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At least 991 records · Page 55

A New Proof That the Number of Linear Elastic Symmetries in Two Dimensions Is Four

In this work, we present an elementary and self-contained proof that there are exactly four symmetry classes of the elasticity tensor in two dimensions: oblique, rectangular, square, and isotropic. In two dimensions, orthogonal transformations are either reflections or rotations. The proof is based on identification of constraints imposed by reflections and rotations on the elasticity tensor, and it simply employs elementary tools from trigonometry, making the proof accessible to a broad audience. For completeness, we identify the sets of transformations (rotations and reflections) for each symmetry class and report the corresponding equations of motions in classical linear elasticity.

42 ENGINEERING↗

Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders

We report the design/discovery of new materials is highly nontrivial owing to the near-infinite possibilities of material candidates and multiple required property/performance objectives. Thus, machine learning tools are now commonly employed to virtually screen material candidates with desired properties by learning a theoretical mapping from material-to-property space, referred to as the forward problem. However, this approach is inefficient and severely constrained by the candidates that the human imagination can conceive. Thus, in this work on polymers, we tackle the materials discovery challenge by solving the inverse problem: directly generating candidates that satisfy desired property/performance objectives. We utilize syntax-directed variational autoencoders (VAE) in tandem with Gaussian process regression (GPR) models to discover polymers expected to be robust under three extreme conditions: (1) high temperatures, (2) high electric field, and (3) high temperature and high electric field, useful for critical structural, electrical, and energy storage applications. This approach to learn from and augment) human ingenuity is general and can be extended to discover polymers with other targeted properties and performance measures.

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Investigating Carboxysome Morphology Dynamics with a Rotationally Invariant Variational Autoencoder

Carboxysomes are a class of bacterial microcompartments that form proteinaceous organelles within the cytoplasm of cyanobacteria and play a central role in photosynthetic metabolism by defining a cellular microenvironment permissive to CO2 fixation. Critical aspects of the assembly of the carboxysomes remain relatively unknown, especially with regard to the dynamics of this microcompartment. Progress in understanding carboxysome dynamics is impeded in part because analysis of the subtle changes in carboxysome morphology with microscopy remains a low-throughput and subjective process. Here we use deep learning techniques, specifically a Rotationally Invariant Variational Autoencoder (rVAE), to analyze fluorescence microscopy images of cyanobacteria bearing a carboxysome reporter and quantitatively evaluate how carboxysome shell remodelling impacts subtle trends in the morphology of the microcompartment over time. Toward this goal, we use a recently developed tool to control endogenous protein levels, including carboxysomal components, in the model cyanobacterium Synechococcous elongatus PCC 7942. By utilization of this system, proteins that compose the carboxysome can be tuned in real time as a method to examine carboxysome dynamics. We find that rVAEs are able to assist in the quantitative evaluation of changes in carboxysome numbers, shape, and size over time. Further, we propose that rVAEs may be a useful tool to accelerate the analysis of carboxysome assembly and dynamics in response to genetic or environmental perturbation and may be more generally useful to probe regulatory processes involving a broader array of bacterial microcompartments.

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Antimicrobial properties of nanostructured surfaces – demonstrating the need for a standard testing methodology

Bioinspired nanostructured materials that exhibit antimicrobial properties are being synthesized and tested at increasing rates for use in healthcare, manufacturing processes, and diagnostics. Although progress has been made in improving and understanding their bactericidal activity, arguably, the biggest problem currently in the field is the lack of a standard testing methodology that allows for optimal characterization and better comparison of emerging nanostructures. In this work, we examine two forms of nanostructured silicon that vary in their ability to kill certain bacterial species due to different physical mechanisms and derive guidelines for the comparative testing. We perform a comprehensive evaluation of methodologies used extensively in the field (e.g., colony counting and live dead analysis) and the novel application of high-throughput flow cytometry. The data reveal how the techniques are complementary but not always directly equivalent or correlative. Therefore, comparison of results obtained using different methodologies on different materials can be grossly misleading. We report significant variations in bactericidal efficiencies depending on experimental environments (medium type, etc.) and methodologies employed. In addition, we demonstrate how cytometry is yet another powerful complementary tool that can aid the mechanistic understanding of antimicrobial activities of rough surfaces. Besides standardization for comparison, ultimately, evaluation methods need to consider anticipated applications. Then and only then can the true potential (or limitation) of a novel material be determined for its suitability for advancement in a particular field of use.

