The Effect of Lossy Data Compression in Computational Fluid Dynamics Applications: Resilience and Data Postprocessing

  • E. OteroEmail author
  • R. Vinuesa
  • P. Schlatter
  • O. Marin
  • A. Siegel
  • E. Laure
Conference paper
Part of the ERCOFTAC Series book series (ERCO, volume 25)


The field of computational fluid dynamics (CFD) is data intensive, particularly for high-fidelity simulations. Direct and large-eddy simulations (DNS and LES), which are framed in this high-fidelity regime, require to capture a wide range of flow scales, a fact that leads to a high number of degrees of freedom. Besides the computational bottleneck, brought by the size of the problem, a slightly overlooked issue is the manipulation of the data. High amounts of disk space and also the slow speed of I/O (input/output) impose limitations on large-scale simulations. Typically the computational requirements for proper resolution of the flow structures are far higher than those of post-processing. To mitigate such shortcomings we employ a lossy data compression procedure, and track the reduction that occurs for various levels of truncation of the data set.



Financial support from the Stiftelsen för strategisk forskning (SSF) and the Swedish e-Science Research Centre (SeRC) via the SESSI project is acknowledged. The computations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC).


  1. 1.
    Ahmed, N., Natarajan, T., Rao, K.R.: Discrete cosine transform. IEEE Trans. Comput. C–23, 90–93 (1974)MathSciNetCrossRefGoogle Scholar
  2. 2.
    El Khoury, G.K., Schlatter, P., Noorani, A., Fischer, P.F., Brethouwer, G., Johansson, A.V.: Direct numerical simulation of turbulent pipe flow at moderately high Reynolds numbers. Flow Turbul. Combust. 91, 475–495 (2013)CrossRefGoogle Scholar
  3. 3.
    Fischer, P.F., Lottes, J.W., Kerkemeier, S.G.: Nek5000: open source spectral element CFD solver (2008).
  4. 4.
    Jeong, J., Hussain, F.: On the identification of a vortex. J. Fluid Mech. 285, 69–94 (1995)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Marin, O., Schanen, M., Fischer, P.F.: Large-scale lossy data compression based on an a priori error estimator in a spectral element code. In: ANL/MCS-P6024-0616 (2016)Google Scholar
  6. 6.
    Peplinski, A., Vinuesa, R., Offermans, N., Schlatter, P.: ExaFLOW use cases for Nek5000: incompressible jet in cross-flow and flow around a NACA4412 wing section. In: H2020 FETHPC-1-2014 D3.1Google Scholar
  7. 7.
    Schmalzl, J.: Using standard image compression algorithms to store data from computational fluid dynamics. Comput. Geosci. 29, 1021–1031 (2003)CrossRefGoogle Scholar
  8. 8.
    Vinuesa, R., Hosseini, S. M., Hanifi, A., Henningson, D.S., Schlatter, P.: Pressure-gradient turbulent boundary layers developing around a wing section. Flow Turbul. Combust. 99, 613–641 (2017)CrossRefGoogle Scholar
  9. 9.
    Vinuesa, R., Schlatter, P.: Skin-friction control of the flow around a wing section through uniform blowing. In: Proceedings of European Drag Reduction and Flow Control Meeting (EDRFCM) (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • E. Otero
    • 1
    Email author
  • R. Vinuesa
    • 1
  • P. Schlatter
    • 1
  • O. Marin
    • 2
  • A. Siegel
    • 2
  • E. Laure
    • 3
  1. 1.Linné FLOW CentreKTH Mechanics and Swedish e-Science Research Centre (SeRC)StockholmSweden
  2. 2.MCSArgonne National LaboratoryLemontUSA
  3. 3.Center for High Performance ComputingKTHStockholmSweden

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