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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 1,207 records · Page 67

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NASA NTRS1989

Extravehicular activity welding experiment

The In-Space Technology Experiments Program (INSTEP) provides an opportunity to explore the many critical questions which can only be answered by experimentation in space. The objective of the Extravehicular Activity Welding Experiment definition project was to define the requirements for a spaceflight experiment to evaluate the feasibility of performing manual welding tasks during EVA. Consideration was given to experiment design, work station design, welding hardware design, payload integration requirements, and human factors (including safety). The results of this effort are presented. Included are the specific objectives of the flight test, details of the tasks which will generate the required data, and a description of the equipment which will be needed to support the tasks. Work station requirements are addressed as are human factors, STS integration procedures and, most importantly, safety considerations. A preliminary estimate of the cost and the schedule for completion of the experiment through flight and postflight analysis are given.

Watson, J. Kevin↗

NASA NTRS1990

Factors shaping the evolution of electronic documentation systems

The main goal is to prepare the space station technical and managerial structure for likely changes in the creation, capture, transfer, and utilization of knowledge. By anticipating advances, the design of Space Station Project (SSP) information systems can be tailored to facilitate a progression of increasingly sophisticated strategies as the space station evolves. Future generations of advanced information systems will use increases in power to deliver environmentally meaningful, contextually targeted, interconnected data (knowledge). The concept of a Knowledge Base Management System is emerging when the problem is focused on how information systems can perform such a conversion of raw data. Such a system would include traditional management functions for large space databases. Added artificial intelligence features might encompass co-existing knowledge representation schemes; effective control structures for deductive, plausible, and inductive reasoning; means for knowledge acquisition, refinement, and validation; explanation facilities; and dynamic human intervention. The major areas covered include: alternative knowledge representation approaches; advanced user interface capabilities; computer-supported cooperative work; the evolution of information system hardware; standardization, compatibility, and connectivity; and organizational impacts of information intensive environments.

Dede, Christopher J.↗

NASA NTRS2020

Recurring Causes of Human Spaceflight Mishaps during Flight Tests and Early Operations

n analysis of recurring causes underlying human spaceflight mishaps that occurred during flight tests and early operations was performed. Eight mishaps from the Apollo, Soyuz, Skylab, Space Shuttle, and Constellation Programs (i.e., the Ares-1X test flight) and commercial suborbital systems were included in the study. Detailed event analyses were performed for the historical mishaps and aggregate data analyses conducted to identify recurring issues. The nine most frequent issues were inadequate technical controls or risk management practices, incomplete procedures, system design and development issues, inadequate inspection or secondary verification requirements, failures of organizations to learn from previous incidents, inadequate schedule controls, inadequate task analyses or design processes, flaws in the design of organizations, and issues with organizational safety cultures.

Timothy S Barth↗

NASA NTRSDate not supplied

TPSAS-NF1676L-18493-DND

The Air Traffic Operations Lab is a multi-fidelity, part-task, aircraft and air traffic operations simulation facility. Used to explore interactions between new airborne and ground-based tools in a NextGeneration ATC environment, including: - Concept-level flight operations research, looking at human decision making, situation awareness, transfer of authority/responsibility, etc. - Performance impact assessments of new operational concepts on the National Airspace System (NAS) - Rapid prototyping of new flight deck tools and automation - Concept-level safety assessments - Identification and evaluation of future communication, navigation, and surveillance (CNS) infrastructure requirements - Controller-pilot data link - Precision trajectory-based ops - Air-to-air data exchange

