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| Hauptseite > Publikationsdatenbank > PMMA pyrolysis simulation – from micro- to real-scale > print |
| 001 | 1015150 | ||
| 005 | 20231124114431.0 | ||
| 024 | 7 | _ | |a 10.1016/j.firesaf.2023.103926 |2 doi |
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| 024 | 7 | _ | |a 0379-7112 |2 ISSN |
| 024 | 7 | _ | |a 1873-7226 |2 ISSN |
| 024 | 7 | _ | |a 10.34734/FZJ-2023-03555 |2 datacite_doi |
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| 037 | _ | _ | |a FZJ-2023-03555 |
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| 100 | 1 | _ | |a Hehnen, Tristan |0 P:(DE-Juel1)174283 |b 0 |
| 245 | _ | _ | |a PMMA pyrolysis simulation – from micro- to real-scale |
| 260 | _ | _ | |a New York, NY [u.a.] |c 2023 |b Elsevier |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a In fire spread simulations, heat transfer and pyrolysis are processes to describe the thermal degradation of solid material. In general, the necessary material parameters cannot be directly measured. They are implicitly deduced from micro- and bench-scale experiments, i.e. thermogravimetric analysis (TGA), micro-combustion (MCC) and cone calorimetry. Using a complex fire model, an inverse modelling process (IMP) is capable to find parameter sets, which are able to reproduce the experimental results. In the real-scale, however, difficulties arise predicting the fire behaviour using the deduced parameter sets. Here, we show an improved model to fit data of multiple small scale experiment types. Primarily, a gas mixture is used to model an average heat of combustion for the surrogate fuel. The pyrolysis scheme is using multiple reactions to match the mass loss (TGA), as well as the energy release (MCC). Additionally, a radiative heat flux map, based on higher resolution simulations, is used in the cone calorimeter setup. With this method, polymethylmetacrylate (PMMA) micro-scale data can be reproduced well. For the bench-scale, IMP setups are used differing in cell size and targets, which all lead to similar and good results. Yet, they show significantly different performance in the real-scale parallel panel setup. |
| 536 | _ | _ | |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5111 |c POF4-511 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Arnold, Lukas |0 P:(DE-Juel1)132044 |b 1 |e Corresponding author |
| 773 | _ | _ | |a 10.1016/j.firesaf.2023.103926 |g Vol. 141, p. 103926 - |0 PERI:(DE-600)1483569-1 |p 103926 - |t Fire safety journal |v 141 |y 2023 |x 0378-7761 |
| 856 | 4 | _ | |u https://www.sciencedirect.com/science/article/pii/S0379711223001947 |
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