identification and quantification of spatial uncertainty, modelled as an interval field. In order to make a realistic assessment of the spatial uncertainty on the model parameters, the dimensionality of the interval field as well as its constituting base functions and interval scalars have to be identified. For this purpose, this work introduces an identification method based on objective measurement data. The specific challenge in this context lies in the fact that a continuous spatial input parameter has to be identified on a high-resolution discretised model of the structure under consideration, based on possibly high-dimensional measurement data set, obtained in the result domain of the analysed model. In the presented method, the field dimensionality is quantified based on the concept of effective dimension of the measurement data. The base functions of the interval field are identified by minimising the difference between the gradients of the halfspaces respectively bounding the measurement data and the realisations of the interval field. The method is illustrated using two case studies: an dynamic model of a cantilever beam and a quasi-static model of a cast pressure vessel. It is shown that the presented methods are capable of accurately identifying the interval field uncertainty that is present on the model parameters, and that this identification is robust against the size of the measurement data set.