Single-View Tomographic Reconstruction Using Learned Primal Dual
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97 MATHEMATICS AND COMPUTING↗
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Virtual Volumes
SAND2022-14692 O Virtual Volumes is a CAD2VR plugin that gives users a powerful and intuitive way of exploring volumetric data in a three-dimensional (3D) virtual reality (VR) environment. It enables viewing of and interaction with volumetric and voxel data derived from sources such computed tomography (CT) scans. Current 2D software solutions for viewing CT scans and other volumetric data forces the user to look through "slices" of their data across anatomical planes. In Virtual Volumes, the user can intuitively interact, scale, crop, colorize, and threshold their data in a 3D VR environment. 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 under contract DE-NA0003525.
Krukar, JohnA.↗
Operational Evolution of FTS3: A DevOps Driven Approach to Elastic Operations
The File Transfer Service (FTS3) is a distributed data movement service developed at CERN and widely used to transfer data across the Worldwide LHC Computing Grid (WLCG). At Fermilab, FTS3 supports data transfers for multiple experiments, including Intensity Frontier experiments such as DUNE, enabling reliable data movement between WebDAV endpoints in Europe and the Americas. At CHEP 2021, we reported on the initial containerized deployment of FTS3 on OKD, the community Kubernetes distribution of Red Hat OpenShift. In this work, we present the subsequent evolution of this deployment, focusing on new operational capabilities introduced to improve scalability, robustness, and long-term maintainability. We describe the adoption of more secure and reproducible container build workflows, the integration of DevOps-driven operational practices, and enhancements in monitoring and automation. A key new result is the introduction of horizontal scaling and elastic resource management, allowing FTS3 components to dynamically adapt to workload variations while maintaining service reliability. We also discuss improvements in fault tolerance and operational procedures derived from production experience. Finally, we summarize lessons learned from operating FTS3 as a Kubernetes-native service and outline how these developments have improved the resilience and efficiency of data movement operations at Fermilab.
Munoz Flores, Victor Leopoldo [Fermilab]↗
Optimal PV Inverter Control in Distribution Systems via Data-Driven Distributionally Robust Optimization
Distribution systems with high penetration of uncertain solar generation call for advanced control strategies of photovoltaics (PVs) inverters. This paper proposes a data-driven distributionally robust optimization (DDDRO) approach to optimally controlling the PV inverters to improve the system operation performance under solar power uncertainties. In the proposed DDDRO approach, a Wasserstein ball-based method is proposed to construct the distributional ambiguity set to model the uncertainties of PV generation through partial observations of historical data without knowing exact probability distributions. We further reformulate the computationally intractable DDDRO model to a mixed integer second order cone programming (MISOCP) problem. The effectiveness and out-of-sample performance of the proposed approach have been demonstrated on a modified IEEE 33-node system. We conduct a comparative study to compare the proposed method with traditional chance constrained programming (CCP). It shows that the proposed DDDRO approach can provide a less conservative yet robust solution to minimize the worse-case expectation of the total network loss while maintaining nodal voltages in a secure range.
Xue, Yaosuo↗
Development of a Multi-Sensor Data Science System Used for Signature Development on Solvent Extraction Processes in support of safeguards- an overview
A new nuclear fuel cycle test bed is being built at Idaho National Laboratory to support the purification of special nuclear material recovered from used fuel. The test bed provides an opportunity to research process flow and the application of computational tools in solvent extraction processes. A deeper understanding of process and equipment behavior coupled with real time data collection can indicate whether a process failure is accidental or purposeful. The goal of this project is to develop a system that utilizes non-traditional measurement sources such as vibration, acoustics, current, light, flow, and temperature in conjunction with data-based, machine learning techniques that will allow for signal discovery. This multi-sensor data can support the development of safeguards by design and security by design measures for such a facility. Additionally, it can aid in early detection and identification of removed materials indicating diversion, which is essential for initiating material recovery and actor identification. This overview encompasses the current research and testing of sensors to develop a spectrum of process signatures. To be followed by planned experiments aimed to characterize said signatures and study potential feature extraction techniques to identify a fault in the system (i.e. flow diversion).
