Publications

A key objective of the United States Department of Energy’s (DOE) Office of Nuclear Energy’s Spent Fuel and Waste Science and Technology (SFWST) Campaign is to better understand the technical basis, risks, and uncertainty associated with the safe and secure disposition of spent nuclear fuel (SNF) and high-level radioactive waste. Commercial nuclear power generation in the United States has resulted in thousands of metric tons of SNF, the disposal of which is the responsibility of the DOE (Nuclear Waste Policy Act of 1982, as amended). Any repository licensed to dispose of SNF must meet requirements regarding the long-term performance of that repository. For an evaluation of the long-term performance of the repository, one of the events that may need to be considered is the SNF achieving a critical configuration during the postclosure period. Of particular interest is the potential behavior of SNF in dualpurpose canisters (DPCs), which are currently licensed and being used to store and transport SNF but were not designed for permanent geologic disposal.

Phase-field modeling is an effective but computationally expensive method for capturing the mesoscale morphological and microstructure evolution in materials. Hence, fast and generalizable surrogate models are needed to alleviate the cost of computationally taxing processes such as in optimization and design of materials. The intrinsic discontinuous nature of the physical phenomena incurred by the presence of sharp phase boundaries makes the training of the surrogate model cumbersome. We develop a framework that integrates a convolutional autoencoder architecture with a deep neural operator (DeepONet) to learn the dynamic evolution of a two-phase mixture and accelerate time-to-solution in predicting the microstructure evolution. We utilize the convolutional autoencoder to provide a compact representation of the microstructure data in a low-dimensional latent space. After DeepONet is trained in the latent space, it can be used to replace the high-fidelity phase-field numerical solver in interpolation tasks or to accelerate the numerical solver in extrapolation tasks.

Strongly coupled nonlinear phenomena such as those described by Earth system models (ESMs) are composed of multiple component models with independent mesh topologies and scalable numerical solvers. A common operation in ESMs is to remap or interpolate component solution fields defined on their computational mesh to another mesh with a different combinatorial structure and decomposition, e.g., from the atmosphere to the ocean, during the temporal integration of the coupled system. Several remapping schemes are currently in use or available for ESMs. However, a unified approach to compare the properties of these different schemes has not been attempted previously. We present a rigorous methodology for the evaluation and intercomparison of remapping methods through an independently implemented suite of metrics that measure the ability of a method to adhere to constraints such as grid independence, monotonicity, global conservation, and local extrema or feature preservation. A comprehensive set of numerical evaluations is conducted based on a progression of scalar fields from idealized and smooth to more general climate data with strong discontinuities and strict bounds. We examine four remapping algorithms with distinct design approaches, namely ESMF Regrid , TempestRemap , generalized moving least squares (GMLS) with post-processing filters, and WLS-ENOR . By repeated iterative application of the high-order remapping methods to the test fields, we verify the accuracy of each scheme in terms of their observed convergence order for smooth data and determine the bounded error propagation using challenging, realistic field data on both uniform and regionally refined mesh cases. In addition to retaining high-order accuracy under idealized conditions, the methods also demonstrate robust remapping performance when dealing with non-smooth data. There is a failure to maintain monotonicity in the traditional L2-minimization approaches used in ESMF and TempestRemap, in contrast to stable recovery through nonlinear filters used in both meshless GMLS and hybrid mesh-based WLS-ENOR schemes. Local feature preservation analysis indicates that high-order methods perform better than low-order dissipative schemes for all test cases. The behavior of these remappers remains consistent when applied on regionally refined meshes, indicating mesh-invariant implementations. The MIRA intercomparison protocol proposed in this paper and the detailed comparison of the four algorithms demonstrate that the new schemes, namely GMLS and WLS-ENOR, are competitive compared to standard conservative minimization methods requiring computation of mesh intersections. The work presented in this paper provides a foundation that can be extended to include complex field definitions, realistic mesh topologies, and spectral element discretizations, thereby allowing for a more complete analysis of production-ready remapping packages.

