Publications

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Carbon fiber epoxy composites are increasingly used in systems requiring a material that is both strong and light weight, as in airplanes, cars, and pressure vessels. In fire environments, carbon fiber epoxy composites are a fuel source subject to oxidation. This literature review seeks to provide material properties as well as uncertainty bounds for those properties for computational models of decomposing carbon fiber epoxy composites. The goal is to guide analysts when measurements are lacking and increase credibility of uncertainty quantification ranges.

Peat fires are a major contributor to greenhouse gas emissions. The estimates of these emissions currently contain major uncertainties, due to the difficulty of determining the mass of peat burned in a fire. To address these uncertainties, we develop a computational physics-based peat smoldering model, which will be leveraged for high-fidelity quantitative estimates of peat fire emissions relevant to climate change. We present the verification of the 2-D axisymmetric model, a first step towards developing a full 3-D model. Verification includes the solution verification against a literature model for the 0-D smoldering case and verification of the heat transfer problem in 1-D and 2-D. Also presented is the effect of reaction mechanism on the smoldering model, for which we found a relatively simple three-step reaction mechanism is able to capture key behavior. These verification results provide the foundation for moving forward with validation against experimental data of the 2-D model.

In this work, thermogravimetric analysis (TGA) was performed on samples of a carbon fiber epoxy composite, a glass fiber epoxy composite, and a mixed carbon fiber/glass fiber epoxy composite, as well on each constituent material (polymer epoxy, carbon fibers and glass fibers). TGA was conducted for heating rates from 1-20 C/min with purified purge gases of nitrogen and dry air. For the fiberglass composite, we find that ~70% of the material remains after heating in air to 1200 C. For the carbon fiber epoxy composite, we observe greater mass loss as the carbon fibers can oxidize, leaving little material by the end of the test. The mixed composite, which has a 2:1 ratio of glass fibers to carbon fibers, experienced a total mass loss between the two other composites. By determining the relationship between the thermal decomposition of a composite material and its constituent materials, we can predict the fire behavior of novel composites during the material design phase.

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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 tradiational 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. 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 de- composition 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 thermogravimetric analysis 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 thermogravimetric analysis data. Uncertainty bounds capture devia- tions of the model from the data. The calibrated parameter distributions are also presented. In conclusion, the distributions may be used in forward propagation of uncertainty in models that leverage this material.

The prevalent use of organic materials in manufacturing is a fire safety concern, and motivates the need for predictive thermal decomposition models. A critical component of predictive modeling is numerical inference of kinetic parameters from bench scale data. Currently, an active area of computational pyrolysis research focuses on identifying efficient, robust methods for optimization. This paper demonstrates that kinetic parameter calibration problems can successfully be solved using classical gradient-based optimization. We explore calibration examples that exhibit characteristics of concern: high nonlinearity, high dimensionality, complicated schemes, overlapping reactions, noisy data, and poor initial guesses. The examples demonstrate that a simple, non-invasive change to the problem formulation can simultaneously avoid local minima, avoid computation of derivative matrices, achieve a computational efficiency speedup of 10x, and make optimization robust to perturbations of parameter components. Techniques from the mathematical optimization and inverse problem communities are employed. By re-examining gradient-based algorithms, we highlight opportunities to develop kinetic parameter calibration methods that should outperform current methods.

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Data from four TGA experiments conducted at Sandia National Laboratories was used for determination of a pyrolysis model using a commercial thermokinetics program developed by Netzsch Instruments (Kinetics NEO, version 2.1). The data measured at 1 K/min and the average of three measurements at 50 K/min were used as input into Kinetics NEO. The model was developed using data in the range 373 to 773 K. An initial estimate of the energy of activation (E) and pre-exponential constant (A) were determined from the model-free Friedman approach.

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