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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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At least 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

DIP - Digital Information Platform

Brief updates of Digital Information Platform (DIP) plans for platform-enabled sustainable aviation services. DIP will provide the capability to integrate key flight information from multiple sources and make it easier to access data critical to developing services using advanced techniques such as machine learning. NASA will share ML-based microservices on the platform for industry to use as reference implementations. The platform will support an ecosystem to share reusable services and promote innovation for digital information.

DIP update↗
DOE OSTI2019

The Effect of Information Organization on Cognitive Workload and Visual Search Performance

This study examined the relationship between how visual information is organized and people’s visual search performance. Specifically, we systematically varied how visual search information was organized (from well-organized to disorganized), and then asked participants to perform a visual search task involving finding and identifying a number of visual targets within the field of visual non-targets. We hypothesized that the visual search task would be easier when the information was well-organized versus when it was disorganized. We further speculated that visual search performance would be mediated by cognitive workload, and that the results could be generally described by the well-established speed-accuracy tradeoff phenomenon. This paper presents the details of the study we designed and our results.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2012

The application of Plant Reliability Data Information System (PRINS) to CANDU reactor

As risk-informed applications (RIAs) are actively implanted in the nuclear industry, an issue associated with technical adequacy of Probabilistic Safety Assessment (PSA) arises in its modeling and data sourcing. In Korea, PSA for all Korean NPPs has been completed and KHNP(Korea Hydro and Nuclear Power Plant Company) developed the database called the Plant Reliability Data Information System (PRinS). It has several characteristics that distinguish it from other database system such as NPRDs (INPO,1994), PRIS (IAEA), and SRDF (EdF). This database has the function of systematic data management such as automatic data-gathering, periodic data deposition and updating, statistical analysis including Bayesian method, and trend analysis of failure rate or unavailability. In recent PSA for CANDU reactor, the component failure data of EPRI ALWR URD and Component Reliability Database were preferentially used as generic data set. The error factor for most component failure data was estimated by using the information NUREG/CR-4550 and NUREG/CR-4639. Also, annual trend analysis was performed for the functional losses of components using the statistical analysis and chart module of PRinS. Furthermore, the database has been updated regularly and maintained as a living program to reflect the current status. This paper presents the failure data analysis using PRinS which provides Bayesian analysis on main components in the CANDU reactor. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2013

Geographic Information System Tools for Management of US DOE Sites - 13489

The DOE Office of Legacy Management (LM) uses a variety of GIS tools to support long-term surveillance and maintenance (LTS and M) activities at DOE closure sites. These geo-spatial applications provide access to data both for external public viewing and for internal analysis and decision making. LM uses a custom geo-spatial application called geo-spatial Environmental Mapping System (GEMS) that draws validated information from a database of 4.6 million analytical results and 232,000 water level measurements for 58 LTS and M sites. These data were collected from transferred sites over a period of 40 years. The database is used to capture and store historical environmental information such as analytical chemistry data, groundwater depths and elevations, well logs, well construction data, geo-referenced boundaries, site physical features, and sampling locations from LTS and M sites. Stakeholders, regulators, and project personnel can use this Web-based application and data to display information in several forms, such as a tabular report, a graph, and a geo-spatial display, or the data can be labeled or highlighted in a map view. Institutional controls, with their LTS and M requirements and documentation, have recently been incorporated into a prototype GEMS Web page for the Weldon Spring, Missouri, Site. LM uses multiple internal GIS viewers to help ensure the future protection of human health and the environment. For example, at the Rocky Flats, Colorado, Site, LM uses a GIS application to display real property interests on authoritative maps. Another project is used to facilitate discussions at stakeholder meetings for the Rocky Flats site's Original Landfill. The Uranium Leasing Program uses multiple interactive maps that assist in ongoing monitoring and the oversight of lease-holders' activities. (authors)

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Oak Ridge environmental information system (OREIS): formalizing the shapes of things on the Oak Ridge Reservation - 15393

As representations of object and area boundaries on Earth's surface, shapes are important components of geographic information system platforms. The US Department of Energy Office of Environmental Management in Oak Ridge, Tennessee recognizes the importance of shapes as boundary representations and has mandated the Oak Ridge Environmental Information System as the official repository of all geographic information system shapes related to CERCLA cleanup on the Oak Ridge Reservation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Robotics Technologies on Knowledge Management Information Tool (KM-IT) Platform - 16465