98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
Multiscale simulation of integrated energy system and electricity market interactions
Accelerating the deep decarbonization of the world's electric grids requires the coordination of complex energy systems and infrastructures across timescales from seconds to decades. Here, we present a new multiscale simulation framework that integrates process- and grid-centric modeling paradigms to better design, operate, and control integrated energy systems (IESs), which combine multiple technologies, in wholesale energy markets. Traditionally, IESs are analyzed with a process-centric paradigm such as levelized cost of electricity (LCOE) or annualized net revenue, ignoring important interactions with electricity markets. This framework explicitly models the complex interactions between an IES's bidding, scheduling, and control decisions and the energy market's clearing and settlement processes, while incorporating operational uncertainties. Through two case studies, we show the importance of understanding and quantifying complex resource-grid interactions. In case study 1, we demonstrate that optimized bidding from one resource shifts the profit distribution for all energy systems in the market. This result suggests new and more flexible IES technologies can disrupt the economics of all market participants, possibly leading to accelerated retirements of less flexible resources. Interestingly, the optimized bidding has little impact on grid-level aggregate statistics, such as total generation costs and renewable penetration rate. While aggregate modeling strategies may remain valid under some IES adoption scenarios for analysis focused on regional outcomes, direct comparisons of IES technologies at specific locations without considering these interactions may lead to misleading or incorrect conclusions. In case study 2, we consider the design and flexible operation of IESs that hybridize conventional generators with energy storage. Through a sensitivity analysis, we find that as the size of the storage system increases, the total number of start-ups for coal- and natural gas-based IESs reduced by 25% and 33.6%, and the total thermal generator ramping (i.e., mileage) reduced by 86.5% and 62.5%, respectively. This shows the primary benefit of storage may not be reduced operational costs (which do not change significantly) but fewer start-ups and less ramping, which may greatly simplify the design, operation, and control of carbon capture systems. The new modeling and optimization capabilities from this work enable the coupling of rigorous, dynamic process models with grid-level production cost models to quantitatively identify the nuanced interdependencies across these vast timescales that must be addressed to realize clean, safe, and secure energy production. Moreover, the proposed general multiscale simulation framework is applicable to all IES technologies and can be easily extended to consider other energy carriers (e.g., hydrogen, ammonia) and energy infrastructures (e.g., natural gas pipelines).
24 POWER TRANSMISSION AND DISTRIBUTION↗
CMIP6-based Multi-model Hydroclimate Projection over the Conterminous US, Version 1.1
This dataset presents a suite of high-resolution downscaled hydro-climate projections over the conterminous United States (CONUS) based on multiple selected Global Climate Models (GCMs) from the Coupled Models Intercomparison Project phase 6 (CMIP6). The CMIP6 GCMs are downscaled using either statistical (DBCCA) or dynamical (RegCM) downscaling approaches based on two meteorological reference datasets (Daymet and Livneh). Subsequently, the downscaled precipitation, temperature, and wind speed are employed to drive two calibrated hydrologic models (VIC and PRMS), enabling the simulation of projected future hydrologic responses across the CONUS. Each ensemble member covers the 1980-2019 baseline and 2020-2059 near-future periods under the high-end (SSP585) emission scenario. Moreover, utilizing only DBCCA and Daymet, the projections are further extended to the 2060-2099 far-future period and across three additional emission scenarios (SSP370, SSP245, and SSP126). This dataset is formulated to support the SECURE Water Act Section 9505 Assessment for the US Department of Energy (DOE) Water Power Technologies Office (WPTO). For further details on this dataset, please refer to Kao et al. (2022) and Rastogi et al. (2022).
13 HYDRO ENERGY↗
Small Molecule Interactions with Membranes (CRADA No. TC02321 Final Report)
This was a collaborative effort between Lawrence Livermore National Security, LLC (LLNS) as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Procter & Gamble (P&G) to develop and use a multiscale simulation method to explore and evaluate the nature of industrially relevant compounds in the presence of models that represent microbial membranes.