Michael T Palmer↗

NASA NTRS2019

Psychophysiological Monitoring of Aerospace Crew State

As next-generation space exploration missions necessitate increasingly autonomous systems, there is a critical need to better detect and anticipate crewmember interactions with these systems. The success of present and future autonomous technology in exploration spaceflight is ultimately dependent upon safe and efficient interaction with the human operator. Optimal interaction is particularly important for surface missions during highly coordinated extravehicular activity (EVA), which consists of high physical and cognitive demands with limited ground support. Crew functional state may be affected by a number of variables including workload, stress, and motivation. Real-time assessments of crew state that do not require a crewmember’s time and attention to complete will be especially important to assess operational performance and behavioral health during flight. In response to the need for objective, passive assessment of crew state, the aim of this work is to develop an accurate and precise prediction model of human functional state for surface EVA using multi-modal psychophysiological sensing. The psychophysiological monitoring approach relies on extracting a set of features from physiological signals and using these features to classify an operator’s cognitive state. This work aims to compile a non-invasive sensor suite to collect physiological data in real-time. Training data during cognitive and more complex functional tasks will be used to develop a classifier to discriminate high and low cognitive workload crew states. The classifier will then be tested in an operationally relevant EVA simulation to predict cognitive workload over time. Once a crew state is determined, further research into specific countermeasures, such as decision support systems, would be necessary to optimize the automation and improve crew state and operational performance.

Wusk, Grace C.↗

NASA NTRSDate not supplied

TPSAS-NF1676L-35370-DND

The above anvil cirrus plume (AACP) is a weather phenomenon that signifies an intense tropopause penetrating updraft which can inject cirrus clouds several kilometers into the stratosphere. Storms that have such intense updrafts are often supercells which generate severe weather such as tornadoes, high winds, and hail. In addition, AACPs moisten the stratosphere and influence the Earth's radiative balance. Though an AACP can be identified by the human eye, no automated AACP detection methods currently exist. Lack of detection inhibits understanding of where and how often AACPs occur, and how these storms influence stratospheric air composition. Previous work involved synthesis of multiple remote sensing and severe weather report/warning data sources to identify AACPs in Geostationary Operational Environmental Satellite system (GOES) satellite imagery and better understand their weather impacts (Bedka et al. (Wea. Forecasting, 2018)). This current study demonstrates an automated AACP identification method based on the application of a deep learning segmentation model known as a U-net. This study documents the development of a U-net model capable of identifying emergent AACPs using only satellite infrared (IR) and visible reflected sunlight imagery. The performance of a U-net is quantitatively benchmarked with human AACP identifications and qualitatively assessed through animations of detections generated from GOES-16 1-minute temporal resolution imagery.

Charles Liles↗

NASA NTRSDate not supplied

Handling Qualities Assessment of Manual Lunar Landing with Display Augmentation

Research and development is being conducted to support data-driven design decisions for manual control and human involvement in the lunar landing task under the Human Landing System (HLS) program within the Artemis campaign. A human-in-the-loop simulator evaluation of the manual control of a lunar landing vehicle in the final approach and landing phase was conducted at NASA Langley Research Center in the Lunar Flight Deck simulator using the Altair Design and Analysis Cycle (DAC)-2 government reference vehicle. The objective was to perform a direct comparison of control law types with display aiding for various rotational control powers being considered under HLS. Ten subjects (four NASA test pilots and six current pilot astronauts) provided Cooper-Harper ratings, NASA Task Load Index workload ratings, and qualitative comments. The piloting task was to assume manual control of the vehicle (including vertical descent rate) at 150 m above the landing zone, fly to a redesignated landing target (which was up to 75 m radially from the center of the landing zone) and to touch down within a position accuracy of 5m. The data showed that the display augmentation in the form of a “hover cue” significantly improved the pilot’s ability to control translation and create satisfactory handling qualities for otherwise sluggish configurations; however, the investigation also showed that display augmentation is not a panacea. Handling qualities problems, including pilot-induced oscillations, and higher workload for the lowest control powers can still be evident.

