Analysis of large data sets using Proper Orthogonal Decomposition (Invited)

In this paper, the attention is directed towards application of Proper Orthogonal Decomposition (POD), with special focus on analysis of large data sets. Using modern tools it is common that simulations or measurements of flows result in very large amounts of data. Today, the principal difficulty is to be able to manage the large data set to post-process it and thereby compute the quantities of interest. To study the dynamics of the large scale structures it is common to apply POD. Here, analysis of three flow cases are presented. The common denominator among the flows is that the amount of data to process is vast. The three cases investigated are: direct numerical simulation (DNS) of stably stratified shear flow, DNS of a channel, and large eddy simulation (LES) of a compressible boundary layer.

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