Principal component analysis of turbulent combustion data: Data pre-processing and manifold sensitivity
Principal component analysis of turbulent combustion data: Data pre-processing and manifold sensitivity
复制标题
湍流燃烧数据的主成分分析：数据预处理和流形灵敏度
DOI：
10.1016/j.combustflame.2012.09.016
复制
发表时间：
2013
影响因子：
4.4
通讯作者：
J. Sutherland
中科院分区：
文献类型：
--
作者：
A. Parente;J. Sutherland
主成分分析已经证明了其识别湍流反应系统中的低维化学流形的能力，通过减少参数化变量的数量，为这种系统的先验参数化提供了基础。以往的主成分分析研究只提到了数据预处理和尺度对主成分分析的重要性，没有详细考虑。本文评估了数据预处理技术对通过主成分分析完成的尺寸缩减过程的影响。特别地，提出了一种方法来识别和去除数据集中的异常观测值，并对其进行主成分分析。此外，详细评估和讨论了定心和缩放技术对主成分分析流形的影响，研究了不同缩放对流形大小和状态空间重建精度的影响。最后，考虑了化学流形对流动特性的敏感性，研究了它对雷诺数的不变性。在本工作中考虑了来自TNF研讨会数据库的几个高保真实验数据集，以证明所提出方法的有效性。
Principal component analysis has demonstrated promise in its ability to identify low-dimensional chemical manifolds in turbulent reacting systems by providing a basis for the a priori parameterization of such systems based on a reduced number of parameterizing variables. Previous studies on PCA have only mentioned the importance of data pre-processing and scaling on the PCA analysis, without detailed consideration. This paper assesses the influence of data-preprocessing techniques on the size-reduction process accomplished through PCA. In particular, a methodology is proposed to identify and remove outlier observations from the datasets on which PCA is performed. Moreover, the effect of centering and scaling techniques on the PCA manifold is assessed and discussed in detail, to investigate how different scalings affect the size of the manifold and the accuracy in the reconstruction of the state-space. Finally, the sensitivity of the chemical manifold to flow characteristics is considered, to investigate its invariance with respect to the Reynolds number. Several high-fidelity experimental datasets from the TNF workshop database are considered in the present work to demonstrate the effectiveness of the proposed methodologies.