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Embedded-Error Bayesian Calibration of Thermal Decomposition of Organic Materials

Journal of Verification, Validation and Uncertainty Quantification

Frankel, Ari L.; Wagman, Ellen B.; Keedy, Ryan M.; Houchens, Brent C.; Scott, Sarah N.

Organic materials are an attractive choice for structural components due to their light weight and versatility. However, because they decompose at low temperatures relative to traditional materials, they pose a safety risk due to fire and loss of structural integrity. To quantify this risk, analysts use chemical kinetics models to describe the material pyrolysis and oxidation using thermogravimetric analysis (TGA). This process requires the calibration of many model parameters to closely match experimental data. Previous efforts in this field have largely been limited to finding a single best-fit set of parameters even though the experimental data may be very noisy. Furthermore, the chemical kinetics models are often simplified representations of the true decomposition process. The simplification induces model-form errors that the fitting process cannot capture. In this work, we propose a methodology for calibrating decomposition models to TGA data that accounts for uncertainty in the model-form and experimental data simultaneously. The methodology is applied to the decomposition of a carbon fiber epoxy composite with a three-stage reaction network and Arrhenius kinetics. The results show a good overlap between the model predictions and TGA data. Uncertainty bounds capture deviations of the model from the data. The calibrated parameter distributions are also presented. The distributions may be used in forward propagation of uncertainty in models that leverage this material.

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Thermal Decomposition Model Development of EN-7 and EN-8 Polyurethane Elastomers

Keedy, Ryan M.; Harrison, Kale W.; Cordaro, Joseph G.

Thermogravimetric analysis - gas chromatography/mass spectrometry (TGA- GC/MS) experiments were performed on EN-7 and EN-8, analyzed, and reported in [1] . This SAND report derives and describes pyrolytic thermal decomposition models for use in predicting the responses of EN-7 and EN-8 in an abnormal thermal environment.

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Modeling Porous PMDI-based Polyurethane Foam Decomposition in Pressurizing Systems

10th U.S. National Combustion Meeting

Scott, Sarah N.; Keedy, Ryan M.; Brunini, Victor B.; Dodd, Amanda B.

Polymer foam encapsulants provide mechanical, electrical, and thermal isolation in engineered systems. In fire environments, gas pressure from thermal decomposition of polymers can cause mechanical failure of sealed systems. A 3-D finite element conduction-radiation model with porous media flow and a chemistry model was created to investigate the heat transfer and pressurization in such scenarios. Experiments show that the rate of pressurization and the temperature of select thermocouples are dependent on orientation with respect to gravity, indicating buoyancy-driven flow. In this work, the gas velocity is solved by applying the Darcy approximation, and the heat transfer and pressurization are determined by solving the continuity, species, and enthalpy equations in the condensed and gas phases. This work will describe the porous media model, explore material parameters (e.g. phase, permeability, conductivity) for use with PMDI polyurethane, compare predictions to experimental data, and recommend values for material properties. It will use multiple heating rates to validate the data, and show that incorporating gas motion into the model captures the divergent nature of the results in different orientations.

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Uncertainty Quantification of Pressurization Due to Organic Material Decomposition

Keedy, Ryan M.

A new approach for describing the uncertainty associated with organic material decomposition in an abnormal thermal environment is des cribed. Rather than applying multiplier s to the pressure predicted by the simulation , the uncertain parameters are incorporated in a broader Latin hypercube sampling study. The resulting distribution gives more information than the pressure multiplier, but similar uncertainty bounds can be derived from a log - normal fit.

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25 Results
25 Results