Publications

The goal of the ExaWind project is to enable predictive simulations of wind farms comprised of many megawatt-scale turbines situated in complex terrain. Predictive simulations will require computational fluid dynamics (CFD) simulations for which the mesh resolves the geometry of the turbines, capturing the thin boundary layers, and captures the rotation and large deflections of blades. Whereas such simulations for a single turbine are arguably petascale class, multi-turbine wind farm simulations will require exascale-class resources.

The tearing parameter criterion and material softening failure method currently used in the multilinear elastic-plastic constitutive model was added as an option to modular failure capabilities. The modular failure implementation was integrated with the multilevel solver for multi-element simulations. Currently, this implementation is only available to the J₂ plasticity model due to the formulation of the material softening approach. The implementation compared well with multilinear elastic-plastic model results for a uniaxial tension test, a simple shear test, and a representative structural problem. Necessary generalizations of the failure method to extend it as a modular option for all plasticity models are highlighted.

The PRO-X program is actively supporting the design of nuclear systems by developing a framework to both optimize the fuel cycle infrastructure for advanced reactors (ARs) and minimize the potential for production of weapons-usable nuclear material. Three study topics are currently being investigated by Sandia National Laboratories (SNL) with support from Argonne National Laboratories (ANL). This multi-lab collaboration is focused on three study topics which may offer proliferation resistance opportunities or advantages in the nuclear fuel cycle. These topics are: 1) Transportation Global Landscape, 2) Transportation Avoidability, and 3) Parallel Modular Systems vs Single Large System (Crosscutting Activity).

Disastrous consequences can result from defects in manufactured parts—particularly the high consequence parts developed at Sandia. Identifying flaws in as-built parts can be done with nondestructive means, such as X-ray Computed Tomography (CT). However, due to artifacts and complex imagery, the task of analyzing the CT images falls to humans. Human analysis is inherently unreproducible, unscalable, and can easily miss subtle flaws. We hypothesized that deep learning methods could improve defect identification, increase the number of parts that can effectively be analyzed, and do it in a reproducible manner. We pursued two methods: 1) generating a defect-free version of a scan and looking for differences (PandaNet), and 2) using pre-trained models to develop a statistical model of normality (Feature-based Anomaly Detection System: FADS). Both PandaNet and FADS provide good results, are scalable, and can identify anomalies in imagery. In particular, FADS enables zero-shot (training-free) identification of defects for minimal computational cost and expert time. It significantly outperforms prior approaches in computational cost while achieving comparable results. FADS’ core concept has also shown utility beyond anomaly detection by providing feature extraction for downstream tasks.

Single photon detection (SPD) plays an important role in many forefront areas of fundamental science and advanced engineering applications. In recent years, rapid developments in superconducting quantum computation, quantum key distribution, and quantum sensing call for SPD in the microwave frequency range. We have explored in this LDRD project a new approach to SPD in an effort to provide deterministic photon-number-resolving capability by using topological Josephson junction structures. In this SAND report, we will present results from our experimental studies of microwave response and theoretical simulations of microwave photon number resolving detector in topological Dirac semimetal Cd₃As₂. These results are promising for SPD at the microwave frequencies using topological quantum materials.

Projection-based model order reduction allows for the parsimonious representation of full order models (FOMs), typically obtained through the discretization of a set of partial differential equations (PDEs) using conventional techniques (e.g., finite element, finite volume, finite difference methods) where the discretization may contain a very large number of degrees of freedom. As a result of this more compact representation, the resulting projection-based reduced order models (ROMs) can achieve considerable computational speedups, which are especially useful in real-time or multi-query analyses. One known deficiency of projection-based ROMs is that they can suffer from a lack of robustness, stability and accuracy, especially in the predictive regime, which ultimately limits their useful application. Another research gap that has prevented the widespread adoption of ROMs within the modeling and simulation community is the lack of theoretical and algorithmic foundations necessary for the “plug-and-play” integration of these models into existing multi-scale and multi-physics frameworks. This paper describes a new methodology that has the potential to address both of the aforementioned deficiencies by coupling projection-based ROMs with each other as well as with conventional FOMs by means of the Schwarz alternating method [41]. Leveraging recent work that adapted the Schwarz alternating method to enable consistent and concurrent multiscale coupling of finite element FOMs in solid mechanics [35, 36], we present a new extension of the Schwarz framework that enables FOM-ROM and ROM-ROM coupling, following a domain decomposition of the physical geometry on which a PDE is posed. In order to maintain efficiency and achieve computation speed-ups, we employ hyper-reduction via the Energy-Conserving Sampling and Weighting (ECSW) approach [13]. We evaluate the proposed coupling approach in the reproductive as well as in the predictive regime on a canonical test case that involves the dynamic propagation of a traveling wave in a nonlinear hyper-elastic material.

