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DOE OSTI2025

The Dynamic Networks Experiments: Virtual Experiments to Quantify Gains in Nuclear Explosion Monitoring

We describe an ongoing series of virtual experiments conducted collaboratively by four United States National Laboratories: Sandia National Laboratories, Los Alamos National Laboratory, Lawrence Livermore National Laboratory, and Pacific Northwest National Laboratory. These Dynamic Network Experiments (DNEs) provide an experimental framework to evaluate the potential impact of new research tools on nuclear explosion monitoring. The second DNE (DNE2), completed in 2024, exploited waveform data (seismic, infrasound, and electromagnetic) that was recorded by multi-modal sensors within and near the Nevada National Security Site and synthetic radionuclide signatures over multiple time periods. During the execution of DNE2, we processed and analyzed data through a multi-stage event processing pipeline that ingested raw data, performed quality control, detected signals, built events from these signals, located these events, and characterized the events’ source types and sizes. For each stage and over the entire event processing pipeline, we evaluated performance changes by comparing the performance of new data processing methods, models, and algorithms against a baseline. We also performed an additional execution phase to assess event processing pipeline function, speed, and efficiency against that of an expert analyst, including computational and manual efforts. Finally, we assessed the impact and effort of modern computing infrastructure on the monitoring pipeline. This paper describes key elements of the DNEs, from formulation through execution, as demonstrated in DNE2. The DNEs introduce several novel concepts to quantitatively measure the potential impact of new methods on explosion monitoring, including the collaborative design of multi-modal datasets, performance and logistical metrics, and integrated analyses.

42 ENGINEERING↗

DOE OSTI2021

Sequential Perturbation-based FDIA Detection using DERs for Unbalanced Distribution System

The power distribution system with its reliance on automated monitoring and control infrastructure makes it a complicated cyber-physical system and vulnerable to various cyber-attacks. This paper proposes a moving target defense inspired false data injection attack (FDIA) detection mechanism for an unbalanced distribution system. In the proposed grid diagnosis service framework, the distribution system operator judiciously manipulates power outputs of a subset of inverter-based distribution energy resources (DERs) to create secret low magnitude voltage perturbations which are inconsequential to the normal operation of the grid. A mixed-integer-linear-programming algorithm is developed to select the optimal set of DERs that can create a voltage perturbation signal of the required magnitude at each sensor location at a minimum cost. Then, a sequential detector is applied that detects for the FDIA as measurements are received from individual sensors. The performance of the proposed perturbation-based FDIA detection framework is demonstrated via simulation of the IEEE 123 bus test system.

Attack Detection↗

DOE OSTI2018

Aqueduct Mesa Photos

These photos document geophysical field work conducted on top of Aqueduct Mesa at the Nevada National Security Site during the weeks of October 14-20 and October 28 –Nov 3, 2018. Field personnel were Charlotte Rowe and Dea Musa, of Los Alamos National Laboratory’s Earth and Environmental Sciences Division (EES). 320 gravity sites were surveyed during the work, with positions obtained via Trimbullhand-held GPS, and gravity measurements made using a LaCoste & Romberg model G gravity meter. During the field work, some archaeological artifacts (stone circle, chirt shards, pottery shard and arrowhead) were noted and photographed with the GPS unit (for which a photographic permit was obtained), which provides latitude and longitude coordinates. On a few occasions the field party was augmented by wild horses. NNSS geologists assisted with navigation and synchronized readings of two gravity meters to tie the survey into a previous gravity survey conducted throughout the P-Tunnel, beneath Aqueduct Mesa, during June 2018.