The Knowledge Management Information Tool (KM-IT) is a web-based system developed to maintain and preserve the deactivation and decommissioning (D and D) knowledge base across the Department of Energy (DOE) complex. The system was developed by the Applied Research Center (ARC) at Florida International University (FIU) with the support of the D and D community, including the DOE Office of Environmental Management (DOE EM), and with the active collaboration and support of the DOE's Energy Facility Contractors Group (EFCOG). The KM-IT system is a D and D community driven system tailored to serve the technical issues faced by the D and D workforce across the DOE Complex. New information, including vendors and technologies related to D and D, are researched and added to KM-IT on an ongoing basis. In late 2014, DOE EM provided a database of 471 robotic technologies, originally developed by NuVision Engineering and Cogentus Consulting, with a request to evaluate the potential for integrating the data into the KM-IT framework for ongoing hosting/maintenance of the information. All of the data from the original database as well as photographs, documents, and videos are now available on KM-IT. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Cyber-Informed Engineering Overview: Joint NERC/INL/E-ISAC Webinar

In July, NERC, the Electricity Information Sharing and Analysis Center (E-ISAC), and Idaho National Laboratory (INL) will host a joint informational webinar to highlight areas focused on integrating cyber and physical security with conventional engineering practices (security integration). NERC's work on these topics represents one of the first focused applications of Cyber-Informed Engineering (CIE). INL is leading the development of philosophy and practices to identify and mitigate the inherent risks of digital technology by including cybersecurity as a core element of engineering risk management. This presentation will provide an overview of CIE, a discussion of how NERC's work represents one of the first new discipline-specific applications of CIE, and an update on significant milestones and resources from the CIE program.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Rapid Inverse Parameter Inference Using Physics-Informed Neural Network

As Li-ion batteries become more essential in today's economy, tools need to be developed to accurately and rapidly diagnose a battery's internal state-of-health. Using a Li-ion battery's (high-rate) voltage response, it is proposed to determine a battery's internal state through Bayesian calibration. However, Bayesian calibration is notoriously slow and requires thousands of model runs. To accelerate parameter inference using Bayesian calibration, a surrogate model is developed to replace the underlying physics-based Li-ion model. Developing a surrogate model for rapid Bayesian calibration analysis is discussed for both the single particle model (SPM) and the pseudo two-dimensional (P2D) model. Surrogate models are constructed using physics-informed neural networks (PINNs) that encode the influence of internal properties on observed voltage responses. In practice, a neural network can be trained by: 1) using simulation results of the physics-based model (i.e., a data-loss approach); 2) using the residuals of the governing equations themselves (i.e., a physics-loss approach); or 3) using a combination of simulation results and governing equation residuals. In the present work, PINNs are developed using a variety of training losses and neural network architectures. In this analysis, it is shown that a PINN surrogate model can be reliably trained with only physics-informed loss. However, using a coupled data-informed and physics-loss approach produced the most accurate PINNs.

Bayesian calibration↗
DOE OSTI2018

Media Relations Strategies in a Post-Information Age - 18452

Many organizations that deal with radioactive materials reluctantly engage in media relations and social media. This paper explores the drivers behind the dramatic coverage of clean-up activities nearest to large media markets. Interviews and media analysis reveal how companies and government actors responded to aggressive media investigations. Insights from professional journalists demonstrate the significance of Freedom of Information Act (FOIA) requests and computer assisted reporting on past and future coverage. Coverage of Hanford and Santa Susana Field Laboratory set the stage for creative engagement strategies developed to support public interest in clean-up efforts. Industrial and engineering firms, particularly small to mid-sized subcontractors, often place less priority on media and public relations. However, recent examples demonstrate that organizations that deal with scientific risk must evolve. Firms that acknowledge changing media trends update their communication strategies in ways that improve public trust and understanding. It's easy to understand why many organizations resist communicating when public concerns arise. Legal issues and contract restrictions may limit communications. The return on investment may seem low. Understanding exposure and hazard requires perspective, context and accuracy. Reporters and citizens also have better access to primary sources. Computer assisted reporting (CAR) and the dramatic rise of requests under the Freedom of Information Act (FOIA) lay bare details of the inner workings of complex situations, often without context. Examples from FOIA requests and challenges filed in the U.S. district courts, are described to help project administrators understand the points of view that can take shape on government projects from filings and disclosures. The range of benefits and risks of ongoing interaction with reporters provides a framework for assessing the right level of effort. For remediation efforts near large media markets, the influence of effective communication is magnified. (authors)

