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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Analysis of Airport Ground Delay Program Decisions Using Data Mining Techniques

Air traffic service providers have to make decisions regarding changes to air traffic flow in the event of major weather disturbances and traffic congestions to maintain safety of the system. The behavior of the air traffic management system will be more predictable if consistent decisions are made under similar traffic and weather conditions. Consistency of deciding on control action depends on the weather and traffic conditions as well as accuracy in predicting these conditions. Weather parameters (defined in terms of forecast and actual weather and traffic conditions) on different days can be used to categorize days into days with little decision consistency, days with moderate decision consistency and days with high decision consistency. Four years of traffic, weather and ground delay program decisions data at major airports in the United States are used in the analysis. This paper examines performance of different data mining methods in the three regions of decision consistency. Not surprisingly, data mining methods have the best performance in the region of most decision consistency and have the poorest performance in the region of little decision consistency. In applications where data mining methods have differing performance in differing regions, it would be more useful to characterize the region specific performance instead of characterizing performance by a single parameter. Finally, the results show no significant variation in the performance of different data mining methods for this particular problem. The fact that different mining methods show no significant variation also provides further confidence in the results of data mining methods. Work in this abstract discusses initial results. This paper describes the results in terms of both forecast and actual environmental conditions and discusses how prediction errors impact decision consistency.

Kulkarni, Deepak↗
DOE OSTI2023

Initial Systems-Level Assessment of a Distributed Direct Air Capture System Concept at the Urban-scale (UrbanDAC)

Direct Air Capture (DAC) systems offer a promising solution for mitigating global carbon emissions by directly removing ambient carbon dioxide (CO 2 ) from the atmosphere. While future DAC facilities are typically envisioned as being large and centralized, small-scale systems present an alternative approach with advantages such as adaptability and lower uptake costs. By harnessing waste heat from the built environment, such small-scale systems become distributed DAC at the urban scale (UrbanDAC) that benefit from existing urban infrastructure, while presenting challenges such as identifying eligible buildings and sustainable transportation and storage of captured CO 2 . Collaborating with engineering experts and developers of a DAC unit that can be co-located with cooling towers of existing commercial buildings, this study explores the systems-level implications of UrbanDAC using a geographically explicit multi-decision criteria analysis (MCDA) framework. By considering various infrastructure and environmental factors, network analysis and geospatial techniques are applied to identify optimal building candidates for distributed DAC units within Knoxville, Tennessee, USA, as a representative mid-size city. The selected outputs of the MCDA are used to explore a scenario that assumes a CO 2 collection and transport route for 20 high-ranking candidate buildings; total carbon emissions, EV energy consumption, and net carbon dioxide removal (CDR) are then calculated. Results suggest that the spatial variation of optimal candidates between thriving commercial areas is an important planning consideration. Examining the feasibility of UrbanDAC at an urban planning level provides valuable insights into the barriers and enabling conditions for CDR in cities, where the vast majority of CO 2 emissions are produced, and supports decision-making processes for the implementation of decarbonization initiatives. Through this initial assessment, this research acts as a pilot study for an emerging technology that highlights the importance of distributed DAC technologies in addressing climate change and emphasizes the need for further research and exploration in this domain.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Metrics for Decision-Making in Energy Justice

Energy equity and justice have become priority considerations for policymakers, practitioners, and scholars alike. To ensure that energy equity is incorporated into actual decisions and analysis, it is necessary to design, use, and continually improve energy equity metrics. In this article, we review the literature and practices surrounding such metrics. We present a working definition for energy justice and equity, and connect them to both criteria for and frameworks of metrics. We then present a large sampling of energy equity metrics, including those focused on vulnerability, wealth creation, energy poverty, life cycle, and comparative country-level dynamics. We conclude with a discussion of the limitations, gaps, and trade-offs associated with these various metrics and their interactions thereof.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Report on Coastal Structure Integrated Wave Energy Converters (CSI-WECs)