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Simultaneous measurements of volume, pressure, optical images, and crystal structure with a dynamic diamond anvil cell: A real-time event monitoring system

The dynamic diamond anvil cell (dDAC) technique has attracted great interest because it possibly provides a bridge between static and dynamic compression studies with fast, repeatable, and controllable compression rates. The dDAC can be a particularly useful tool to study the pathways and kinetics of phase transitions under dynamic pressurization if simultaneous measurements of physical quantities are possible as a function of time. We report the development of a real-time event monitoring (RTEM) system with dDAC, which can simultaneously record the volume, pressure, optical image, and structure of materials during dynamic compression runs. In particular, the volume measurement using both Fabry–Pérot interferogram and optical images facilitates the construction of an equation of state (EoS) using the dDAC in a home-laboratory. We also developed an in-line ruby pressure measurement (IRPM) system to be deployed at a synchrotron x-ray facility. This system provides simultaneous measurements of pressure and x-ray diffraction in low and narrow pressure ranges. The EoSs of ice VI obtained from the RTEM and the x-ray diffraction data with the IRPM are consistent with each other. The complementarity of both RTEM and IRPM systems will provide a great opportunity to scrutinize the detailed kinetic pathways of phase transitions using dDAC.

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TEAM Project Review, Year 2

This report summarizes our research activities within the TEAM project between December 2020 and December 2021, funded by the ASCR Advanced Research in Quantum Computing program. During the reporting period the LLNL-MSU team has made progress on several fronts. An overarching goal of the team is to provide a comprehensive suite of software tools that can be used for the Characterize-Optimize-Compute loop needed to implement and execute algorithms on quantum devices. We are concurrently developing lightweight solvers that can be used on desktop computers to find optimal control pulses and to characterize small quantum systems (consisting of a few transmons and cavities). However, desktop computers are insufficient for simulating and characterizing larger quantum systems. We have therefore also developed parallel, distributed memory, simulators and optimization solvers, both for open and closed quantum systems. These parallel solvers have, for example, been used to study quantum optimal control for pure-state preparation, utilizing 1000’s of cores on a modern high-performance computing (HPC) platform.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Advancing Vision-based Feedback and Convolutional Neural Networks for Visual Outlier Detection

Machine learning has matured into a technology that has immediate applicability to the surveillance needs of nuclear material storage containers. These containers at LANL are the barrier preventing release of radioactive material to the workers, public, and environment during the storage period of the material. Annual surveillance activities can only provide coverage on a handful of containers. There is a significant need for surveillance tools to identify potential issues and precursors to containment failure that can be used during opportunistic inspections and, more generally, outside of annual surveillance activities. In this report we provide details on the advancement of our proposed embodiment that combines an automation system for taking pictures and a high-accuracy machine learning-driven object detection software. We further showcase the improvements on the software side with progress on extracting unique identification features and advances in detecting damage. The current state of the system captures subject matter expert training and a space-conscious design whose implementation is envisioned in the near future.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

Complete Demonstration of a Prototype Version of FORCE User Interface and Conduct Analyst Survey Collecting Feedback on Interface Features and Usability

In 2024 the US Department of Energy (DOE) Office of Nuclear Energy (NE) Integrated Energy System (IES) program continued to develop the Framework for Optimization of Resources and Economics (FORCE) analysis ecosystem into a more traditional toolset with simplified software installation, automated workflows, and interactive results visualization. The DOE-NE Nuclear Energy Advanced Modeling and Simulation (NEAMS) Workbench continued to be leveraged for user input, application workflow and runtime environment, and interactive results visualization capabilities. This report documents the demonstration of a FORCE User Interface (UI) prototype and the results of a survey of analysts’ using the Holistic Energy Resource Optimization Network (HERON) tool in FORCE with the prototype UI.

