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At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

CO2 Storage Site Analysis, Screening, and Resource Estimation for Cenozoic Offshore Reservoirs in the Central Gulf of Mexico

The storage potential of hydrocarbon reservoirs in the central Gulf of Mexico (GOM) makes future development of CO2 storage projects in those areas promising for secure, large-scale, and long-term storage purposes. Focusing on the producing and depleted hydrocarbon fields in the continental slope of the central GOM, this paper analyzed, assessed, and screened the producing sands and evaluated their CO2 storage potential. A live interactive CO2 storage site screening system was built in the SAS® Viya system with a broad range of screening criteria combined from published studies. This offers the users a real-time assessment of the storage sites and enables them to adjust the filters and visualize the results to determine the most suitable filter range. The CO2 storage resources of the sands were estimated using a volumetric equation and the correlation developed by the National Energy Technology Laboratory (NETL). The results of this study indicate that 1.05 gigatons of CO2 storage resources are available in the developed reservoirs at the upper slope area of the central GOM. The Mississippi Canyon and Green Canyon protraction areas contain the fields with the largest storage resources.

Hu, Xitong↗
DOE OSTI2024

ACCELERATING MICROREACTOR DEVELOPMENT AND DEPLOYMENT THROUGH JOINT PUBLIC TEST BEDS AND PRIVATE ADVANCED REACTOR DEVELOPMENT

The United States Department of Energy (DOE) has funded the National Reactor Innovation Center (NRIC) to accelerate the development and commercialization of small modular reactors and other advanced reactor technologies. As part of this effort NIRC is building two strategic assets at the Idaho National Laboratory (INL) to facilitate public-private partnerships for the development and testing of advanced nuclear microreactors and Small Modular Reactors (SMR). One will provide a safe, secure, and affordable location for High-Assay Low Enriched Uranium (HALEU) fueled reactors and a second for Highly Enriched Uranium (HEU) fueled reactors. Together these testbeds fill a nuclear testing infrastructure gap. The availability of such comprehensive testing facilities at the national labs eliminates the significant financial and operational burdens associated with each developer building and licensing their own test facilities. Consequently, this approach will not only accelerate technological innovation and reduce time-to-market for advanced nuclear solutions but it also underscores the DOE's commitment to fostering an ecosystem where nuclear energy will thrive as a clean, reliable, and efficient source of power. The Demonstration of Microreactor Experiments (DOME) and Laboratory for Operations and Testing in the United States (LOTUS) have planned availability in 2026, and 2027 respectively.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Scalable edge clustering of dynamic graphs via weighted line graphs

Timestamped relational datasets consisting of records (or connections) between pairs of entities are ubiquitous in network science. For applications like peer-to-peer communication, email, various social network interactions, and computer network security, it is useful to organize these records into groups based on how and when they are occurring. Weighted line graphs offer a natural way to model how records are related in such datasets but for large real-world graph topologies, building and utilizing the line graph is prohibitively expensive. Here, we present the framework to cluster the edges of a dynamic graph via the associated line graph that contains two major contributions. The first is a method to work with the line graph implicitly and the second is a distributed scale implementation of an agglomerative hierarchical graph clustering algorithm. We outline a novel hierarchical dynamic graph edge clustering approach that efficiently breaks massive relational datasets into small sets of edges containing events at various timescales. This is in stark contrast to traditional graph clustering algorithms that prioritize highly connected (clique-like) community structures. Our approach relies on constructing a sufficient subgraph of a weighted line graph and applying a hierarchical agglomerative clustering. This approach is related to scalable techniques from spatial clustering, nonlinear-dimension reduction, topological data analysis, and draws particular inspiration from HDBSCAN. As an edge clustering, this method yields an overlapping node clustering. Our algorithm is parallelizable and we demonstrate efficient clustering of a billion-scale, real-world dynamic graph into small edge sets that correlate in topology and time. The entire clustering process for a graph with tens of billions of edges takes just a few minutes of run time on 256 nodes of a distributed compute environment. We argue how the output of the edge clustering is useful for a multitude of data visualization and powerful machine learning tasks, both involving the original massive dynamic graph data and metadata associated with the nodes and edges. Finally, we describe how this approach can be extended to dynamic hypergraphs and dynamic graphs/hypergraphs with unstructured data living on vertices and edges.