58 GEOSCIENCES↗

DOE OSTI2024

ARPA-E Grid Optimization (GO) Competition Challenge 1

The ARPA-E Grid Optimization (GO) Competition Challenge 1, from 2018 to 2019, focused on the basic Security Constrained AC Optimal Power Flow problem (SCOPF) for a single time period. The Challenge utilized sets of unique datasets generated by the ARPA-E GRID DATA program. Each dataset consisted of a collection of power system network models of different sizes with associated operating scenarios (snapshots in time defining instantaneous power demand, renewable generation, generator and line availability, etc.). The datasets were of two types: Real-Time, which included starting-point information, and Online, which did not. Week-Ahead data is also provided for some cases but was not used in the Competition. Although most datasets were synthetic and generated by GRIDDATA, a few came from industry and were only used in the Final Event. All synthetic Input Data and Team Results for the GO Competition Challenge 1 for the Sandbox, Trial Events 1 to 3, and the Final Event along with problem, format, scoring and rules descriptions are available here. Data for industry scenarios will not be made public. Challenge 1, a minimization problem, required two computational steps. Solver 1 or Code 1 solved the base SCOPF problem under a strict wall clock time limit, as would be the case in industry, and reported the base case operating point as output, which was used to compute the Objective Function value that was used as the scenario score. The feasibility of the solution was provided by the Solver 2 or Code 2, which solves the power flow problem for all contingencies based on the results from Solver 1. This is not normally done in industry, so the time limits were relaxed. In fact, there were no time limits for Trial Event 1. This proved to be a mistake, with some codes running for more than 90 hours, and a time limit of 2 seconds per contingency was imposed for all other events. Entrants were free to use their own Solver 2 or use an open-source version provided by the Competition. Containers, such as Docker, were considered to improve the portability of codes, but none that could reliably support a multi-node parallel computing environment, e.g., MPI, could be found. For more information on the competition and challenge see the "GO Competition Challenge 1 Information" and "GO Competition Challenge 1 Additional Information" resources below.

ACOPF↗

NASA NTRS2000

Information Power Grid: Distributed High-Performance Computing and Large-Scale Data Management for Science and Engineering

We use the term "Grid" to refer to distributed, high performance computing and data handling infrastructure that incorporates geographically and organizationally dispersed, heterogeneous resources that are persistent and supported. This infrastructure includes: (1) Tools for constructing collaborative, application oriented Problem Solving Environments / Frameworks (the primary user interfaces for Grids); (2) Programming environments, tools, and services providing various approaches for building applications that use aggregated computing and storage resources, and federated data sources; (3) Comprehensive and consistent set of location independent tools and services for accessing and managing dynamic collections of widely distributed resources: heterogeneous computing systems, storage systems, real-time data sources and instruments, human collaborators, and communications systems; (4) Operational infrastructure including management tools for distributed systems and distributed resources, user services, accounting and auditing, strong and location independent user authentication and authorization, and overall system security services The vision for NASA's Information Power Grid - a computing and data Grid - is that it will provide significant new capabilities to scientists and engineers by facilitating routine construction of information based problem solving environments / frameworks. Such Grids will knit together widely distributed computing, data, instrument, and human resources into just-in-time systems that can address complex and large-scale computing and data analysis problems. Examples of these problems include: (1) Coupled, multidisciplinary simulations too large for single systems (e.g., multi-component NPSS turbomachine simulation); (2) Use of widely distributed, federated data archives (e.g., simultaneous access to metrological, topological, aircraft performance, and flight path scheduling databases supporting a National Air Space Simulation systems}; (3) Coupling large-scale computing and data systems to scientific and engineering instruments (e.g., realtime interaction with experiments through real-time data analysis and interpretation presented to the experimentalist in ways that allow direct interaction with the experiment (instead of just with instrument control); (5) Highly interactive, augmented reality and virtual reality remote collaborations (e.g., Ames / Boeing Remote Help Desk providing field maintenance use of coupled video and NDI to a remote, on-line airframe structures expert who uses this data to index into detailed design databases, and returns 3D internal aircraft geometry to the field); (5) Single computational problems too large for any single system (e.g. the rotocraft reference calculation). Grids also have the potential to provide pools of resources that could be called on in extraordinary / rapid response situations (such as disaster response) because they can provide common interfaces and access mechanisms, standardized management, and uniform user authentication and authorization, for large collections of distributed resources (whether or not they normally function in concert). IPG development and deployment is addressing requirements obtained by analyzing a number of different application areas, in particular from the NASA Aero-Space Technology Enterprise. This analysis has focussed primarily on two types of users: the scientist / design engineer whose primary interest is problem solving (e.g. determining wing aerodynamic characteristics in many different operating environments), and whose primary interface to IPG will be through various sorts of problem solving frameworks. The second type of user is the tool designer: the computational scientists who convert physics and mathematics into code that can simulate the physical world. These are the two primary users of IPG, and they have rather different requirements. The results of the analysis of the needs of these two types of users provides a broad set of requirements that gives rise to a general set of required capabilities. The IPG project is intended to address all of these requirements. In some cases the required computing technology exists, and in some cases it must be researched and developed. The project is using available technology to provide a prototype set of capabilities in a persistent distributed computing testbed. Beyond this, there are required capabilities that are not immediately available, and whose development spans the range from near-term engineering development (one to two years) to much longer term R&D (three to six years). Additional information is contained in the original.