This document provides an overview of the economic and technical challenges related to bringing small modular reactors to market and then presents an outline for how to address the new challenges. The purpose of this project was to proactively design software for its intended use to provide a strategic positioning for work in the future. This project seeks to augment the short-term stop-gap approach of trying to use legacy software well outside of its range of applicability.

The Sandia National Laboratories, California (SNL/CA) site comprises approximately 410 acres and is located in the eastern portion of Livermore, Alameda County, California. The property is owned by the United States Department of Energy and is being managed and operated by National Technology & Engineering Solutions of Sandia, LLC. The facility location is shown on the Site Map(s) in Appendix A. This Stormwater Pollution Prevention Plan (SWPPP) is designed to comply with California’s General Permit for Stormwater Discharges Associated with Industrial Activities (General Permit) Order No. 2015-0122-DWQ (NPDES No. CAS000001) issued by the State Water Resources Control Board (State Water Board) (Ref. 6.1). This SWPPP has been prepared following the SWPPP Template provided on the California Stormwater Quality Association Stormwater Best Management Practice Handbook Portal: Industrial and Commercial (CASQA 2014). In accordance with the General Permit, Section X.A, this SWPPP contains the following required elements: Facility Name and Contact Information; Site Map; List of Significant Industrial Materials; Description of Potential Pollution Sources; Assessment of Potential Pollutant Sources; Minimum BMPs; Advanced BMPs, if applicable; Monitoring Implementation Plan (MIP); Annual Comprehensive Facility Compliance Evaluation (Annual Evaluation); and, Date that SWPPP was Initially Prepared and the Date of Each SWPPP Amendment, if Applicable.

High-level purpose of this work: This report summarizes work carried out by Sandia National Laboratories (SNL) in the fiscal year 2022 (FY22) to evaluate the potential occurrence of stress corrosion cracking (SCC) on spent nuclear fuel (SNF) dry storage canisters. The U.S. currently lacks a repository for permanent disposal of SNF; thus, dry storage systems will be in use for much longer time periods than originally intended. Gap analyses by the US Department of Energy (DOE), the Nuclear Regulatory Commission (NRC), the Nuclear Waste Technical Review Board (NWTRB), and the Electric Power Research Institute (EPRI) have all determined that an improved understanding of the occurrence and risk of canister SCC is critical to demonstrating the safety of long-term dry storage. Should canister penetration by SCC occur, the containment boundary represented by the canister would be breached. A loss of the inert environment (helium) within the canister could occur and intrusion of air and moisture could react with and damage the fuel within the canister. For this reason, the DOE is funding an effort to evaluate the potential occurrence and consequences of dry storage canister SCC and to develop prevention, mitigation, and repair technologies for this degradation mechanism.

Solar Thermal Ammonia Production has potential to produce green ammonia using CSP, air, and water. Air separation to purify N₂ was successfully demonstrated with BSF1585 in packed bed reactor; on-sun reduction reactor under construction. Metal nitrides (MNy) were successfully synthesized and characterized under both ambient and pressurized conditions. Co₃Mo₃N shown to successfully produce NH₃ when exposed to pure H₂ at pressures between 5 – 20 bar 600 – 750 °C. Ambient reaction experiments imply there may be a catalytic aspect as well. Technoeconomic and systems analyses show a path towards scale-up.

The ASC program seeks to use machine learning to improve efficiencies in its stockpile stewardship mission. Moreover, there is a growing market for technologies dedicated to accelerating AI workloads. Many of these emerging architectures promise to provide savings in energy efficiency, area, and latency when compared to traditional CPUs for these types of applications — neuromorphic analog and digital technologies provide both low-power and configurable acceleration of challenging artificial intelligence (AI) algorithms. If designed into a heterogeneous system with other accelerators and conventional compute nodes, these technologies have the potential to augment the capabilities of traditional High Performance Computing (HPC) platforms [5]. This expanded computation space requires not only a new approach to physics simulation, but the ability to evaluate and analyze next-generation architectures specialized for AI/ML workloads in both traditional HPC and embedded ND applications. Developing this capability will enable ASC to understand how this hardware performs in both HPC and ND environments, improve our ability to port our applications, guide the development of computing hardware, and inform vendor interactions, leading them toward solutions that address ASC’s unique requirements.

CO₂-neutral ammonia production with concentrated solar technology is theoretically possible based on advanced solar thermochemical looping technology. STAP offers price stability achieving a target price <250 $/tonne NH₃ without including the H₂. The nitride cost is the most significant expense, accounting for more than the 50% of the total CapEx.

