Solutions to aliasing in time-resolved flow data

Creative Commons License

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

THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS, cilt.36, sa.6, ss.887-914, 2022 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 36 Sayı: 6
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1007/s00162-022-00630-1
  • Dergi Adı: THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS
  • Derginin Tarandığı İndeksler: 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
  • Sayfa Sayıları: ss.887-914
  • Anahtar Kelimeler: Aliasing, Signal processing, High-fidelity simulation, Derivative-based de-aliasing, DYNAMIC-MODE DECOMPOSITION, LARGE-EDDY SIMULATIONS, SPECTRAL-ANALYSIS, NUMERICAL ERRORS, AMPLIFICATION, TURBULENCE
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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.