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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 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Mapping analysis and planning system for the John F. Kennedy Space Center

Environmental management, impact assessment, research and monitoring are multidisciplinary activities which are ideally suited to incorporate a multi-media approach to environmental problem solving. Geographic information systems (GIS), simulation models, neural networks and expert-system software are some of the advancing technologies being used for data management, query, analysis and display. At the 140,000 acre John F. Kennedy Space Center, the Advanced Software Technology group has been supporting development and implementation of a program that integrates these and other rapidly evolving hardware and software capabilities into a comprehensive Mapping, Analysis and Planning System (MAPS) based in a workstation/local are network environment. An expert-system shell is being developed to link the various databases to guide users through the numerous stages of a facility siting and environmental assessment. The expert-system shell approach is appealing for its ease of data access by management-level decision makers while maintaining the involvement of the data specialists. This, as well as increased efficiency and accuracy in data analysis and report preparation, can benefit any organization involved in natural resources management.

Hall, C. R.↗
NASA NTRS2003

Integrated Human Centered Systems Analysis for Aircraft Separation from Icing Conditions

This document contains five papers on coping with ice formation during the operation of aircraft. The first paper uses a pilot survey to identify the desired attributes of future information systems for aircraft icing. The second paper investigates the influence of potential remote ice-detection system features on pilot decision making. The third paper investigates the effectiveness of aviation weather forecasting along aircraft trajectories. The fourth paper applied a human-centered systems analysis to the adverse aircraft weather encounter problem in order to identify desirable functions of weather and icing information. The objective of the fifth paper, a viewgraph presentation, is to propose means to improve aviation weather information, training, and procedures based on a human-centered systems approach.

Hansman, Robert John↗
DOE OSTI2021

Computational framework for behind-the-meter DER techno-economic modeling and optimization: REopt Lite

The energy system is undergoing a major transformation with the global emphasis on decarbonization. Distributed generation is projected to play a significant role in the new energy system, and energy models are informing how distributed generation can be integrated reliably and economically. In this work, we present an end-to-end computational framework for distributed energy resource (DER) modeling, REopt Lite™, which captures the interface of technology, economics, and policy in the energy modeling process. We describe the problem space, the building blocks of the model, the scaling capabilities of the design, the optimization formulation, and the extensibility of the model. We present a framework for accelerating the techno-economic analysis of behind-the-meter distributed energy resources to enable rapid planning and decision-making, thereby enabling greater renewable energy deployment. This computation framework is open-sourced to facilitate transparency, flexibility, and wider collaboration opportunities within the worldwide energy modeling community.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Hypothesis-Agnostic Network-Based Analysis of Real-World Data Suggests Ondansetron is Associated with Lower COVID-19 Any Cause Mortality

Background: The COVID-19 pandemic generated a massive amount of clinical data, which potentially hold yet undiscovered answers related to COVID-19 morbidity, mortality, long-term effects, and therapeutic solutions.Objectives: The objectives of this study were (1) to identify novel predictors of COVID-19 any cause mortality by employing artificial intelligence analytics on real-world data through a hypothesis-agnostic approach and (2) to determine if these effects are maintained after adjusting for potential confounders and to what degree they are moderated by other variables.Methods: A Bayesian statistics-based artificial intelligence data analytics tool (bAIcis®) within the Interrogative Biology® platform was used for Bayesian network learning and hypothesis generation to analyze 16,277 PCR+ patients from a database of 279,281 inpatients and outpatients tested for SARS-CoV-2 infection by antigen, antibody, or PCR methods during the first pandemic year in Central Florida. This approach generated Bayesian networks that enabled unbiased identification of significant predictors of any cause mortality for specific COVID-19 patient populations. These findings were further analyzed by logistic regression, regression by least absolute shrinkage and selection operator, and bootstrapping.Results: We found that in the COVID-19 PCR+ patient cohort, early use of the antiemetic agent ondansetron was associated with decreased any cause mortality 30 days post-PCR+ testing in mechanically ventilated patients.Conclusions: The results demonstrate how a real-world COVID-19-focused data analysis using artificial intelligence can generate unexpected yet valid insights that could possibly support clinical decision making and minimize the future loss of lives and resources.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Intertwined impacts of water, energy development, and carbon emissions in China

