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

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

Orbital transfer vehicle launch operations study: Automated technology knowledge base, volume 4

A simplified retrieval strategy for compiling automation-related bibliographies from NASA/RECON is presented. Two subsets of NASA Thesaurus subject terms were extracted: a primary list, which is used to obtain an initial set of citations; and a secondary list, which is used to limit or further specify a large initial set of citations. These subject term lists are presented in Appendix A as the Automated Technology Knowledge Base (ATKB) Thesaurus.

Source record↗
NASA NTRS2023

Leveraging Automated Fiber Placement Computer Aided Process Planning Framework for Defect Validation and Dynamic Layup Strategies

Process planning represents an essential stage of the Automated Fiber Placement (AFP) workflow. It develops useful and efficient machine processes based upon the working material, composite design, and manufacturing resources. The current state of process planning requires a high degree of interaction from the process planner and could greatly benefit from increased automation. Therefore, a list of key steps and functions are created to identify the more difficult and time-consuming phases of process planning. Additionally, a set of metrics must exist by which to evaluate the effectiveness of the manufactured laminate from the machine code created during the Process Planning stage. Layup strategies, in addition to dog ears, stagger shifts, steering constraints, and starting points, represented the group of functions labeled as process optimization and ranked the highest in terms of priority for automation. The laminates resulting from the selected parameters are evaluated through the occurrences of principal defect metrics such as fiber gaps, overlaps, angle deviation and steering violations. This document presents an automated software solution to the layup strategy and starting point selection phase of process planning. A series of ply scenarios are generated with variations of these ply parameters and evaluated according to a set of metrics entered by the Process Planner. These metrics are generated through use of the Analytical Hierarchy Process (AHP), where relative importance between each of the fiber features are defined. The ply scenarios are selected which reduce the overall fiber feature scores based on the defects the Process Planner wishes to minimize.

HiCAM↗
DOE OSTI2021

An Investigation of Strategies for Sensing Window Attachment Position: Survey of Candidate Technologies, Evaluation, and Deployment Recommendations

While windows are desirable feature in buildings for views, daylighting, and aesthetics, it is important to recognize that windows are a major contributor to a building’s energy load. Interior window coverings, such as shades and blinds, can help improve the energy performance of windows without sacrificing the benefits; however, while appropriate use of window coverings provide the potential for energy savings, there is little consensus regarding how occupants typically use these window attachments, which sometimes puts into question how much energy they actually save in an occupied home. Automated shading control strategies can reduce the uncertainty of energy savings; however, users can overwrite pre-defined schedules or write their own shade control programs to reach their personal comfort and privacy goals. Because it is difficult to identify how occupants interact with window shades, it is difficult to identify the energy savings potential of blinds. To address this problem, this study was developed to identify a candidate sensor that can be deployed to sense blind position. The intended application for this sensor is deployment in energy efficiency field studies so that shade usage can be measured to better understand results and identify the energy impact of specific window attachment technologies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Development and Implementation of Fault-Correction Algorithms in Fault Detection and Diagnostics Tools

A fault detection and diagnostics (FDD) tool is a type of energy management and information system that continuously identifies the presence of faults and efficiency improvement opportunities through a one-way interface to the building automation system and the application of automated analytics. Building operators on the leading edge of technology adoption use FDD tools to enable median whole-building portfolio savings of 8%. Although FDD tools can inform operators of operational faults, currently an action is always required to correct the faults to generate energy savings. A subset of faults, however, such as biased sensors, can be addressed automatically, eliminating the need for staff intervention. Automating this fault “correction” can significantly increase the savings generated by FDD tools and reduce the reliance on human intervention. Doing so is expected to advance the usability and technical and economic performance of FDD technologies. This paper presents the development of nine innovative fault auto-correction algorithms for Heating, Ventilation, and Air Conditioning pi(HVAC) systems. When the auto-correction routine is triggered, it overwrites control setpoints or other variables to implement the intended changes. It also discusses the implementation of the auto-correction algorithms in commercial FDD software products, the integration of these strategies with building automation systems and their preliminary testing.

