期刊
THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS
卷 36, 期 6, 页码 887-914出版社
SPRINGER
DOI: 10.1007/s00162-022-00630-1
关键词
Aliasing; Signal processing; High-fidelity simulation; Derivative-based de-aliasing
资金
- Clean Sky 2 Joint Undertaking under the European Union [785303]
- ONR [N00014-22-1-2561]
- NAVAIR SBIR project
- TUBITAK 2236 Co-funded Brain Circulation Scheme 2 [121C061]
- H2020 Societal Challenges Programme [785303] Funding Source: H2020 Societal Challenges Programme
This paper presents a set of alternative strategies for mitigating aliasing in flow data for large datasets, including using time-derivative data and spatial filtering methods, as well as proposing strategies for preventing aliasing.
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.
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