Data Analysis↗
DOE OSTI2026

nnopf

SAND2026-19433O The nnopf tool addresses the optimal power flow (OPF) problem by optimizing electricity delivery from generating plants to consumers. At the same time, it minimizes costs and adheres to power grid constraints. While traditional methods for solving OPF can be computationally intensive, the tool mitigates this challenge by training neural networks on power grids to predict optimal solutions for the OPF problem. 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.

Hart, William [Sandia National Lab. (SNL-CA), Live↗
DOE OSTI2021

Tensor Text-Mining Methods for Malware Identification and Detection, Malware Dynamics Characterization, and Hosts Ranking

Malware is one of the most persistent and costly cyber threats endangering reputation, confidentiality, integrity, and availability for organizations and national security. Consequently, many of the incident detection and prevention systems, and incident responders have begun to utilize machine learning as a helper in the fight against malware and other cyber threats. However, cyber defenders rely on interpretability and generalizability, yet the popular machine learning methods are black-box and often use traditional supervised solutions that do not generalize to novel malware. Therefore, there is a need to improve the existing solutions. At the same time, the majority of the prior research ignored essential evaluation criteria when reporting the results of their methods, which disables the safe reproducibility of the methods in a production environment. Tensor decomposition, on the other hand, enables interpretable unsupervised analysis of the large-scale data for the discovery of hidden patterns. Our findings, performed on real-world and large-scale experiments, show that tensor factorization-based methods yield performance results that surpasses or competes with existing supervised solutions with the added benefit of interpretability and generalizability. With the ability to analyze complex and large-scale data using tensors, we report results that reflect real-world production environments. We propose to develop new game- changing tools for malware identification and characterization that can trace malware evolution, rank the infected or malicious hosts, and streamline the work of incident response teams, malware analysts, and incident detection and prevention systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Deep anomaly detection for industrial systems: a case study

We explore the use of deep neural networks for anomaly detection of industrial systems where the data are multivariate time series measurements. We formulate the problem as a self-supervised learning where data under normal operation are used to train a deep neural network autoregressive model, i.e., use a window of time series data to predict future data values. The aim of such a model is to learn to represent the system dynamic behavior under normal conditions, while expect higher model vs. measurement discrepancies under faulty conditions. In real world applications, many control settings are discrete in nature. In this paper, vector embedding and joint losses are employed to deal with such situations. Both LSTM and CNN based deep neural network backbones are studied on the Secure Water Treatment (SWaT) testbed datasets. Also, Support Vector Data Description (SVDD) method is adapted to such anomaly detection settings with deep neural networks. Evaluation methods and results are discussed based on the SWaT dataset along with potential pitfalls.

anomaly detection, deep neural network↗
DOE OSTI2020

Load altering attack-tolerant defense strategy for load frequency control system

Cyber attacks are emerging threats to every information-oriented energy management system. By violating the cyber systems, the hacker can disrupt the security and stability due to the strong coupling between the cyber and physical facilities. In this paper, one type of cyber attacks designated as the load altering attack is studied for the power system frequency control, and corresponding defense strategies are proposed to improve the frequency control performance. Considering the difficulty of the application of model-based controller into large-scale power systems, a novel model-free defense framework is for the first time presented. Under this framework, both active defense and passive defense strategies are designed. The former assumes that the defender has the initiative to learn different attack scenarios. Adaptive defense strategies are implemented using the online attack identification information and off-line trained strategy pool. The latter assumes that the defender passively tolerates various attack scenarios via the pre-trained off-line strategy. Both approaches prove to be effective through validation based on the IEEE benchmark systems. The proposed defense framework and defense strategies can be extended to other energy control systems to enhance their attack tolerance capability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Neutron calorimeter development at fission and fusion pulsed neutron sources