field testing↗
NASA NTRS2004

Space Technology 5: Pathfinder for Future Micro-Sat Constellations

The Space Technology 5 (ST-5) Project, currently in the implementation phase, is part of the National Aeronautics and Space Administration (NASA) s New Millennium Program (NMP). ST-5 will consist of a constellation of three miniature satellites, each with mass less than 25 kg and size approximately 60 cm by 30 cm. ST-5 addresses technology challenges, as well as fabrication, assembly, test and operations strategies for future micro-satellite missions. ST-5 will be deployed into a highly eccentric, geo-transfer orbit (GTO). This will expose the spacecraft to a high radiation environment as well as provide a low level magnetic background. A three-month flight demonstration phase is planned to validate the technologies and demonstrate concepts for future missions. Each ST-5 spacecraft incorporates NMP competitively-selected breakthrough technologies. These include Cold Gas Micro-Thrusters for propulsion and attitude control, miniature X-band transponder for space-ground communications, Variable Emittance Coatings for dynamic thermal control, and CULPRiT ultra low power logic chip used for Reed-Solomon encoding. The ST-5 spacecraft itself is a technology that can be infused into future missions. It is a fully functional micro-spacecraft built within tight volume and mass constraints. It is built to withstand a high radiation environment, large thermal variations, and high launch loads. The spacecraft power system is low-power and low-voltage, and is designed to turn on after separation &om the launch vehicle. Some of the innovations that are included in the ST-5 design are a custom spacecraft deployment structure, magnetometer deployment boom, nutation damper, X-band antenna, miniature spinning sun sensor, solar array with triple junction solar cells, integral card cage assembly containing single card Command and Data Handling and Power System Electronics, miniature magnetometer, and lithium ion battery. ST-5 will demonstrate the ability of a micro satellite to perform research-quality science. Each ST-5 spacecraft will deploy a precision magnetometer to be used both for attitude determination and as a representative science instrument. The spacecraft has been developed with a low magnetic signature to avoid interference with the magnetometer. The spacecraft will be able to detect and respond autonomously to science events, i.e. significant changes in the magnetic field measurements. The three spacecraft will be a pathfinder for future constellation missions. They will be deployed to demonstrate an appropriate geometry for scientific measurements as a constellation. They will be operationally managed as a constellation, demonstrating automation and communication strategies that will be useful for future missions. The technologies and future mission concepts will be validated both on the ground and in space. Technologies will be validated on the ground by a combination of component level and system level testing of the flight hardware in a thermal vacuum environment. In flight, specific validation runs are planned for each of the technologies. Each validation run consists of one or more orbits with a specific validation objective. This paper will describe the ST-5 mission, and the applicability of the NMP technologies, spacecraft, and mission concepts to future missions. It will also discuss the validation approach for the ST-5 technologies and mission concepts.

Carlisle, Candace↗
DOE OSTI2021

Z-Sequence: photometric redshift predictions for galaxy clusters with sequential random k-nearest neighbours

ABSTRACT We introduce Z-Sequence, a novel empirical model that utilizes photometric measurements of observed galaxies within a specified search radius to estimate the photometric redshift of galaxy clusters. Z-Sequence itself is composed of a machine learning ensemble based on the k-nearest neighbours algorithm. We implement an automated feature selection strategy that iteratively determines appropriate combinations of filters and colours to minimize photometric redshift prediction error. We intend for Z-Sequence to be a standalone technique but it can be combined with cluster finders that do not intrinsically predict redshift, such as our own DEEP-CEE. In this proof-of-concept study, we train, fine-tune, and test Z-Sequence on publicly available cluster catalogues derived from the Sloan Digital Sky Survey. We determine the photometric redshift prediction error of Z-Sequence via the median value of |Δ$z$|/(1 + $z$) (across a photometric redshift range of 0.05 ≤ $z$ ≤ 0.6) to be ∼0.01 when applying a small search radius. The photometric redshift prediction error for test samples increases by 30–50 per cent when the search radius is enlarged, likely due to line-of-sight interloping galaxies. Eventually, we aim to apply Z-Sequence to upcoming imaging surveys such as the Legacy Survey of Space and Time to provide photometric redshift estimates for large samples of as yet undiscovered and distant clusters.