This report investigates a largely underexplored, and underexploited, application for wave energy: coastal structure integrated wave energy converters (CSI-WECs). These WEC types have significant potential to rapidly advance the wave energy industry while addressing the challenges of coastal community resiliency and marine energy integration and application, and the urgent need for increased coastal protection. To further support the value proposition investigation of this work, three wave energy developers agreed to conduct an energy production analysis of their technology for the four sites identified. Additionally, a geographic information system-based tool was developed that supports efficient and comprehensive site assessment for optimal and high-value deployments.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

Ensemble Federated Machine Learning‐Based Cybersecurity Situational Awareness in Microgrid Network

Cyber-physical microgrids are vulnerable to stealthy cybersecurity threats that disguise their actions through the exploitation of system knowledge. Such actions can severely impacts microgrids deployed in defense bases, slowing the response time of military forces during national emergencies. Several machine-learning algorithms have been proposed to detect intrusions in the grid networks; however, these traditional machine-learning algorithms lack data privacy and are subject to several adversarial machine-learning threats. This paper proposes a novel federated machine learning (FML)-based three-model framework to detect and identify stealthy data-integrity attacks while ensuring data privacy in microgrid networks. The proposed architecture uses a variational mode decomposition technique to extract derived features from incoming measurement and control datasets. The extraction of these derived features allows FML models to learn minute variations in data patterns that allow them to perform significantly better than the models trained with generic datasets consisting of raw features. Our experimental results show the efficient performance of the proposed methodology against different types of data integrity attacks while considering primary and secondary controllers in microgrids. Further, the applied FML-integrated random forest ensemble algorithm outperforms the existing generic FML algorithms during noisy and noise-free datasets with prediction latencies of only 91–134 µs per sample within the 0.1 s sampling interval and requires communication bandwidth of around ∼8.25 KB/s at the control center and ∼2.7 KB/s per edge client for communication.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

MAFLD identifies patients with significant hepatic fibrosis better than NAFLD

Abstract Background & Aims Diagnostic criteria for metabolic associated fatty liver disease (MAFLD) have been proposed, but not validated. We aimed to compare the diagnostic accuracy of the MAFLD definition vs the existing NAFLD criteria to identify patients with significant fibrosis and to characterize the impact of mild alcohol intake. Methods We enrolled 765 Japanese patients with fatty liver (median age 54 years). MAFLD and NAFLD were diagnosed in 79.6% and 70.7% of patients respectively. Significant fibrosis was defined by FIB‐4 index ≥1.3 and liver stiffness ≥6.6 kPa using shear wave elastography. Mild alcohol intake was defined as <20 g/day. Factors associated with significant fibrosis were analysed by logistic regression and decision‐tree analyses. Results Liver stiffness was higher in MAFLD compared to NAFLD (7.7 vs 6.8 kPa, P = .0010). In logistic regression, MAFLD (OR 4.401; 95% CI 2.144‐10.629; P < .0001), alcohol intake (OR 1.761; 95% CI 1.081‐2.853; P = .0234), and NAFLD (OR 1.721; 95%CI 1.009‐2.951; P = .0463) were independently associated with significant fibrosis. By decision‐tree analysis, MAFLD, but not NAFLD or alcohol consumption was the initial classifier for significant fibrosis. The sensitivity for detecting significant fibrosis was higher for MAFLD than NAFLD (93.9% vs 73.0%). In patients with MAFLD, even mild alcohol intake was associated with an increase in the prevalence of significant fibrosis (25.0% vs 15.5%; P = .0181). Conclusions The MAFLD definition better identifies a group with fatty liver and significant fibrosis evaluated by non‐invasive tests. Moreover, in patients with MAFLD, even mild alcohol consumption is associated with worsening of hepatic fibrosis measures.

