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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The causal relationship between melt pool geometry and energy absorption measured in real time during laser-based manufacturing

During laser powder bed fusion additive manufacturing, laser power absorption is governed by a pro -tean pool of molten metal that can present as a highly reflective surface, a deeply absorbing cavity, or some amalgamation thereof. These melt pool dynamics have been linked to defect creation, porosity, and surface finish quality. Although these are therefore critical for determining final part quality, their in-stantaneous influence on laser absorption have only been explored through simulation. To date, direct real-time observations have been elusive due to the locally extreme environment. In this work, we fo-cus a laser on Ti-6Al-4V powder and bare plate while quantifying the time-dependent, absolute energy absorption by monitoring omnidirectional backscattered laser intensity. We also simultaneously record the projective melt pool geometries with high-speed synchrotron x-ray imaging. We find that laser ab-sorption strongly reflects the stability of the vapor depression over a wide range of applied laser powers, oxygen content in the processing atmosphere, and with the presence of powder. During laser scanning of a powder bed surface, we find a significant absorption reduction after 400 mu s due to a dramatic change in the vapor depression aspect ratio-an event known to create porosity. Furthermore, as several industrial scan strate-gies necessitate thousands of these events during a build, their identification and control is of significant practical importance. Lastly, a normalized enthalpy model is demonstrated to be effective in quantifying the relationship between the laser absorption and cavity depth, even under transient conditions. In addi-tion to providing vital quantitative data for simulation calibration, the correlation of melt pool geometry with laser absorption during realistic processing conditions suggests the use of a total backscattered light detection system for real-time process control.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Qualitative Strategy for Fusion of Physics into Empirical Models for Process Anomaly Detection

To facilitate the automated online monitoring of power plants, a systematic and qualitative strategy for anomaly detection is presented. This strategy is essential to provide credible reasoning on why and when an empirical versus hybrid (i.e., physics-supported) approach should be used and to determine the ideal mix of these two approaches for a defined anomaly detection scope. Empirical methods are usually based on pattern, statistical, and causal inference. Hybrid methods include the use of physics models to train and test data methods, reduce data dimensionality, reduce data-model complexity, augment data, and reduce empirical uncertainty; hybrid methods also include the use of data to tune physics models. The presented strategy is driven by key decision points related to data relevance, simple modeling feasibility, data inference, physics-modeling value, data dimensionality, physics knowledge, method of validation, performance, data availability, and suitability for training and testing, cause-effect, entropy inference, and model fitting. The strategy is demonstrated through a pilot use case for the application of anomaly detection to capture a valve packing leak at the high-pressure coolant injection system of a nuclear power plant.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

An improved Method for Determining Transient Fuel Dilution of Oil in an Internal-Combustion Engine Using Laser-Induced Florescence and Multivariate Least Square Calibration

An optical diagnostic, based on laser-induced fluorescence (LIF), has been developed for on-engine measurements of real-time fuel dilution of engine oil or fuel in oil (FIO). Fuel dilution of oil is broadly relevant to advancing engine technology including durability, calibration, and catalyst-system management, and believed to promote destructive stochastic pre-ignition (SPI) during high-load engine operations. While standard (e.g., ASTM D3524-90) methods are not capable of real-time transient measurements, the LIF technique resolves transient dilution on the minutes time scale. We have expanded on our original FIO instrument development by introducing an improved analysis based on multivariate least square chemometrics analysis. The measurement uses a fuel dye (180–1300 parts per million, by mass) and monitors for its presence in the oil using 532 nm excitation and LIF. While the original FIO instrument utilized a two-color ratio method for analysis, the improved chemometric analysis uses the fully resolved LIF dye spectra to provide better predictive FIO accuracy (>92%) over a wide FIO range (1.5–14%) typical of engine application. We also investigate the effect of oil temperature on the LIF signal. Limited engine applications for demonstrating and validating the improved FIO instrument are shown, and the related data used to quantify practical detection limit and sensitivity. The improved analysis is insensitive to laser power fluctuation and change in detector integration time, providing an excellent FIO sensitivity (1–2%) and detection limit (0.01 %FIO) over a wide range of loads and injection timings, illustrating this updated approach to be a promising tool for advancing engine technology.