China is rapidly expanding its alternative and non-conventional energy production capabilities. Although renewable electricity remains the focus, considerable investment has supported construction of coal liquefaction and coal gasification facilities in the desert steppes of north-central China, new coal mines in arid Inner Mongolia, and tight oil and gas extraction in the Ordos to supplement limited domestic supplies of oil and gas. At the same time, China is also facing severe drought and water scarcity in these same regions and in response has expanded various water supply technologies such as desalination and wastewater treatment. Recent government goals and measures for reducing energy and water consumption and increasing efficiency introduced in national policies, however, are poorly or not coordinated, resulting in contradictory objectives for which physical interlinkages are not well understood. This research intends to provide insights for future energy-water nexus management decisions in China, through systematic, comprehensive modeling of the water-energy nexus in China based on comprehensive, bottom-up technology characterizations. Existing studies fail to adequately characterize the details on specific technologies, nor do they comprehensively cover all energy sectors, including energy conversion for non-energy products. We developed integrated assessment (IA) capabilities to allow stakeholders to observe the tradeoffs between various technology options and policy decisions and to test hypotheses/premises in a scenario-driven environment. The results of our analysis underscore the growing interconnection between water and energy in China, the mixed trade-offs from developing low-carbon technologies such as renewable energy and inland nuclear, and the importance of water-energy nexus issues at the regional and local scales. This study lays the groundwork for an integrated resource policy planning process in China and provides an assessment methodology and research directions for future studies of the water-energy nexus. To conclude, this study contributes to the water-energy nexus literature by providing systematic data and policy implications for China, where data are typically less accessible, as well as providing references for other regions in the world that are facing similar water and energy use and planning challenges.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2013

Automated Detection of Anomalous Shipping Manifests to Identify Illicit Trade

We describe an approach to analyzing trade data which uses clustering to detect similarities across shipping manifest records, classification to evaluate clustering results and categorize new unseen shipping data records, and visual analytics to provide to support situation awareness in dynamic decision making to monitor and warn against the movement of radiological threat materials through search, analysis and forecasting capabilities. The evaluation of clustering results through classification and systematic inspection of the clusters show the clusters have strong semantic cohesion and offer novel ways to detect transactions related to nuclear smuggling.

classification↗
DOE OSTI2024

Toward Accelerating Discovery via Physics-Driven and Interactive Multifidelity Bayesian Optimization

Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and often nondifferentiable parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, processing spaces, and molecular embedding spaces. Often these systems are expensive or time consuming to evaluate a single instance, and hence classical approaches based on exhaustive grid or random search are too data intensive. This resulted in strong interest toward active learning methods such as Bayesian optimization (BO) where the adaptive exploration occurs based on human learning (discovery) objective. However, classical BO is based on a predefined optimization target, and policies balancing exploration and exploitation are purely data driven. In practical settings, the domain expert can pose prior knowledge of the system in the form of partially known physics laws and exploration policies often vary during the experiment. Here, we propose an interactive workflow building on multifidelity BO (MFBO), starting with classical (data-driven) MFBO, then expand to a proposed structured (physics-driven) structured MFBO (sMFBO), and finally extend it to allow human-in-the-loop interactive interactive MFBO (iMFBO) workflows for adaptive and domain expert aligned exploration. These approaches are demonstrated over highly nonsmooth multifidelity simulation data generated from an Ising model, considering spin–spin interaction as parameter space, lattice sizes as fidelity spaces, and the objective as maximizing heat capacity. Detailed analysis and comparison show the impact of physics knowledge injection and real-time human decisions for improved exploration with increased alignment to ground truth. Here, the associated notebooks allow to reproduce the reported analyses and apply them to other systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Markov Decision Processes for Intelligent, Risk-Informed Asset-Management Decision-Making

Advanced nuclear reactors are a promising option for aiding the world in achieving its net-zero carbon emission goals, however, there are significant challenges to attaining and maintaining economic competitiveness with other sources of electricity. To improve the economic competitiveness of advanced reactor designs, a project was initiated to explore the use of Markov Decision Processes (MDPs) to guide asset-management decision-making during advanced reactor operation. MDPs are a powerful tool for optimizing decision-making in complex environments and their application to advanced reactors can aid in planning maintenance and repair activities to minimize downtime and maximize generation. The described approach expands on previous work regarding the use of MDPs for operational decision-making through the direct incorporation of real-time plant information. The integral MDP analysis includes information from online component diagnostic tools and the plant’s real-time generation risk assessment (GRA) and probabilistic risk assessment (PRA), which evaluate plant risk from both an economic and safety perspective. The result is an asset-management optimization framework that is based on real-time data regarding plant component status and the current best-estimate of plant risk. The paper presents an overview of the theoretical framework to incorporate the different information pathways into an integral MDP analysis, along with example analyses.

