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Pyrolysis Model Validation (PMMA)
In preparation for the MaCFP-3 Workshop to be held on October 22, 2023 in Tsukuba Japan, the MaCFP Condensed Phased Subgroup Organized a pyrolysis model validation exercise to recommend a material property set for use in MaCFP-3 fire growth simulations. This recommended material property set is identified based on its ability to most closely reproduce validation experiments of the NIST-Gasification dataset.
On March 23, 2023 a virtual meeting was organized to introduce the experimental results and modeling targets of each of the five target cases for the MaCFP-3 Workshop, including this pyrolysis model validation case. Presentation slides shared during the March 2023 meeting are available: MaCFP-3 Virtual Presentation March 2023.pdf. A video recording of this presentation is also available online.
This presentation described:
- Apparatus boundary conditions,
- Sample (and backing insulation) preparation and configuration, and
- Measurement data (video/photos + time-resolved measurements of MaCFP-PMMA mass loss rate and back surface temperature rise [7 cm discs, nominally 6 mm thick] during anaerobic heating at either 25 kW/m2 or 50 kW/m2).
Modelers were asked to:
- Compare experimental measurements and model predictions of this validation dataset
- Recalibrate models (material property sets) if/as needed
On June 20, 2023 the results of this pyrolysis model validation exercise were shared in a virtual meeting. Presentation slides shared during the June 2023 virtual meeting are available: MaCFP-3 Virtual Presentation June 2023.pdf.
At this virtual meeting, the following were presented and discussed:
- Model Validation: Comparison of experimental measurements and model predictions of the NIST-Gasification dataset
- Suggested material property set(s): Identification of a recommended material property set for use in MaCFP-3 fire growth cases (UMD-SBI and NIST-Parallel-Panel Tests)
- Model Sensitivity (preliminary analysis) - relative impact of variability in material properties on model predictions of burning behavior