42 ENGINEERING↗
DOE OSTI2026

Microwave-Assisted Hydrogen Generation from Hydrocarbon-Bearing Reservoir Rocks: Stage-Dependent Thermal Runaway and In-Situ Carbonate Engineering

Microwave-assisted hydrogen generation from hydrocarbon-bearing reservoir rocks is strongly influenced by mineralogy, methane activation, carbonate reactions, and thermal runaway behavior. This study investigates a new approach in which carbonate phases are generated in-situ through the reaction of internally produced CO2 with Ca(OH)2 under microwave heating conditions. The objective is to evaluate how rock mineralogy, methane injection, and Ca(OH)2 addition influence hydrogen generation, carbon redistribution, and stage-dependent reaction pathways during microwave exposure. Microwave heating experiments were conducted using Permian Basin reservoir rocks under three experimental conditions: rock-only experiments under Ar atmosphere, CH4–Ar experiments without additive, and CH4–Ar experiments containing 5 wt% Ca(OH)2. Methane-assisted experiments were performed under continuous injection of 30 standard cubic centimeters per minute (sccm) CH4 and 30 sccm Ar. Based on thermal runaway behavior, each experiment was divided into three operational stages: Before Thermal Runaway (BR), After Thermal Runaway–Decrease in Microwave Power (ARD), and After Thermal Runaway– Increase in Microwave Power (ARI). Temperature and gas composition were continuously monitored throughout the experiments. The rock-only experiments demonstrated that hydrogen generation can occur intrinsically from hydrocarbon-bearing rocks under microwave heating, even without externally injected methane. However, hydrogen production did not correlate solely with kerogen content, indicating that mineralogy strongly influences hydrogen-generation pathways. Correlation analyses suggested that kerogen decomposition initially generated CH4, CO, and CO2, followed by secondary hydrocarbon reactions associated with H2 and C2 hydrocarbon formation. Methane-assisted experiments substantially increased hydrogen production; however, identical methane injection rates produced significantly different hydrogen yields among the rock samples, confirming that mineralogical composition controls methaneconversion behavior under microwave heating conditions. The addition of Ca(OH)2 significantly altered carbon evolution behavior in a stage-dependent manner. During the BR stage, Ca(OH)2 reduced gas-phase CO2 production, particularly in carbonate-rich rocks, indicating favorable conditions for in-situ carbonation and carbonate deposition prior to extensive thermal decomposition. The suppression of CO2 during BR became more pronounced with increasing carbonate content of the rock system. After thermal runaway, carbonate-containing systems exhibited enhanced hydrogen generation behavior, suggesting that carbonatederived mineral transformations and carbonate-mediated reactions contribute to high-temperature hydrogen-generation pathways. In carbonate-poor rocks, Ca(OH)2 addition enabled simultaneous URTeC 4493775 2 enhancement of hydrogen production and partial suppression of CO2 release during the post-runaway stages. Overall, the results demonstrate that microwave-assisted hydrogen generation is governed by dynamically evolving interactions among kerogen decomposition, methane activation, mineral transformations, carbonate formation/decomposition, and thermal runaway behavior. This work introduces in-situ carbonate engineering with Ca(OH)2 as a strategy for coupling hydrogen generation with partial insitu carbon management under microwave heating conditions.

03 NATURAL GAS↗
DOE OSTI2024

From Simple Labels to Time-Use Integrations: Supporting the Spectrum of Qualitative Travel Behavior Data

In transportation research, applications of travel behavior data collection are context-specific and require different types of qualitative inputs. These inputs can be viewed as spanning a spectrum of user burden and data quality, from simple trip labels to complex time-use surveys. However, each currently active smartphone-based travel diary platform appears to only support one type of qualitative input, and the effort required for customization is unclear. In this paper, we characterize the spectrum by defining four canonical use cases: (i) trip labels, (ii) trip questionnaire, (iii) counterfactual trips, and (iv) time-use surveys. We then outline a mechanism for supporting configurable user inputs on the same underlying smartphone-based sensing mechanism and demonstrate that it can support all the use cases without any code changes. We further demonstrate that the flexible data model that underpins this mechanism can enable real-time monitoring and analysis. Finally, we evaluate per-user data collection and engagement metrics for large-scale deployments of three canonical use cases, spanning 10 programs, 435 users, and 251,041 trips, and a maximum duration of 800 days. Future efforts may support additional use cases through an expanded configuration and provide greater insight into user engagement. We hope that these insights enable the research community to look at qualitative inputs through a new lens and experiment with novel use cases to fill in the spectrum.