In this project, we demonstrated stable nanoscale fracture in single-crystal silicon using an in-situ wedge-loaded double cantilever beam (DCB) specimen. The fracture toughness KIC was calculated directly from instrumented measurement of force and displacement via finite element analysis with frictional corrections. Measurements on multiple test specimens were used to show K_IC = 0.72 ± 0.07 MPa m^1/2 on {111} planes and observe the crack-growth resistance curve in <500 nm increments. The exquisite stability of crack growth, instrumented measurement of material response, and direct visual access to observe nanoscale fracture processes in an ideally brittle material differentiate this approach from prior DCB methods.

Optimal mitigation planning for highly disruptive contingencies to a transmission-level power system requires optimization with dynamic power system constraints, due to the key role of dynamics in system stability to major perturbations. We formulate a generalized disjunctive program to determine optimal grid component hardening choices for protecting against major failures, with differential algebraic constraints representing system dynamics (specifically, differential equations representing generator and load behavior and algebraic equations representing instantaneous power balance over the transmission system). We optionally allow stochastic optimal pre-positioning across all considered failure scenarios, and optimal emergency control within each scenario. This novel formulation allows, for the first time, analyzing the resilience interdependencies of mitigation planning, preventive control, and emergency control. Using all three strategies in concert is particularly effective at maintaining robust power system operation under severe contingencies, as we demonstrate on the Western System Coordinating Council (WSCC) 9-bus test system using synthetic multi-device outage scenarios. Towards integrating our modeling framework with real threats and more realistic power systems, we explore applying hybrid dynamics to power systems. Our work is applied to basic RL circuits with the ultimate goal of using the methodology to model protective tripping schemes in the grid. Finally, we survey mitigation techniques for HEMP threats and describe a GIS application developed to create threat scenarios in a grid with geographic detail.

Computed tomography (CT) resolution has become high enough to monitor morphological changes due to aging in materials in long-term applications. We explored the utility of the critic of a generative adversarial network (GAN) to automatically detect such changes. The GAN was trained with images of pristine Pharmatose, which is used as a surrogate energetic material. It is important to note that images of the material with altered morphology were only used during the test phase. The GAN-generated images visually reproduced the microstructure of Pharmatose well, although some unrealistic particle fusion was seen. Calculated morphological metrics (volume fraction, interfacial line length, and local thickness) for the synthetic images also showed good agreement with the training data, albeit with signs of mode collapse in the interfacial line length. While the critic exposed changes in particle size, it showed limited ability to distinguish images by particle shape. The detection of shape differences was also a more challenging task for the selected morphological metrics that related to energetic material performance. We further tested the critic with images of aged Pharmatose. Subtle changes due to aging are difficult for the human analyst to detect. Both critic and morphological metrics analysis showed image differentiation.

Widespread integration of social media into daily life has fundamentally changed the way society communicates, and, as a result, how individuals develop attitudes, personal philosophies, and worldviews. The excess spread of disinformation and misinformation due to this increased connectedness and streamlined communication has been extensively studied, simulated, and modeled. Less studied is the interaction of many pieces of misinformation, and the resulting formation of attitudes. We develop a framework for the simulation of attitude formation based on exposure to multiple cognitions. We allow a set of cognitions with some implicit relational topology to spread on a social network, which is defined with separate layers to specify online and offline relationships. An individual’s opinion on each cognition is determined by a process inspired by the Ising model for ferromagnetism. We conduct experimentation using this framework to test the effect of topology, connectedness, and social media adoption on the ultimate prevalence of and exposure to certain attitudes.

Gaining a proper understanding of how Earth structure and other near-source properties affect estimates of explosion yield is important to the nonproliferation mission. The yields of explosion sources are often based on seismic moment or waveform amplitudes. Quantifying how the seismic waveforms or estimates of the source characteristics derived from those waveforms are influenced by natural or man-made structures within the near-source region, where the wavefield behaves nonlinearly, is required to understand the full range of uncertainty in those yield estimates. We simulate tamped chemical explosions using a nonlinear, shock physics code and couple the ground motions beyond the elastic radius to a linear elastic, full waveform seismic simulation algorithm through 3D media. In order to isolate the effects of simple small-scale 3D structures on the seismic wavefield and linear seismic source estimates, we embed spheres and cylinders close to the fully- tamped source location within an otherwise homogenous half-space. The 3 m diameters spheres, given their small size compared to the predominate wavelengths investigated, not surprisingly are virtually invisible with only negligible perturbations to the far-field waveforms and resultant seismic source time functions. Similarly, the 11 m diameter basalt sphere has a larger, but still relatively minor impact on the wavefield. However, the 11 m diameter air-filled sphere has the largest impact on both waveforms and the estimated seismic moment of any of the investigated cases with a reduction of ~25% compared to the tamped moment. This significant reduction is likely due in large part to the cavity collapsing from the shock instead of being solely due to diffraction effects . Although the cylinders have the same diameters as the 3 m spheres, their length of interaction with the wavefield produces noticeable changes to the seismic waveforms and estimated source terms with reductions in the peak seismic moment on the order of 10%. Both the cylinders and 11 m diameter spheres generate strong shear waves that appear to emanate from body force sources.