Lynda J. Kramer↗

NASA NTRSDate not supplied

Handling Qualities Assessment of Manual Lunar Landing with Display Augmentation

Lynda Kramer↗

NASA NTRS2023

Psychophysiological Research Methods to Assess Airline Flight Crew Resilient Performance in High-Fidelity Flight Simulation Scenarios

New concepts in aviation system safety thinking have emerged to consider not only what may go wrong, but also what can be learned when things go right. This approach forms a more comprehensive approach to system safety thinking. A need exists for methods to enable a better understanding of human contributions to aviation safety and how they may inform Safety Management Systems (SMS). A high-fidelity 737-800 simulation study was conducted to study how current type-rated commercial airline flight crews anticipate, monitor, respond to, and learn from expected and unexpected disturbances during line operations. A number of dependent measures were collected that included traditional SMS data types, but also non-traditional safety data to include multiple psychophysiological metrics. This paper describes the psychophysiological measures results that evinced the capability of measures to help identify resilient flight crews. Implications for future research and design of future In-time Aviation Safety Management Systems are discussed.

Psychophysiology↗

NASA NTRS1992

The Language Research Center's Computerized Test System for environmental enrichment and psychological assessment

In the spring of 1987, we undertook to provide environmental enrichment to nonhuman primate subjects in ways that would complement and even contribute to the bio-behaviorial science that justified the monkeys' captivity. Of course, the psychological well-being of captive primates--and indeed all research species-- has been an area of intense research activity since the 1985 amendment of the Animal Welfare Act. This mandate for researchers to ensure the psychological, as well as physical, fitness of experimental animals catalyzed the humane and scientific interests of the research community. The contemporary literature is replete with proposed means both of assaying and of providing enrichment and well-being. Notwithstanding, consensus on either assessment or intervention has yet to be reached. The paradigm we employed was modelled after successful efforts with chimpanzees. An automated test system was constructed in which subjects responded to computer tasks by manipulating a joystick. The tasks, interactive game-like versions of many of the classic testing paradigms of cognitive and comparative psychology, permitted the controlled presentation of stimuli and demands without the required presence of a human experimenter. Despite significant barriers to the success, rhesus monkeys (Macaca mulatta) and a variety of other primate species (including, of course, humans) have mastered the skills necessary for testing in this paradigm. Previous experiments have illustrated the utility of the test system for addressing questions of learning, memory, attention, perception, and motivation. Additional data have been reported to support the contention that the Language Research Center's Computerized Test System (LRC-CTS) serves its other raison d'etre--providing environmental enrichment and assessing psychological well-being. This paper is designed to augment previous descriptions of the technology and the paradigm for scientists and caretakers interested in environmental enrichment and performance assessment with primates.

Non-NASA Center↗

DOE OSTI2023

The impact of human expert visual inspection on the discovery of strong gravitational lenses

We investigate the ability of human ‘expert’ classifiers to identify strong gravitational lens candidates in Dark Energy Survey like imaging. We recruited a total of 55 people that completed more than 25 percent of the project. During the classification task, we present to the participants 1489 images. The sample contains a variety of data including lens simulations, real lenses, non-lens examples, and unlabelled data. We find that experts are extremely good at finding bright, well-resolved Einstein rings, while arcs with g-band signal to noise less than ~25 or Einstein radii less than ~1.2 times the seeing are rarely recovered. Very few non-lenses are scored highly. There is substantial variation in the performance of individual classifiers, but they do not appear to depend on the classifier’s experience, confidence or academic position. These variations can be mitigated with a team of 6 or more independent classifiers. Our results give confidence that humans are a reliable pruning step for lens candidates, providing pure and quantifiably complete samples for follow-up studies.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2003

The biomedical challenges of space flight

Space medicine has evolved considerably through past U.S. missions. It has been proven that humans can live and work in space for long durations and that humans are integral to mission success. The space medicine program of the National Aeronautics and Space Administration (NASA) looks toward future long-duration missions. Its goal is to overcome the biomedical challenges associated with maintaining the safety, health, and optimum performance of astronauts and cosmonauts. This program investigates the health effects of adaptation to microgravity: the nature of their pathologies, the effects of microgravity on pathophysiology, and the alterations in pharmacodynamics and treatment. A critical capability in performing research is the monitoring of the health of all astronauts and of the spacecraft environment. These data support the evidence-based approach to space medicine, incorporating past studies of microgravity-related conditions and their terrestrial counterparts. This comprehensive approach will enable safe and effective exploration beyond low Earth orbit.