97 MATHEMATICS AND COMPUTING↗

FY22 Grid Modernization & Energy Storage Program: Accomplishments & Impacts

Sandia’s Grid Modernization and Energy Storage program works to advance a national vision of a secure, resilient, and sustainable electric system for all users. Our achievements reflect a strategic approach combining technology development; modeling, simulation, and data analytics; and partnered demonstrations and outreach to further the adoption of advanced grid and storage technologies. Our FY22 efforts leverage the strengths of our partnerships—spanning Sandia’s core science and technology competencies as well as external technology leaders—to develop the solutions today which enable the grid of tomorrow. Much of the material in this report comes from the separate 2022 Accomplishments Report compiled by our Energy Storage subprogram team, a cornerstone of our grid research and achievements. The Grid Energy Storage Program at Sandia is focused on making energy storage cost-effective through research and development (R&D) in new battery technologies, advanced power electronics and power conversion systems, improved safety and reliability for energy storage systems, analytical tools for the valuation of energy storage, and the validation of new energy storage technologies through demonstration projects. During the 2022 fiscal year, Sandia executed R&D work supported by the U.S. Department of Energy’s (DOE) Office of Electricity – Energy Storage Program under the leadership of Dr. Imre Gyuk. This report indicates key areas of research and engagement and summarizes the impact of Sandia’s contributions through notable accomplishments, journal publications, patents, and technical conferences and presentations. It is provided with the hope that readers discover ways we can further team to create our modern grid and apply the outcomes of our efforts. The bulk of work described herein is funded by the DOE Office of Electricity and key programs within the DOE Office of Energy Efficiency and Renewable Energy. As we indicated in our report from last year, the contributors to our successes are too numerous to name here, though our team wishes to express our deep gratitude to the numerous program and project sponsors at the US Department of Energy, who often function equally as technical collaborators; our many partners in industry, academia, utilities, and other national labs; and fellow researchers and business partners at Sandia whose leadership and creativity have enabled the accomplishments described herein.

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Power-to-Gas System Valuation. Final Report

To provide a framework for evaluating the technical and financial benefits of a power-to-gas (P2G) system deployed in the Holyoke Gas and Electric (HG&E) system, Pacific Northwest National Laboratory modified its energy storage valuation tools to run a one-year simulation of P2G operations. The simulation was used to evaluate the benefits of P2G for multiple grid and industrial applications over the economic life of the unit, or units, depending on the asset configuration.

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NETL Plastic Pipes Project - June 2022 Progress Report

Plastic or composite pipelines have been the bane of the utility locating industry because conduits are neither conductive nor magnetic, which are the properties traditionally used to locate buried utilities. Ground penetrating radar (GPR) is an effective geophysical tool for locating plastic/composite pipeline where the resistive cover allows for adequate penetration of radar energy. However, GPR cannot be used in areas where the soil cover is conductive due to significant clay and/or salt content. This study takes a comprehensive look at near-surface geophysical methods that potentially are useful for locating buried plastic/composite pipelines, either singly or in combination with other geophysical methods. Specifically, this modeling study uses computational numerical methods to forward model the response of GPR, resistivity, seismic, and gravity gradiometry methods to plastic/composite pipelines for various scenarios including: (1) pipe diameters ranging between 2 in. to 12 in.; (2) burial depths ranging between 3 ft to 4 ft; (3) various degrees in contrast in physical properties (i.e., permittivity, elasticity, electrical resistivity, density); and (4) various experimental acquisition choices (e.g., GPR radar frequencies, seismic source frequency, electrode spacing).

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NETL Plastic Pipes Project (Final Report)

