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- ItemDevelopment of a Semiglobal Reaction Mechanism for the Thermal Decomposition of a Polymer Containing Reactive Flame Retardants: Application to Glass-Fiber-Reinforced Polybutylene Terephthalate Blended with Aluminum Diethyl Phosphinate and Melamine Polyphosphate(MDPI, 2018-09-17) Ding, Yan; Stoliarov, Stanislav I.; Kraemer, Roland H.This work details a methodology for parameterization of the kinetics and thermodynamics of the thermal decomposition of polymers blended with reactive additives. This methodology employs Thermogravimetric Analysis, Differential Scanning Calorimetry, Microscale Combustion Calorimetry, and inverse numerical modeling of these experiments. Blends of glass-fiber-reinforced polybutylene terephthalate (PBT) with aluminum diethyl phosphinate and melamine polyphosphate were used to demonstrate this methodology. These additives represent a potent solution for imparting flame retardancy to PBT. The resulting lumped-species reaction model consisted of a set of first- and second-order (two-component) reactions that defined the rate of gaseous pyrolyzate production. The heats of reaction, heat capacities of the condensed-phase reactants and products, and heats of combustion of the gaseous products were also determined. The model was shown to reproduce all aforementioned experiments with a high degree of detail. The model also captured changes in the material behavior with changes in the additive concentrations. Second-order reactions between the material constituents were found to be necessary to reproduce these changes successfully. The development of such models is an essential milestone toward the intelligent design of flame retardant materials and solid fuels.
- ItemTowards Data-Driven Operational Wildfire Spread Modeling: A Report of the NSF-Funded WIFIRE Workshop(2015-01-13) Gollner, Michael; Trouve, Arnaud; Altintas, Ilkay; Block, Jessica; de Callafon, Raymond; Clements, Craig; Cortes, Anna; Ellicott, Evan; Filippi, Jean Baptiste; Finney, Mark; Ide, Kayo; Jenkins, Mary Ann; Jimenez, Daniel; Lautenberger, Christopher; Mandel, Jan; Rochoux, Melanie; Simeoni, AlbertThis report presents a record of the discussions that took place during the workshop entitled “Towards Data-Driven Operational Wildfire Spread Modeling” held on January 12-13, 2015, at the University of California, San Diego. The workshop was organized as part of WIFIRE, a collaborative project sponsored by the National Science Foundation (NSF) between San Diego Supercomputer Center, Calit2's Qualcomm Institute and Jacobs School of Engineering at the University of California at San Diego (UCSD) and the Department of Fire Protection Engineering at the University of Maryland (UMD). The objective of WIFIRE is to build a cyberinfrastructure for real-time and data-driven simulation, prediction and visualization of wildfire behavior (see http://wifire.ucsd.edu). WIFIRE is funded by NSF Award #1331615 as part of the Interdisciplinary Research in Hazards and Disasters (Hazards SEES) program. The objectives of the WIFIRE workshop were: (1) to identify technical barriers and milestones that need to be overcome in order to develop validated data-driven wildfire spread models and make them operational; and (2) to bring together leading representatives of the wildfire research community, the geosciences community and the fire science community. The wildfire research community has relevant expertise on wildfire operations; the geosciences community has relevant expertise on large-scale effects in wildfires (e.g., the coupling with atmospheric phenomena); the fire science community has relevant expertise on flame-scale effects in wildfires (e.g., the response of the fire to changing local conditions). The workshop was organized around four main topical areas and corresponding breakout groups, including operational rate-of-spread models for wildfire spread, CFD models, wildfire data, and data assimilation (see Appendix A for a description of the WIFIRE workshop program). Our goal in this report is to document and share the substance and scope of the workshop discussions and to thereby invite the wider research community to support, engage in, and contribute to the general effort to develop operational data-driven tools for wildfire spread predictions.