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Lossy Data Compression Effects on Wall-bounded Turbulence: Bounds on Data Reduction
KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering, Aerodynamics. KTH Mech, Linne FLOW Ctr, SE-10044 Stockholm, Sweden.;Swedish E Sci Res Ctr SeRC, Stockholm, Sweden..ORCID iD: 0000-0001-9902-6216
KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW. KTH, School of Engineering Sciences (SCI), Mechanics, Stability, Transition and Control. KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH Mech, Linne FLOW Ctr, SE-10044 Stockholm, Sweden.;Swedish E Sci Res Ctr SeRC, Stockholm, Sweden..ORCID iD: 0000-0001-6570-5499
Argonne Natl Lab, MCS, Lemont, IL 60439 USA..
PDC KTH, Ctr High Performance Comp, SE-10044 Stockholm, Sweden..
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2018 (English)In: Flow Turbulence and Combustion, ISSN 1386-6184, E-ISSN 1573-1987, Vol. 101, no 2, p. 365-387Article in journal (Refereed) Published
Abstract [en]

Postprocessing and storage of large data sets represent one of the main computational bottlenecks in computational fluid dynamics. We assume that the accuracy necessary for computation is higher than needed for postprocessing. Therefore, in the current work we assess thresholds for data reduction as required by the most common data analysis tools used in the study of fluid flow phenomena, specifically wall-bounded turbulence. These thresholds are imposed a priori by the user in L (2)-norm, and we assess a set of parameters to identify the minimum accuracy requirements. The method considered in the present work is the discrete Legendre transform (DLT), which we evaluate in the computation of turbulence statistics, spectral analysis and resilience for cases highly-sensitive to the initial conditions. Maximum acceptable compression ratios of the original data have been found to be around 97%, depending on the application purpose. The new method outperforms downsampling, as well as the previously explored data truncation method based on discrete Chebyshev transform (DCT).

Place, publisher, year, edition, pages
Springer, 2018. Vol. 101, no 2, p. 365-387
Keywords [en]
Lossy data compression, Data reduction, Turbulence statistics, Orthogonal polynomials, Resilience
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:kth:diva-237136DOI: 10.1007/s10494-018-9923-5ISI: 000446583900006Scopus ID: 2-s2.0-85047423287OAI: oai:DiVA.org:kth-237136DiVA, id: diva2:1258704
Funder
Swedish Foundation for Strategic Research Knut and Alice Wallenberg FoundationSwedish Research Council
Note

QC 20181025

Available from: 2018-10-25 Created: 2018-10-25 Last updated: 2018-10-25Bibliographically approved

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Otero, EvelynVinuesa, RicardoSchlatter, Philipp

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Flow Turbulence and Combustion
Bioinformatics (Computational Biology)

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