Mir Project↗

DOE OSTI2025

Uncertainty quantification of a physics-informed model based on sparse identification of a Thermal Energy Distribution System

Integrated energy systems (IES)s are crucial for enhancing the economy and efficiency of power generation sources (e.g., nuclear energy) necessary to unleash American energy dominance. These systems can be integrated with thermal energy storage (TES) and intermittent renewable energies to optimize overall energy use, peak-load regulation, and demand-side responses. However, the stabilization of energy generation, transport, and utilization introduces operational complexities that exceed the challenges of managing each sub-component individually. Currently, though IESs rely on human operators for efficiency and stability, reducing human error risk and enhancing performance through automation is highly desirable. Recent advances at Idaho National Laboratory have demonstrated successful control of the Thermal Energy Distributed System (TEDS). However, the automatic control system depends on a deterministic Sparse Identification of Nonlinear Dynamics with Control (SINDyC) model, which are trained based on simulation data from physics-based simulations. Because of uncertainties in physics-based simulation, SINDyC model results in large discrepancies against experimental data and cannot be reliably used in automatic control. In this paper, we present an innovative approach to address these discrepancies by quantifying uncertainties and developing a more robust model. We first generated trajectories by using first-principles physics codes to encapsulate the experiment. Next, we trained thousands of models by randomly sampling these trajectories. We then collapsed all those models into one probabilistic SINDyC by fitting a multivariate Gaussian distribution onto the resulting coefficient’s distribution. Despite its simplicity, our approach successfully produced 95% confidence intervals that captured the experimental trajectories. It even did so with a higher probability and better U-pooling score across six of the seven relevant quantities of interest (QoIs), as compared to other classical approaches. In conclusion, ongoing research is focusing on generating new experimental trajectories to validate this approach, and on employing Bayesian calibration to refine parametric uncertainties and guide future model development efforts.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2015

TU-EF-204-04: Feasibility Study of a Novel Stationary Spectral CT Scanner

Purpose: To evaluate a cadmium zinc telluride (CZT) detector module and to develop a proof-of-concept bench-top system to examine the concept and feasibility of a stationary spectral scanner that would consist of a CZT detector ring and a distributed source for rotationless imaging. Methods: The bench-top system employs a double-rotation set-up with two concentric but independent rotating stages, one for turning the object at sparse angles to mimic the imaging from a sequentially activated source distributed on a ring, while the other for translating the detector module to simulate a projection arc along a virtual detector ring. A source of 30-degree fan is used in the set-up, and the ring is designed to be 85cm in diameter in order to provide a 22cm field-of-view that is large enough for an average human head. The CZT detector module has 16×16 0.5mm pixels and eight global energy thresholds, and is attached to an experimental data acquisition board from the same vendor (Nova R&D). Its pixel gain is first calibrated using radioisotope Tc-99m to align the photo-peaks. Count rate performance is assessed using the X-ray source. Air and offset corrections are done prior to each CT scan. A 10cm acrylic phantom with 5 different inserts is imaged at 100kVp, 2mA, with an effective exposure window of 150ms per projection. Reconstruction is done using a total variation-type iterative method. Results: The detector module exhibited large gain variations among pixels. Average count rate was over 1Mcps per pixel. Parallax effect was observed along the arcs but was corrected. Reconstructed images from two energy bins demonstrated good contrast, spatial resolution and image uniformity, with little artifacts. Conclusion: The CZT detector tested is suitable for imaging relatively small objects. A stationary spectral CT appears feasible should we have enough buttable modules and a distributed source. This work was supported by Ministry of Science and Technology of China through grants 11YQ040082 and 2012BAI13B04.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