ADVANCED PROPULSION SYSTEMS,POWER TRANSMISSION AND↗
DOE OSTI2025

AI-powered municipal solid waste management: a comprehensive review from generation to utilization

The accumulation of municipal solid waste (MSW) continues to rise due to burgeoning population, rapid global urbanization and economic growth, intensifying ecological concerns associated with landfills and greenhouse gas (GHG) emissions. Over the past 2 decades, global waste generation has surged by 50%, with one-third remaining uncollected and about 70% sent to landfills. This review examines the critical role of integrating emerging technologies, such as advanced sensors and artificial intelligence (AI), into end-to-end MSW management to alleviate landfill burdens. The suitability of various AI tools for different stages of MSW management is assessed, alongside the deployment of advanced sensors including hyperspectral cameras, computer vision systems, and internet of things (IoT) devices for material identification. Applications of genetic algorithms and reinforcement learning for optimizing collection routes, reducing costs, and lowering emissions are highlighted. Life cycle assessment (LCA) across all stages of MSW management is also reviewed, along with future trends in leveraging generative AI, natural language processing (NLP), and agent-based AI systems to analyze waste generation patterns and public sentiment. Efficient collection and handling can be enhanced through route optimization with geographic information systems and real-time bin-level monitoring. Furthermore, sensor-embedded, real-time object detection systems paired with robotics enable material characterization and automated sorting, thereby lowering costs and diverting waste from landfills into value-added products for diverse industrial sectors including packaging, chemicals, textiles, metals and glass, transportation, and electronics industries. Without intervention, global waste is projected to reach 4.54 billion tons by 2050, contributing direct economic costs of $\$$400 billion and roughly 2.38 billion tons of CO 2 -equivalent emissions annually. This review demonstrates how AI-driven, end-to-end solutions for MSW management can mitigate economic and environmental challenges, while directly supporting the United Nations Sustainable Development (UNDP) goals related to innovation and infrastructure (SDG 9), sustainable cities (SDG 11), responsible consumption and production (SDG 12), and climate action (SDG 13).

09 BIOMASS FUELS↗
DOE OSTI2020

Dynamic Undervolting to Improve Energy Efficiency on Multicore X86 CPUs

Chip manufacturers introduce redundancy at various levels of CPU design to guarantee correct operation, even for worst-case combinations of non-idealities in process variation and system operating conditions. This redundancy is implemented partly in the form of voltage margins. However, for a wide range of real-world execution scenarios these margins are excessive and merely translate to increased power consumption, hindering the effort towards higher-energy efficiency in both HPC and general purpose computing. Our study on the x86-64 Haswell and Skylake multicore microarchitectures reveals-wide voltage margins, which vary across different microarchitectures, different chip parts of the same microarchitecture, and across different workloads. We find that it is necessary to quantify-voltage margins using multi-threaded and multi-instance workloads, as characterization with single-threaded and single-instance workloads that do not stress the CPU to its full capacity typically identifies overly optimistic margins that lead to errors when applied in realistic program execution scenarios. In addition, we introduce, deploy and evaluate a run-time governor that dynamically reduces the supply voltage of modern multicore x86-64 CPUs. Our governor employs a model that takes as input a set of performance metrics which are directly measurable via performance monitoring counters and have high predictive value for the minimum tolerable supply voltage (V min ), to predict and apply the appropriate reduction for the workload at hand. Compared with the conventional DVFS governor, our approach in this study achieves up to 42 percent energy savings for the Skylake family and 34 percent for the Haswell family for complex, real-world applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Respirator Cartridge Performance on Mixed Vapors from Hanford Tank Headspaces and Exhausters - 20427

