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| Hauptseite > Publikationsdatenbank > Sensitivity Study of Input Parameters in Modeling Flame Spread in Bench-Scale Experiments Using the SPyro Model in FDS |
| Hauptseite > Publikationsdatenbank > Sensitivity Study of Input Parameters in Modeling Flame Spread in Bench-Scale Experiments Using the SPyro Model in FDS |
| Contribution to a conference proceedings | FZJ-2025-05689 |
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2025
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Please use a persistent id in citations: doi:10.34734/FZJ-2025-05689
Abstract: The heat release rate (HRR) of a burning solid material depends on the interaction of several physical phenomena, such as the decomposition of the condensed phase, the exothermic combustion reaction in the gas phase, and the transfer of heat back to the material surface. While the fire safety community has made significant progress in quantifying material and reaction properties in recent years, it is still the focus of on going research in the community. As a result, simplified engineering approaches are often used in performance-based design (PBD) in fire safety applications. The scaling-based pyrolysis (SPyro) model is a recently developed engineering model to bridge the gap between detailed pyrolysis models and engineering practice. SPyro uses the concept of heat of gasification to scale a measured bench-scale material response to dynamic exposure conditions predicted within a computational fluid dynamics (CFD) model. The model estimates the flame heat flux occurring in a cone calorimeter experiment to calculate the reference heat flux for use in scaling. To date, this heat flux has been estimated based on an empirical formulation for a cone calorimeter in a horizontal configuration. However, these results are often applied to predict material behavior in a vertical configuration, for example wall linings. In this study, cone calorimeter experiments were conducted using cast black polymethyl methacrylate (PMMA) across a range of configurations, including both horizontal and vertical orientations. The experimental data were used to calibrate and validate the SPyro model, enabling cross-prediction between configurations. Furthermore, the SPyro model was also applied to simulate fire growth in another benchscale experiment.
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