Chan, Matthew C.↗
DOE OSTI2022

Introduction to Special Section: Machine Learning for Image-based Geologic Interpretation

Image-based geological interpretation has been a labor-intensive and time-consuming process because it requires well-trained geoscientists to identify geological structures, features, and textures from various types of images. These images include scanning electron microscopic images, optical microscopic images, optical photos, resistivity images, seismic volumes, remote-sensing images, etc. With fast-evolving machine learning (ML) technology and computing power in recent decades, computers can achieve nearhuman-level to super-human-level performance with scalable high efficiency in the computer vision field. These technological revolutions facilitated image-based geological interpretation in petroleum exploration and production. For example, a fault picking method applied to 3-D seismic volume data using deep learning can achieve superior performance in comparison to conventional auto-picking methods. In addition, under the new normal of low oil prices, the petroleum industry seeks cost-effective strategies such as automating traditionally labor-intensive processes. Nevertheless, the potential of applying ML to geological image interpretation is still facing a few key challenges including data scarcity, data distribution, poor data and/or label quality, data leakage, learning algorithms, model architecture, training methodologies, testing and evaluation metrics, hyper-parameters optimization, model drift, production deployment, and the like.

58 GEOSCIENCES↗
NASA NTRS1991

Automation for deep space vehicle monitoring

Information on automation for deep space vehicle monitoring is given in viewgraph form. Information is given on automation goals and strategy; the Monitor Analyzer of Real-time Voyager Engineering Link (MARVEL); intelligent input data management; decision theory for making tradeoffs; dynamic tradeoff evaluation; evaluation of anomaly detection results; evaluation of data management methods; system level analysis with cooperating expert systems; the distributed architecture of multiple expert systems; and event driven response.

Schwuttke, Ursula M.↗
NASA NTRS2012

Techniques for Targeted Fermi-GBM Follow-Up of Gravitational-Wave Events

The Advanced LIGO and Advanced Virgo ground-based gravitational-wave (GW) detectors are projected to come online 2015 2016, reaching a final sensitivity sufficient to observe dozens of binary neutron star mergers per year by 2018. We present a fully-automated, targeted search strategy for prompt gamma-ray counterparts in offline Fermi-GBM data. The multi-detector method makes use of a detailed model response of the instrument, and benefits from time and sky location information derived from the gravitational-wave signal.

Blackburn, L.↗
NASA NTRS2016

3D-CDTI User Manual v2.1

The 3D-Cockpit Display of Traffic Information (3D-CDTI) is a flight deck tool that presents aircrew with: proximal traffic aircraft location, their current status and flight plan data; strategic conflict detection and alerting; automated conflict resolution strategies; the facility to graphically plan manual route changes; time-based, in-trail spacing on approach. The CDTI is manipulated via a touchpad on the flight deck, and by mouse when presented as part of a desktop flight simulator.

