DNS 1-3 Statistical Data: Difference between revisions

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==Additional data==
==Additional data==
Here additional statistical data should be provided such as data on solid surfaces and integral
No additional data yet.
time scales as specified respectively in sections 4 and 5 of [1]. Further global quantities of interest
( e.g. drag/lift coefficients, mass flow rates, efficiencies, . . . ) should be given here and if need be
the procedure should be described in detail. Indicate how the data were non-dimensionalised.
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{{ACContribs
{{ACContribs
| authors=
| authors=Oriol Lehmkuhl, Arnau Miro
| organisation=
| organisation=Barcelona Supercomputing Center (BSC)
}}
}}
{{DNSHeaderLib
{{DNSHeaderLib

Revision as of 12:17, 15 February 2021


Front Page

Description

Computational Details

Quantification of Resolution

Statistical Data

Instantaneous Data

Storage Format

Statistical data

Volume data

Volumetric data on the statistics computed is provided in HDF5 files. 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.


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:

An example to read this file with python's h5 library would be:

import h5py

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

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

f.close()

Recover gradients and Reynolds stress tensor (indices are these of the list above:

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. Taylor microscale
  2. Kolmogorov length scale
  3. Kolmorogov time scale

Additional data

No additional data yet.



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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