Publications

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Sandia National Laboratories has tested and evaluated an updated version of the MB3a infrasound sensor, designed by CEA and manufactured by SeismoWave. The purpose of this infrasound sensor evaluation is to measure the performance characteristics in such areas as power consumption, sensitivity, full scale, self-noise, dynamic range, response, passband, sensitivity variation due to changes in barometric pressure and temperature, and sensitivity to acceleration. The MB3a infrasound sensors are being evaluated for use in the International Monitoring System (IMS) of the Preparatory Commission to the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO).

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We present an adaptive algorithm for constructing surrogate models of multi-disciplinary systems composed of a set of coupled components. With this goal we introduce “coupling” variables with a priori unknown distributions that allow surrogates of each component to be built independently. Once built, the surrogates of the components are combined to form an integrated-surrogate that can be used to predict system-level quantities of interest at a fraction of the cost of the original model. The error in the integrated-surrogate is greedily minimized using an experimental design procedure that allocates the amount of training data, used to construct each component-surrogate, based on the contribution of those surrogates to the error of the integrated-surrogate. The multi-fidelity procedure presented is a generalization of multi-index stochastic collocation that can leverage ensembles of models of varying cost and accuracy, for one or more components, to reduce the computational cost of constructing the integrated-surrogate. Extensive numerical results demonstrate that, for a fixed computational budget, our algorithm is able to produce surrogates that are orders of magnitude more accurate than methods that treat the integrated system as a black-box.

Achieving practical, high-energy-density calcium batteries requires controlling the stability of Ca2+electrolytes during calcium metal cycling. Because of the highly reactive nature of calcium, most typical electrolyte constituents are unstable, leading to electrode passivation and low Coulombic efficiency. Among various commercially available salts, calcium bis(trifluoromethylsulfonyl)imide (Ca(TFSI)2) is attractive because of its oxidative stability and high solubility in a variety of solvents. However, this salt does not allow for calcium metal plating, and it has been proposed that TFSI-instability induced by Ca2+coordination is to blame. In this work, we test the ability of strongly coordinating Ca2+cosalts such as halides and borohydrides to displace TFSI-from the first coordination shell of Ca2+and thereby stabilize TFSI-based electrolytes to enable calcium plating. Through spectroscopic analysis, we find that the effectiveness of these cosalts at displacing the TFSI-anion is dependent on the solvent's coordination strength toward Ca2+. Surprisingly, electrochemical calcium deposition behavior is not correlated to the population of bound or free TFSI-. Instead, the nature of the coordination interaction between Ca2+and the cosalt anion is more important for determining stability. Our findings indicate that TFSI-anions are inherently unstable during calcium deposition even in the nominally free state. Therefore, strategies aimed at eliminating the interactions of these anions with the electrode surface via interface/interphase design are required.

This report represents the milestone deliverable M4SF-22SN010309092 “Modeling Activities Related to Waste Form Degradation: Progress Report” that describes the progress of R&D activities of ongoing modeling investigations specifically on nuclear waste glass degradation, Density Functional Theory (DFT) studies on clarkeite structure and stability, and electrochemical model development of spent nuclear fuel (SNF). These activities are part of the Waste Form Testing, Modeling, and Performance work package at Sandia National Laboratories (SNL). This work package is part of the “Inventory and Waste Form Characteristics and Performance” control account that includes various experimental and modeling activities on nuclear waste degradation conducted at Oak Ridge National Laboratory (ORNL), SNL, Argonne National Laboratory (ANL), and Pacific Northwest National Laboratory (PNNL).

A series of experiments will be performed to test the integral effects of molybdenum on the reactivity of a critical system. These experiments will use the 7uPCX assembly with the 1.55 cm triangular pitch grid plates. Molybdenum sleeves, consisting of 19.6 inch long 0.5-inch nominal outside diameter molybdenum tubes with 0.031-inch nominal wall thickness and centering hardware, will be placed on some of the fuel rods in the array. The purpose of this analysis is to examine two configurations of the 7uPCX using the 1.55 cm triangular pitch grid plates in fully-reflected approach-to-critical experiments with the number of fuel rods in the array as the approach parameter. This document presents the results of the analysis that was done to allow completion of the 7uPCX Configuration Checklist from Appendix A of SPRF-AP-005 [SNL 2020] for the cores noted above. The checklists for these cores are shown in Appendix A.

