Computational Models of Polyurethane Foam Formation for Property Prediction.
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Engineering topics
Publications and source records attributed to Mondy, Lisa Ann.
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We present a new collection of data on the load-stress relaxation-unload behavior of MinK and FiberFrax Board (FF) insulation materials used as pellets in-line with thermal battery electrochemical stacks. Both materials were subjected to standard thermal preparations, and then tested at room temperature. Intermediate term stress relaxation tests are presented (order 104 minutes of relaxation) showing that FF relaxation is not significantly stress or deformation dependent, but MinK is moderately so. Moreover, stress-strain curves associated with specimen unloading, reloading, and unloading again are presented for both materials. FF and MinK are substantially different here. Acute material variability is observed though test conditions and material preparations are standardized. A modeling approach is presented to empirically estimate the amount of stress relaxation at room temperature, and from this state, represent the unloading stress-strain behavior of both materials. This effort provides a complete framework for representing (in an engineering sense) both materials in thermal battery performance simulations.
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Polyurethane is a complex multiphase material that evolves from a viscous liquid to a system of percolating bubbles, which are created via a CO2 generating reaction. The continuous phase polymerizes to a solid during the foaming process generating heat. Foams introduced into a mold increase their volume up to tenfold, and the dynamics of the expansion process may lead to voids and will produce gradients in density and degree of polymerization. These inhomogeneities can lead to structural stability issues upon aging. For instance, structural components in weapon systems have been shown to change shape as they age depending on their molding history, which can threaten critical tolerances. The purpose of this project is to develop a Cradle-to-Grave multiphysics model, which allows us to predict the material properties of foam from its birth through aging in the stockpile, where its dimensional stability is important.
The recent boom in the oil and natural gas industry of hydraulic fracture of source rocks has caused a new era in oil and gas production worldwide. However, there are many parts of this process that are poorly understood and thus hard to control. One of the few things that can be controlled is the process of injection to create the fractures in the subsurface and the subsequent injection of proppants to maintain the permeability of the fractured formation, allowing hydrocarbons to be extracted. The goal of this work was to better understand the injection process and resulting proppant distribution in the fracture through a combination of lab-scale experiments and computational models.
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We are developing computational models to elucidate the expansion and dynamic filling process of a polyurethane (PMDI) foam used to encapsulate electronic components or to produce lightweight structural parts. The polyurethane of interest is a chemically blown foam, where carbon dioxide is produced via the reaction of water, a blowing agent, and isocyanate. Here, we take a careful look at the evolution of the bubble sizes during blowing. This information will help the development of subgrid models to predict bubble formation, growth, coalescence and collapse, drainage, and, hence, eventually the development of engineering models to predict foam expansion into a mold. Close-up views of bubbles at a transparent wall of a narrow, temperature-controlled channel are recorded during the foaming reaction and analyzed with image processing. Because these bubbles are pressed against the wall, the bubble sizes in the last frames after the expansion has stopped are compared to scanning electron microscope (SEM) images of the interior of some of the cured samples to determine if the presence of the wall significantly changes the bubble sizes. In addition, diffusing wave spectroscopy (DWS) is used to determine the average bubble sizes across the width of a similar channel as the bubbles change with time. DWS also gives information about microstructural changes as bubbles rearrange upon bubble collapse or coalescence. In this paper we conclude qualitatively that the bubble size distribution is heavily dependent on the formulation of foam being tested, temperature, the height in the foam bar, the proximity to a wall, and the degree of over-packing.
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This report details the study of PMDI structural foam and the development of a computational model to represent it. This polyurethane has a fast catalyst, such that foaming and polymerization occur simultaneously. The foam is over-packed to twice or more of its free rise density to reach the density of interest. Our approach is to combine model development closely with experiments to discover underlying physical phenomena, to parameterize models, and to validate the models once they have been developed. The model must be able to represent the expansion, filling of molds, curing, and final foam properties. PMDI is a chemically blown foam, where carbon dioxide is produced via the reaction of water and isocyanate. The isocyanate also reacts with polyol in a competing reaction, which produces the polymer. A new kinetic model is developed and implemented, which follows a simplified mathematical formalism that decouples these two reactions. The model predicts the polymerization reaction via condensation chemistry, where vitrification and glass transition temperature evolution must be included to correctly predict the temperature dependence of the kinetics. The foam gas generation kinetics are determined by tracking the molar concentration of both water and carbon dioxide. Understanding the thermal history and loads on the foam due to exothermicity and oven curing is very important to the results, since the kinetics and material properties are all sensitive to temperature. The conservation equations, including the equations of motion, an energy balance, and three rate equations are solved via a stabilized finite element method. We assume generalized-Newtonian rheology that is dependent on the extent of cure, gas fraction, and temperature. The conservation equations are combined with a level set method to determine the location of the free surface over time. Results from the model are compared to experimental flow visualization data and post-test X-ray computed tomography (CT) data for the density. Several geometries are investigated including a mock encapsulation part, two configurations of a mock structural part, and a bar geometry to specifically test the density model. We have found that the model predicts both average density and filling profiles well. However, it under predicts density gradients, especially in the gravity direction. Further model improvements are also discussed.