Johnston, William E.↗

NASA NTRS2009

Developing a Standard Method for Link-Layer Security of CCSDS Space Communications

Communications security for space systems has been a specialized field generally far removed from considerations of mission interoperability and cross-support in fact, these considerations often have been viewed as intrinsically opposed to security objectives. The space communications protocols defined by the Consultative Committee for Space Data Systems (CCSDS) have a twenty-five year history of successful use in over 400 missions. While the CCSDS Telemetry, Telecommand, and Advancing Orbiting Systems protocols for use at OSI Layer 2 are operationally mature, there has been no direct support within these protocols for communications security techniques. Link-layer communications security has been successfully implemented in the past using mission-unique methods, but never before with an objective of facilitating cross-support and interoperability. This paper discusses the design of a standard method for cryptographic authentication, encryption, and replay protection at the data link layer that can be integrated into existing CCSDS protocols without disruption to legacy communications services. Integrating cryptographic operations into existing data structures and processing sequences requires a careful assessment of the potential impediments within spacecraft, ground stations, and operations centers. The objective of this work is to provide a sound method for cryptographic encapsulation of frame data that also facilitates Layer 2 virtual channel switching, such that a mission may procure data transport services as needed without involving third parties in the cryptographic processing, or split independent data streams for separate cryptographic processing.

Biggerstaff, Craig↗

DOE OSTI2016

Final LDRD Report: Using Linkography of Cyber Attack Patterns to Inform Honeytoken Placement

The war to establish cyber supremacy continues, and the literature is crowded with strictly technical cyber security measures. We present the results of a three year LDRD project using Linkography, a methodology new to the field of cyber security, we establish the foundation necessary to track and profile the microbehavior of humans attacking cyber systems. We also propose ways to leverage this understanding to influence and deceive these attackers. We studied the science of linkography, applied it to the cyber security domain, implemented a software package to manage linkographs, generated the preprocessing blocks necessary to ingest raw data, produced machine learning models, created ontology refinement algorithms and prototyped a web application for researchers and practitioners to apply linkography. Machine learning produced some of our key results: We trained and validated multinomial classifiers with a real world data set and predicted the attacker's next category of action with 86 to 98% accuracy; dimension reduction techniques indicated that the linkography-based features were among the most powerful. We also discovered ontology refinement algorithms that advanced the state of the art in linkography in general and cyber security in particular. We conclude that linkography is a viable tool for cyber security; we look forward to expanding our work to other data sources and using our prediction results to enable adversary deception techniques.

