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.

Milestone accomplishments staged the ExaWind team for successful completion of KPP-2 challenge problem in FY23, which requires the simulation on Frontier of at least four MW-scale turbines in an atmospheric boundary layer with at least 20B gridpoints. The ExaWind project and software stack is many faceted, with team members working on multiple areas, including linear-system solvers (Trilinos, hypre, AMReX), overset meshes, turbulence modeling, and in situ visualization, all with an aim for high fidelity predictions and performance portability. This milestone marks significant improvements on many fronts and provides the team with a pathway to exascale wind farm simulations in FY23.

X-ray computed tomography is generally a primary step in characterization of defective electronic components, but is generally too slow to screen large lots of components. Super-resolution imaging approaches, in which higher-resolution data is inferred from lower-resolution images, have the potential to substantially reduce collection times for data volumes accessible via x-ray computed tomography. Here we seek to advance existing two-dimensional super-resolution approaches directly to three-dimensional computed tomography data. Multiple scan resolutions over a half order of magnitude of resolution were collected for four classes of commercial electronic components to serve as training data for a deep-learning, super-resolution network. A modular python framework for three-dimensional super-resolution of computed tomography data has been developed and trained over multiple classes of electronic components. Initial training and testing demonstrate the vast promise for these approaches, which have the potential for more than an order of magnitude reduction in collection time for electronic component screening.

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.

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.

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.

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).

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.

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.

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 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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Abstract not provided.