Uncertainty transmission of fluid data upon proper orthogonal decompositions

Jie Ren, Xuerui Mao*

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

1 引用 (Scopus)

摘要

Proper orthogonal decomposition (POD) serves as a principal approach for modal analysis and reduced-order modeling of complex flows. The method works robustly with most types of fluid data and is fundamentally trusted. While, in reality, one has to discern the input spatiotemporal data as passively contaminated globally or locally. To understand this problem, we formulate the relation for uncertainty transmission from input data to individual POD modes. We incorporate a statistical model of data contamination, which can be independently established based on experimental measurements or credible experiences. The contamination is not necessarily a Gaussian white noise, but a structural or gusty modification of the data. Through case studies, we observe a general decaying trend of uncertainty toward higher modes. The uncertainty originates from twofold: self-correlation and cross correlation of the contamination terms, where the latter could become less influential, given the narrow correlation width measured in experiments. Mathematically, the self-correlation is determined by the inner product of the data snapshot and the mode itself. Therefore, the similarity between the input data and the resulting POD modes becomes a critical and intuitive indicator when quantifying the uncertainty. A scaling law is shown to be applicable for self-correlation that promotes fast quantification on sparse grids.

源语言英语
文章编号071702
期刊Physics of Fluids
35
7
DOI
出版状态已出版 - 1 7月 2023

指纹

探究 'Uncertainty transmission of fluid data upon proper orthogonal decompositions' 的科研主题。它们共同构成独一无二的指纹。

引用此