Application Cases of Inverse Modelling with the PROPTI Framework - Data Set

Contents Set of simulation data, supplementary for a paper submitted to (published: 15 June 2019) the Fire Safety Journal, with the title "Application Cases of Inverse Modelling with the PROPTI Framework". See also our project at ResearchGate. This repository contains the complete input data for each IMP run of the mass loss calorimeter, shown in this paper. This comprises of the experimental data files, the templates for the simulation models and the input file for PROPTI. The data base files are provided. This includes the original ones created by PROPTI during the run, as well as the cleaned data base files, used to create the plots, and the extracted best parameter sets per generation. Plots, created during the IMP runs as means of monitoring the progress are also included. Furthermore, the repository contains a small collection of Jupyter notebooks which have been used to process the data base files and create the plots presented in this paper. The full factorial simulations were set up from within a Jupyter notebook. This notebook and the conducted simulations are also part of this repository. Data of the various TGA simulations are provided within a very similar repository, linked to a conference paper (ESFSS 2018, Nancy, France). Finally, the simulation input files, PROPTI input, as well as the custom script for file handling in concert with OpenFOAM, are provided.   Technical Information Each ZIP archive represents a sub-directory of the original directory. For the analysis scripts, the Jupyter notebooks, to work properly out of the box it is necessary to keep this structure. Thus, simply extract all archives into the same directory. Note: Size on disc, after extraction, is about 4.1 GB. Version 2 adds about 5.1 GB.   Version 2: Version 2 contains new IMP runs that address an error in determining the normalised residual mass, see Jupyter Notebook "RevisedTargetAssessment.ipynb", as well as input from the reviewers. The IMP runs are denoted by "08" after the optimisation algorithm label, e.g. "MLC_FSCABC_08_new_75kw_Ins".

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PID https://www.doi.org/10.5281/zenodo.3234541
PID https://www.doi.org/10.5281/zenodo.2538847
PID https://www.doi.org/10.5281/zenodo.2538846
URL https://zenodo.org/record/3234541
URL https://figshare.com/articles/Application_Cases_of_Inverse_Modelling_with_the_PROPTI_Framework_-_Data_Set/7592351
URL http://dx.doi.org/10.5281/zenodo.2538847
URL https://zenodo.org/record/2538847
URL http://dx.doi.org/10.5281/zenodo.2538846
URL http://dx.doi.org/10.5281/zenodo.3234541
URL https://figshare.com/articles/Application_Cases_of_Inverse_Modelling_with_the_PROPTI_Framework_-_Data_Set/8203052
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Access Right Open Access
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Author Arnold, Lukas, 0000-0002-5939-8995
Author Hehnen, Tristan
Author Lauer, Patrick
Author Trettin, Corinna
Author Vinayak, Ashish
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Collected From Zenodo; Datacite; figshare
Hosted By Zenodo; figshare
Publication Date 2019-05-29
Publisher Zenodo
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Language English
Resource Type Dataset
system:type dataset
Management Info
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Source https://science-innovation-policy.openaire.eu/search/dataset?datasetId=dedup_wf_001::bbda834a4d7fd4e7075eae01dec7e709
Author jsonws_user
Version Version 2
Last Updated 28 December 2020, 16:18 (CET)
Created 28 December 2020, 16:18 (CET)