Between 2016 and 2018, the Hanford Tank Operations Contractor - Washington River Protection Solutions (WRPS) - conducted a series of tests of air-purifying respirator (APR) chemical cartridges commonly used at Hanford tank farms to determine the period of time for which the cartridges would provide adequate performance when used in APRs and powered-air-purifying respirators (PAPRs) to protect workers when exposed to a mixture of vapors exiting tank headspaces. Although cartridge manufacturers provide service life estimating tools for individual chemical compounds, the projected performance of these cartridges on complex vapor mixtures is not available, and the adequacy of APRs for tank farm applications represents an important workforce concern. The Occupational Safety and Health Administration identifies cartridge testing as a valid approach for establishing cartridge service life. The primary function of the WRPS Cartridge Test Program was to obtain objective data to determine service lives for the APR and PAPR cartridges used at Hanford tank farms. WRPS contracted with Pacific Northwest National Laboratory to analyze the test data and offer an independent analysis of and recommendations based on respirator cartridge performance. A total of 28 APR and 10 PAPR cartridge tests were conducted between 2016 and 2018 on 12 different tank headspaces and tank farm exhauster slipstreams. Two APR cartridges from SCOTT (now 3M) and two PAPR cartridges from MSA Safety, Inc. and 3M were evaluated using a cartridge testing system specifically designed to measure and monitor test conditions and sample cartridge inlet and outlet vapor streams for important chemical compounds. Testing focused on analysis of approximately 61 tank vapor chemicals of potential concern (COPCs) that have been previously detected in tank vapors at levels above 10% of their occupational exposure limits (OELs). Each test was conducted over 16 hours of run time. Evidence of chemical breakthrough was assessed by comparing inlet and outlet COPC concentrations over the duration of each test. The breakthrough threshold was normally defined as exceeding 10% of the compounds OEL at the cartridge outlet. Ammonia breakthrough was observed in a majority of the cartridge tests and occurred earlier than breakthrough of any other chemical compound. Several other COPCs did exhibit breakthrough behavior, including mercury, 1,3-butadiene, furan, 2,5 dihydrofuran, and N-nitrosodimethylamine (NDMA), but only in a very limited number of cartridge tests and only after ammonia breakthrough had occurred. In addition, tests results suggest that breakthrough of some of these COPCs may have been affected by competition with and breakthrough of other non-COPC organics, such as ethanol and acetone, with substantially lower toxicological hazard. Comparison of cartridge manufacturers' service life estimates with the experimentally derived breakthrough times indicates that manufacturers' estimates are generally conservative, even in the presence of the complex mixed vapor streams experienced in these tests. With consideration of appropriate safety margins, these results provide valuable insights on cartridge performance to inform industrial hygiene professionals in establishing appropriate cartridge change-out schedules for APR and PAPR use in the Hanford tank farms. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Distributed Fiber Sensors With High Spatial Resolution in Extreme Radiation Environments in Nuclear Reactor Cores

This paper is a comprehensive experimental report on the neutron radiation effects of distributed optical fiber sensors with enhanced Rayleigh scattering profiles in an in-pile environment. Femtosecond laser direct writing was used to inscribe Type-II modifications in standard telecom fibers and radiation-hardened fibers with fluorine-doped cores. Rayleigh backscattering signals were enhanced for continuous 1.5 m. In-pile lead-out sensors tests were carried out at the MIT Research Reactor for two months, which was operated at a nominal power of 5.7 MW with fast neutron (>0.1 MeV) flux of 1.29 × 1014 n/cm2/s and an in-core temperature of up to 560°C. Using the Optical Frequency Domain Reflectometry technique, the backscattering profiles of fiber sensors were interrogated with a 3-cm spatial resolution to monitor the temperature profile of the reactor. Results show that laser inscribed Type-II modifications in the form of nanogratings are highly stable against extreme temperature and ionizing radiation. Both standard telecom fibers and radiation-hardened fibers with laser-enhanced Rayleigh profiles can continuously perform distributed temperature measurements over the entire duration of the in-pile testing. Temperature coefficients of sensors and spectral shift quality were studied as functions of total radiation fluence. To the best of our knowledge, we present for the first time, the temperature profile of an operating nuclear reactor core with 3-cm spatial resolution, enabled by distributed fiber sensors with laser-enhanced Rayleigh scattering profiles. The high spatial resolution measurements can provide valuable data for the design and validation of digital twin and virtual reality of nuclear energy systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Development of Short-Term Forecasting Models Using Plant Asset Data and Feature Selection

