Empirical and physics based mathematical models of uranium hydride decomposition kinetics with quantified uncertainties.

Metal particle beds have recently become a major technique for hydrogen storage. In order to extract hydrogen from such beds, it is crucial to understand the decomposition kinetics of the metal hydride. We are interested in obtaining a a better understanding of the uranium hydride (UH3) decomposition kinetics. We first developed an empirical model by fitting data compiled from different experimental studies in the literature and quantified the uncertainty resulting from the scattered data. We found that the decomposition time range predicted by the obtained kinetics was in a good agreement with published experimental results. Secondly, we developed a physics based mathematical model to simulate the rate of hydrogen diffusion in a hydride particle during the decomposition. We used this model to simulate the decomposition of the particles for temperatures ranging from 300K to 1000K while propagating parametric uncertainty and evaluated the kinetics from the results. We compared the kinetics parameters derived from the empirical and physics based models and found that the uncertainty in the kinetics predicted by the physics based model covers the scattered experimental data. Finally, we used the physics-based kinetics parameters to simulate the effects of boundary resistances and powder morphological changes during decomposition in a continuum level model. We found that the species change within the bed occurring during the decomposition accelerates the hydrogen flow by increasing the bed permeability, while the pressure buildup and the thermal barrier forming at the wall significantly impede the hydrogen extraction

Formation of algae growth constitutive relations for improved algae modeling.

This SAND report summarizes research conducted as a part of a two year Laboratory Directed Research and Development (LDRD) project to improve our abilities to model algal cultivation. Algae-based biofuels have generated much excitement due to their potentially large oil yield from relatively small land use and without interfering with the food or water supply. Algae mitigate atmospheric CO2 through metabolism. Efficient production of algal biofuels could reduce dependence on foreign oil by providing a domestic renewable energy source. Important factors controlling algal productivity include temperature, nutrient concentrations, salinity, pH, and the light-to-biomass conversion rate. Computational models allow for inexpensive predictions of algae growth kinetics in these non-ideal conditions for various bioreactor sizes and geometries without the need for multiple expensive measurement setups. However, these models need to be calibrated for each algal strain. In this work, we conduct a parametric study of key marine algae strains and apply the findings to a computational model

Development of the Sandia Cooler.

This report describes an FY13 effort to develop the latest version of the Sandia Cooler, a breakthrough technology for air-cooled heat exchangers that was developed at Sandia National Laboratories. The project was focused on fabrication, assembly and demonstration of ten prototype systems for the cooling of high power density electronics, specifically high performance desktop computers (CPUs). In addition, computational simulation and experimentation was carried out to fully understand the performance characteristics of each of the key design aspects. This work culminated in a parameter and scaling study that now provides a design framework, including a number of design and analysis tools, for Sandia Cooler development for applications beyond CPU cooling

Formation of algae growth constitutive relations for improved algae modeling.

This SAND report summarizes research conducted as a part of a two year Laboratory Directed Research and Development (LDRD) project to improve our abilities to model algal cultivation. Algae-based biofuels have generated much excitement due to their potentially large oil yield from relatively small land use and without interfering with the food or water supply. Algae mitigate atmospheric CO2 through metabolism. Efficient production of algal biofuels could reduce dependence on foreign oil by providing a domestic renewable energy source. Important factors controlling algal productivity include temperature, nutrient concentrations, salinity, pH, and the light-to-biomass conversion rate. Computational models allow for inexpensive predictions of algae growth kinetics in these non-ideal conditions for various bioreactor sizes and geometries without the need for multiple expensive measurement setups. However, these models need to be calibrated for each algal strain. In this work, we conduct a parametric study of key marine algae strains and apply the findings to a computational model

Characterization and modeling of thermal diffusion and aggregation in nanofluids.

Fluids with higher thermal conductivities are sought for fluidic cooling systems in applications including microprocessors and high-power lasers. By adding high thermal conductivity nanoscale metal and metal oxide particles to a fluid the thermal conductivity of the fluid is enhanced. While particle aggregates play a central role in recent models for the thermal conductivity of nanofluids, the effect of particle diffusion in a temperature field on the aggregation and transport has yet to be studied in depth. The present work separates the effects of particle aggregation and diffusion using parallel plate experiments, infrared microscopy, light scattering, Monte Carlo simulations, and rate equations for particle and heat transport in a well dispersed nanofluid. Experimental data show non-uniform temporal increases in thermal conductivity above effective medium theory and can be well described through simulation of the combination of particle aggregation and diffusion. The simulation shows large concentration distributions due to thermal diffusion causing variations in aggregation, thermal conductivity and viscosity. Static light scattering shows aggregates form more quickly at higher concentrations and temperatures, which explains the increased enhancement with temperature reported by other research groups. The permanent aggregates in the nanofluid are found to have a fractal dimension of 2.4 and the aggregate formations that grow over time are found to have a fractal dimension of 1.8, which is consistent with diffusion limited aggregation. Calculations show as aggregates grow the viscosity increases at a faster rate than thermal conductivity making the highly aggregated nanofluids unfavorable, especially at the low fractal dimension of 1.8. An optimum nanoparticle diameter for these particular fluid properties is calculated to be 130 nm to optimize the fluid stability by reducing settling, thermal diffusion and aggregation

A coupled transport and solid mechanics formulation with improved reaction kinetics parameters for modeling oxidation and decomposition in a uranium hydride bed.