A recently discovered, enhanced Ge diffusion mechanism along the oxidizing interface of Si/SiGe nanostructures has enabled the formation of single-crystal Si nanowires and quantum dots embedded in a defect-free, single-crystal SiGe matrix. Here, we report oxidation studies of Si/SiGe nanofins aimed at gaining a better understanding of this novel diffusion mechanism. A superlattice of alternating Si/Si0.7Ge0.3layers was grown and patterned into fins. After oxidation of the fins, the rate of Ge diffusion down the Si/SiO2interface was measured through the analysis of HAADF-STEM images. The activation energy for the diffusion of Ge down the sidewall was found to be 1.1 eV, which is less than one-quarter of the activation energy previously reported for Ge diffusion in bulk Si. Through a combination of experiments and DFT calculations, we propose that the redistribution of Ge occurs by diffusion along the Si/SiO2interface followed by a reintroduction into substitutional positions in the crystalline Si.

The inverse methods team provides a set of tools for solving inverse problems in structural dynamics and thermal physics, and also sensor placement optimization via Optimal Experimental Design (OED). These methods are used for designing experiments, model calibration, and verification/validation analysis of weapons systems. This document provides a user’s guide to the input for the three apps that are supported for these methods. Details of input specifications, output options, and optimization parameters are included.

Climate change is an existential threat to the vast global permafrost domain. The diverse human cultures, ecological communities, and biogeochemical cycles of this tenth of the planet depend on the persistence of frozen conditions. The complexity, immensity, and remoteness of permafrost ecosystems make it difficult to grasp how quickly things are changing and what can be done about it. Here, we summarize terrestrial and marine changes in the permafrost domain with an eye toward global policy. While many questions remain, we know that continued fossil fuel burning is incompatible with the continued existence of the permafrost domain as we know it. If we fail to protect permafrost ecosystems, the consequences for human rights, biosphere integrity, and global climate will be severe. The policy implications are clear: the faster we reduce human emissions and draw down atmospheric CO2, the more of the permafrost domain we can save. Emissions reduction targets must be strengthened and accompanied by support for local peoples to protect intact ecological communities and natural carbon sinks within the permafrost domain. Some proposed geoengineering interventions such as solar shading, surface albedo modification, and vegetation manipulations are unproven and may exacerbate environmental injustice without providing lasting protection. Conversely, astounding advances in renewable energy have reopened viable pathways to halve human greenhouse gas emissions by 2030 and effectively stop them well before 2050. We call on leaders, corporations, researchers, and citizens everywhere to acknowledge the global importance of the permafrost domain and work towards climate restoration and empowerment of Indigenous and immigrant communities in these regions.

Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some data are also available. In some instances, the objective is to discover part of the hidden physics from the available data, and PIML has been shown to be particularly effective for such problems for which conventional methods may fail. Unlike commercial machine learning where training of deep neural networks requires big data, in PIML big data are not available. Instead, we can train such networks from additional information obtained by employing the physical laws and evaluating them at random points in the space-time domain. Such PIML integrates multimodality and multifidelity data with mathematical models, and implements them using neural networks or graph networks. Here, we review some of the prevailing trends in embedding physics into machine learning, using physics-informed neural networks (PINNs) based primarily on feed-forward neural networks and automatic differentiation. For more complex systems or systems of systems and unstructured data, graph neural networks (GNNs) present some distinct advantages, and here we review how physics-informed learning can be accomplished with GNNs based on graph exterior calculus to construct differential operators; we refer to these architectures as physics-informed graph networks (PIGNs). We present representative examples for both forward and inverse problems and discuss what advances are needed to scale up PINNs, PIGNs and more broadly GNNs for large-scale engineering problems.