This document provides very basic background information and initial enabling guidance for computational analysts to develop and utilize GitOps practices within the Common Engineering Environment (CEE) and High Performance Computing (HPC) computational environment at Sandia National Laboratories through GitLab/Jacamar runner based workflows.

Using a newly developed coupling of the ElectroMagnetic Plasma In Realistic Environments (EMPIRE) code with the Integrated Tiger Series (ITS) code, radiation environment calculations have been performed. The effort was completed as part of the Saturn Recapitalization (Recap) program that represents activities to upgrade and modernize the Saturn accelerator facility. The radiation environment calculations performed provide baseline results with current or planned hardware in the facility. As facility design changes are proposed and implemented as part of Saturn Recap, calculations of the radiation environment will be performed to understand how the changes impact the output of the Saturn accelerator.

This report documents the results of an FY22 ASC V&V level 2 milestone demonstrating new algorithms for multifidelity uncertainty quantification. Part I of the report describes the algorithms, studies their performance on a simple model problem, and then deploys the methods to a thermal battery example from the open literature. Part II (restricted distribution) applies the multifidelity UQ methods to specific thermal batteries of interest to the NNSA/ASC program.

Steady-state fluctuations in the friction force of molybdenum disulfide (MoS2), a prototypical lamellar solid, were analyzed experimentally for newton-scale forces and computationally via molecular dynamics simulations for nanonewton-scale forces. A mean field model links the statics and the dynamics of the friction behavior across these eight orders of magnitude in friction force and six orders of magnitude in friction force fluctuations (i.e., avalanches). Both the statistics and dynamics of the avalanches match model predictions, indicating that friction can be characterized as a series of avalanches with properties that are predictable over a wide range of scales.

In this work we present a novel method for improving the high-temperature performance of silicon photomultipliers (SiPMs) via focused ion beam (FIB) modification of individual microcells. The literature suggests that most of the dark count rate (DCR) in a SiPM is contributed by a small percentage (<5%) of microcells. By using a FIB to electrically deactivate this relatively small number of microcells, we believe we can greatly reduce the overall DCR of the SiPM at the expense of a small reduction in overall photodetection efficiency, thereby improving its high temperature performance. In this report we describe our methods for characterizing the SiPM to determine which individual microcells contribute the most to the DCR, preparing the SiPM for FIB, and modifying the SiPM using the FIB to deactivate the identified microcells.

Th e U.S. Strategic Petroleum Reserve (SPR) is a crude oil storage system administered by the U.S. Department of Energy. The reserve consists of 60 active storage caverns located in underground salt domes spread across four sites in Louisiana and Texas, near the Gulf of Mexico. Beginning in 2016, the SPR started executing C ongressionally mandated oil sales. The configuration of the reserve, with a total capacity of greater than 700 million barrels ( MMB ) , re quires that unsaturated water (referred to herein as ?raw? water) is injected into the storage caverns to displace oil for sales , exchanges, and drawdowns . As such, oil sales will produce cavern growth to the extent that raw water contacts the salt cavern walls and dissolves (leaches) the surrounding salt before reaching brine saturation. SPR injected a total of over 45 MMB of raw water into twenty - six caverns as part of oil sales in CY21 . Leaching effects were monitored in these caverns to understand how the sales operations may impact the long - term integrity of the caverns. While frequent sonars are the most direct means to monitor changes in cavern shape, they can be resource intensive for the number of caverns involved in sales and exchanges. An interm ediate option is to model the leaching effects and see if any concerning features develop. The leaching effects were modeled here using the Sandia Solution Mining Code , SANSMIC . The modeling results indicate that leaching - induced features do not raise co ncern for the majority of the caverns, 15 of 26. Eleven caverns, BH - 107, BH - 110, BH - 112, BH - 113, BM - 109, WH - 11, WH - 112, WH - 114, BC - 17, BC - 18, and BC - 19 have features that may grow with additional leaching and should be monitored as leaching continues in th ose caverns. Additionally, BH - 114, BM - 4, and BM - 106 were identified in previous leaching reports for recommendation of monitoring. Nine caverns had pre - and post - leach sonars that were compared with SANSMIC results. Overall, SANSMIC was able to capture the leaching well. A deviation in the SANSMIC and sonar cavern shapes was observed near the cavern floor in caverns with significant floor rise, a process not captured by SANSMIC. These results validate that SANSMIC continues to serve as a useful tool for mon itoring changes in cavern shape due to leaching effects related to sales and exchanges.