3D CDTI↗
NASA NTRSDate not supplied

Flight Testing of In-Time Safety Assurance Technologies for UAS Operations

Ongoing research at NASA is driven by a strategic plan defined by the Aeronautics Research Mission Directorate and a vision for future In-Time Aviation Safety Management Systems (IASMS) as described by the National Academies. In both visions, system safety awareness and provision are expanded through increased access to relevant data; integrated analysis and predictive capabilities; improved real-time detection and alerting of domain-specific hazards; decision support, and in some cases, automated risk mitigation strategies. One primary research focus is to develop means by which more timely (i.e., “in-time”) actions may be taken to mitigate precursors, anomalies, or trends that are observed during operations. In this paper, we describe such means as a collection of Services, Functions, and Capabilities (SFCs) that are supported by an underlying information system. For example, an integrated risk assessment capability is envisioned that continuously monitors safety-related metrics and margins and recommends timely operational changes. Assessment functions and/or services can be based on data analytics and predictive models derived from heterogeneous data sets that span relevant indicator metrics and their time histories. Likewise, on-board functions can identify and reduce susceptibility to precursor conditions that have led (and can lead) to aircraft loss-of-control or out-of-control accidents. This paper summarizes development and testing of such an information system tailored to hazards anticipated for future highly autonomous flight missions near and over densely populated areas. Testing is accomplished via simulation and by using small, unmanned aircraft operating over a test range at NASA’s Langley Research Center. Flight plans and test scenarios are defined to emulate several use-cases, including package delivery; reconnaissance; fire management; and urban air taxi vertiport operations. Two test phases are summarized with Phase 1 occurring in (2019-2020) and Phase 2 ongoing (2021-present). Results focus on SFC performance, technology readiness level assessment, and requirements discovery/validation. Companion papers are cited throughout for additional details on the recent testing.

safety management↗
NASA NTRSDate not supplied

Nasa Pilot Fatigue Research: Past, Present & Future

Pilot fatigue research at NASA Ames has a long history, initiated by a Congressional request to investigate the magnitude of fatigue, sleep loss and circadian disruption in flight operations, and how these factors impact crew performance. Research has been conducted in a broad range of aviation operations, including short-haul, long-haul, rotorcraft, overnight cargo, corporate/business, and regional airlines. Studies have been conducted in the field during actual flight operations, in simulated settings, focus group meetings, and with surveys. Other investigations have examined mitigation strategies including in-flight rest periods and lighting applications. Other activities have included education and training, publications, accident investigation support and the development of research methods and tools. Future research may include further lighting interventions, controlled rest strategies, use of automation, the effectiveness of modeling tools, and implementation of organizational fatigue management programs.

aviation↗
NASA NTRS1997

Automation Bias and Countermeasures in Flight Crews

Results of recent research investigating the use of automated systems have indicated the presence of automation bias, a term describing errors made when decision makers rely on automated cues as a heuristic replacement for vigilant information seeking and processing. Automation commission errors, i.e., errors made when decision makers take inappropriate action because they over-attend to automated information or directives, and automation omission errors, i.e., errors made when decision makers do not take appropriate action because they are not informed of an imminent problem or situation by automated aids, can result from this tendency. In a series of studies, participants who perceived themselves as "accountable" for their strategies of interaction with automation were significantly more likely to verify its correct functioning, and committed significantly fewer automation-related errors than those who did not report this perception. In this study, we focus on two manipulations to encourage verification behaviors within cockpit crews. The first, an information/training manipulation, will focus on explicit information on automation bias, and training for verification of automated functioning. The second manipulation will consist of display prompts to verify automated functioning. Participants (glass-cockpit crews) will fly a series of approaches on the Mini-ACFS, a two-person pan-task flight simulator. Approach scenarios include several automation "events," that is, glitches, malfunctions, or inappropriate recommendations, that can be caught if cross-checked with other cockpit indicators. We anticipate that these manipulations will ameliorate automation bias. Final data analysis will be completed prior to the OSU symposium, and is expected to support the hypotheses that: a) reduced errors are associated with verification behaviors; b) crews will be more likely to verify under training/display conditions than when in the control condition; and c) effects will persist when subjects return for a follow-up exercise 6-9 weeks after their first session. Implications for training and automation design will be discussed.

Mosier, Kathleen, L.↗
NASA NTRS2002

The Relationship of Self-Efficacy and Complacency in Pilot-Automation Interaction

Pilot 'complacency' has been implicated as a contributing factor in numerous aviation accidents and incidents. The term has become more prominent with the increase in automation technology in modern cockpits and, therefore, research has been focused on understanding the factors that may mitigate its effect on pilot-automation interaction. The study examined self-efficacy of supervisory monitoring and the relationship between complacency on strategy of pilot use of automation for workload management under automation schedules that produce the potential for complacency. The results showed that self-efficacy can be a 'double-edged' sword in reducing potential for automation-induced complacency but limiting workload management strategies and increasing other hazardous states of awareness.

