Application Cases of Inverse Modelling with the PROPTI Framework - Data Set Article Swipe
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· 2019
· Open Access
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· DOI: https://doi.org/10.5281/zenodo.2538846
· OA: W4393700292
<strong>Contents</strong> 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. <strong>Technical Information</strong> 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. <strong>Version 2:</strong> 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".
