Numerical simulations and experimental measurements of the ULB semi-industrial furnace for the development of a Digital Twin
Authors/Creators
- 1. Université Libre de Bruxelles
Description
This dataset contains the numerical and experimental data used to build the Digital Twin in Aversano et al. (https://doi.org/10.1016/j.proci.2020.06.045) and the adaptive Digital Twin in Procacci et al. (https://doi.org/10.1016/j.proci.2022.07.029).
The directory "Numerical_data" includes 45 text files containing the data coming from the CFD simulations of the ULB furnace.
In each file, for each computational cell the features reported are:
- the cell's position in x, y, z coordinates and in meters.
- the cell's temperature in K.
- the cell's species mass fraction of NO (mf-pollut-pollutant-0), CO, OH, H2, H2O, CO2, O2, CH4.
The details of the setup of the numerical simulations are reported in Aversano et al.
The numerical simulations have been computed for different values of the equivalence ratio (phi), blend of H2-CH4 (H2) and
inlet diameter (D).
The simulations for different inlet diameter where computed using different meshes, with slightly different numbers of cells.
In the file 'cases_parameters.csv', the value of the parameters is reported for of each simulation. There is a
discrepancy between the naming of the simulations in Aversano et al. and the one used in naming the files, so both are reported.
The experimental measurements used to validate the numerical simulations can be found in the directory "Experimental_data". Each
file contains the value of the measured temperature along with the position in x and z in meters (y being 0). The temperature is
in K. The experimental uncertainty is estimated at 10 K.
The file 'grid.vtu' contains the computational grid used to solve the CFD simulations. It can be opened using VTK-based software
such as Paraview or Pyvista.
Changelog:
- In version V1, some simulations were corrupted during data export.
- Added the grid file in V3
Files
DT_dataset.zip
Files
(785.1 MB)
| Name | Size | Download all |
|---|---|---|
|
md5:c9aeb72b44088a7cc53c7470d3d435f6
|
785.1 MB | Preview Download |
Additional details
Related works
- Cites
- Journal article: 10.1016/j.proci.2020.06.045 (DOI)
- Journal article: 10.1016/j.proci.2022.07.029 (DOI)