59 BASIC BIOLOGICAL SCIENCES↗
Cyber-Informed Engineering (CIE) Integration into Model- Based Systems Engineering (MBSE)
Engineering design in the field of industrial engineering, such as designing automated factories or warehouses, is critical for the effective operation of facilities. Any design flaws introduced early can result in significant capital expenses to correct. However, early-stage engineering design is inherently complex. The systems are not yet built, requiring designers to integrate various aspects, including digital engineering and cybersecurity, to support virtual representations throughout the design process. In this study, we propose an approach to integrate Cyber-Informed Engineering (CIE) principles into model-based systems engineering (MBSE). This approach facilitates the development of a digital thread for engineering systems, ensuring secure digital artifacts in the design of industrial engineering systems.
42 - ENGINEERING↗
Building a FAIR data ecosystem for incorporating single-cell transcriptomics data into agricultural genome to phenome research
Introduction The agriculture genomics community has numerous data submission standards available, but the standards for describing and storing single-cell (SC, e.g., scRNA- seq) data are comparatively underdeveloped. Methods To bridge this gap, we leveraged recent advancements in human genomics infrastructure, such as the integration of the Human Cell Atlas Data Portal with Terra, a secure, scalable, open-source platform for biomedical researchers to access data, run analysis tools, and collaborate. In parallel, the Single Cell Expression Atlas at EMBL-EBI offers a comprehensive data ingestion portal for high-throughput sequencing datasets, including plants, protists, and animals (including humans). Developing data tools connecting these resources would offer significant advantages to the agricultural genomics community. The FAANG data portal at EMBL-EBI emphasizes delivering rich metadata and highly accurate and reliable annotation of farmed animals but is not computationally linked to either of these resources. Results Herein, we describe a pilot-scale project that determines whether the current FAANG metadata standards for livestock can be used to ingest scRNA-seq datasets into Terra in a manner consistent with HCA Data Portal standards. Importantly, rich scRNA-seq metadata can now be brokered through the FAANG data portal using a semi-automated process, thereby avoiding the need for substantial expert curation. We have further extended the functionality of this tool so that validated and ingested SC files within the HCA Data Portal are transferred to Terra for further analysis. In addition, we verified data ingestion into Terra, hosted on Azure, and demonstrated the use of a workflow to analyze the first ingested porcine scRNA-seq dataset. Additionally, we have also developed prototype tools to visualize the output of scRNA-seq analyses on genome browsers to compare gene expression patterns across tissues and cell populations. This JBrowse tool now features distinct tracks, showcasing PBMC scRNA-seq alongside two bulk RNA-seq experiments. Discussion We intend to further build upon these existing tools to construct a scientist-friendly data resource and analytical ecosystem based on Findable, Accessible, Interoperable, and Reusable (FAIR) SC principles to facilitate SC-level genomic analysis through data ingestion, storage, retrieval, re-use, visualization, and comparative annotation across agricultural species.
Genetics & Heredity↗
Scribe Network API v.1.0.0
SAND2023-07864O Scribe Network API is an add-on tool for Scribe3D software. Scribe Network API documents tabletop exercises in trainings and plays back simulated videos of the scenarios and responses. Users can apply the software to compiled projects, or to a simple visual studio project solution package. The software runs a web application that relays information between computers using Scribe3D through a web application, or web app. The web app involves a representational state transfer (REST) application programming interface that handles sending and receiving Scribe save files and an SQL server that stores the save files. This follow-on package allows users to facilitate a networked tabletop exercise. 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 under contract DE-NA0003525.