A method is developed to calculate the length into a sample to which a Frequency Domain Thermoreflectance (FDTR) measurement is sensitive. Sensing depth and sensing radius are defined as limiting cases for the spherically spreading FDTR measurement. A finite element model for FDTR measurements is developed in COMSOL multiphysics and used to calculate sensing depth and sensing radius for silicon and silicon dioxide samples for a variety of frequencies and laser spot sizes. The model is compared to experimental FDTR measurements. Design recommendations for sample thickness are made for experiments where semi-infinite sample depth is desirable. For measurements using a metal transducer layer, the recommended sample thickness is three thermal penetration depths, as calculated from the lowest measurement frequency.

Aria is a Galerkin finite element based program for solving coupled-physics problems described by systems of PDEs and is capable of solving nonlinear, implicit, transient and direct-to-steady state problems in two and three dimensions on parallel architectures. The suite of physics currently supported by Aria includes thermal energy transport, species transport, and electrostatics as well as generalized scalar, vector and tensor transport equations. Additionally, Aria includes support for manufacturing process ows via the incompressible Navier-Stokes equations specialized to a low Reynolds number (Re < 1) regime. Enhanced modeling support of manufacturing processing is made possible through use of either arbitrary Lagrangian-Eulerian (ALE) and level set based free and moving boundary tracking in conjunction with quasi-static nonlinear elastic solid mechanics for mesh control. Coupled physics problems are solved in several ways including fully-coupled Newton’s method with analytic or numerical sensitivities, fully-coupled Newton-Krylov methods and a loosely-coupled nonlinear iteration about subsets of the system that are solved using combinations of the aforementioned methods. Error estimation, uniform and dynamic ℎ-adaptivity and dynamic load balancing are some of Aria’s more advanced capabilities.

The SIERRA Low Mach Module: Fuego, henceforth referred to as Fuego, is the key element of the ASC fire environment simulation project. The fire environment simulation project is directed at characterizing both open large-scale pool fires and building enclosure fires. Fuego represents the turbulent, buoyantly-driven incompressible flow, heat transfer, mass transfer, combustion, soot, and absorption coefficient model portion of the simulation software. Using MPMD coupling, Scefire and Nalu handle the participating-media thermal radiation mechanics. This project is an integral part of the SIERRA multi-mechanics software development project. Fuego depends heavily upon the core architecture developments provided by SIERRA for massively parallel computing, solution adaptivity, and mechanics coupling on unstructured grids.

SIERRA/Aero is a compressible fluid dynamics program intended to solve a wide variety compressible fluid flows including transonic and hypersonic problems. This document describes the commands for assembling a fluid model for analysis with this module, henceforth referred to simply as Aero for brevity. Aero is an application developed using the SIERRA Toolkit (STK). The intent of STK is to provide a set of tools for handling common tasks that programmers encounter when developing a code for numerical simulation. For example, components of STK provide field allocation and management, and parallel input/output of field and mesh data. These services also allow the development of coupled mechanics analysis software for a massively parallel computing environment.

The present study has used a variety of characterization techniques to determine the products and reaction pathways involved in the rechargeable Li-FeS2 system. We revisit both the initial lithiation and subsequent cycling of FeS2 employing an ionic liquid electrolyte to investigate the intermediate and final charge products formed under varying thermal conditions (room temperature to 100 °C). The detection of Li2S and hexagonal FeS as the intermediate phases in the initial lithiation and the electrochemical formation of greigite, Fe3S4, as a charge product in the rechargeable reaction differ significantly from previous reports. The conditions for Fe3S4 formation are shown to be dependent on both the temperature (∼60 °C) and the availability of sulfur to drive a FeS to Fe3S4 transformation. Upon further cycling, Fe3S4 transforms to a lower sulfur content iron sulfide phase, a process which coincides with the loss of sulfur based on the new reaction pathways established in this work. The connection between sulfur loss, capacity fade, and charge product composition highlights the critical need to retain sulfur in the active material upon cycling.

The SNL Sierra Mechanics code suite is designed to enable simulation of complex multiphysics scenarios. The code suite is composed of several specialized applications which can operate either in standalone mode or coupled with each other. Arpeggio is a supported utility that enables loose coupling of the various Sierra Mechanics applications by providing access to Framework services that facilitate the coupling. More importantly Arpeggio orchestrates the execution of applications that participate in the coupling. This document describes the various components of Arpeggio and their operability. The intent of the document is to provide a fast path for analysts interested in coupled applications via simple examples of its usage.