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| Book/Dissertation / PhD Thesis | FZJ-2025-05728 |
2025
Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag
Jülich
ISBN: 978-3-95806-875-9
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Please use a persistent id in citations: doi:10.34734/FZJ-2025-05728
Abstract: Uncontrolled fires in buildings pose significant threats to occupants, the environment, property, and the continuity of operations. Fire risk assessments are critical tools used to identify hazards, determine the likelihood of their occurrence, assess potential consequences, and propose mitigation measures. Several methods exist for quantitative analysis, ranging from simple hand calculations to advanced computational fluid dynamics (CFD) simulations. Typically, heat release rates are prescribed in these assessments and are static. Primarily because the modelling of thermal decomposition (pyrolysis) of combustible materials is difficult. This dissertation consists of three publications that deal with the design of parameter sets that allow the simulation of pyrolysis, based on the heat feedback to a sample material. From common small-scale fire experiments, data are used to create the material models. The first publication deals with the simulation of fire propagation in cable tray installations. A proof of concept for a methodology is presented in which material parameters can be determined in an inverse modelling process (IMP). This IMP uses experimental data as the target. The second publication builds on the work of the first publication and improves it. Several adjustments needed to be made in the previous work after the IMP concluded to enable fire propagation. These adjustments are here directly implemented in the IMP. Further improvements are implemented to the process, such as higher fluid cell resolution during the parameter estimation process. The third publication moves away from the complex structure of electrical cables and focuses on a pure polymer: poly (methyl methacrylate) (PMMA). Further improvements are conducted with even higher fluid cell resolution. Furthermore, a wider range of experimental data sets is available, which is used to improve the parameter estimation.
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