Yamamura, Sakura↗
DOE OSTI2021

Multiscale Reduced Order Modeling and Parameter Estimation for Climate Sciences

Several problems in earth system modeling are dependent on highly multiscale phenomena, such as turbulence, where computational modeling is challenging and expensive. This issue is exacerbated in atmospheric and oceanic domains, due to inherent high-dimensionality of the problem. One approach to this problem has been reduced order modeling (ROM); which aims to represent the key physics of the phenomena as a low-dimensional system. AI methods have huge potential in building accurate, stable ROMs and parameter estimation for these ROMs, as it requires extracting nonlinearities and patterns from simulation and/or observational data. Developing physics-based AI approaches specialized for the complexities of multiscale data, along with strategies to account for uncertainties, will revolutionize rapid modeling, analysis and decision making for earth system problems of practical interest.

58 GEOSCIENCES↗
DOE OSTI2022

Developing a Power Plant Suitability Model for the Energy Zones Mapping Tool

This report provides an example of designing, developing, and running a power plant suitability model in the Energy Zones Mapping Tool (EZMT), a public, web-based mapping tool with a large spatial database focused on energy infrastructure, energy resources, and related siting factors. The example focuses on natural gas combined cycle (NGCC) power plants, which have several unique and interesting characteristics. NGCC plants typically provide peaking power to the electrical grid. Such plants can be started or stopped relatively quickly and are often used to supplement base load plants (such as nuclear or coal) during times of peak electrical consumption or to counterbalance lulls in variable power generation (such as wind or solar). Due to these and other factors, NGCC plants have been projected to increase in number under energy planning studies such as Hadley et al. The scope of the Hadley et. al. study is the Eastern Interconnection (EI), the electrical transmission grid serving much of the United States and Canada east of the Rocky Mountains. Results in the study are organized according to 22 Multi-region National—North American Electricity and Environment Model (NEEM) regions within the EI. NGCC plants usually have lower water requirements than other thermoelectric power plants they may replace; therefore, they are expected to have an interesting role in reducing the overall water requirements of energy generation.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2020

An Applied Strategy for Using Empirical and Hybrid Models in Online Monitoring

The monitoring of plant equipment for failure prediction is one of the key contributors to operation and maintenance (O&M) costs for a nuclear power plant (NPP) because O&M monitoring depends on labor-intensive activities that are required to meet high equipment reliability standards. These activities rely primarily on humans for information gathering, condition diagnosis, and predictive analysis. Online monitoring aims to automate these activities by relying on sensors to replace human information gathering and machine learning to replace human analysis and decision making. To facilitate automated monitoring, a systematic strategy for anomaly detection is needed to optimally use the available sensor data, empirical models, and physics-supported models. This strategy is essential to provide credible reasoning on why and when an empirical (i.e., purely data-driven) 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. The extant methods usually adopt an ad hoc trial-and-error approach that, in addition to being time-consuming and costly, is also highly subjective; it is impacted by the background and the skill set of the personnel making the decisions. Thus, such an approach cannot guarantee an optimum outcome. This represents the motivation of the current research effort, which is focused on devising a scientifically supported strategy for the optimum selection of anomaly detection methods. This report presents a detailed assessment of the main anomaly detection techniques within the empirical or hybrid method streams. Empirical methods include 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 listed techniques within these two streams represent the vast majority of techniques performed for anomaly detection. Using the techniques as outcomes, a strategy was developed to enable a systematic decision-making process to lead to one of these techniques. The 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. Each of these decision points in the strategy is explained in detail in this report with examples, along with the scientific basis behind the decisions and outcomes in common and simplified terminology. The strategy is developed for use by any NPP staff with basic engineering or science knowledge. A user-friendly graphical state flow diagram was also developed as a visual presentation of the strategy. The strategy was tested and demonstrated through two pilot projects for the application of anomaly detection at an NPP. Each pilot had two use cases: an initial case in which certain decisions were made that resulted in one or more empirical techniques and a revised use case where one or more key decisions were modified resulting in using a set of hybrid methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1979

Biomedical systems analysis program

Biomedical monitoring programs which were developed to provide a system analysis context for a unified hypothesis for adaptation to space flight are presented and discussed. A real-time system of data analysis and decision making to assure the greatest possible crew safety and mission success is described. Information about man's abilities, limitations, and characteristic reactions to weightless space flight was analyzed and simulation models were developed. The predictive capabilities of simulation models for fluid-electrolyte regulation, erythropoiesis regulation, and calcium regulation are discussed.