Plastic or composite pipelines have been the bane of the utility locating industry because they are neither conductive nor magnetic which are the properties traditionally used to locate buried utilities. Ground penetrating radar (GPR) is an effective geophysical tool for locating plastic/composite pipelines where resistive cover allows for adequate penetration of radar energy. However, GPR has limited applicability in areas where the soil cover is conductive due to significant clay and/or salt content. This study examines complementary near-surface geophysical methods that are potentially useful for locating buried plastic/composite pipelines, either singly or in combination. Specifically, this modeling study used computational numerical methods to forward model the response of GPR, resistivity, seismic, gravity gradiometry, and photoacoustic/thermoacoustic imaging methods to plastic/composite pipelines for various scenarios including: (1) pipe diameters ranging between 2 in. to 12 in.; (2) burial depths ranging between 3 ft. to 4 ft.; (3) various degrees in contrast in physical properties (i.e., electrical permittivity, elasticity, resistivity, density); and (4) various experimental acquisition choices (e.g., GPR radar and seismic source frequencies, electrode spacing). Numerical modeling performed herein reconfirmed that GPR is the preferred method for detecting/locating plastic pipelines. A caveat for GPR detection is that the material covering the plastic pipe (trench fill material and adjacent soil) must be sufficiently resistive to allow the two-way propagation to the required depth of investigation and back to the surface. GPR was the only method modeled in this study that can be used to directly detect plastic pipelines of 2-in.-diameter and larger when buried 3-ft-deep. GPR data processing and imaging also can determine pipe depth, pipe diameter, trench dimensions, and moisture conditions. Seismic modeling results suggest that direct detection of a 12-in.-diameter plastic pipe at 3-ft.-depth may be possible under favorable conditions; however, the associated signature would be weak (e.g., surface- to S-wave, backscattered surface-waves, and/or forward scattered surface-waves to S-wave). Direct pipe detection under field conditions with noise and strong lateral geologic heterogeneity is doubtful. Numerical modeling also suggests that plastic pipelines can be indirectly located by detecting the trench in which they are buried. GPR, direct current (DC) resistivity, and seismic methods have the potential to locate the pipeline trench if there is sufficient contrast between the trench-wall and trench-fill materials for the physical property being measured by each method (i.e., electrical permittivity for GPR; resistivity for DC resistivity; or density, compressional velocity, or shear velocity for seismic). Modeling also indicated that currently available (commercial) gravity gradiometers would be unable to directly detect/locate plastic pipelines ≤ 8-in.-diameter when buried 3-ft.-deep given the typical instrument noise floor for field surveying as well as the expected density variations due to geologic heterogeneity. The numerical modeling performed in this project did not identify a universal geophysical technology that can locate buried plastic pipelines in all parts of the United States (although GPR is suggested for all areas with resistive cover). However, the project results suggest that a towed land streamer simultaneously acquiring multiple geophysical data types including multi-offset GPR, multi-channel DC resistivity, seismic geophone- and/or distributed acoustic sensing (DAS), and potentially photoacoustic/thermoacoustic data would be an appropriate platform for locating buried plastic pipeline. Moreover, the complementary multiphysics data acquired by a towed land streamer would permit the use of joint and/or cooperative inversion frameworks for a more rigorous and consistent data interpretation.

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Powering Circularity Through Data Reporting and Collection

Sustainability and Circular Economy have many metrics for evaluation. Calculating mass intensity, energy return on investment, financial payback, and recycling rate for proposed technology changes and lifecycle management can support decision making. Robust data with modeling tools can perform these calculations, informing good decision making. Analyses show that reliability is more critical than recyclability. Improved data gathering and tool accessibility will support our industry to make more circular choices for PV lifecycle management.

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CALORIE: A Constraint Language and Optimizing Runtime for Exascale Power Management (Final Report)

This final technical report summarizes the key accomplishments on the CALORIE project, a DOE Early Career award received by PI Henry Hoffmann at the University of Chicago. CALORIE’s main goal was to create principled methodologies, tools, and practices to help scientists and high-performance computing (HPC) operators maximize the performance and insights obtained from scientific computing applications in the face of exascale power constraints. The project accomplished these goals by completing the following objectives: • Designing a language for describing application goals (including constraints and objectives) and system capabilities. The application goals will include things like which simulations or simulations plus in situ analysis will be run together, what the power constraints are, and what requirements there are for in situ analysis (for example, a desired frame rate for visualization). The system capabilities include all components that can be adjusted to tradeoff power and performance. • Designing a runtime system that takes specified goals and capabilities and dynamically determines what capabilities to use to meet the goals. This runtime adapts to changes in goals, application behavior, or available capabilities to automatically maintain the goals despite unexpected disturbances. • Developing a foundational understanding of how power constraints affect the problem of scheduling applications in large-scale systems. This report provides an overview of the accomplishments related to each of these key objectives.