Noel, Todd↗
Artificial Intelligence and Machine Learning for Bioenergy Research: Opportunities and Challenges
The integration of artificial intelligence and machine learning (AI/ML) with automated experimentation, genomics, biosystems design, and bioprocessing technologies is poised to revolutionize scientific investigation and, particularly, bioenergy research. To identify the opportunities and challenges in this emerging research area, the U.S. Department of Energy’s (DOE) Biological and Environmental Research program (BER) and Bioenergy Technologies Office (BETO) held a joint virtual workshop on AI/ML for Bioenergy Research (AMBER) on August 23–25, 2022. These interests have since been amplified in a September 2022 Executive Order, “Advancing Biotechnology and Biomanufacturing Innovation for a Sustainable, Safe, and Secure U.S. Bioeconomy,” to promote a whole-of government approach to biotechnology development (White House 2022). Approximately 50 scientists with various backgrounds and expertise from academia, industry, and DOE national laboratories met to discuss the opportunities and challenges of AI/ML for bioenergy research. Workshop participants were tasked with assessing the potential for AI/ML and laboratory automation to advance biological understanding and engineering in general. They particularly examined how integrating AI/ML tools with laboratory automation could accelerate biosystems design and optimize biomanufacturing. Discussions included the data and computational infrastructure needed to augment biosystems design applications and the expertise and workforce development efforts urgently required to shift integrated systems toward bioenergy research more broadly. Participants discussed many existing and future applications of AI/ML for biosystems design ranging from enzymes to plants and microbes, microbiomes, and bioprocess development. They also identified three key categories of scientific and technical opportunities and challenges: high-quality data, AI/ML algorithms, and laboratory automation. Several main takeaways emerged from the workshop: 1. Numerous AI/ML and automated experimentation applications exist for a variety of DOE mission needs in energy and the environment; 2. Exemplary research grand challenges for which AI/ML could provide solutions include: building microbes and microbial communities to specifications, developing closed-loop autonomous design and control for biosystems design, and advancing scale-up and automation; 3. Lack of sufficient high-quality, annotated data hinders the development of AI/ML applications; 4. New and improved AI/ML tools are needed, particularly those meeting the specific needs of the BER and BETO research communities; 5. Trade-offs in performance, cost, and reliability exist between deploying commercially available versus building custom-developed instrumentation and software for automated or autonomous experimentation; translation of manual to automated or autonomous methods is often a nontrivial endeavor; 6. Training a new generation of young scientists who can develop and apply AI/ML tools is needed to solve long-standing scientific challenges in bioenergy research. The integration of AI/ML tools and automated experimentation represents a new data-driven research paradigm complementary to the traditional hypothesis-driven research paradigm. This paradigm accelerates design and optimization of biological systems and processes for a variety of DOE mission needs in energy and the environment. The AMBER workshop broadly explored the potential of this new paradigm for bioenergy research, of particular interest to BER and BETO, and identified key challenges and opportunities that DOE can address in the coming years by leveraging its unique capabilities and resources.
59 BASIC BIOLOGICAL SCIENCES↗
LLM Generation of Online Courses from a Curated Set of Documents in the Nuclear Safeguards Domain
A multidisciplinary team at Argonne National Laboratory explores the application of advanced technologies to enhance knowledge transfer and retention within the nuclear safeguards domain. Specifically, it examines the feasibility of leveraging secure large language models (LLMs) to streamline the creation of e-learning modules for the U.S. National Nuclear Security Administration (NNSA) Office of International Nuclear Safeguards (NA-241). The initiative addresses the critical need for preserving institutional memory and accelerating skill development amidst the imminent retirement of senior professionals in the field in addition to supporting good knowledge management practices. The project integrates instructional design theory with cutting-edge AI technologies to transform curated document sets from the Safeguards Knowledge Repository (SKR) into modular online courses. By automating the generation of learning objectives and instructional content, the effort aims to reduce manual effort while maintaining high-quality educational outcomes. A limited measure of human supervision, however, ensures accuracy, relevance, and alignment with NNSA’s strategic priorities. Key findings highlight the potential of AI-assisted course generation to support safeguards professionals by creating structured, interactive learning experiences. The report underscores the importance of SME validation to address limitations in AI-generated content, such as terminology errors and gaps in coverage. Recommendations include adopting a structured workflow combining LLM acceleration with expert oversight to ensure accuracy, usability, and alignment with learner needs. This work demonstrates Argonne’s commitment to advancing national security and scientific excellence through innovative knowledge management solutions.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
Cybersecurity at the Grid Edge: Protecting LA's Energy Systems in a Connected World
Join SoCalGas and the National Laboratory of the Rockies (NLR) for an insightful webinar on securing operational technology and energy systems. Los Angeles has the second-largest metro by population in the nation, making it vital to protect the energy infrastructure that powers day-to-day life. However, cybersecurity for energy systems is an immense challenge due to an increasing number of interconnected devices and stakeholders. While there was traditionally a limited need to secure energy infrastructure, the grid is only becoming smarter and more software-defined. Amid aging infrastructure and evolving cyber threats, the need to ensure the security of energy is at an all-time high. Together, SoCalGas and NLR are assessing the current state-of-the-art of the energy ecosystem to understand gaps in current best practices and technologies relevant to powering the city of Los Angeles.