The deluge of sensors and data generating devices has driven a paradigm shift in modern computing from arithmetic-logic centric to data-centric processing. Data-centric processing require innovations at the device level to enable novel compute-in-memory (CIM) operations. A key challenge in the construction of CIM architectures is the conflicting trade-off between the performance and their flexibility for various essential data operations. Here, we present a transistor-free CIM architecture that permits storage, search, and neural network operations on sub-50 nm thick Aluminum Scandium Nitride ferroelectric diodes (FeDs). Our circuit designs and devices can be directly integrated on top of Silicon microprocessors in a scalable process. By leveraging the field-programmability, nonvolatility, and nonlinearity of FeDs, search operations are demonstrated with a cell footprint <0.12 μm2when projected onto 45 nm node technology. We further demonstrate neural network operations with 4-bit operation using FeDs. Our results highlight FeDs as candidates for efficient and multifunctional CIM platforms.

The recent growth in multifidelity uncertainty quantification has given rise to a large set of variance reduction techniques that leverage information from model ensembles to provide variance reduction for estimates of the statistics of a high-fidelity model. In this paper we provide two contributions: (1) we utilize an ensemble estimator to account for uncertainties in the optimal weights of approximate control variate (ACV) approaches and derive lower bounds on the number of samples required to guarantee variance reduction; and (2) we extend an existing multifidelity importance sampling (MFIS) scheme to leverage control variates. Our approach directly addresses a limitation of many multifidelity sampling strategies that require the usage of pilot samples to estimate covariances. As such we make significant progress towards both increasing the practicality of approximate control variates—for instance, by accounting for the effect of pilot samples—and using multifidelity approaches more effectively for estimating low-probability events. The numerical results indicate our hybrid MFIS-ACV estimator achieves up to 50% improvement in variance reduction over the existing state-of-the-art MFIS estimator, which had already shown an outstanding convergence rate compared to the Monte Carlo method, on several problems of computational mechanics.

Uncertainty quantification (UQ) plays a major role in verification and validation for computational engineering models and simulations, and establishes trust in the predictive capability of computational models. In the materials science and engineering context, where the process-structure-property-performance linkage is well known to be the only road mapping from manufacturing to engineering performance, numerous integrated computational materials engineering (ICME) models have been developed across a wide spectrum of length-scales and time-scales to relieve the burden of resource-intensive experiments. Within the structure-property linkage, crystal plasticity finite element method (CPFEM) models have been widely used since they are one of a few ICME toolboxes that allows numerical predictions, providing the bridge from microstructure to materials properties and performances. Several constitutive models have been proposed in the last few decades to capture the mechanics and plasticity behavior of materials. While some UQ studies have been performed, the robustness and uncertainty of these constitutive models have not been rigorously established. In this work, we apply a stochastic collocation (SC) method, which is mathematically rigorous and has been widely used in the field of UQ, to quantify the uncertainty of three most commonly used constitutive models in CPFEM, namely phenomenological models (with and without twinning), and dislocation-density-based constitutive models, for three different types of crystal structures, namely face-centered cubic (fcc) copper (Cu), body-centered cubic (bcc) tungsten (W), and hexagonal close packing (hcp) magnesium (Mg). Our numerical results not only quantify the uncertainty of these constitutive models in stress-strain curve, but also analyze the global sensitivity of the underlying constitutive parameters with respect to the initial yield behavior, which may be helpful for robust constitutive model calibration works in the future.

The sCO₂ system located in 916/160A, Sandia National Laboratories, CA, was constructed in 2014, for testing of materials in the presence of supercritical carbon dioxide (sCO₂) at high pressures (up to 3500 psi) and temperatures (up to 650°C). The basic design of the system consists of a thermally insulated IN625 autoclave, a high-pressure supercritical CO₂ compressor, autoclave heaters, temperature controllers, gas manifold, and temperature and pressure diagnostics. This system was modified in 2016 (sCO₂ compressor was removed) to enable corrosion studies with metal alloys in gaseous CO₂ at lower pressure (up to 300 psi) at 500°C. The capability was not used much afterwards until 2020, when preliminary tests using this capability (again without the supercritical CO₂ compressor) involved the exposure of fatigue and tensile specimens of HN 230 and 800H alloys to CO₂ gas for 168 hours in gaseous CO₂. Using this capability, we finished experiments with low pressure (450 psi/ 3 MPa), high temperature (650°C) exposure of fatigue and tensile specimens of HN 230 and 800H alloys to CO₂ gas for 168 hours. The data from these experiments will be compared to that gathered from experiments performed in 2020 using the tube furnace and presented in a future report. It is to be noted that the tube furnace experiments ran 500-1500 hours, unlike the 168 hours of exposure in the recent experiment. This can help validate the use of the sCO₂ autoclave for both CO₂ and sCO₂ experiments.