Artificial Intelligence

Artificial intelligence is the study of intelligence agents as demonstrated by machines. It is an interdisciplinary field involving computer science as well as, various kinds of engineering and science, for example, robotics, bio-medical engineering, that accentuates automation of human acts and intelligence through machines. AI represents state-of-the-art use of machines to bring about algorithmic computation and understanding of tasks that include learning, problem solving, mapping, perception, and reasoning. Given the data and a description of its properties and relations between objects of interest, AI methods can perform the aforementioned tasks. Widely applied AI capabilities, e.g. learning, are now achievable at large scale through machine learning (ML), large volumes of data and specialized computational machines. ML encompasses learning without any kind of supervision (unsupervised learning) and learning with full supervision (supervised learning). Widely applied supervised learning techniques include deep learning and other machine learning methods that require less data than deep learning e.g. support vector machines, random forests. Unsupervised learning examples include dictionary learning, independent component analysis, and autoencoders. For application tasks with less labeled data, both supervised and unsupervised techniques can be adapted in a semi-supervised manner to produce accurate models and to increase the size of the labeled training data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

ML-based Real-Time Control at the Edge: An Approach Using hls4ml

This study focuses on implementing a real-time control system for a particle accelerator facility that performs high energy physics experiments. A critical operating parameter in this facility is beam loss, which is the fraction of particles deviating from the accelerated proton beam into a cascade of secondary particles. Accelerators employ a large number of sensors to monitor beam loss. The data from these sensors is monitored by human operators who predict the relative contribution of different sub-systems to the beam loss. Using this information, they engage control interventions. In this paper, we present a controller to track this phenomenon in real-time using edge-Machine Learning (ML) and support control with low latency and high accuracy. We implemented this system on an Intel Arria 10 SoC. Optimizations at the algorithm, high-level synthesis, and interface levels to improve latency and resource usage are presented. Our design implements a neural network, which can predict the main source of beam loss (between two possible causes) at speeds up to 575 frames per second (fps) (average latency of 1.74 ms). The practical deployed system is required to operate at 320 fps, with a 3ms latency requirement, which has been met by our design successfully.

Shi, R.↗

NASA NTRS1973

Identification of human operator performance models utilizing time series analysis

The results of an effort performed by Sperry Systems Management Division for AMRL in applying time series analysis as a tool for modeling the human operator are presented. This technique is utilized for determining the variation of the human transfer function under various levels of stress. The human operator's model is determined based on actual input and output data from a tracking experiment.

Holden, F. M.↗

NASA NTRS1995

Ventilatory inhomogeneity determined from multiple-breath washouts during sustained microgravity on Spacelab SLS-1

We used multiple-breath N2 washouts (MBNW) to study the homogeneity of ventilation in four normal humans (mean age 42.5 yr) before, during, and after 9 days of exposure to microgravity on Spacelab Life Sciences-1. Subjects performed 20-breath MBNW at tidal volumes of approximately 700 ml and 12-breath MBNW at tidal volumes of approximately 1,250 ml. Six indexes of ventilatory inhomogeneity were derived from data from (1) distribution of specific ventilation (SV) from mixed-expired and (2) end-tidal N2, (3) change of slope of N2 washout (semilog plot) with time, (4) change of slope of normalized phase III of successive breaths, (5) anatomic lead dead space, and (6) Bohr dead space. Significant ventilatory inhomogeneity was seen in the standing position at normal gravity (1 G). When we compared standing 1 G with microgravity, the distributions of SV became slightly narrower, but the difference was not significant. Also, there were no significant changes in the change of slope of the N2 washout, change of normalized phase III slopes, or the anatomic and Bohr dead spaces. By contrast, transition from the standing to supine position in 1 G resulted in significantly broader distributions of SV and significantly greater changes in the changes in slope of the N2 washouts, indicating more ventilatory inhomogeneity in that posture. Thus these techniques can detect relatively small changes in ventilatory inhomogeneity. We conclude that the primary determinants of ventilatory inhomogeneity during tidal breathing in the upright posture are not gravitational in origin.

Prisk, G. Kim↗