This document is intended to be utilized with the Equipment Test Environment being developed to provide a standard process by which the ETE can be validated. The ETE is developed with the intent of establishing cyber intrusion, data collection and through automation provide objective goals that provide repeatability. This testing process is being developed to interface with the Technical Area V physical protection system. The document will overview the testing structure, interfaces, device and network logging and data capture. Additionally, it will cover the testing procedure, criteria and constraints necessary to properly capture data and logs and record them for experimental data capture and analysis.

Tritium has a unique physical and chemical behavior which causes it to be highly mobile in the environment. As it behaves similarly to hydrogen in the environment, it may also be readily incorporated into the water cycle and other biological processes. These factors and other environmental transformations may also cause the oxidation of an elemental tritium release, resulting in a multiple order of magnitude increase in dose coefficient and radiotoxicity. While source term development and understanding for advanced reactors are still underway, tritium may be a radionuclide of interest. It is thus important to understand how tritium moves through the environment and how the MACCS accident consequence code handles acute tritium releases in an accident scenario. Additionally, existing tritium models may have functionalities that could inform updates to MACCS to handle tritium. In this report tritium transport is reviewed and existing tritium models are summarized in view of potential updates to MACCS.

This report documents a method for the quantitative identification of radionuclides of potential interest for accident consequence analysis involving advanced nuclear reactors. Based on previous qualitative assessments of radionuclide inventories for advanced reactors coupled with the review of a radiological inventory developed for a heat pipe reactor, a 1 Ci activity airborne release was calculated for 137 radionuclides using the MACCS 4.1 code suite. Several assumptions regarding release conditions were made and discussed herein. The potential release of a heat pipe reactor inventory was also modeled following the same assumptions. Results provide an estimation of the relative EARLY and CHRONC phase dose contribution from advanced reactor radionuclides and are normalized to doses from equivalent releases of I-131 and Cs-137, respectively. Ultimately, a list of 69 radionuclides with EARLY or CHRONC dose contributions at least 1/100th that of I-131 or Cs-137, respectively – 48 of which are currently considered for LWR consequence analyses – was identified of being of potential importance for analyses involving a heat pipe reactor.

International safeguards currently rely on material accountancy to verify that declared nuclear material is present and unmodified. Although effective, material accountancy for large bulk facilities can be expensive to implement due to the high precision instrumentation required to meet regulatory targets. Process monitoring has long been considered to improve material accountancy. However, effective integration of process monitoring has been met with mixed results. Given the large successes in other domains, machine learning may present a solution for process monitoring integration. Past work has shown that unsupervised approaches struggle due to measurement error. Although not studied in depth for a safeguards context, supervised approaches often have poor generalization for unseen classes of data (e.g., unseen material loss patterns). This work shows that engineered datasets, when used for training, can improve the generalization of supervised approaches. Further, the underlying models needed to generate these datasets need only accurately model certain high importance features.

The National Solar Thermal Test Facility (NSTTF) at Sandia National Laboratories New Mexico (SNL/NM) developed this Life Cycle Management Plan (LCMP) to document its process for executing, monitoring, controlling and closing-out Phase 3 of the Gen 3 Particle Pilot Plant (G3P3). This plan serves as a resource for stakeholders who wish to be knowledgeable of project objectives and how they will be accomplished.