97 MATHEMATICS AND COMPUTING↗
LUMIS: A Land Use Management Information System for urban planning
The Land Use Management Information System (LUMIS) consists of a methodology of compiling land use maps by means of air photo interpretation techniques, digitizing these and other maps into machine-readable form, and numerically overlaying these various maps in two computer software routines to provide land use and natural resource data files referenced to the individual census block. The two computer routines are the Polygon Intersection Overlay System (PIOS) and an interactive graphics APL program. A block referenced file of land use, natural resources, geology, elevation, slope, and fault-line items has been created and supplied to the Los Angeles Department of City Planning for the City's portion of the Santa Monica Mountains. In addition, the interactive system contains one hundred and seventy-three socio-economic data items created by merging the Third Count U.S. Census Bureau tapes and the Los Angeles County Secured Assessor File. This data can be graphically displayed for each and every block, block group, or tract for six test tracts in Woodland Hills, California. Other benefits of LUMIS are the knowledge of air photo availability, flight pattern coverage and frequencies, and private photogrammetry companies flying Southern California, as well as a formal Delphi study of relevant land use informational needs in the Santa Monicas.
Paul, C. K.↗
Provable Security and Resilience in Critical Infrastructure – Next Steps
With the conclusion of the Laboratory Directed Research and Development (LDRD) project on Provable Security and Resilience (PSaR) in Critical Infrastructure, we present forward-looking technical concepts and strategies that build on the project’s outcomes and INL’s long-standing expertise in infrastructure protection. The challenge is to protect critical infrastructure and functions much more efficiently at scale than capable adversaries can attack at scale. After summarizing progress and ongoing work we’ll discuss what are the challenges that remain and what are new/emerging technologies, strategies, and processes to meet those challenges. Finally, we’ll layout concepts that integrate with other protection work in the coming year and beyond. For example, building secure function-specific platforms based on the seL4 microkernel, and considering the successes of Cyber-Informed Engineering as a model for engage, collaboration, and adoption. We look forward to your feedback and collaboration as we refine and expand this vision.
97 - MATHEMATICS AND COMPUTING↗
Mechanistic within-host mathematical model of inhalational anthrax
We present a mathematical model of the dynamics of Bacillus anthracis bacteria within the lymph nodes and blood of a host, following inhalation of an initial dose of spores. We also incorporate the dynamics of protective antigen, which is the binding component of the anthrax toxin produced by the bacteria. The model offers a mechanistic description of the early infection dynamics of inhalational anthrax, while its stochastic nature allows us to study the probabilities of different outcomes (for example, how likely it is that the infection will be cleared for a given inhaled dose of spores) in order to explain dose-response data for inhalational anthrax. The model is calibrated via a Bayesian approach, using in vivo data from New Zealand white rabbit and guinea pig infection studies, enabling within-host parameters to be estimated. We also leverage incubation-period data from the Sverdlovsk 1979 anthrax outbreak to show that the model can accurately describe human time-to-symptoms data under reasonable parameter regimes. Finally, we derive a simple approximate formula for the probability of symptom onset before time t, assuming that the number of inhaled spores has a Poisson distribution.
59 BASIC BIOLOGICAL SCIENCES↗
Earth Sciences Applications for Energy and Global Security: A Few Vignettes [Slides]
Understanding the physics of flow and transport in the subsurface environment is crucial to several National Security applications. In the Energy Security realm, some examples include characterizing radioactive waste disposal, exploring natural gas extraction and sequestering CO2. An example in the Global Security arena is to be able to distinguish between anthropogenic and naturally occurring seismic disturbances, the former being of particular concern as underground nuclear testing activity has seen an increase in recent years. A common theme across these applications is the ability to accurately model fluid flow in porous and/or fractured subsurface media. For decades, researchers have grappled with how to include the effects of uncertainties in these models, including but not limited to heterogeneity, unknown initial and boundary conditions and issues with scalability. More recently advances in computing and machine learning methodologies have allowed a broader exploration of the uncertainty space as we strive for real time decision making. I will present an overview of the field and discuss a few examples of how my own research in these topics has evolved over nearly two decades.
58 GEOSCIENCES↗