Prinzel, Lawrence J., III↗
NASA NTRS1996

Decision Making In A High-Tech World: Automation Bias and Countermeasures

Automated decision aids and decision support systems have become essential tools in many high-tech environments. In aviation, for example, flight management systems computers not only fly the aircraft, but also calculate fuel efficient paths, detect and diagnose system malfunctions and abnormalities, and recommend or carry out decisions. Air Traffic Controllers will soon be utilizing decision support tools to help them predict and detect potential conflicts and to generate clearances. Other fields as disparate as nuclear power plants and medical diagnostics are similarly becoming more and more automated. Ideally, the combination of human decision maker and automated decision aid should result in a high-performing team, maximizing the advantages of additional cognitive and observational power in the decision-making process. In reality, however, the presence of these aids often short-circuits the way that even very experienced decision makers have traditionally handled tasks and made decisions, and introduces opportunities for new decision heuristics and biases. Results of recent research investigating the use of automated aids have indicated the presence of automation bias, that is, errors made when decision makers rely on automated cues as a heuristic replacement for vigilant information seeking and processing. Automation commission errors, i.e., errors made when decision makers inappropriately follow an automated directive, or automation omission errors, i.e., errors made when humans fail to take action or notice a problem because an automated aid fails to inform them, can result from this tendency. Evidence of the tendency to make automation-related omission and commission errors has been found in pilot self reports, in studies using pilots in flight simulations, and in non-flight decision making contexts with student samples. Considerable research has found that increasing social accountability can successfully ameliorate a broad array of cognitive biases and resultant errors. To what extent these effects generalize to performance situations is not yet empirically established. The two studies to be presented represent concurrent efforts, with student and professional pilot samples, to determine the effects of accountability pressures on automation bias and on the verification of the accurate functioning of automated aids. Students (Experiment 1) and commercial pilots (Experiment 2) performed simulated flight tasks using automated aids. In both studies, participants who perceived themselves as accountable for their strategies of interaction with the automation were significantly more likely to verify its correctness, and committed significantly fewer automation-related errors than those who did not report this perception.

Mosier, Kathleen L.↗
NASA NTRS1993

Modeling strategic behavior in human-automation interaction - Why an 'aid' can (and should) go unused

Task-offload aids (e.g., an autopilot, an 'intelligent' assistant) can be selectively engaged by the human operator to dynamically delegate tasks to automation. Introducing such aids eliminates some task demands but creates new ones associated with programming, engaging, and disengaging the aiding device via an interface. The burdens associated with managing automation can sometimes outweigh the potential benefits of automation to improved system performance. Aid design parameters and features of the overall multitask context combine to determine whether or not a task-offload aid will effectively support the operator. A modeling and sensitivity analysis approach is presented that identifies effective strategies for human-automation interaction as a function of three task-context parameters and three aid design parameters. The analysis and modeling approaches provide resources for predicting how a well-adapted operator will use a given task-offload aid, and for specifying aid design features that ensure that automation will provide effective operator support in a multitask environment.

Kirlik, Alex↗
NASA NTRS1991

Automated Predictive Diagnosis (APD): A 3-tiered shell for building expert systems for automated predictions and decision making

The APD software features include: On-line help, Three level architecture, (Logic environments, Setup/Application environment, Data environment), Explanation capability, and File handling. The kinds of experimentation and record keeping that leads to effective expert systems is facilitated by: (1) a library of inferencing modules (in the logic environment); (2) an explanation capability which reveals logic strategies to users; (3) automated file naming conventions; (4) an information retrieval system; and (5) on-line help. These aid with effective use of knowledge, debugging and experimentation. Since the APD software anticipates the logical rules becoming complicated, it is embedded in a production system language (CLIPS) to insure the full power of the production system paradigm of CLIPS and availability of the procedural language C. The development is discussed of the APD software and three example applications: toy, experimental, and operational prototype for submarine maintenance predictions.

Steib, Michael↗