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This paper focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 +- 0.014, 0.0026 +- 0, and 0.063 +- 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

SSTDR and FDR Detection of Un-Energized and Energized Cable Anomalies Including Thermal Degradation Using Machine Learning

Historically, cables are initially qualified for nuclear power plant use for 40 years. As plants extend their operating license to 60 and 80 years, continued use of these cables must shift to a performance-based approach since it is cost prohibitive to completely replace cables that are likely still capable of performing their design function. A variety of cable tests are available and are commonly applied during outages when the cables can be taken out of service. Frequency domain reflectometry (FDR) is one of these test methods that is being more broadly accepted and used because it not only detects anomalies along the cable with a low-voltage signal that does not stress the cable insulation, but the technique also locates the anomalies. This supports follow-up local inspection and local repair or partial replacement of a damaged cable segment. Currently, FDR testing is only applied to cables that are taken out of service since the test instrument would be damaged by operational voltages. A related technology that has found some acceptance in the aircraft and rail industry is spread spectrum time domain reflectometry (SSTDR). This technology has been implemented with a custom commercial instrument by LiveWire Innovation that is designed to operate on live cables up to 1000 volts and with a bandwidth of 48 MHz. Initial evaluation by the Pacific Northwest National Laboratory (PNNL) of the Live Wire system indicated that a broader bandwidth (BW) SSTDR may be better for many kinds of flaws. This led PNNL to develop an SSTDR laboratory instrument suitable for tests up to 500 MHz bandwidth. Testing on energized cables is also desirable for online monitoring systems so an inductive clamshell coupler was developed that allows energized cables to be tested up to at least 5 kV and likely higher voltage levels. Dielectric spectroscopy and tan delta testing plus various laboratory destructive tests were included in this data acquisition campaign directed to feed a machine learning (ML) study. With these kinds of developments, online energized cable tests may be possible with industrial adoption of such hardware advances but it will be completely impractical to have highly skilled data analysts continually examine these complex signals for indications of damage or compromised conditions. If online testing is to be implemented in new test hardware, it must be accompanied by software that can interpret the signals and alert plant operators of changing or degraded conditions. The thermally aged, shielded cable investigated here was separately treated for ML analysis. Visual analysis of electrical data showed generally increasing peaks where the cable entered and exited the oven. These peaks were not exactly aligned with expected locations, but these differences were attributed to velocity of propagation calibration errors. Only supervised ML was applied to the thermally aged data as this data was only available shortly before the committed publication date of this report. The supervised ML was structured to divide the 0 to 70-day responses as ‘normal’ from 0 to 35 days or ‘anomalous’ from 36 to 70 days, based on cable tensile elongation at break (EAB) insulation characterization. Using 80% of the data for training and 20% for testing, the supervised ML predicted normal versus anomalous was 70% accurate. Important conclusions include: • Accuracy to predict the presence of cable damage is improved from the 2023 effort by more training data. Weighted accuracies for comparisons among the instruments ranged from 67 to 89 % for unsupervised ML and 71 to 99% for supervised ML. • Based on the synthetic data tests, the unsupervised models are more generalizable to unseen anomalies. The Multi-Layer Perceptron classifier (MLP) model reported as high as 99.7% accuracy on the test data, but this dropped to 58.3% when tested on the synthetic data. In contrast, the unsupervised Pointwise model only achieved 89.7% accuracy on the experimental data but reported 78.3% accuracy on the synthetic data. • The best anomaly indicators are higher frequency (400 MHz BW) FDR data. Other tests may be interesting but for this study, this was the best predicter.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Dynamic Model Development of a Wind Power Plant Using Neural Net Method to Forecast Wind Power Output (CRADA Final Report)

This project is intended to model wind power plant based on monitored data at the wind power plant. This project will promote the university research in Renewable Energy area and trains the future highly qualified engineers. The dynamic model will be based on neural net model with the input from the two met towers (12 inputs), and the number of turbines in operation (one input). The overall input will be 13 inputs to drive the simulations. The output power at the point of interconnection will be used to tune the neural net weight coefficients. Two neural net concepts will be investigated (the back propagation neural net and the dynamic recurrent neural net with feedback).

