Solutions to aliasing in time-resolved flow data

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Karban U., Martini E., Jordan P., Bres G. A., Towne A.

THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS, vol.36, no.6, pp.887-914, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 36 Issue: 6
  • Publication Date: 2022
  • Doi Number: 10.1007/s00162-022-00630-1
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, Geobase, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.887-914
  • Keywords: Aliasing, Signal processing, High-fidelity simulation, Derivative-based de-aliasing, DYNAMIC-MODE DECOMPOSITION, LARGE-EDDY SIMULATIONS, SPECTRAL-ANALYSIS, NUMERICAL ERRORS, AMPLIFICATION, TURBULENCE
  • Middle East Technical University Affiliated: Yes


Avoiding aliasing in time-resolved flow data obtained through high-fidelity simulations while keeping the computational and storage costs at acceptable levels is often a challenge. Well-established solutions such as increasing the sampling rate or low-pass filtering to reduce aliasing can be prohibitively expensive for large datasets. This paper provides a set of alternative strategies for identifying and mitigating aliasing that are applicable even to large datasets. We show how time-derivative data, which can be obtained directly from the governing equations, can be used to detect aliasing and to turn the ill-posed problem of removing aliasing from data into a well-posed problem, yielding a prediction of the true spectrum. Similarly, we show how spatial filtering can be used to remove aliasing for convective systems. We also propose strategies to prevent aliasing when generating a database, including a method tailored for computing nonlinear forcing terms that arise within the resolvent framework. These methods are demonstrated using a nonlinear Ginzburg-Landau model and large-eddy simulation data for a subsonic turbulent jet.