Ternary nitrides in the family A₃B_xN (A=Co, Ni, Fe; B=Mo; x=2,3) identified and synthesized. Experiments with Co₃Mo₃N in Ammonia Synthesis Reactor demonstrate cyclable NH3 production from bulk nitride under pure H₂. Production rates were approx. constant in all the reduction steps with no evident dependence on the consumed solid-state nitrogen up to formation of 661. Material can be re-nitridized under pure N₂ (or 10% H₂/N₂). Bulk N utilization per reduction step averaged between 25 – 40% of the total (2-3 hours). Rate equations and parameters extracted from data. NH₃ selectivity exceeds gas phase equilibrium at higher temperatures (in a large excess of H₂). Selectivity begins to decrease significantly above 650 C, N₂ production rapidly increases above 650 C seemingly due to reaction that is zero order in H₂ (thermal reduction of the nitride?). Poised to begin the systematics studies of relationships between materials and reactions.

In this LDRD we investigated the application of machine learning methods to understand dimensionality reduction and evolution of the Rayleigh-Taylor instability (RTI). As part of the project, we undertook a significant literature review to understand current analytical theory and machine learning based methods to treat evolution of this instability. We note that we chose to refocus on assessing the hydrodynamic RTI as opposed to the magneto-Rayleigh-Taylor instability originally proposed. This choice enabled utilizing a wealth of analytic test cases and working with relatively fast running open-source simulations of single-mode RTI. This greatly facilitated external collaboration with URA summer fellowship student, Theodore Broeren. In this project we studied the application of methods from dynamical systems learning and traditional regression methods to recover behavior of RTI ranging from the fully nonlinear to weakly nonlinear (wNL) regimes. Here we report on two of the tested methods SINDy and a more traditional regression-based approach inspired by analytic wNL theory with which we had the most success. We conclude with a discussion of potential future extensions to this work that may improve our understanding from both theoretical and phenomenological perspectives.

Under IER-305, critical experiments will be done with and without molybdenum sleeves on 7uPCX fuel rods. New critical assembly hardware has been designed and procured to accomplish the experiments with the fuel supported by in a 1.55 cm triangular-pitched array.

Recent progress in photoinitiated ring-opening metathesis polymerization (photoROMP) has enabled the lithographic production of patterned films from olefinic resins. Recently, we reported the use of a latent ruthenium catalyst (HeatMet) in combination with a photosensitizer (2-isopropylthioxanthone) to rapidly photopolymerize dicyclopentadiene (DCPD) formulations upon irradiation with UV light. While this prior work was limited in terms of catalyst and photosensitizer scope, a variety of alternative catalysts and photosensitizers are commercially available that could allow for tuning of thermomechanical properties, potlifes, activation rates, and irradiation wavelengths. Herein, 14 catalysts and 8 photosensitizers are surveyed for the photoROMP of DCPD and the structure-activity relationships of the catalysts examined. Properties relevant to stereolithography additive manufacturing (SLA AM)-potlife, irradiation dose required to gel, conversion-are characterized to develop catalyst and photosensitizer libraries to inform development of SLA AM resin systems. Two optimized catalyst/photosensitizer systems are demonstrated in the rapid SLA printing of complex, multidimensional pDCPD structures with microscale features under ambient conditions.

The course goal is to provide participants with better understanding of the dynamic evolution between space policy, technology and world events in order to (1) anticipate the potential impacts of evolving space security policy on technical research and development needs for current and future space operations; (2) anticipate how technical research and development advancements might shape future directions and implementation of space security policy; and (3) develop more impactful research and development proposals and effective policy initiatives.

Hole spins in Ge quantum wells have shown success in both spintronic and quantum applications, thereby increasing the demand for high-quality material. We performed material analysis and device characterization of commercially grown shallow and undoped Ge/SiGe quantum well heterostructures on 8-in. (100) Si wafers. Material analysis reveals the high crystalline quality, sharp interfaces, and uniformity of the material. We demonstrate a high mobility (1.7 × 105cm2V-1s-1) 2D hole gas in a device with a conduction threshold density of 9.2 × 1010cm-2. We study the use of surface preparation as a tool to control barrier thickness, density, mobility, and interface trap density. We report interface trap densities of 6 × 1012eV-1. Our results validate the material's high quality and show that further investigation into improving device performance is needed. We conclude that surface preparations which include weak Ge etchants, such as dilute H2O2, can be used for postgrowth control of quantum well depth in Ge-rich SiGe while still providing a relatively smooth oxide-semiconductor interface. Our results show that interface state density is mostly independent of our surface preparations, thereby implying that a Si cap layer is not necessary for device performance. Transport in our devices is instead limited by the quantum well depth. Commercially sourced Ge/SiGe, such as studied here, will provide accessibility for future investigations.

Abstract not provided.

Irradiance transposition models seem to perform well, except the Isotropic with -11.25 W/m² underestimation. Most temperature models could not capture behavior when ΔΤ between module and ambient is negative. Uncertainties due to derate factors: modelers overbudgeted resulting in significant power underestimation; maybe ~10% is appropriate for commercial systems but not lab-scale? Most software and models cluster together showing good reproducibility among participants. Modeler’s skills seem to be more important than the PV model itself (flat efficiency with irradiance, positive power temperature coefficients, etc.). Results and best practices will be communicated in a journal article.

