2026-10-13T07:15:23Zhttps://www.radar-service.eu/oai/OAIHandler10.35097/26za20q32xsz43yk2026-10-09T10:46:19Zradar4kit
https://dx.doi.org/10.35097/26za20q32xsz43yk
Neuhauser, Jonathan
Dataset: Conjugate Heat Transfer in Turbulent Pipe Flows with Non-Uniform Heating Effects at Two Prandtl Numbers
Karlsruhe Institute of Technology
2026
Engineering
dataset
Dataset
Hunter (HLRS)
Instrument
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
This dataset contains time-averaged statistics for turbulent forced convection in a pipe (Re_b = 5300) at two Prandtl numbers (Pr = 0.025 and 0.71). Conjugate heat transfer is considered. Data are generated by means of a so-called Thermal DNS in NekRS v26.0.
Data are provided as tar archives of .zarr version 3 files (https://zarr.dev/), created with Xarray (https://xarray.dev/).
The bulk Reynolds number u_b D / nu = 5300 corresponds to Re_tau approx. 180.
Three meshes are considered:
- long (50 D)
- short (12.5 D), including one with slightly finer resolution
Different scalars are considered; they correspond to the "Pr" and "isc" axes. On the isc axis, the associated coordinates "type", "K", "lambda_sf" and "bccode" further specify solid properties and BC code.
type:
- MBC (mixed boundary condition; corresponds to K -> 0); only bccode = 0
- IF (isoflux, corresponds to K -> infty)
- CHT (conjugate heat transfer)
bccode:
- 0: azimuthally uniform heat flux
- 2: half-sinusoidal heat flux: q_w = pi*sin(phi) if phi < np.pi; 0 otherwise
- 3: q_w = sin(phi)
- 4: q_w = sin(2*phi)
- 5: q_w = sum_{k=0}^{10} sin(k*phi)
lambda_sf / K:
- K = 1: lambda_sf = 1/2, 1, 2
- lambda_sf = 1: K = 0.25, 1, 4
For technical reasons, the list of scalars was split into various subcases during simulation (and thus the velocity fields are slightly different); therefore u is broadcasted correctly over the list of scalars.
The following statistics are contained in the dataset:
Primary Variables:
- p
- t
- u
- v
- w
Correlations:
- p*p
- u*u
- v*v
- w*w
- w*w*w
- w*w*w*w
- u*v
- u*w
- v*p
- v*w
- w*p
- t*t
- t*t*t
- t*u
- t*v
- t*w
- u*p
- t*t*u
- t*t*v
- t*t*w
Derivatives and their products determined using SEM differentiation:
- d(t)dx
- d(t)dy
- d(t)dz
- d(w)dx
- d(w)dy
- d(t)dx*d(t)dx
- d(t)dx*d(t)dy
- d(t)dy*d(t)dx
- d(t)dy*d(t)dy
- d(t)dz*d(t)dz
For each Reynolds number, two datasets are provided:
1) joined_datasets.zarr(.tar) contains the z-averaged data on the original NekRS grid (i.e. coordinates are isc, element, i, j, k) with one element in z direction. The time axis is preserved to allow for batched-means estimation of uncertainties.
2) joined_datasets.interp.zarr(.tar) contains polar-interpolated data (i.e. coordinates are isc, r, phi). Interpolation points are concentrated near the start of the heated region to resolve the high mean gradients occurring in this region. Interpolation was performed using the gfldr utility in nek5000. Fluid and solid domains were interpolated separately to account for the discontinuity of some quantities. The time axis is preserved.
3) monitor.zarr(.tar) contains runtime statistics like TMax, TMin, TBulk, uMax, wMax over the entire domain.
In addition, Python scripts are provided for the evaluations of the PhD thesis of J. Neuhauser [1].
Simulations were performed on the national supercomputer HPE Cray EX4000 Hunter at the High Performance Computing Center Stuttgart (HLRS) under the grant number ctbctpf.
[1] J. Neuhauser (2026). Transport Properties of Flow over Spatially Non-Uniform Boundary Conditions. PhD Thesis. Karlsruhe Institute of Technology.
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