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Klingenberg, Dario (2025): A dataset of nonlinear optimals in turbulent channel flow [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.983358

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Published: 2025-07-01 • DOI registered: 2025-07-31

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Abstract:
We investigate the energy transfer from the mean profile to velocity fluctuations in channel flow by calculating nonlinear optimal disturbances, i.e. the initial condition of a given finite energy that achieves the highest possible energy growth during a given fixed time horizon. It is found that for a large range of time horizons and initial disturbance energies, the nonlinear optimal exhibits streak spacing and amplitude consistent with DNS at least at Re_tau = 180, which suggests that they isolate the relevant physical mechanisms that sustain turbulence. Moreover, the time horizon necessary for a nonlinear disturbance to outperform a linear optimal is consistent with previous DNS-based estimates using eddy turnover time, which offers a new perspective on how some turbulent time scales are determined. In this dataset, the initial conditions and temporal evolutions of all calculated optimals are compiled, along with post-processing scripts.
Keyword(s):
nonlinear optimisation; turbulent channel flow
Parameter(s):
#NameShort NameUnitPrincipal InvestigatorMethod/DeviceComment
1 Binary ObjectBinaryKlingenberg, DarioNumerical simulated
2 Binary Object (File Size)Binary (Size)BytesKlingenberg, DarioNumerical simulated
3 FigureFigKlingenberg, DarioNumerical simulated
4 TitleTitleKlingenberg, DarioNumerical simulated
5 File nameFile nameKlingenberg, DarioNumerical simulated
6 VariableVariableKlingenberg, DarioNumerical 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:
64 data points

Data

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1 
Binary		2 
Binary (Size) [Bytes]		3 
Fig		4 
Title		5 
File name		6 
Variable
fig1.zip4.7 GBytes1Energy gain as a function of initial energy for time horizon of 0.7 h/u_taufig1.zipVarious python scripts to produce figure 1 along with hdf files describing the underlying cases.
fig2.zip9.8 GBytes2Long-term evolution of quasilinear and nonlinear optimalsfig2.zipVarious python scripts to produce figure 2 along with hdf files describing the underlying cases.
fig3.zip430.4 MBytes3Evolution of the linear optimalfig3.zipVarious python and bash scripts to produce figure 3 along with hdf files describing the underlying case.
fig4.zip364.1 MBytes4Evolution of the spectrum of the linear optimalfig4.zipVarious python and bash scripts to produce figure 4 along with hdf files describing the underlying case.
fig5_to_9.zip447.4 MBytes5-9Evolution of the nonlinear optimalfig5_to_9.zipVarious python and bash scripts to produce figures 5 to 9 along with hdf files describing the underlying case.
fig10.zip1.5 GBytes10Velocity amplitudes over time for various the nonlinear optimalsfig10.zipVarious python and bash scripts to produce figure 10 along with hdf files describing the underlying cases.
fig11.zip1.1 GBytes11Evolution of various the nonlinear optimalsfig11.zipVarious python and bash scripts to produce figure 11 along with hdf files describing the underlying cases.
fig_12_16_17.zip33.9 GBytes12,16,17Evolution of various the nonlinear optimalsfig12_16_17.zipVarious python and bash scripts to produce figures 12, 16 and 17 along with hdf files describing the underlying cases (this directory contains all cases considered in the publication).
fig13.zip1.9 GBytes13Evolution of a nonlinear nonlocalised optimalfig13.zipVarious python and bash scripts to produce figure 13 along with hdf files describing the underlying case.
fig14.zip4.3 GBytes14Energy over time for nonlinear nonlocalised optimal compared with nonlinear localised optimalsfig14.zipVarious python and bash scripts to produce figure 14 along with hdf files describing the underlying cases.
fig15.zip693.5 MBytes15Early part of the evolution of a nonlinear optimalfig15.zipVarious python and bash scripts to produce figure 15 along with hdf files describing the underlying case.
fig18.zip330.6 MBytes18Evolution of the short-time high-energy nonlinear optimalfig18.zipVarious python and bash scripts to produce figure 18 along with hdf files describing the underlying case.
jax-spectral-dns.zip64.4 MBytesThe simulation code used to obtain the results of the paper and for postprocessingjax-spectral-dns.zipA python package that is installable with pip.