A neutron calorimeter fielded in pulsed neutron irradiation experiments at the National Ignition Facility (NIF) and White Sands Missile Range Fast Burst Reactor (WSMR FBR) is described, and results are provided and discussed. The calorimeter is modular in design to allow fielding of various neutron absorber materials and thicknesses, as well as use at both fission and fusion sources, all of which has been done. Neutron calorimeters designed for fission sources have been fielded at fusion sources as well, but these instances are not documented in available literature—making this work novel. The functional calorimeter consists of a neutron absorber and two sheathed, grounded thermocouples secured within a subassembly made of thermally insulating materials. This subassembly is placed into a sealed vessel for material and facility safety considerations. The device was fielded at NIF in four polar-direct-drive exploding pusher shots and eleven WSMR FBR pulses. Calorimeter data from NIF experiments show a discernable thermal response accredited to neutron heating within the first 10 s, but the data are dominated by noise from ambient X-ray heating after time = 10 s post-pulse. This response is highly consistent between shots and linearly related to incident neutron fluence and neutron absorber thickness. A temperature rise of 1.1E-2 K/(10 13 neutrons/cm 2 ) per millimeter of absorber thickness was determined for both 0.4 mm and 1 mm thick depleted uranium absorbers with linear best fit R 2 of 0.97 and 0.98 for each thickness and y-axis offsets of 8.8E-3 and 1.3E-2, respectively. WSMR FBR experiment data show a temperature rise of 6.2E-2 K/(10 13 neutrons/cm 2 ) with linear best fit R 2 of 0.14 and y-axis offset of 7.6E-3 for a calorimeter with a 1 mm thick enriched boron carbide ( 10 B 4 C) absorber, though it must be noted that device-to-device variability is significant at both facilities. So these data are also affected by noise on a similar timescale as at NIF, though the noise is significantly lesser in magnitude but oscillatory.

42 ENGINEERING↗
DOE OSTI2023

Transformer Health Monitoring via Synchrophasors

Traditional transformer protection schemes such as differential protection operate once the transformer is in a critical state. Proactive means of transformer health evaluation such as Sweep Frequency Response Analysis, can only be performed periodically with the transformer out-of-service. This work presents a framework where the health and structural integrity of transformers is monitored via synchrophasors in real-time. Synchrophasors and subspace estimation techniques are used to build reduced-order models which are then analyzed across multiple domains. A diagnostics algorithm is developed and tested via Matlab simulations. Test results are promising, opening up new prospects in transformer protection research.

cyber-physical security↗
DOE OSTI2024

ARPA-E Grid Optimization (GO) Competition Challenge 3

Synthetic Input Data and Team Results for the GO Competition Challenge 3 for Events 1 - 4 and the Sandbox, along with problem and format descriptions and code to validate data and solutions, are available here. Data for industry scenarios will not be made public. The Grid Optimization (GO) Competition Challenge 3 focused on the security-constrained optimal power flow (SCOPF) problem. It is part of a continuing effort begun with Challenges 1 and 2, to successfully discover, develop, and test innovative and disruptive software solutions for critical energy challenges and to overcome existing barriers. The broader goal of the of the GO Competition is to accelerate the development of transformational and disruptive methods for solving problems related to the electric power grid and to provide a transparent, fair, and comprehensive evaluation of new solution methods. Challenge 3 used multiperiod dynamic markets, including advisory models for extreme weather events, day-ahead markets, and the real-time markets with an extended look-ahead. In Event 4, whose submission window was August 31-September 4, 2023, 14 teams solved for the objective values of 669 scenarios (39 scenarios required solutions both with and without line switching being allowed). The 591 synthetic scenarios from 9 network models (3.6 GB) are available here. Ten teams were funded to participate and 7 won prizes totaling $2,400,000. The largest prize ($550,000) went to Mississippi State University. An additional $600,000 was awarded in Event 3 (6/15-16/2023). No prizes were awarded in Events 1 (1/25-27/2023) or 2 (4/13-14/2023). For more information on the competition and challenge see the "GO Competition Challenge 3 Information" resource below.

ACOPF↗
DOE OSTI2020

Thermal Energy Storage Using Solid Particles for Long-Duration Energy Storage

The rapid growth of renewable energy increases the importance of economically firming the electricity supply from variable solar photovoltaic- and wind-power generators. Energy storage will be the key to manage variability and to bridge the generation gap over time scales of hours or days for high renewable grid integration. The integration of renewable power and storage of excess electricity has several significant and positive impacts including: 1) expanding the renewable energy portion of total electricity generation, 2) improving the peak-load response, and 3) coordinating the electricity supply and demand over the grid. Long-duration energy storage can potentially complement the reduction of fossil-fuel baseload generation that otherwise would risk grid security when a large portion of grid power comes from variable renewable sources. Several energy storage methods are deployed or under development, including mechanical, chemical or electrochemical, and thermal energy storage (TES). Comparing their economic potential for different scales and applications helps identify suitable technology to support high renewable grid integration. Despite the progress of TES technologies developed and deployed with concentrating solar power (CSP) systems, TES has been undervalued for its potential role in electric energy storage. This paper introduces TES methods applicable to grid energy storage and particularly focuses on solid-particle-based TES to serve the purpose of long-duration energy storage (LDES). The objective of this paper is to present a standalone particle-based TES system for electric storage and to show the potential of TES systems for LDES applications over other energy storage methods such as batteries, compressed-air energy storage, or pumped-storage hydropower.