Mechanical draft cooling towers (MDCTs) serve a critical heat management role in a variety of industries. For nuclear reactors in particular, the consistent, predictable operation of MDCTs is required to avoid damage to infrastructure and reduce the potential for catastrophic failure. Accurate, reliable measurement of MDCT fan speed is therefore an important maintenance and safety requirement. To that end, we have developed an algorithm for automatically predicting the rotational speeds of multiple, simultaneously operating fan rotors using contactless, infrasound measurements. The algorithm is based on identifying the blade passing frequencies (BPFs), their harmonics, as well as the motor frequencies (MFs) for each fan in operation. Using the algorithm, these frequencies can be automatically identified in the acoustic waveform’s short-time Fourier transform spectrogram. Attribution is aided by a set of filters that rely on the unique spectral and temporal characteristics of fan operation, as well as the intrinsic frequency ratios of the BPF harmonics and the BPF/MF signals. The algorithm was tested against infrasound data acquired from infrasound sensors deployed at two research reactors: the Advanced Test Reactor (ATR) located at Idaho National Laboratory (INL) and the High Flux Isotope Reactor (HFIR) located at Oak Ridge National Laboratory (ORNL). After manually identifying the MDCT gearbox ratio, the algorithm was able to quickly yield fan speeds at both reactors in good agreement with ground truth. Ultimately, this work demonstrates the ease by which MDCT fans may be monitored in order to optimize operational conditions and avoid infrastructure damage.

Artificial intelligence and machine learning (AI/ML) are becoming important tools for scientific modeling and simulation as in several other fields such as image analysis and natural language processing. ML techniques can leverage the computing power available in modern systems and reduce the human effort needed to configure experiments, interpret and visualize results, draw conclusions from huge quantities of raw data, and build surrogates for physics based models. Domain scientists in fields like fluid dynamics, microelectronics and chemistry can automate many of their most difficult and repetitive tasks or improve the design times by use of the faster ML-surrogates. However, modern ML and traditional scientific highperformance computing (HPC) tend to use completely different software ecosystems. While ML frameworks like PyTorch and TensorFlow provide Python APIs, most HPC applications and libraries are written in C++. Direct interoperability between the two languages is possible but is tedious and error-prone. In this work, we show that a compiler-based approach can bridge the gap between ML frameworks and scientific software with less developer effort and better efficiency. We use the MLIR (multi-level intermediate representation) ecosystem to compile a pre-trained convolutional neural network (CNN) in PyTorch to freestanding C++ source code in the Kokkos programming model. Kokkos is a programming model widely used in HPC to write portable, shared-memory parallel code that can natively target a variety of CPU and GPU architectures. Our compiler-generated source code can be directly integrated into any Kokkosbased application with no dependencies on Python or cross-language interfaces.

Plasma formation from intensely ohmically heated conductors is known to be highly non-uniform, as local overheating can be driven by micron-scale imperfections. Detailed understanding of plasma formation is required to predict the performance of magnetically driven physics targets and magnetically-insulated transmission lines (MITLs). Previous LDRD-supported work (projects 178661 and 200269) developed the electrothermal instability (ETI) platform, on the Mykonos facility, to gather high-resolution images of the self-emission from the non-uniform ohmic heating of z-pinch rods. Experiments studying highly inhomogeneous alloyed aluminum captured complex heating topography. To enable detailed comparison with magnetohydrodynamic (MHD) simulation, 99.999% pure aluminum rods in a z-pinch configuration were diamond-turned to ~10nm surface roughness and then further machined to include well-characterized micron-scale "engineered" defects (ED) on the rod's surface (T.J. Awe, et al., Phys. Plasmas 28, 072104 (2021)). In this project, the engineered defect hardware and diagnostic platform were used to study ETI evolution and non-uniform plasma formation from stainless steel targets. The experimental objective was to clearly determine what, if any, role manufacturing, preparation, or alloy differences have in encouraging nonuniform heating and plasma formation from high-current density stainless steel. Data may identify improvements that may be implemented in the fabrication/preparation of electrodes used on the Z machine. Preliminary data shows that difference in manufacturer has no observed effect on ETI evolution, stainless alloy 304L heated more uniformly than alloy 310 at similar current densities, and that stainless steel undergoes the same evolutionary ETI stages as ultra-pure aluminum, with increased emission tied to areas of elevated surface roughness.