97 MATHEMATICS AND COMPUTING↗

NASA NTRSDate not supplied

Tracking and Recovery of the Low-Earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) Reentry Vehicle (RV)

The LOFTID mission launched from Vandenberg on Nov 10, 2022, and successfully demonstrated the reentry of a 6m diameter inflatable aeroshell from low Earth orbit. This paper will cover the design features implemented to enable recovery of the flight vehicle, and will discuss the splashdown calculations, in-flight tracking, recovery from the ocean, and post-flight inspection of the flight vehicle. To support recovery of the RV and ejected data recorder after splashdown, a recovery ship was pre-positioned near the predicted splashdown ellipse in the Pacific Ocean. The splashdown ellipse was repeatedly updated as launch approached. In-flight tracking included transmission from the RV of GPS data through both the Iridium satellite network and the LoRa direct RF link, along with IR video cameras on the recovery ship and airborne imagery from the SCIFLI Team. Using both the GPS data and the IR imagery, the recovery ship tracked down the RV, and deployed an inflatable boat to approach the RV and attach it to the ship’s crane, after which the RV was hoisted on board and secured in its GSE recovery stand. The ship then tracked down the ejected data recorder, which was also broadcasting its GPS data, and pulled it from the water. Once the ship returned to port, the RV was hoisted ashore for additional inspection, removal of the data recorders, and repackaging for shipment back to NASA Langley.

Robert A. Dillman↗

NASA NTRSDate not supplied

Tracking and Recovery of the LOFTID RV (Low-Earth Orbit Flight Test of an Inflatable Decelerator Reentry Vehicle)

Robert A Dillman↗

DOE OSTI2025

Transforming Energy Through Computational Excellence: A View From NREL

At the National Renewable Energy Laboratory (NREL)—a U.S. Department of Energy laboratory—computational science, high-performance computing, applied mathematics, advanced computer science, visualization, and data play a pivotal role in advancing energy abundance, affordability, security, and reliability. From fundamental scientifc discovery to systems engineering and analysis, NREL researchers tackle market-relevant challenges to develop solutions for an independent energy system that is reliable, resilient and secure. Collaborative partnerships with industry, government, and academia ensure that our research remains cutting edge, impactful, applicable, and aligned with real-world energy needs. This special issue of Computing in Science & Engineering highlights exemplary NREL projects where computational tools and methodologies drive discovery and accelerate innovation in scalable and integrated energy systems. The featured articles explore the role of computational modeling, high-performance computing, generative AI, and adaptive computing in advancing independent energy solutions, optimizing sustainability research, and enhancing decision-making for energy solutions using a broad mix of energy technologies. Here, these contributions demonstrate how NREL’s computational research bridges the gap between theoretical advancements and practical implementation, emphasizing interdisciplinary collaboration and a commitment to innovation, with a focus on translating computational excellence into real-world impact, thus accelerate progress toward national energy goals. By showcasing cutting-edge research at the intersection of computational science and energy systems, this issue aims to inspire and inform researchers, practitioners, and policymakers dedicated to shaping a more reliable energy future.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Self-powered Wireless Through-wall Data Communication for Nuclear Environments

In the nuclear industry, many vital components, such as spent fuel storage canisters and nuclear reactor pressure vessels (RPV), are entirely enclosed by metal and surrounded by thick concrete walls that manage the potentially harmful radiation and prevent release to the environment. Due to the casks’ long storage, monitoring temperature, pressure, radiation, humidity, structural health, etc., within these enclosed vessels is crucial to ensure the fuel containment safety and security. In this paper, a self-powered wireless through-wall data communication system for nuclear environments was designed. The whole package includes: a radiation energy harvester with power management; ultrasound wireless communication using high-temperature piezoelectric transducers; electronics modules for harvesting, sensing, and data transmission; and radiation shielding for electronics and sensors. The package is able to harvest tens to hundreds of mW power from a nuclear canister environment directly and provides a path forward for continuous monitoring of the dry cask for 50 years.