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2023

Hydrologic Impacts of a Strike-Slip Fault Zone: Insights from Joint 3D Body-Wave Tomography of Rock Valley

The Rock Valley fault zone (RVFZ), an intraplate strike-slip fault zone in the southern Nevada National Security Site (NNSS), hosted a series of very shallow (<3 km) earthquakes in 1993. The RVFZ may also have hydrological significance within the NNSS, potentially playing a role in regional groundwater flow, but there is a lack of local hydrological data. In the Spring of 2021, we collected active-source accelerated weight drop seismic data over part of the RVFZ to better characterize the shallow subsurface. Here, we manually picked ~17,000 P-wave travel times and over 14,000 S-wave travel times, which were inverted for P-wave velocity (V P ), S-wave velocity (V S ), and V P /V S ratio in a 3D joint tomographic inversion scheme. Seismic velocities are imaged as deep as ~700 m in areas and generally align with geologic and structural expectations. V P and V S are relatively reduced near mapped and inferred faults, with the most prominent lower V P and V S zone around the densest collection of faults. We image V P /V S ratios ranging from ~1.5 to ~2.4, the extremes of which occur at a depth of ~100 m and are juxtaposed across a fault. One possible interpretation of the imaged seismic velocities is enhanced fault damage near the densest collection of faults with relatively higher porosity and/or crack density at ~100 m depth, with patches of semiperched groundwater present in the sedimentary rock in higher V P /V S areas and drier rock in lower V P /V S areas. A relatively higher V P /V S area beneath the densest faults persists at depth, which suggests percolation of groundwater via the fault damage zone to the regionally connected lower carbonate aquifer. Potentially, the presence and movement of groundwater may have played a role in the 1993 earthquake aftershocks.

58 GEOSCIENCES↗
DOE OSTI2021

Baylor University Campus-Wide Deep Dive

In January 2020, staff members from the Engagement and Performance Operations Center (EPOC) and the Lonestar Education And Research Network (LEARN) met with researchers and staff at Baylor University for the purpose of a Campus-Wide Deep Dive into research drivers. The goal of this meeting was to help characterize the requirements for five campus research use cases and to enable cyberinfrastructure support staff to better understand the needs of the researchers they support. Profiled scientific use cases included: - Experimental High Energy Physics (HEP) - Proton Computed Tomography (pCT) - Nutrition and Relation to Digestive Microbiome - Baylor University Core Research Facilities - Molecular Quantum-dot Cellular Automata (QCA), and Material Science of Quantum Computing - Modeling and Simulation of Low-Dimensional and Nano-Structured Materials - Computational Fluid Dynamics Material for this event included the written documentation from each of the research areas at Baylor University, documentation about the current state of technology support, and a write-up of the discussion that took place in person. The Case Studies highlighted the ongoing challenges that Baylor University has in supporting a cross-section of established and emerging research use cases. Each Case Study mentioned unique challenges which were summarized into common needs. These included: - Tradeoffs for network/software security, and usability of the resulting infrastructure. Better communication to set expectations and understand realities is required. - Computation use on campus is widespread and healthy. While no major problems were uncovered, upgrades to maintain current usage patterns and encourage growth will be required. - Storage is a critical need for enterprise use cases and research. In particular, a campus wide ‘storage architecture’ to support research use cases (e.g. instruments, data sharing) is required in the 2-5 year time window. - Instrumentation on campus is healthy and expanding. Technology must scale with this in the form of computation and storage. - Working with LEARN to upgrade network capacity (in multiples of 10G, or upgrades to 100G) will be required in the 1-3 year time frame. - Network monitoring and visibility will help to establish external science use cases. - Data sharing via portal systems is not currently a critical need, but growing in scope. EPOC can assist Baylor with options.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Ultrafast Laser-Excited Optical Emission of Xe under Loose-Focusing Conditions