Cement production for concrete has been responsible for ~7–8% of global greenhouse gas (GHG) emissions, and nearly equally contribution for steel production processes (EPA, 2020). In order to achieve carbon neutrality by 2050, a novel solution has to be investigated. This project aims to develop fundamental mechanistic understanding and experimental characterization to create a 3D printable biopolymer concrete using plant-based polyurethane as an innovative and sustainable alternative for Portland cement concrete, with significantly low carbon footprint. Future construction will utilize the advances in digital additive manufacturing (3D printing) to produce optimal geometries with a minimum waste of materials. Understanding the polymerization process, factors impacting the composite rheology, and the structural behavior of this biopolymer concrete will enable us to engineer the next generation of concrete structures with low carbon footprint. This project aims to improve the nation’s ability to control Greenhouse Gas emission neutrality for the set goal of 2050 via introducing a structurally viable bio-based polymer concrete.

The purpose of this report is to document updates on the apparatus to simulate commercial vacuum drying procedures at the Nuclear Energy Work Complex at Sandia National Laboratories. Validation of the extent of water removal in a dry spent nuclear fuel storage system based on drying procedures used at nuclear power plants is needed to close existing technical gaps. Operational conditions leading to incomplete drying may have potential impacts on the fuel, cladding, and other components in the system during subsequent storage and disposal. A general lack of data suitable for model validation of commercial nuclear canister drying processes necessitates well-designed investigations of drying process efficacy and water retention. Scaled tests that incorporate relevant physics and well-controlled boundary conditions are essential to provide insight and guidance to the simulation of prototypic systems undergoing drying processes. This report documents a new test apparatus, the Advanced Drying Cycle Simulator (ADCS). This apparatus was built to simulate commercial drying procedures and quantify the amount of residual water remaining in a pressurized water reactor (PWR) fuel assembly after drying. The ADCS was constructed with a prototypic 17×17 PWR fuel skeleton and waterproof heater rods to simulate decay heat. These waterproof heaters are the next generation design to heater rods developed and tested at Sandia National Laboratories in FY20. This report describes the ADCS vessel build that was completed late in FY22, including the receipt of the prototypic length waterproof heater rods and construction of the fuel basket and the pressure vessel components. In addition, installations of thermocouples, emissivity coupons, pressure and vacuum lines, pressure transducers, and electrical connections were completed. Preliminary power functionality testing was conducted to demonstrate the capabilities of the ADCS. In FY23, a test plan for the ADCS will be developed to implement a drying procedure based on measurements from the process used for the High Burnup Demonstration Project. While applying power to the simulated fuel rods, this procedure is expected to consist of filling the ADCS vessel with water, draining the water with applied pressure and multiple helium blowdowns, evacuating additional water with a vacuum drying sequence at successively lower pressures, and backfilling the vessel with helium. Additional investigations are expected to feature failed fuel rod simulators with engineered cladding defects and guide tubes with obstructed dashpots to challenge the drying system with multiple water retention sites.

Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration. The National Nuclear Security Administration’s Sandia Field Office administers the contract and oversees contractor operations at Sandia National Laboratories, Kaua‘i Test Facility in Hawai‘i. Activities at the site are conducted in support of U.S. Department of Energy weapons programs, and the site has operated as a rocket preparation launching and tracking facility since 1962.

CTE (coefficient of thermal expansion) mismatch between two wafers has potential for brittle failure when large areas are bonded on top of one another (wafer to wafer or wafer to die bonds). To address this type of failure, we proposed patterning a polymer around metallic interconnects. For this project, utilized benzo cyclobutene (BCB) to form the bond and accommodate stress. For the metal interconnects, we used indium. To determine the benefits of utilizing BCB, mechanical shear testing of die bonding with just BCB were compared to die bonded just with oxide. These tests demonstrated that BCB, when cured for only 30 minutes and bonded at 200°C, the BCB was able to withstand shear forces similar to oxide. Furthermore, when the BCB did fail, it experienced a more ductile failure, allowing the silicon to crack, rather than shatter. To demonstrate the feasibility of using BCB between indium interconnects, wafers were pattered with layers of BCB with vias for indium or ENEPIG (electroless nickel, electroless palladium, immersion gold). Subsequently, these wafers were pattered with a variety of indium or ENEPIG interconnect pitches, diameters, and heights. These dies were bonded under a variety of conditions, and those that held a bond, were cross-sectioned and imaged. Images revealed that certain bonding conditions allow for interconnects and BCB to achieve a void-less bond and thus demonstrate that utilizing polymers in place of oxide is a feasible way to reduce CTE stress.

