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DOI: 10.1615/ICHMT.2010.RAD-6.540
pages 59-61

Gabriel Wecel
Institute of Thermal Technology, Silesian University of Technology Konarskiego 22, Gliwice 44-100, Poland

Ziemowit Ostrowski
Institute of Thermal Technology, Silesian University of Technology Konarskiego 22, Gliwice 44-100, Poland

Ryszard A. Bialecki
Institute of Thermal Technology, Silesian University of Technology, Gliwice, Poland


Proper Orthogonal Decomposition (POD) technique has been used in many application for more than a century and appears under many names: Karhunen-Loeve Decomposition, Principal Component Analysis, or Singular Value Decomposition. The overview of the POD related literature, theory and history has been recently described1. POD offers elegant way to build low dimensional description of large and correlated data sets consisting of a sequence of vectors of the same dimensions. The most striking feature of the POD is its optimality: it provides the most efficient way of capturing the dominant components of a data set using only few degrees of freedom. The technique is similar to discrete Fourier expansion of function. In both cases the approximated function is expressed as a linear combination of orthogonal vectors (modes). The scalar coefficients of this combination are referred to as amplitudes. While the Fourier modes are user defined, the POD modes are calculated by a procedure which extracts the correlation between the members of the vectors sequence.

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