Wu, Yongjia↗

DOE OSTI2021

Systems and methods for control system security

A resilient security agent determines a cyber and/or physical health of a control system by, inter alia, communicating cyber-physical key data through cyber-physical control paths of the system, and determining error introduced by the communication. The resilient security agent may be further configured to verify the integrity of acquired cyber-physical state information. The cyber health of the control system may be evaluated by comparing the acquired cyber state information to one or more cyber state profiles. The physical health of the control system may be evaluated by comparing the acquired physical state information to one or more physical state profiles.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2017

Behavioral Assessment of Spaceflight Effects on Neurocognitive Performance: Extent and Longevity

Exposure to the microgravity environment during spaceflight missions impacts crewmembers' sensorimotor function. Bock et al. [1] studied the cognitive demands of human sensorimotor performance and dual tasking during long duration missions and concluded that both stress and scarcity of cognitive resources required for sensorimotor adaptation may be responsible for these deficits during spaceflight. Therefore, in consideration of the health and performance of crewmembers in- and post-flight, we are conducting this study to investigate the effects of spaceflight on the extent, longevity and neural bases of sensorimotor, cognitive, and neural changes. The data presented will focus on the behavioral measures that were collected pre-, in- and post-flight including spatial cognition, processing speed, bimanual coordination, functional mobility, computerized dynamic posturography (CDP), and vibrotactile induced vestibular evoked myogenic potential (VEMP). To date, data were collected over the course of two pre-flight sessions and four post-flight sessions on five crewmembers (n=13) using the protocol described in Koppelmans et al. [2]. Balance control was assessed using CDP, with eyes closed and a sway-referenced base of support (Sensory Organization Test 5), with and without head movements in the pitch plane. Spatial working memory was assessed using Thurston's Card Rotation Test and a Mental Rotation Test. The Rod and Frame Test was performed to test visual dependence. The Digit Symbol Substitution Test was performed to evaluate processing speed, and the Purdue Pegboard Task was performed to test bimanual coordination. Vestibular function was assessed by eliciting ocular VEMP via a hand held striker on the side of the head as subjects lay supine on a gurney. Subjects also performed the Functional Mobility Test of walking through an obstacle course to assess rate of early motor learning. Data were also collected on the same crewmembers during three in-flight sessions on the International Space Station (ISS). In-flight, spatial working memory was assessed using the Mental Rotation Test, adaptation to visuo-motor transformation in manual control was assessed using the Sensorimotor Adaptation Test, and multi-tasking ability was assessed using the Dual Task Test. These three tests were performed in a strapped-in configuration mimicking a seated position - waist bungees pulled the crewmember toward the "floor" with feet secured in foot loops. The Mental Rotation Test was also performed in a free-floating configuration while the crewmember floated while holding on to the gamepad controller used to provide input that was secured to the equipment rack on the ISS. Preliminary findings from data collected to date, will be included in the presentation. Eventual comparison to results from supporting bed rest and longitudinal studies will enable the parsing out of the multiple mechanisms contributing to any observed spaceflight-induced sensorimotor and cognitive behavioral changes.

De Dios, Y. E.↗

DOE OSTI2017

Weapon Physicist Declassifies Rescued Nuclear Test Films

The U.S. conducted 210 atmospheric nuclear tests between 1945 and 1962, with multiple cameras capturing each event at around 2,400 frames per second. But in the decades since, around 10,000 of these films sat idle, scattered across the country in high-security vaults. Not only were they gathering dust, the film material itself was slowly decomposing, bringing the data they contained to the brink of being lost forever. For the past five years, Lawrence Livermore National Laboratory (LLNL) weapon physicist Greg Spriggs and a crack team of film experts, archivists and software developers have been on a mission to hunt down, scan, reanalyze and declassify these decomposing films. The goals are to preserve the films’ content before it’s lost forever, and provide better data to the post-testing-era scientists who use computer codes to help certify that the aging U.S. nuclear deterrent remains safe, secure and effective.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2018

Yucca Flat Well Development and Testing Analyses for Wells ER-3-3 and ER-4-1, Nevada National Security Site, Nye County, Nevada