Direct air capture (DAC) of CO₂ is one of the negative emission technologies under development to limit the impacts of climate change. The dilute concentration of CO₂ in the atmosphere (~400 ppm) requires new materials for carbon capture with increased CO₂ selectivity that is not met with current materials. Porous liquids (PLs) are an emerging material that consist of a combination of solvents and porous hosts creating a liquid with permanent porosity. PLs have demonstrated excellent CO₂ selectivity, but the features that control how and why PLs selectively capture CO₂ is unknown. To elucidate these mechanisms, density functional theory (DFT) simulations were used to investigate two different PLs. The first is a ZIF-8 porous host in a water/glycol/2-methylimidazole solvent. The second is the CC13 porous organic cage with multiple bulky solvents. DFT simulations identified that in both systems, CO₂ preferentially bound in the pore window rather than in the internal pore space, identifying that the solvent-porous host interface controls the CO₂ selectivity. Additionally, SNL synthesized ZIF-8 based PL compositions. Evaluation of the long-term stability of the PL identified no change in the ZIF-8 crystallinity after multiple agitation cycles, identifying its potential for use in carbon capture systems. Through this project, SNL has developed a fundamental understanding of solvent-host interactions, as well as how and where CO₂ binds in PLs. Through these results, future efforts will focus not on how CO₂ behaves inside the pore, but on the porous host-solvent interface as the driving force for PL stability and CO₂ selectivity.

Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration. The National Nuclear Security Administration’s Sandia Field Office administers the contract and oversees contractor operations at Sandia National Laboratories, Tonopah Test Range. Activities at the site are conducted in support of U.S. Department of Energy weapons programs and have operated at the site since 1957.

Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration. The National Nuclear Security Administration’s Sandia Field Office administers the contract and oversees contractor operations at Sandia National Laboratories, New Mexico. Activities at the site support research and development programs with a wide variety of national security missions, resulting in technologies for nonproliferation, homeland security, energy and infrastructure, and defense systems and assessments.

Nonlocal models provide a much-needed predictive capability for important Sandia mission applications, ranging from fracture mechanics for nuclear components to subsurface flow for nuclear waste disposal, where traditional partial differential equations (PDEs) models fail to capture effects due to long-range forces at the microscale and mesoscale. However, utilization of this capability is seriously compromised by the lack of a rigorous nonlocal interface theory, required for both application and efficient solution of nonlocal models. To unlock the full potential of nonlocal modeling we developed a mathematically rigorous and physically consistent interface theory and demonstrate its scope in mission-relevant exemplar problems.

Fully characterizing high energy density (HED) phenomena using pulsed power facilities (Z machine) and coherent light sources is possible only with complementary numerical modeling for design, diagnostic development, and data interpretation. The exercise of creating numerical tests, that match experimental conditions, builds critical insight that is crucial for the development of a strong fundamental understanding of the physics behind HED phenomena and for the design of next generation pulsed power facilities. The persistence of electron correlation in HED materials arising from Coulomb interactions and the Pauli exclusion principle is one of the greatest challenges for accurate numerical modeling and has hitherto impeded our ability to model HED phenomena across multiple length and time scales at sufficient accuracy. An exemplar is a ferromagnetic material like iron, while familiar and widely used, we lack a simulation capability to characterize the interplay of structure and magnetic effects that govern material strength, kinetics of phase transitions and other transport properties. Herein we construct and demonstrate the Molecular-Spin Dynamics (MSD) simulation capability for iron from ambient to earth core conditions, all software advances are open source and presently available for broad usage. These methods are multi-scale in nature, direct comparisons between high fidelity density functional theory (DFT) and linear-scaling MSD simulations is done throughout this work, with advancements made to MSD allowing for electronic structure changes being reflected in classical dynamics. Main takeaways for the project include insight into the role of magnetic spins on mechanical properties and thermal conductivity, development of accurate interatomic potentials paired with spin Hamiltonians, and characterization of the high pressure melt boundary that is of critical importance to planetary modeling efforts.

Air-cooled heat exchangers are used to reject excess heat from a concentrated source to the surrounding atmosphere for a variety of mechanical and electrical systems. Advancements in heat exchanger design have been very limited in recent years for most product applications. In support of heat exchanger advancement, Sandia developed the Sandia Cooler.

The electrodeposition of rhenium on to a metal seed layer on flexible substrates is presented as a means to creating superconducting flexible cable connectors in an enabling plug-and-play approach for quantum computing. Cryogenic quantum electronics are currently connected using masses of stainless-steel coaxial cables that are bulky, rigid - both in form and design - and lead to significant joule heating, thermal noise, and cross talk. Here, we present an unprecedented approach to integrate an aerosol jet printed (AJP) metal seed layer with rhenium electrodeposition on a flexible substrate in the advancement of superconducting interconnect technologies. Silver and gold were printed using the ‘Nanojet’ aerosol jet printer on Kapton films. Adhesion of gold was found to be far superior to that of silver and adhesion on roughened Kapton surpassed that of its smooth counterpart. Electrodeposition of rhenium was successful on both silver and gold and an amorphous Re film was confirmed by XRD. Results for both materials are presented however due to the poor adhesion of silver to Kapton it was ruled out as a viable candidate. Composite materials were characterized by profilometry, EDS, XRD and FIBSEM. Electrical measurements of the composite at ambient temperature showed a critical temperature (T_c), where the resistance drops to 0, of 5.8 K, well above 4.2 K, the temperature of liquid helium. Stress-strain tests of the Ag-Re and Au-Re composites on roughened and smooth Kapton were completed. Cyclic flexure testing (200 cycles) to 1.25% strain showed smooth Kapton samples reach a stress of ~16 MPa, while Kapton roughened with sandpaper, reaches ~20MPa of stress for the same 1.25% strain.