Grabaskas, David↗
DOE OSTI2013

Development of a statistically based access delay timeline methodology.

The charter for adversarial delay is to hinder access to critical resources through the use of physical systems increasing an adversary s task time. The traditional method for characterizing access delay has been a simple model focused on accumulating times required to complete each task with little regard to uncertainty, complexity, or decreased efficiency associated with multiple sequential tasks or stress. The delay associated with any given barrier or path is further discounted to worst-case, and often unrealistic, times based on a high-level adversary, resulting in a highly conservative calculation of total delay. This leads to delay systems that require significant funding and personnel resources in order to defend against the assumed threat, which for many sites and applications becomes cost prohibitive. A new methodology has been developed that considers the uncertainties inherent in the problem to develop a realistic timeline distribution for a given adversary path. This new methodology incorporates advanced Bayesian statistical theory and methodologies, taking into account small sample size, expert judgment, human factors and threat uncertainty. The result is an algorithm that can calculate a probability distribution function of delay times directly related to system risk. Through further analysis, the access delay analyst or end user can use the results in making informed decisions while weighing benefits against risks, ultimately resulting in greater system effectiveness with lower cost.

Rivera, W. Gary↗
DOE OSTI2014

Development and Demonstration of a Security Core Component

In recent years, the convergence of a number of trends has resulted in Cyber Security becoming a much greater concern for electric utilities. A short list of these trends includes: · Industrial Control Systems (ICSs) have evolved from depending on proprietary hardware and operating software toward using standard off-the-shelf hardware and operating software. This has meant that these ICSs can no longer depend on “security through obscurity. · Similarly, these same systems have evolved toward using standard communications protocols, further reducing their ability to rely upon obscurity. · The rise of the Internet and the accompanying demand for more data about virtually everything has resulted in formerly isolated ICSs becoming at least partially accessible via Internet-connected networks. · “Cyber crime” has become commonplace, whether it be for industrial espionage, reconnaissance for a possible cyber attack, theft, or because some individual or group “has something to prove.” Electric utility system operators are experts at running the power grid. The reality is, especially at small and mid-sized utilities, these SCADA operators will by default be “on the front line” if and when a cyber attack occurs against their systems. These people are not computer software, networking, or cyber security experts, so they are ill-equipped to deal with a cyber security incident. Cyber Security Manager (CSM) was conceived, designed, and built so that it can be configured to know what a utility’s SCADA/EMS/DMS system looks like under normal conditions. To do this, CSM monitors log messages from any device that uses the syslog standard. It can also monitor a variety of statistics from the computers that make up the SCADA/EMS/DMS: outputs from host-based security tools, intrusion detection systems, SCADA alarms, and real-time SCADA values – even results from a SIEM (Security Information and Event Management) system. When the system deviates from “normal,” CSM can alert the operator in language that they understand that an incident may be occurring, provide actionable intelligence, and informing them what actions to take. These alarms may be viewed on CSM’s built-in user interface, sent to a SCADA alarm list, or communicated via email, phone, pager, or SMS message. In recognition of the fact that “real world” training for cyber security events is impractical, CSM has a built-in Operator Training Simulator capability. This can be used stand alone to create simulated event scenarios for training purposes. It may also be used in conjunction with the recipient’s SCADA/EMS/DMS Operator Training Simulator. In addition to providing cyber security situational awareness for electric utility operators, CSM also provides tools for analysts and support personnel; in fact, the majority of user interface displays are designed for use in analyzing current and past security events. CSM keeps security-related information in long-term storage, as well as writing any decisions it makes to a (syslog) log for use forensic or other post-event analysis.

Turke, Andy↗
DOE OSTI2024

A Hydropower facility as an Energy Water Signal Processor

In recent times, various efforts have been made to address the challenge of adequately representing hydropower systems in modeling frameworks, accounting for the lack of data to represent the multiple constraints in hydropower operation. This research is a pilot data-driven methodology for characterizing, classifying, and comparing the water-to-energy and energy-to-water signal transformations that hydropower facilities as signal processors accomplish. In this study, a Box Jenkins transfer function/noise model is used to identify the relationship between reservoir inflows and outflows. For examining the feasibility of this methodology, 5-minute fleet data for five storage and five run-of-river facilities was provided by the Tennessee Valley Authority (TVA) and transfer function models are developed. The influence of past inflow and outflow values on the current outflow decisions was investigated and summarized by examining the results of Box Jenkins methodology. Finally, dominance analysis was introduced to add value to the Box Jenkins model results and provide different stakeholders with a set of concepts to convey the functionality of hydropower.