The optical filament-based radioxenon sensing can potentially overcome the constraints of conventional detection techniques that are relevant for nuclear security applications. This study investigates the spectral signatures of pure xenon (Xe) when excited by ultrafast laser filaments at near-atmosphericpressure and in short and loose-focusing conditions. The two focusing conditions lead to laser intensity differences of several orders of magnitude and different plasma transient behavior. The gaseous sample was excited at atmospheric pressure using ∼7 mJ pulses with a 35 fs pulse duration at 800 nm wavelength. The optical signatures were studied by time-resolved spectrometry and imaging in orthogonal light collection configurations in the ∼400 nm (VIS) and ∼800 nm (NIR) spectral regions. The most prominent spectral lines of atomic Xe are observable in both focusing conditions. An on-axis light collection from an atmospheric air–Xe plasma mixture demonstrates the potential of femtosecond filamentation for the remote sensing of noble gases.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

The role of current and emerging technologies in meeting Japan’s mid- to long-term carbon reduction goals

Using Japan as a proxy for a developed nation, we investigated the role of existing and nascent technologies in curbing carbon emissions. We simulated possible pathways to meeting 2030 and 2050 emission targets within the Japanese electricity supply sector using a single-region model in The Integrated MARKAL-EFOM System (TIMES). Critically, our simulations incorporate novel technologies like hydrogen electrolysers, carbon capture, photochemical water splitting, and emerging photovoltaic cells, assess long-term impacts up to the year 2100, and include life-cycle emissions and learning curves for parameters such as investment cost, efficiency, and emission coefficients. Results indicate that a hybrid approach, using nuclear power and hydrogen from renewable energy-based electrolysis, is cost-effective and provides long-term emission reduction along with energy security. Finally, nuclear, wind, solar, and hydrogen from renewables emerge as key emission reduction technologies, while natural gas with carbon capture plays a minor role in achieving emission reduction targets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Fossil Energy Workshop on Quantum Information Science & Technology (Summary Report)

This report summarizes findings and discussion from the Fossil Energy Workshop on Quantum Information Science & Technology which took place on November 19-21, 2019. Quantum Information Science (QIS) is expected to profoundly change the practice of science and engineering in the coming decades. It is a rapidly progressing field, fueled by large investments from the private sector and governments. Its importance to the U.S. economy and national security is underscored by the National Quantum Initiative Act passed in December 2018. QIS includes quantum sensing, quantum communications, quantum simulation experiments, and quantum computation. QIS technology exploits quantum phenomena for performing tasks that are impossible to do today, such as finding prime factors of large numbers or elucidating reaction mechanisms in complex chemical systems. The opportunities for applying QIS to problems encountered in fossil energy technology development are not known today. This workshop brought together, for the first time, experts in these fields to exchange information and explore potential research opportunities for QIS to advance fossil energy. The goal of the workshop was to develop a set of priority research opportunities that can inform future research efforts in QIS and build a community of next-generation researchers at the intersection of QIS and fossil energy.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

A distributed voltage inference framework for cyber-physical attacks detection and localization in active distribution grids

The transition to active distribution grids with real-time monitoring and control depends on the proliferation of advanced communication networks and devices. This paradigm shift towards a cyber-physical architecture also introduces new vulnerabilities for adversaries to exploit and launch sophisticated cyber-physical attacks targeting grid observability. Current research highlights the challenges in distinguishing attacks on voltage phasor or nodal injection measurements and isolating multi-source attack locations in a multiphase distribution grid. The attack detection and localization methods in literature face accuracy issues, applications across diverse attack scenarios, or scalability limits. Here, to bridge these gaps, this paper proposes a distributed Voltage Inference framework for real-time detection and localization of cyber-physical attacks, addressing scalability, adaptability, and accuracy challenges in state-of-the-art methods. The proposed methodology leverages the distributed nature of the Voltage Inference framework through a two-step process of prediction and correction, together with a tractable graph partitioning approach, providing a reliable solution to identify compromised measurement sources and facilitate isolation. Extensive testing on IEEE 13 and 123-node distribution feeders underscores the algorithm’s efficacy, enhancing the security and resilience of active distribution grids against evolving cyber threats. Additionally, Hardware-in-the-Loop (HIL) implementation validates the proposed strategy’s practical applicability in real-world scenarios.

active distribution grids↗