This report summarizes research performed in the context of a REHEDS LDRD project that explores methods for measuring electrical properties of vessel joints. These properties, which include contact points and associated contact resistance, are “hidden” in the sense that they are not apparent from a computer-assisted design (CAD) description or visual inspection. As is demonstrated herein, the impact of this project is the development of electromagnetic near-field scanning capabilities that allow weapon cavity joints to be characterized with high spatial and/or temporal resolution. Such scans provide insight on the hidden electrical properties of the joint, allowing more detailed and accurate models of joints to be developed, and ultimately providing higher fidelity shielding effectiveness (SE) predictions. The capability to perform high-resolution temporal scanning of joints under vibration is also explored, using a multitone probing concept, allowing time-varying properties of joints to be characterized and the associated modulation to SE to be quantified.

Fractured media models comprise discontinuities of multiple lengths (e.g. fracture lengths and apertures, wellbore area) that fall into the relatively insignificant length scales spanning millimeter-scale fractures to centimeter-scale wellbores in comparison to the extensions of the field of interest, and challenge the conventional discretization methods imposing highly-fine meshing and formidably large numerical cost. By utilizing the recent developments in the finite element analysis of electromagnetics that allow to represent material properties on a hierarchical geometry, this project develops computational capabilities to model fluid flow, heat conduction, transport and induced polarization in large-scale geologic environments that possess geometrically-complex fractures and man-made infrastructures without explosive computational cost. The computational efficiency and robustness of this multi-physics modeling tool are demonstrated by considering various highly-realistic complex geologic environments that are common in many energy and national security related engineering problems.

Space-based and airplane-based synthetic aperture RADAR (SAR) can monitor ground height using interferometric SAR (InSAR) collections. However, fielding the airplane-based SAR is expensive and coordinating the frequency and timing of ground experiments with space-based SAR is challenging. This research explored the possibility of using a small, mobile unmanned aerial vehicle- base (UAV) SAR to see if it could provide a quick and inexpensive InSAR option for the Source Physics Experiment (SPE) Phase III project. Firstly, a local feasibility collection using a UAV-based SAR showed that InSAR products and height measurements were possible, but that in-scene fiducials were needed to assist in digital elevation model (DEM) construction. Secondly, an InSAR collection was planned and executed over the SPE Phase III site using the same platform configuration. We found that the image formation by the SAR manufacturer creates discontinuities, and that noise impacted the generation and accuracy of height maps. These processing artifacts need to be overcome to generate an accurate height map.

Broadly applicable solutions to multimodal and multisensory fusion problems across domains remain a challenge because effective solutions often require substantive domain knowledge and engineering. The chief questions that arise for data fusion are in when to share information from different data sources, and how to accomplish the integration of information. The solutions explored in this work remain agnostic to input representation and terminal decision fusion approaches by sharing information through the learning objective as a compound objective function. The objective function this work uses assumes a one-to-one learning paradigm within a one-to-many domain which allows the assumption that consistency can be enforced across the one-to-many dimension. The domains and tasks we explore in this work include multi-sensor fusion for seismic event location and multimodal hyperspectral target discrimination. We find that our domain- informed consistency objectives are challenging to implement in stable and successful learning because of intersections between inherent data complexity and practical parameter optimization. While multimodal hyperspectral target discrimination was not enhanced across a range of different experiments by the fusion strategies put forward in this work, seismic event location benefited substantially, but only for label-limited scenarios.

The purpose of this effort is to investigate whether large acoustic pressure waves can be transmitted inside beverage containers to enable pasteurization. Acoustic waves are known to induce large nonlinear compressive forces and shock waves in fluids, suggesting that compression waves may be capable of damaging bacteria inside beverage containers without appreciably increasingly the temperature or altering the freshness and flavor of the beverage contents. Although a combined process such as thermosonication (e.g., sonication with heating) is likely more efficient, it is instructive to compute the acoustic pressure field distribution inside the beverage container. The COMSOL simulations used two and three-dimensional models of beverage containers placed in a water bath to compute the acoustic pressure field. A limitation of these COMSOL models is that they cannot determine the bacterial lysis efficiency, rather the models provide an indirect metric of bacterial lysis based on the magnitude of the pressure field and its distribution.

Abstract not provided.