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2017

Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information

Reservoirs are fundamental human–built infrastructures that collect, store, and deliver fresh surface water in a timely manner for many purposes. Efficient reservoir operation requires policy makers and operators to understand how reservoir inflows are changing under different hydrological and climatic conditions to enable forecast–informed operations. Over the last decade, the uses of Artificial Intelligence and Data Mining [AI & DM] techniques in assisting reservoir streamflow subseasonal to seasonal forecasts have been increasing. In this study, Random Forest [RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR) are employed and compared with respect to their capabilities for predicting 1 month–ahead reservoir inflows for two headwater reservoirs in USA and China. Both current and lagged hydrological information and 17 known climate phenomenon indices, i.e., PDO and ENSO, etc., are selected as predictors for simulating reservoir inflows. Here, the results show three methods are capable of providing monthly reservoir inflows with satisfactory statistics; the results obtained by Random Forest have the best statistical performances compared with the other two methods; another advantage of Random Forest algorithm is its capability of interpreting raw model inputs; climate phenomenon indices are useful in assisting monthly or seasonal forecasts of reservoir inflow; and different climate conditions are autocorrelated with up to several months, and the climatic information and their lags are cross correlated with local hydrological conditions in our case studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Physics-informed Deep Reinforcement Learning-based Control in Power systems

Incorporating physics information into the deep reinforcement learning (DRL) process is a promising approach for addressing the challenges faced in learning-based control design problems for physical systems. Power grid dynamics, being a physical system, adheres to specific physical laws, constraints, as well as operational and control rules. Therefore, consideration of such physics-based law improves the learning process drastically. In general, traditional grid control schemes rely on rule-based mechanisms that cannot adapt to changing operating conditions. To improve the adaptability and computation time, recent research has seen a surge of DRL-based applications in power grid control. A generic DRL-based control design imposes the system performance requirements through the design of reward functions. In some cases, some of the important physics information is injected through this reward function. However, due to the complex dynamics and large state-action space, learning an optimal DRL policy often becomes challenging. Inspired by the latest developments in general machine learning (ML) research, power system researchers have been investigating more direct ways of incorporating physics knowledge into DRL training. This chapter specifically focuses on these aspects of physics-informed DRL designs in grid control. It discusses the significance, applications, research gaps, and open problems that need to be addressed in future research.

artificial intelligence, machine learning↗
DOE OSTI2016

Informativeness of wind data in linear Madden–Julian oscillation prediction

Abstract Linear inverse models (LIMs) are used to explore predictability and information content of the Madden–Julian Oscillation (MJO). Hindcast skill for outgoing longwave radiation (OLR) related to the MJO on intraseasonal timescales in the tropics has been examined for a variety of LIMs using OLR and optionally 200 and 850 hPa zonal wind information channels. The dependence of OLR hindcast skill on wind channels was evaluated by randomizing in time, averaging in space, or omitting data entirely. Results show positive prediction skill (relative to climatology) up to 3 weeks and wind information, mostly at the largest scales, adds 1–2 days of skill.

Allen, Theodore L.↗
DOE OSTI2018

Graphics to facilitate informative discussion and team decision making

Everyone knows the expression “A picture is worth a thousand words,” and this effectively summarizes the ability of graphical summaries to convey information and persuade. However, in many cases, the goal for the right visualization is to encourage and guide discussion while helping focus a team to make carefully considered, defensible, and data-driven decisions. The aims of graphics differ if we are trying to communicate the merits of a single choice versus outlining several contending alternatives for further comparison and discussion. These choices each have their own strengths and weaknesses depending on how we value different criteria. They also serve different purposes at various stages of decision making. Often the role of statisticians is not to provide a single answer but to provide rich information and summaries in a manageable and compact form to enable productive discussion among team members. Through a series of diverse examples, this work present principles and strategies for encouraging discussion and informed decision making and discuss how they can be integrated with versatile use of graphical tools for examining multiple objectives, framing trade-offs between alternatives, and examining the impact of subjective priorities and uncertainty on the final decision.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Test–retest reliability for a social discounting of personal information task