Modeling of reacting flows in porous media has become particularly important with the increased interest in hydrogen solid-storage beds. An advanced type of storage bed has been proposed that utilizes oxidation of uranium hydride to heat and decompose the hydride, releasing the hydrogen. To reduce the cost and time required to develop these systems experimentally, a valid computational model is required that simulates the reaction of uranium hydride and oxygen gas in a hydrogen storage bed using multiphysics finite element modeling. This SAND report discusses the advancements made in FY12 (since our last SAND report SAND2011-6939) to the model developed as a part of an ASC-P&EM project to address the shortcomings of the previous model. The model considers chemical reactions, heat transport, and mass transport within a hydride bed. Previously, the time-varying permeability and porosity were considered uniform. This led to discrepancies between the simulated results and experimental measurements. In this work, the effects of non-uniform changes in permeability and porosity due to phase and thermal expansion are accounted for. These expansions result in mechanical stresses that lead to bed deformation. To describe this, a simplified solid mechanics model for the local variation of permeability and porosity as a function of the local bed deformation is developed. By using this solid mechanics model, the agreement between our reacting bed model and the experimental data is improved. Additionally, more accurate uranium hydride oxidation kinetics parameters are obtained by fitting the experimental results from a pure uranium hydride oxidation measurement to the ones obtained from the coupled transport-solid mechanics model. Finally, the coupled transport-solid mechanics model governing equations and boundary conditions are summarized and recommendations are made for further development of ARIA and other Sandia codes in order for them to sufficiently implement the model

A coupled transport and solid mechanics formulation with improved reaction kinetics parameters for modeling oxidation and decomposition in a uranium hydride bed.

Modeling of reacting flows in porous media has become particularly important with the increased interest in hydrogen solid-storage beds. An advanced type of storage bed has been proposed that utilizes oxidation of uranium hydride to heat and decompose the hydride, releasing the hydrogen. To reduce the cost and time required to develop these systems experimentally, a valid computational model is required that simulates the reaction of uranium hydride and oxygen gas in a hydrogen storage bed using multiphysics finite element modeling. This SAND report discusses the advancements made in FY12 (since our last SAND report SAND2011-6939) to the model developed as a part of an ASC-P&EM project to address the shortcomings of the previous model. The model considers chemical reactions, heat transport, and mass transport within a hydride bed. Previously, the time-varying permeability and porosity were considered uniform. This led to discrepancies between the simulated results and experimental measurements. In this work, the effects of non-uniform changes in permeability and porosity due to phase and thermal expansion are accounted for. These expansions result in mechanical stresses that lead to bed deformation. To describe this, a simplified solid mechanics model for the local variation of permeability and porosity as a function of the local bed deformation is developed. By using this solid mechanics model, the agreement between our reacting bed model and the experimental data is improved. Additionally, more accurate uranium hydride oxidation kinetics parameters are obtained by fitting the experimental results from a pure uranium hydride oxidation measurement to the ones obtained from the coupled transport-solid mechanics model. Finally, the coupled transport-solid mechanics model governing equations and boundary conditions are summarized and recommendations are made for further development of ARIA and other Sandia codes in order for them to sufficiently implement the model

Rapid hydrogen gas generation using reactive thermal decomposition of uranium hydride.

Oxygen gas injection has been studied as one method for rapidly generating hydrogen gas from a uranium hydride storage system. Small scale reactors, 2.9 g UH{sub 3}, were used to study the process experimentally. Complimentary numerical simulations were used to better characterize and understand the strongly coupled chemical and thermal transport processes controlling hydrogen gas liberation. The results indicate that UH{sub 3} and O{sub 2} are sufficiently reactive to enable a well designed system to release gram quantities of hydrogen in {approx} 2 seconds over a broad temperature range. The major system-design challenge appears to be heat management. In addition to the oxidation tests, H/D isotope exchange experiments were performed. The rate limiting step in the overall gas-to-particle exchange process was found to be hydrogen diffusion in the {approx}0.5 {mu}m hydride particles. The experiments generated a set of high quality experimental data; from which effective intra-particle diffusion coefficients can be inferred