Source record↗
NASA NTRS1990

Deep space telecommunications, navigation, and information management - Support of the Space Exploration Initiative

The principal challenges in providing effective deep space navigation, telecommunications, and information management architectures and designs for Mars exploration support are presented. The fundamental objectives are to provide the mission with the means to monitor and control mission elements, obtain science, navigation, and engineering data, compute state vectors and navigate, and to move these data efficiently and automatically between mission nodes for timely analysis and decision making. New requirements are summarized, and related issues and challenges including the robust connectivity for manned and robotic links, are identified. Enabling strategies are discussed, and candidate architectures and driving technologies are described.

Hall, Justin R.↗
NASA NTRS1992

An expert system/ion trap mass spectrometry approach for life support systems monitoring

Efforts to develop sensor and control system technology to monitor air quality for life support have resulted in the development and preliminary testing of a concept based on expert systems and ion trap mass spectrometry (ITMS). An ITMS instrument provides the capability to identify and quantitate a large number of suspected contaminants at trace levels through the use of a variety of multidimensional experiments. An expert system provides specialized knowledge for control, analysis, and decision making. The system is intended for real-time, on-line, autonomous monitoring of air quality. The key characteristics of the system, performance data and analytical capabilities of the ITMS instrument, the design and operation of the expert system, and results from preliminary testing of the system for trace contaminant monitoring are described.

Palmer, Peter T.↗
NASA NTRS1994

Dual-Use Space Technology Transfer Conference and Exhibition, Volume 1

This document contains papers presented at the Dual-Use Space Technology Transfer Conference and Exhibition held at the Johnson Space Center February 1-3, 1994. Possible technology transfers covered during the conference were in the areas of information access; innovative microwave and optical applications; materials and structures; marketing and barriers; intelligent systems; human factors and habitation; communications and data systems; business process and technology transfer; software engineering; biotechnology and advanced bioinstrumentation; communications signal processing and analysis; new ways of doing business; medical care; applications derived from control center data systems; human performance evaluation; technology transfer methods; mathematics, modeling, and simulation; propulsion; software analysis and decision tools systems/processes in human support technology; networks, control centers, and distributed systems; power; rapid development perception and vision technologies; integrated vehicle health management; automation technologies; advanced avionics; ans robotics technologies. More than 77 papers, 20 presentations, and 20 exhibits covering various disciplines were presented b experts from NASA, universities, and industry.

Kumar Krishen↗
NASA NTRS1994

Dual-Use Space Technology Transfer Conference and Exhibition

This is the second volume of papers presented at the Dual-Use Space Technology Transfer Conference and Exhibition held at the Johnson Space Center February 1-3, 1994. Possible technology transfers covered during the conference were in the areas of information access; innovative microwave and optical applications; materials and structures; marketing and barriers; intelligent systems; human factors and habitation; communications and data systems; business process and technology transfer; software engineering; biotechnology and advanced bioinstrumentation; communications signal processing and analysis; medical care; applications derived from control center data systems; human performance evaluation; technology transfer methods; mathematics, modeling, and simulation; propulsion; software analysis and decision tools; systems/processes in human support technology; networks, control centers, and distributed systems; power; rapid development; perception and vision technologies; integrated vehicle health management; automation technologies; advanced avionics; and robotics technologies.

Krishen, Kumar↗