Increasing cybercrime rates means identifying potential victims is critically important. Social discounting tasks show that individuals share less personally identifying information as social distance increases. However, the test–retest reliability and uniqueness of this measure is unclear. The current study assessed social discounting for personally identifying information (SDPII), delay discounting, risk taking, and personality at two measurement waves 30 days apart for 64 undergraduate students. Test–retest reliability was statistically significant for the SDPII and all other measures, replicating previous studies. SDPII rates were not significantly correlated with other measures during both measurement waves, showing discriminant validity. SDPII rates were lower than those reported in a previous study but were still well described by a hyperbolic discounting function, suggesting replicability across studies. Furthermore, the high test–retest reliability, uniqueness, and replicability of the SDPII suggests that it may quantitatively identify cybercrime victimization. Future research should test which measure or combination of measures can accurately predict scam and cybercrime victimization to inform data-based interventions.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Large-d phase transitions in holographic mutual information

In the AdS/CFT correspondence, the entanglement entropy of subregions in the boundary CFT is conjectured to be dual to the area of a bulk extremal surface at leading order in G N in the holographic limit. Under this dictionary, distantly separated regions in the CFT vacuum state have zero mutual information at leading order, and only attain nonzero mutual information at this order when they lie close enough to develop significant classical and quantum correlations. Previously, the separation at which this phase transition occurs for equal-size ball-shaped regions centered at antipodal points on the boundary was known analytically only in 3 spacetime dimensions. Inspired by recent explorations of general relativity at large- d , we compute the separation at which the phase transition occurs analytically in the limit of infinitely many spacetime dimensions, and find that distant regions cannot develop large correlations without collectively occupying the entire volume of the boundary theory. We interpret this result as illustrating the spatial decoupling of holographic correlations in the large- d limit, and provide intuition for this phenomenon using results from quantum information theory. We also compute the phase transition separation numerically for a range of bulk spacetime dimensions from 4 to 21, where analytic results are intractable but numerical results provide insight into the dimension-dependence of holographic correlations. For bulk dimensions above 5, our exact numerical results are well approximated analytically by working to next-to-leading order in the large- d expansion.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

On quantum information before the Page time

While recent progress in the black hole information problem has shown that the entropy of Hawking radiation follows a unitary Page curve, the quantum state of Hawking radiation prior the Page time is still treated as purely thermal, containing no information about the microstructure of the black hole. We demonstrate that there is significant quantum information regarding the quantum state of the black hole in the Hawking radiation prior to the Page time. By computing of the quantum fidelity in a 2D boundary conformal field theory (BCFT) model of black hole evaporation, we demonstrate that an observer outside of an evaporating black hole may distinguish different black holes via measurements of the Hawking radiation at any time during the evaporation process, albeit with an exponentially large number of measurements. Furthermore, our results are universal, applicable to general BCFTs including those with large central charge and rational BCFTs. The techniques we develop for computing the fidelity are more generally applicable to excited states in CFT. As such, we are able to characterize more general aspects of thermalization in 2D conformal field theory.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Replica wormhole and information retrieval in the SYK model coupled to Majorana chains

Motivated by recent studies of the information paradox in (1+1)-D anti-de Sitter spacetime with a bath described by a (1+1)-D conformal field theory, we study the dynamics of second Ŕenyi entropy of the Sachdev-Ye-Kitaev (SYK) model (χ) coupled to a Majorana chain bath (ψ). The system is prepared in the thermofield double (TFD) state and then evolved by HL + HR. For small system-bath coupling, we find that the second Rényi entropy $S^(2)_{χL,χR}$ of the SYK model undergoes a first order transition during the evolution. In the sense of holographic duality, the long-time solution corresponds to a “replica wormhole”. The transition time corresponds to the Page time of a black hole coupled to a thermal bath. We further study the information scrambling and retrieval by introducing a classical control bit, which controls whether or not we add a perturbation in the SYK system. The mutual information between the bath and the control bit shows a positive jump at the Page time, indicating that the entanglement wedge of the bath includes an island in the holographic bulk.

1/N Expansion↗
DOE OSTI2021

Mutual information superadditivity and unitarity bounds

We derive the property of strong superadditivity of mutual information arising from the Markov property of the vacuum state in a conformal field theory and strong subadditivity of entanglement entropy. We show this inequality encodes unitarity bounds for different types of fields. These unitarity bounds are precisely the ones that saturate for free fields. This has a natural explanation in terms of the possibility of localizing algebras on null surfaces. A particular continuity property of mutual information characterizes free fields from the entropic point of view. We derive a general formula for the leading long distance term of the mutual information for regions of arbitrary shape which involves the modular flow of these regions. We obtain the general form of this leading term for two spheres with arbitrary orientations in spacetime, and for primary fields of any tensor representation. For free fields we further obtain the explicit form of the leading term for arbitrary regions with boundaries on null cones.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