Extracting the Spectrum by Spatial Filtering
Mahmoud Sadek, Hussein Aluie

TL;DR
This paper introduces a local physical-space filtering method to extract flow spectra, demonstrating its effectiveness and advantages over wavelet analysis in various turbulent flow scenarios.
Contribution
The authors develop simple stencil kernels with sufficient vanishing moments to extract spectra steeper than $k^{-3}$, enabling local spectral analysis in complex flows.
Findings
Method accurately extracts spectra from synthetic and turbulent flow data.
Constructed kernels can handle spectra steeper than $k^{-3}$.
Guarantees energy conservation and handles non-quadratic quantities.
Abstract
We show that the spectrum of a flow field can be extracted within a local region by straightforward filtering in physical space. We find that for a flow with a certain level of regularity, the filtering kernel must have a sufficient number of vanishing moments in order for the "filtering spectrum" to be meaningful. Our derivation follows a similar analysis by Perrier et al. 1995 for the wavelet spectrum, where we show that the filtering kernel has to have at least vanishing moments in order to correctly extract a spectrum with . For example, any flow with a spectrum shallower than can be extracted by a straightforward average on grid-cells of a stencil. We construct two new "simple stencil" kernels, and , with only two and three fixed stencil weight coefficients, respectively, and that have sufficient…
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Taxonomy
TopicsFluid Dynamics and Turbulent Flows · Meteorological Phenomena and Simulations · Wind and Air Flow Studies