17 WIND ENERGY↗
DOE OSTI2024

Challenges and Technology-Driven Opportunities for Safeguarding Microreactors

Nuclear microreactors (MRs) represent a new class of reactors characterized by their compactness, portability, and low power output. These features enable MRs to supply electricity and process heat to remote areas like military bases; inaccessible locations; small grids, such as on islands; or disaster impacted areas. Compared to traditional light water reactors, MRs have a unique set of attributes that need to be considered for the implementation of safeguard strategies. Current safeguard methodologies are reactor technology specific and are employed on large, stationary reactors where there is easy access by safeguards inspectors and where safeguard equipment can be easily installed and retrofitted. While there are numerous benefits to MRs, their compact size, portability, scalability, and operational lifetime create challenges to the traditional safeguard approaches, thus needing novel safeguard strategies. Here, this paper addresses the unique challenges posed by MRs to the international nuclear safeguards regime, including limited human resources, and explores how technology advancements can help mitigate these challenges. Specifically, it examines novel technologies that could contribute to establishing a comprehensive safeguards framework for MRs. These safeguards-enabling technologies encompass safeguards by design, remote sensing and monitoring technologies, applications of artificial intelligence and machine learning algorithms, utilization of digital twins, and system of systems assessments. While each of these safeguards-enabling technologies offers partial solutions to the challenges posed by MRs for the international safeguards regime, none of them alone can entirely address these challenges. Consequently, a combination of the safeguards-enabling technologies outlined in this paper is recommended to establish a robust safeguards regime for MRs.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

Vegetation Warming Experiment: Landscape-scale digital camera imagery for vegetation phenology, Utqiagvik, Alaska, 2021

Images captured using a StarDot NetCam SC phenocamera looking east from the top of the Barrow Environmental Observatory (BEO) Sled Shed, Utqiagvik, Alaska. The camera was installed to remotely monitor plant phenology and operation of the Brookhaven National Laboratory (BNL) TEST group's ZPW (Zero Power Warming) chambers during the growing season of 2021. Images were captured from early spring (1 April) through to mid fall (22 October). Snowmelt, vegetation growth and senescence, and snow accumulation were captured. Images were uploaded to the BNL FTP server every hour, then from 2021-07-09 images were recorded every 10 minutes until the end of data collection on 2021-10-22. Images have been combined in *.zip format (6.1 GB). Closer fields of view (northeasterly) were also captured using 4 Wingscapes TimelapseCam cameras mounted on a mast on the sled shed. These cameras were operated from 2021-06-19 to 2021-09-22, with images recorded every 30 minutes from 9:00 to 16:30 Alaska daylight time (AKDT, UTC-8). Individual jpg images from each camera have been combined in zip format. The data package includes a metadata document with example fields of view from each camera (*.pdf). The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Statistical Analysis of Inter-Area Oscillations in the U.S. Eastern Interconnection: A 2017-2023 Perspective

Recent advancements and the accumulation of high-resolution, long-term phasor measurement unit (PMU) data have provided detailed insights into inter-area oscillations in power grids. This study conducts a comprehensive statistical analysis of inter-area oscillations within the United States Eastern Interconnection from 2017 to 2023. Utilizing data captured by the advanced wide-area Frequency Monitoring Network (FNET/GridEye), this investigation examines the occurrence patterns, dominant frequencies, damping ratios, and excitation mechanisms of these oscillations. Our analysis sheds light on the evolving statistical behaviors of inter-area oscillations, offering updated and critical information for grid operators and planners. The insights gained from this study can be instrumental in enhancing the operational resilience of the power network and guiding strategic developments in grid infrastructure to accommodate future challenges. Additionally, the study discusses emerging challenges associated with the modernization of the power grid, including increased renewable penetration, dynamic load variability, and cyber-physical vulnerabilities that complicate oscillation monitoring and control.

