Feldmann, Daniel; Umair, Mohammad; Avila, Marc; von Kameke, Alexandra (2025): Pipe flow simulation data illustrating the influence of filter kernels on the scale-energetics of near-wall turbulence [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.982843
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Published: 2025-09-01 • DOI registered: 2025-10-18
Abstract:
Originally developed for large-eddy simulation (LES) modelling and error estimation, the concept of inter-scale energy fluxes, π λ, is now widely used as a diagnostic tool to study how energy is transferred across different length scales, λ, in high-resolution turbulence data---independent of LES modelling. In Feldmann et al. (2025), we examine how the choice of filter kernel affects the computed energy fluxes and their interpretation when π is used in post-processing to explore scale interactions in near-wall turbulence. To this end, spatial filtering is applied to a turbulent pipe flow simulation using three commonly used kernels: sharp spectral, Gaussian, and box. The dataset enables a detailed comparison of how these different filters influence both the local structure and statistical characteristics of π at constant filter width. These data support the findings presented in Feldmann et al. (2025) and are provided to facilitate further studies on filtering effects and energy transfer in wall-bounded turbulent flows. This dataset includes velocity fields and energy flux fields based on three different filter kernels, as well as one- and two-point statistics for all field data sets.
Keyword(s):
References:
Feldmann, Daniel; Umair, Mohammad; Avila, Marc; von Kameke, Alexandra (preprint): Effect of filter kernel on scale-energetics of near-wall turbulent structures. arXiv, https://doi.org/10.48550/ARXIV.2008.03535
Parameter(s):
| # | Name | Short Name | Unit | Principal Investigator | Method/Device | Comment |
|---|---|---|---|---|---|---|
| 1 | Binary Object | Binary | Feldmann, Daniel | Numerical simulated | ||
| 2 | Binary Object (File Size) | Binary (Size) | Bytes | Feldmann, Daniel | Numerical simulated | |
| 3 | File format | File format | Feldmann, Daniel | Numerical simulated | ||
| 4 | Figure | Fig | Feldmann, Daniel | Numerical simulated | ||
| 5 | Title | Title | Feldmann, Daniel | Numerical simulated | ||
| 6 | Description | Description | Feldmann, Daniel | Numerical simulated |
License:
Creative Commons Attribution 4.0 International (CC-BY-4.0)
Status:
Curation Level: Enhanced curation (CurationLevelC) * Processing Level: PANGAEA data processing level 2 (ProcLevel2)
Size:
55 data points
Data
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| 1 Binary | 2 Binary (Size) [Bytes] | 3 File format | 4 Fig | 5 Title | 6 Description |
|---|---|---|---|---|---|
| codeFigures.tar.gz | 9.7 MBytes | Python (*.py) | 1,2,3,4,5,6,7,8,9 | Python code figures | Python code to reproduce the figures presented in Feldmann et al. 2025. |
| codeSimulations.tar.gz | 1 GBytes | Fortran (*.f90) | 1,2,3,4,5,6,7,8,9 | nsPipe simulation code | Direct numerical simulation (DNS) code nsPipe/nsCouette including parameter files and initial conditions to continue/reproduce the turbulent flow simulations presented in Feldmann et al. 2025. nsPipe is based on a pseudo-spectral formulation in cylindrical coordinates with radial ($r$), azimuthal ($\theta$), and axial ($z$) directions. For further information see https://doi.org/10.1016/j.softx.2019.100395 |
| dataSimPiCorrelationTh.tar.gz | 625.1 kBytes | NumPy (*.npy) | 6,7,8,9 | Azimuthal two-point statistics | Energy flux ($\Pi$) and velocity ($u_r$, $u_\theta$, $u_z$) field auto- and cross-correlations in azimuthal ($\theta$) direction. |
| dataSimPiCorrelationThZ.tar.gz | 1 GBytes | NumPy (*.npy) | 7,8,9 | Wall-parallel two-point statistics | Energy flux ($\Pi$) and velocity ($u_r$, $u_\theta$, $u_z$) field auto- and cross-correlations in azimuthal ($\theta$) and axial ($z$) directions. |
| dataSimPiCorrelationZ.tar.gz | 3.6 MBytes | NumPy (*.npy) | 6,7,8,9 | Axial two-point statistics | Energy flux ($\Pi$) and velocity($u_r$, $u_\theta$, $u_z$) field auto- and cross-correlations in axial ($z$) direction. |
| dataSimPiField.tar.gz | 1.5 GBytes | HDF5 (*.h5) | 1,3,4 | Energy flux field | Full, 3d energy flux fields ($\Pi$) for one fixed filter width computed from the turbulent velocity field based on three different filter kernels (sharp spectral, Gaussian, box) used for scale separation. |
| dataSimPiStatistics.tar.gz | 81.6 kBytes | NumPy (*.npy) | 1,5 | Energy flux one-point statistics | Mean, RMS, skewness, and flatness profiles for the energy flux field. |
| dataSimVelocityField.tar.gz | 4 GBytes | HDF5 (*.h5) | 1,2,4 | Velocity field | Full, scale-resolved 3d velocity field of turbulent pipe flow ($Re_\tau = 180$) in cylindrical coordinates ($u_r$, $u_\theta$, $u_z$). |
| dataSimVelocityFieldFiltered.tar.gz | 7.5 GBytes | HDF5 (*.h5) | 2 | Filtered velocity field | Spatially filtered velocity field based on three different filter kernels (sharp spectral, Gaussian, box) used for scale separation. |
| dataSimVelocityStatistics.tar.gz | 28.7 kBytes | NumPy (*.npy) | 1,2,4,6,8,9 | Velocity one-point statistics | Mean, RMS, skewness, and flatness profiles for the turbulent velocity field |
| journalPublications.tar.gz | 6.4 MBytes | Paper (*.pdf) | 1,2,3,4,5,6,7,8,9 | Journal publication | Journal publications relevant to this data set. |