97 MATHEMATICS AND COMPUTING↗

The Incorporation of Pump-and-Treat Data in the New Tracking Restoration and Closure (TRAC)Web-Based Mapping Tool - 20339

Groundwater pump-and-treat (P and T) systems are a common remediation strategy for sites with contaminated groundwater within the U.S. Department of Energy (DOE) Office of Environmental Management (EM). Currently, there are six DOE-EM sites with active P and T systems, but integrated information on individual systems is only primarily available in separate annual reports. To provide a broad view of both current and historical P and T operations, data has been collected on all of the P and T systems. This summary information not only includes capital and average annual costs, but also identifies contaminants treated and forecasted P and T closure dates. Principal contaminants of concern treated with P and T systems include trichloroethylene, chromium (VI), strontium-90, technetium-99, and uranium. DOE-EM is continuing to create a web-based mapping tool that compiles information on groundwater contaminants, remediation strategies and, current data related to plumes and cleanup progress within all DOE-EM sites. The information collected on P and T systems will be added to the TRAC (Tracking Restoration and Closure) tool using interactive maps to quickly access and share information. This information sharing supports the transition from active to passive remediation methods and long-term monitoring approaches. TRAC can ensure that managers, stakeholders, regulators, and contractors remain up to date on cleanup progress for all sites within the complex. The information on P and T systems expands the use of TRAC and can assist in project monitoring and budget planning and support potential shifts in management plans based on plume data. (authors)

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The origin of CEMA and its relation to CSP

Currently, there exist two methods for analysing an explosive mode introduced by chemical kinetics in a reacting process: the Computational Singular Perturbation (CSP) algorithm and the Chemical Explosive Mode Analysis (CEMA). CSP was introduced in 1989 and addressed both dissipative and explosive modes encountered in the multi-scale dynamics that characterize the process, while CEMA was introduced in 2009 and addressed only the explosive modes. It is shown that (i) the algorithmic tools incorporated in CEMA were developed previously on the basis of CSP and (ii) the examination of explosive modes has been the subject of CSP-based works, reported before the introduction of CEMA.

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Recombinant expression of computationally designed peptide-bundlemers in Escherichia coli

Computational design of fully artificial peptides is extensively researched by material scientists and engineers for the construction of novel nanostructures and biomaterials. Such design has yielded a peptide-based building block or bundlemer, a coiled coil peptide assembly that undergoes further physical-covalent interactions to form 1D, 2D and, potentially, 3D hierarchical assemblies and displays targeted and biomimetic material properties. Recombinant expression is a convenient, flexible tool to synthesize such artificial and modified peptides in large quantities while also enabling economical synthesis of isotopically labeled peptides and longer protein-like artificial peptides. This report describes the protocol for recombinant expression of a 31-amino acid, computationally designed bundlemer-forming peptide in Escherichia coli. Peptide yields of 10 mgs per liter of media were achieved which highlights complementary advantages of recombinant expression technique relative to conventional laboratory-scale synthesis, such as solid-phase peptide synthesis.

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Speciation and Organic Phase Structure in Nitric Acid Extraction with Trioctylamine

Understanding chemical speciation and intermolecular interactions in multicomponent liquids is essential to understanding their phase and chemical equilibria, which underpin chemical separation processes, including solvent extraction. Here we report on the extraction of nitric acid from its aqueous solutions into organic solutions of trioctylamine (TOA) in toluene, investigated with spectroscopic, X-ray scattering, and computational tools to understand molecular speciation in the organic phase and its relationship with the nanoscale structure of the organic phase. Trends in acid and water extraction clearly show two and three regimes, respectively, indicating different stoichiometric relationships, but speciation of HNO 3 , water, and amine in these regimes is not apparent. 1 H NMR of the organic phase shows that there are at least two distinct acidic protons in the organic phase while ATR-FTIR results show that the organic phase with excess acid extraction is a mixture of trioctylammonium-nitrate ion pairs (TOAHNO 3 ), and undissociated HNO 3 molecules. Comparison with DFT-computed IR spectra show that the chain-like configurations of TOAHNO 3 HNO 3 H 2 O are favored over TOAHNO 3 H 2 OHNO 3 , i.e., direct interaction between the nitrate and HNO 3 molecules is more favored compared to a water-mediated interaction. SAXS of the organic phases were modeled as sums of Ornstein-Zernike (O-Z) scattering and a prepeak feature in the higher Q region that corresponds to extractant packing. The extraction of undissociated HNO 3 by the ion pairs leads to an increased X-ray scattering contrast in the organic phase without any significant change in the correlation length. These results show that the organic phase nanostructure is more sensitive to the concentration of TOAHNO 3 and is relatively unaffected by excess acid extraction. Finally, these findings will enable a molecular understanding of the mechanisms behind metal extraction from acidic media with basic extractants.

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