Inter-area oscillations↗
DOE OSTI2023

A Review of Visualization Methods for Cyber-Physical Security: Smart Grid Case Study

Cyber-Physical Systems (CPSs) are becoming increasingly complex and interconnected as they attempt to meet the demands of evolving society. As a result, monitoring and maintaining them becomes a more complex and demanding task for control system operators and cyber defenders. While the literature on visualization techniques in the context of cybersecurity is extensive, the same cannot be said for studies on visualization for the security of cyber-physical systems. This paper aims to fill that gap by: 1) defining the main features of a visualizations workflow for security visualizations in cyber-physical systems. The workflow includes the acquisition of cyber and physical data, processing of data, selection, and configuration of both visualization tools and end-user interactions. 2) Providing an overview of cyber-physical security visualization systems, with a focus on smart grids as a case study. Finally, we use the perspectives gained from this analysis to provide insights and directions for future research and design of cyber-physical visualization techniques.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Towards real-time monitoring: data assimilated time-lapse full waveform inversion for seismic velocity and uncertainty estimation

SUMMARY Rapid development of time-lapse seismic monitoring instrumentations has made it possible to collect dense time-lapse data for tomographically retrieving time-lapse (even continuous) images of subsurface changes. While traditional time-lapse full waveform inversion (TLFWI) algorithms are designed for sparse time-lapse surveys, they lack of effective temporal constraint on time-lapse data, and, more importantly, lack of the uncertainty estimation of the TLFWI results that is critical for further interpretation. Here, we propose a new data assimilation TLFWI method, using hierarchical matrix powered extended Kalman filter (HiEKF) to quantify the image uncertainty. Compared to existing Kalman filter algorithms, HiEKF allows to store and update a data-sparse representation of the cross-covariance matrices and propagate model errors without expensive operations involving covariance matrices. Hence, HiEKF is computationally efficient and applicable to 3-D TLFWI problems. Then, we reformulate TLFWI in the framework of HiEKF (termed hereafter as TLFWI-HiEKF) to predict time-lapse images of subsurface spatiotemporal velocity changes and simultaneously quantify the uncertainty of the inverted velocity changes over time. We demonstrate the validity and applicability of TLFWI–HiEKF with two realistic CO2 monitoring models derived from Frio-II and Cranfield CO2 injection sites, respectively. In both 2-D and 3-D examples, the inverted high-resolution time-lapse velocity results clearly reveal a continuous velocity reduction due to the injection of CO2. Moreover, the accuracy of the model is increasing over time by assimilating more time-lapse data while the standard deviation is decreasing over lapsed time. We expect TLFWI-HiEKF to be equipped with real-time seismic monitoring systems for continuously imaging the distribution of subsurface gas and fluids in the future large-scale CO2 sequestration experiments and reservoir management.

58 GEOSCIENCES↗
DOE OSTI2024

Clean Energy Cybersecurity Accelerator: Cohort 2 - runZero Public Report

The U.S. Department of Energy (DOE) Office of Cybersecurity, Energy Security, and Emergency Response (CESER) sponsors the Clean Energy Cybersecurity Accelerator™ (CECA) to expedite the deployment of emerging security technologies that address the most urgent security concerns facing modern and future electric grids. CECA Cohort 2 assessed solutions focused on hidden risks due to incomplete system visibility and device security and configuration. Improving visibility can be achieved through operational technology (OT) asset identification solutions, including capabilities like automatic discovery, vulnerability reporting, and configuration monitoring. Solutions that monitor and identify assets in information technology (IT) networks in other domains are widely used; however, there is far less adoption of monitoring solutions for operational technology environments. Wider adoption may increase with increased confidence in the ability for these solutions to understand and respond to the specific requirements of OT environments. CECA Cohort 2 evaluated the active and passive asset discovery capabilities of market-ready solutions, documented and analyzed results, and identified gaps in functionality or capabilities. This report and describes how these results can help advance the adoption of these and similar solutions in the electric sector.

24 POWER TRANSMISSION AND DISTRIBUTION↗