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Title: Lossy Data Compression Effects on Wall-bounded Turbulence: Bounds on Data Reduction

Journal Article · · Flow, Turbulence and Combustion
ORCiD logo [1];  [1];  [2];  [3];  [1]
  1. Linne FLOW Centre, Stockholm (Sweden); Swedish e-Science Research Centre (SeRC), Stockholm (Sweden)
  2. Argonne National Lab. (ANL), Lemont, IL (United States)
  3. Center for High Performance Computing, Stockholm (Sweden)
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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 L2-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. Furthermore, the new method outperforms downsampling, as well as the previously explored data truncation method based on discrete Chebyshev transform (DCT).

Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Organization:
Knut and Alice Wallenberg Foundation; Swedish Research Council (SRC); Swedish Foundation for Strategic Research (SSF); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
Grant/Contract Number:
AC02-06CH11357
OSTI ID:
1493736
Journal Information:
Flow, Turbulence and Combustion, Vol. 101, Issue 2; ISSN 1386-6184
Publisher:
European Research Community on Flow, Turbulence and Combustion (ERCOFTAC)Copyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 3 works
Citation information provided by
Web of Science

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Cited By (2)

Compression Challenges in Large Scale Partial Differential Equation Solvers journal September 2019
Compression challenges in large scale PDE solvers text January 2019

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Related Subjects

97 MATHEMATICS AND COMPUTING
Lossy data compression
Data reduction
Turbulence statistics
Orthogonal polynomials
Resilience