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International Collaborations on Radioactive Waste Disposal in Salt (FY20)

Kuhlman, Kristopher L.; Matteo, Edward N.; Mills, Melissa M.; Jayne, Richard S.; Reedlunn, Benjamin R.; Sobolik, Steven R.; Laros, James H.; Stein, Emily S.; Gross, Michael B.

This report is a summary of the international collaboration work conducted by Sandia and funded by the US Department of Energy Office (DOE) of Nuclear Energy Spent Fuel and Waste Science & Technology (SFWST) as part of the Sandia National Laboratories Salt R&D and Salt International work packages. This report satisfies milestone level-three milestone M3SF-205N010303062. Several stand-alone sections make up this summary report, each completed by the participants. The first two sections discuss international collaborations on geomechanical benchmarking exercises (WEIMOS), granular salt reconsolidation (KOMPASS), engineered barriers (RANGERS), and documentation of Features, Events, and Processes (FEPs).

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TYPE Other Report YEAR 2020
DOIOSTI

Complexion dictated thermal resistance with interface density in reactive metal multilayers

Physical Review B

Saltonstall, Christopher B.; Mcclure, Zachary D.; Abere, Michael J.; Guzman, David; Reeve, Samuel T.; Strachan, Alejandro; Kotula, Paul G.; Adams, David P.; Laros, James H.

Multilayers composed of aluminum (Al) and platinum (Pt) exhibit a nonmonotonic trend in thermal resistance with bilayer thickness as measured by time domain thermoreflectance. The thermal resistance initially increases with reduced bilayer thickness only to reach a maximum and then decrease with further shrinking of the multilayer period. These observations are attributed to the evolving impact of an intermixed amorphous complexion approximately 10 nm in thickness, which forms at each boundary between Al- and Pt-rich layers. Scanning transmission electron microscopy combined with energy dispersive x-ray spectroscopy find that the elemental composition of the complexion varies based on bilayer periodicity as does the fraction of the multilayer composed of this interlayer. These variations in complexion mitigate boundary scattering within the multilayers as shown by electronic transport calculations employing density-functional theory and nonequilibrium Green's functions on amorphous structures obtained via finite temperature molecular dynamics. The lessening of boundary scattering reduces the total resistance to thermal transport leading to the observed nonmonotonic trend thereby highlighting the central role of complexion on thermal transport within reactive metal multilayers.

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TYPE Journal Article YEAR 2020
DOIOSTIScopus

Characterization of Partially Observed Epidemics - Application to COVID-19

Safta, Cosmin S.; Ray, Jaideep R.; Laros, James H.; Catanach, Thomas A.; Chowdhary, Kamaljit S.; Debusschere, Bert D.; Galvan, Edgar; Geraci, Gianluca G.; Khalil, Mohammad K.; Portone, Teresa P.

This report documents a statistical method for the "real-time" characterization of partially observed epidemics. Observations consist of daily counts of symptomatic patients, diagnosed with the disease. Characterization, in this context, refers to estimation of epidemiological parameters that can be used to provide short-term forecasts of the ongoing epidemic, as well as to provide gross information for the time-dependent infection rate. The characterization problem is formulated as a Bayesian inverse problem, and is predicated on a model for the distribution of the incubation period. The model parameters are estimated as distributions using a Markov Chain Monte Carlo (MCMC) method, thus quantifying the uncertainty in the estimates. The method is applied to the COVID-19 pandemic of 2020, using data at the country, provincial (e.g., states) and regional (e.g. county) levels. The epidemiological model includes a stochastic component due to uncertainties in the incubation period. This model-form uncertainty is accommodated by a pseudo-marginal Metropolis-Hastings MCMC sampler, which produces posterior distributions that reflect this uncertainty. We approximate the discrepancy between the data and the epidemiological model using Gaussian and negative binomial error models; the latter was motivated by the over-dispersed count data. For small daily counts we find the performance of the calibrated models to be similar for the two error models. For large daily counts the negative-binomial approximation is numerically unstable unlike the Gaussian error model. Application of the model at the country level (for the United States, Germany, Italy, etc.) generally provided accurate forecasts, as the data consisted of large counts which suppressed the day-to-day variations in the observations. Further, the bulk of the data is sourced over the duration before the relaxation of the curbs on population mixing, and is not confounded by any discernible country-wide second wave of infections. At the state-level, where reporting was poor or which evinced few infections (e.g., New Mexico), the variance in the data posed some, though not insurmountable, difficulties, and forecasts were able to capture the data with large uncertainty bounds. The method was found to be sufficiently sensitive to discern the flattening of the infection and epidemic curve due to shelter-in-place orders after around 90% quantile for the incubation distribution (about 10 days for COVID-19). The proposed model was also used at a regional level to compare the forecasts for the central and north-west regions of New Mexico. Modeling the data for these regions illustrated different disease spread dynamics captured by the model. While in the central region the daily counts peaked in the late April, in the north-west region the ramp-up continued for approximately three more weeks.

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TYPE SAND Report YEAR 2020
DOIOSTI
Results 876–900 of 2,290
Results 876–900 of 2,290