This report documents the well development and testing (WDT) data and analysis of Wells ER-3-3 and ER-4-1 during fiscal year (FY) 2017. Wells ER-3-3 and ER-4-1 were constructed to evaluate possible radionuclides (RNs) in groundwater from nearby underground tests (UGTs), to provide hydrogeologic information to support refinement of the Yucca Flat hydrostratigraphic framework model (HFM) (BN, 2006), and to provide supplemental data to the Yucca Flat groundwater flow and contaminant transport model (N-I, 2013) to help address priority concerns and recommendations of the Yucca Flat External Peer Review Team. Of particular interest is the characterization of specific groundwater flow pathways (i.e., faults, fractured aquifers) along which RNs in groundwater could migrate from individual UGTs. Another important objective is to determine the hydraulic properties of the volcanic aquifers (VAs) and carbonate aquifers (CAs) in the former underground testing areas in Yucca Flat, and specifically in the areas proximal to existing UGTs.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2019

The Resilient Energy Platform

The provision of reliable, secure, and affordable electricity is essential to power economic growth and development. The power system is at risk from an array of natural, human-caused, and technological threats, which can cause everything from power interruption to chronic undersupply of energy. It is critical for policymakers, planners, and system operators to safeguard their power systems from these threats by proactively planning for future needs and investing in resilient power systems. Developed through the USAID-NREL Partnership, the Resilient Energy Platform is a website that provides expertly curated resources, training materials, data, tools, and direct technical assistance in planning resilient, sustainable, and secure power systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2019

Velocity Structure at the Source Physics Experiment Phase I Site Obtained with the Large-N Array Data

The Source Physics Experiment (SPE) consists of a series of chemical explosions at the Nevada National Security Site. The goal of the SPE is to understand and model seismic-wave generation and propagation from these explosions. To achieve this goal, we need an accurate velocity model of the SPE site. A large-N seismic array deployed at the SPE site during one of the chemical explosions (SPE-5) provides great data for this purpose. The array consists of 996 geophones and covers an area of approximately 2 × 2.5 km. In addition to the SPE-5 explosion, the array recorded 53 large weight drops. Using the large-N seismic array recordings, we perform first-arrival analysis and obtain a 2D P-wave velocity model of the SPE site. We image a sharp transition from high-velocity Cretaceous granite to low-velocity Quaternary alluvium. Other geological units such as the Tertiary volcanic rocks and Paleozoic sedimentary rocks are also clearly shown. The results of this work provide important local geological information and will be incorporated into the larger 3D modeling effort of the SPE program to validate the predictive models developed for the site.

58 GEOSCIENCES↗

DOE OSTI2020

How Low Can You Go? Using Synthetic 3D Imagery to Drastically Reduce Real-World Training Data for Object Detection

Deep convolutional neural networks (DCNNs) currently provide state-of-the-art performance on image classification and object detection tasks, and there are many global security mission areas where such models could be extremely useful. Crucially, the success of these models is driven in large part by the widespread availability of high-quality open source data sets such as Image Net, Common Objects in Context (COCO), and KITTI, which contain millions of images with thousands of unique labels. However, global security relevant objects-of-interest can be difficult to obtain: relevant events are low frequency and high consequence; the content of relevant images is sensitive; and adversaries and proliferators seek to obscure their activities. For these cases where exemplar data is hard to come-by, even fine-tuning an existing model with available data can be effectively impossible. Recent work demonstrated that models can be trained using a combination of real-world and synthetic images generated from 3D representations; that such models can exceed the performance of models trained using real-world data alone; and that the generated images need not be perfectly realistic (Tremblay, et al., 2018). However, this approach still required hundreds to thousands of real-world images for training and fine tuning, which for sparse, global security-relevant datasets can be an unrealistic hurdle. In this research, we validate the performance and behavior of DCNN models as we drive the number of real-world images used for training object detection tasks down to a minimal set. We perform multiple experiments to identify the best approach to train DCNNs from an extremely small set of real-world images. In doing so, we: Develop state-of-the-art, parameterized 3D models based on real-world images and sample from their parameters to increase the variance in synthetic image training data; Use machine learning explainability techniques to highlight and correct through targeted training the biases that result from training using completely synthetic images; and Validate our results by comparing the performance of the models trained on synthetic data to one another, and to a control model created by fine-tuning an existing ImageNet-trained model with a limited number (hundreds) of real-world images.

97 MATHEMATICS AND COMPUTING↗