We theoretically studied the feasibility of building a long-term read-write quantum memory using the principle of parity-time (PT) symmetry, which has already been demonstrated for classical systems. The design consisted of a two-resonator system. Although both resonators would feature intrinsic loss, the goal was to apply a driving signal to one of the resonators such that it would become an amplifying subsystem, with a gain rate equal and opposite to the loss rate of the lossy resonator. Consequently, the loss and gain probabilities in the overall system would cancel out, yielding a closed quantum system. Upon performing detailed calculations on the impact of a driving signal on a lossy resonator, our results demonstrated that an amplifying resonator is physically unfeasible, thus forestalling the possibility of PT-symmetric quantum storage. Our finding serves to significantly narrow down future research into designing a viable quantum hard drive.

Accurate estimation of greenhouse gases (GHGs) emissions is very important for developing mitigation strategies to climate change by controlling and reducing GHG emissions. This project aims to develop multiple deep learning approaches to estimate anthropogenic greenhouse gas emissions using multiple types of satellite data. NO2 concentration is chosen as an example of GHGs to evaluate the proposed approach. Two sentinel satellites (sentinel-2 and sentinel-5P) provide multiscale observations of GHGs from 10-60m resolution (sentinel-2) to ~kilometer scale resolution (sentinel-5P). Among multiple deep learning (DL) architectures evaluated, two best DL models demonstrate that key features of spatio-temporal satellite data and additional information (e.g., observation times and/or coordinates of ground stations) can be extracted using convolutional neural networks and feed forward neural networks, respectively. In particular, irregular time series data from different NO₂ observation stations limit the flexibility of long short-term memory architecture, requiring zero-padding to fill in missing data. However, deep neural operator (DNO) architecture can stack time-series data as input, providing the flexibility of input structure without zero-padding. As a result, the DNO outperformed other deep learning architectures to account for time-varying features. Overall, temporal patterns with smooth seasonal variations were predicted very well, while frequent fluctuation patterns were not predicted well. In addition, uncertainty quantification using conformal inference method is performed to account for prediction ranges. Overall, this research will lead to a new groundwork for estimating greenhouse gas concentrations using multiple satellite data to enhance our capability of tracking the cause of climate change and developing mitigation strategies.

As heterogeneous systems become increasingly popular for both mobile and high-performance computing, conventional efficiency techniques such as dynamic voltage and frequency scaling (DVFS) fail to account for the tightly coupled and varied nature of systems on a chip (SoCs). In this work, we explore the impact of system unaware DVFS techniques on a mobile SoC under three benchmark suites: Chai, Rodinia, and Antutu. We then analyze performance trends across the suites to identify a set of consistent operating points that optimally balance power and performance across the system. The consistent operating points are then constructed into a dependency graph which can be leveraged to produce a more effective, SoC-wide governor.

The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Spent Fuel & Waste Disposition (SFWD) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). A high priority for SFWST disposal R&D is disposal system modeling (Sassani et al. 2021). The SFWST Geologic Disposal Safety Assessment (GDSA) work package is charged with developing a disposal system modeling and analysis capability for evaluating generic disposal system performance for nuclear waste in geologic media. This report describes fiscal year (FY) 2022 advances of the Geologic Disposal Safety Assessment (GDSA) performance assessment (PA) development groups of the SFWST Campaign. The common mission of these groups is to develop a geologic disposal system modeling capability for nuclear waste that can be used to assess probabilistically the performance of generic disposal options and generic sites. The modeling capability under development is called GDSA Framework (pa.sandia.gov). GDSA Framework is a coordinated set of codes and databases designed for probabilistically simulating the release and transport of disposed radionuclides from a repository to the biosphere for post-closure performance assessment. Primary components of GDSA Framework include PFLOTRAN to simulate the major features, events, and processes (FEPs) over time, Dakota to propagate uncertainty and analyze sensitivities, meshing codes to define the domain, and various other software for rendering properties, processing data, and visualizing results.

This report investigates free expansion of Aluminum and provides a take home message of "The physically realistic SNAP machine-learning potential captures liquid-vapor coexistence behavior for free expansion of aluminum at a level not generally accessible to hydrocodes".

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 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.

Presented in this document is a portion of the tests that exist in the Sierra Thermal/Fluids verification test suite. Each of these tests is run nightly with the Sierra/TF code suite and the results of the test checked under mesh refinement against the correct analytic result. For each of the tests presented in this document the test setup, derivation of the analytic solution, and comparison of the code results to the analytic solution is provided. This document can be used to confirm that a given code capability is verified or referenced as a compilation of example problems.

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.

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 flows 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.

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.