DNS 1-3 Statistical Data

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Statistical Data

Instantaneous Data

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Statistical data

Volume data

Volumetric data on the statistics computed for the whole computational domain are provided in HDF5 format. This can be easily read through the HDF5 library or python's h5py. The tree scheme of the dataset is the following:

• Statistics.h5
  • 01_Info
    • Dimensions
  • 02_Entries
    • Inputs
    • 01_Output
      • AdditionalQuantities
      • Convection
      • Production
      • TurbulentDiffusion01
      • TurbulentDiffusion02
      • PressureStrain
      • Dissipation
      • TripleCorrelation
      • PressureVelocity
  • 03_Nodes
    • Nodes

The available files are:

Statistics (9.1 kB) Is the h5 masterfile that contains the data tree and relates with the other files.

Nodes (2.8 GB) Contains the node positions where data is stored. An example of how to read this data in python would be:

import h5py

f = h5py.File('Statistics.h5','r')

xyz = np.array( f.get('03_Nodes').get('Nodes') )

f.close()

Inputs (26.0 GB) Contains the averaged pressure and velocity along with their gradients, the shear stress and the Reynolds stress tensor in the following order:

1. ${\overline {P}}$
2. ${\overline {U}}$
3. ${\overline {V}}$
4. ${\overline {W}}$
5. ${{\overline {\tau }}_{11}}$
6. ${{\overline {\tau }}_{12}}$
7. ${{\overline {\tau }}_{22}}$
8. ${{\overline {\tau }}_{13}}$
9. ${{\overline {\tau }}_{23}}$
10. ${{\overline {\tau }}_{33}}$
11. ${R_{11}}$
12. ${R_{12}}$
13. ${R_{22}}$
14. ${R_{13}}$
15. ${R_{23}}$
16. ${R_{33}}$
17. ${{\overline {P}},1}$
18. ${{\overline {U}},1}$
19. ${{\overline {V}},1}$
20. ${{\overline {W}},1}$
21. ${{\overline {P}},2}$
22. ${{\overline {U}},2}$
23. ${{\overline {V}},2}$
24. ${{\overline {W}},2}$
25. ${{\overline {P}},3}$
26. ${{\overline {U}},3}$
27. ${{\overline {V}},3}$
28. ${{\overline {W}},3}$

An example to read this file in python and recover the gradients and Reynolds stress tensor (indices are these of the list above) would be:

inp = np.array( f.get('02_Entries').get('Inputs') )

grad_velocity = inp[:,[17,21,25,18,22,26,19,23,27]].astype(np.double)

Rij = inp[:,[10,11,13,11,12,14,13,14,15]].astype(np.double)

AdditionalQuantities (3.7 GB) Contains additional quantities such as the Taylor and Kolmogorov scales and the pressure autocorrelation in the following order:

1. ${\overline {pp}}$
2. Taylor microscale
3. Kolmogorov length scale
4. Kolmorogov time scale

TripleCorrelation (9.3 GB) Contains the components of the velocity triple correlation in the following order:

1. ${\overline {uuu}}$
2. ${\overline {uvu}}$
3. ${\overline {vvu}}$
4. ${\overline {uwu}}$
5. ${\overline {vwu}}$
6. ${\overline {wwu}}$
7. ${\overline {vvv}}$
8. ${\overline {vwv}}$
9. ${\overline {wwv}}$
10. ${\overline {www}}$

Pressure Velocity (2.8 GB)

Finally, the components of the Reynolds stress budget equation come in the following order (for a generic budget component $\phi$):

1. ${\phi _{11}}$
2. ${\phi _{12}}$
3. ${\phi _{22}}$
4. ${\phi _{13}}$
5. ${\phi _{23}}$
6. ${\phi _{33}}$

Convection (5.6 GB) Contains the convection budget.

Production (5.6 GB) Contains the production budget.

Dissipation (5.6 GB) Contains the dissipation budget.

Molecular Diffusion (5.6 GB) Contains the molecular diffusion budget.

Pressure Strain (5.6 GB) Contains the pressure strain budget.

Turbulent Diffusion 01 (5.6 GB) Contains the turbulent diffusion 1 budget.

Turbulent Diffusion 02 (5.6 GB) Contains the turbulent diffusion 2 budget.

Additional data

No additional data yet.

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Contributed by: Oriol Lehmkuhl, Arnau Miro — Barcelona Supercomputing Center (BSC)

Front Page

Description

Computational Details

Quantification of Resolution

Statistical Data

Instantaneous Data

Storage Format

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