13 HYDRO ENERGY↗
DOE OSTI2024

Transforming Agricultural Productivity with AI-Driven Forecasting: Innovations in Food Security and Supply Chain Optimization

Global food security is under significant threat from climate change, population growth, and resource scarcity. This review examines how advanced AI-driven forecasting models, including machine learning (ML), deep learning (DL), and time-series forecasting models like SARIMA/ARIMA, are transforming regional agricultural practices and food supply chains. Through the integration of Internet of Things (IoT), remote sensing, and blockchain technologies, these models facilitate the real-time monitoring of crop growth, resource allocation, and market dynamics, enhancing decision making and sustainability. The study adopts a mixed-methods approach, including systematic literature analysis and regional case studies. Highlights include AI-driven yield forecasting in European hydroponic systems and resource optimization in southeast Asian aquaponics, showcasing localized efficiency gains. Furthermore, AI applications in food processing, such as plasma, ozone and Pulsed Electric Field (PEF) treatments, are shown to improve food preservation and reduce spoilage. Key challenges—such as data quality, model scalability, and prediction accuracy—are discussed, particularly in the context of data-poor environments, limiting broader model applicability. The paper concludes by outlining future directions, emphasizing context-specific AI implementations, the need for public–private collaboration, and policy interventions to enhance scalability and adoption in food security contexts.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2016

Scaling-up permafrost thermal measurements in western Alaska using an ecotype approach

Permafrost temperatures are increasing in Alaska due to climate change and in some cases permafrost is thawing and degrading. In areas where degradation has already occurred the effects can be dramatic, resulting in changing ecosystems, carbon release, and damage to infrastructure. However, in many areas we lack baseline data, such as subsurface temperatures, needed to assess future changes and potential risk areas. Besides climate, the physical properties of the vegetation cover and subsurface material have a major influence on the thermal state of permafrost. These properties are often directly related to the type of ecosystem overlaying permafrost. In this paper we demonstrate that classifying the landscape into general ecotypes is an effective way to scale up permafrost thermal data collected from field monitoring sites. Additionally, we find that within some ecotypes the absence of a moss layer is indicative of the absence of near-surface permafrost. As a proof of concept, we used the ground temperature data collected from the field sites to recode an ecotype land cover map into a map of mean annual ground temperature ranges at 1 m depth based on analysis and clustering of observed thermal regimes. In conclusion, the map should be useful for decision making with respect to land use and understanding how the landscape might change under future climate scenarios.

58 GEOSCIENCES↗
NASA NTRS1974

117.6-kilobit telemetry from Mercury in-flight system analysis

This paper discusses very specifically the mode of the Mariner Venus/Mercury 1973 (MVM'73) telecommunications system in the interplexed dual channel 117.6 kilobits per second (kbps) and 2.45 kbps telemetry. This mode, originally designed for only Venus encounter, was also used at Mercury despite significantly less performance margin. Detailed analysis and careful measurement of system performance before and during flight operations allowed critical operational decisions, which made maximum use of the system capabilities.

Evanchuk, V. L.↗
NASA NTRS1975

Estimating vegetative biomass from LANDSAT-1 imagery for range management

Evaluation of LANDSAT-1, band 5 data for use in estimation of vegetative biomass for range management decisions was carried out for five selected range sites in the Sandhills region of Nebraska. Analysis of sets of optical density-vegetative biomass data indicated that comparisons of biomass estimation could be made within one frame but not between frames without correction factors. There was high correlation among sites within sets of radiance value-vegetative biomass data and also between sets, indicating comparisons of biomass could be made within and between frames. Landsat-1 data are shown to be a viable alternative to currently used methods of determining vegetative biomass production and stocking rate recommendations for Sandhills rangeland.

Seevers, P. M.↗
NASA NTRS1989

Analysis of the 60-Hz power system at KSC: The Orsino substation

An analysis of the Orsino Substation, a component (50 percent) of the 60-Hertz electric power system at the Kennedy Space Center, is presented. Presented here are separate single-line diagrams of the sixteen feeder circuits to permit easy access to information on the individual feeders for future planning. The load condition of each feeder and load break switch are presented and a heuristic reliability analysis of the system is performed. Information is given about the system fashion useful for decision making purposes. The beauty of it is in the simplified manner by which information about the system can be obtained.

Kalu, Alex O.↗