Proper orthogonal decomposition on compressed data

Oana Marin, Elia Merzari, Philipp Schlatter, Andrew Siegel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The main approach to identifying coherent structures in a flow field is the Proper Orthogonal Decomposition, due to its simplicity and effectiveness. However it is a data intensive method which becomes more expensive as the data field increases in size. The difficulty pertains mostly when a three-dimensional decomposition is performed, and the limiting factor is storing and loading large data fields of up to billions of gridpoints. This restriction is a conseuqence of the fact that the I/O bandwidth of supercomputers has not been at the same developmental pace as the CPUs. Lossy compression can reduce the size of the data fields and accelerate the computations. The strategy we suggest here relies on data compression via Discrete Chebyshev Transform (or alternatively Discrete Legendre Transform) which leaves invariant the auto-correlation matrix which lies at the core of the POD method. We show that by discarding over 90% of the data we can still retrieve a good proper ortohonal basis of the data set which deviates from the original by 10-2 in the L2 norm.

Original languageEnglish (US)
Title of host publication10th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2017
PublisherInternational Symposium on Turbulence and Shear Flow Phenomena, TSFP10
ISBN (Electronic)9780000000002
StatePublished - Jan 1 2017
Event10th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2017 - Chicago, United States
Duration: Jul 6 2017Jul 9 2017

Publication series

Name10th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2017
Volume1

Conference

Conference10th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2017
CountryUnited States
CityChicago
Period7/6/177/9/17

All Science Journal Classification (ASJC) codes

  • Atmospheric Science
  • Aerospace Engineering

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