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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS1978

Transfer of training on manual control systems differing in short period frequency and damping characteristics

Each of four groups of 16 subjects was trained on one of four compensatory tracking tasks that differed with regard to short period natural frequency and damping characteristics. After completion of the training sessions, the members of each group either transferred to a task on which they had not been trained or continued with their original task. Analysis of the training data indicated that relative task difficulty was largely determined by system damping which, however, had little effect on the amount of transfer during the transfer trials. The effect of system frequency was essentially reversed, and a marked interaction between training and transfer frequencies was observed in the transfer data. Similar results were obtained both with relative error scores and transinformation scores. Positive transfer was exhibited by most of the groups when they transferred to tasks on which they had not been trained.

Lincoln, R. S.↗
NASA NTRS1990

Users manual for the Automated Performance Test System (APTS)

The characteristics of and the user information for the Essex Automated Performance Test System (APTS) computer-based portable performance assessment battery are given. The battery was developed to provide a menu of performance test tapping the widest possible variety of human cognitive and motor functions, implemented on a portable computer system suitable for use in both laboratory and field settings for studying the effects of toxic agents and other stressors. The manual gives guidance in selecting, administering and scoring tests from the battery, and reviews the data and studies underlying the development of the battery. Its main emphasis is on the users of the battery - the scientists, researchers and technicians who wish to examine changes in human performance across time or as a function of changes in the conditions under which test data are obtained. First the how to information needed to make decisions about where and how to use the battery is given, followed by the research background supporting the battery development. Further, the development history of the battery focuses largely on the logical framework within which tests were evaluated.

Lane, N. E.↗
NASA NTRS2016

Development of an Objective Space Suit Mobility Performance Metric Using Metabolic Cost and Functional Tasks

Existing methods for evaluating EVA suit performance and mobility have historically concentrated on isolated joint range of motion and torque. However, these techniques do little to evaluate how well a suited crewmember can actually perform during an EVA. An alternative method of characterizing suited mobility through measurement of metabolic cost to the wearer has been evaluated at Johnson Space Center over the past several years. The most recent study involved six test subjects completing multiple trials of various functional tasks in each of three different space suits; the results indicated it was often possible to discern between different suit designs on the basis of metabolic cost alone. However, other variables may have an effect on real-world suited performance; namely, completion time of the task, the gravity field in which the task is completed, etc. While previous results have analyzed completion time, metabolic cost, and metabolic cost normalized to system mass individually, it is desirable to develop a single metric comprising these (and potentially other) performance metrics. This paper outlines the background upon which this single-score metric is determined to be feasible, and initial efforts to develop such a metric. Forward work includes variable coefficient determination and verification of the metric through repeated testing.

Norcross, J. R.↗
NASA NTRS2016

Development of an Objective Space Suit Mobility Performance Metric Using Metabolic Cost and Functional Tasks

Existing methods for evaluating EVA suit performance and mobility have historically concentrated on isolated joint range of motion and torque. However, these techniques do little to evaluate how well a suited crewmember can actually perform during an EVA. An alternative method of characterizing suited mobility through measurement of metabolic cost to the wearer has been evaluated at Johnson Space Center over the past several years. The most recent study involved six test subjects completing multiple trials of various functional tasks in each of three different space suits; the results indicated it was often possible to discern between different suit designs on the basis of metabolic cost alone. However, other variables may have an effect on real-world suited performance; namely, completion time of the task, the gravity field in which the task is completed, etc. While previous results have analyzed completion time, metabolic cost, and metabolic cost normalized to system mass individually, it is desirable to develop a single metric comprising these (and potentially other) performance metrics. This paper outlines the background upon which this single-score metric is determined to be feasible, and initial efforts to develop such a metric. Forward work includes variable coefficient determination and verification of the metric through repeated testing.

McFarland, Shane M.↗
NASA NTRS2013

Prediction of the Arctic Oscillation in Boreal Winter by Dynamical Seasonal Forecasting Systems

This study assesses the prediction skill of the boreal winter Arctic Oscillation (AO) in the state-of-the-art dynamical ensemble prediction systems (EPSs): the UKMO GloSea4, the NCEP CFSv2, and the NASA GEOS-5. Long-term reforecasts made with the EPSs are used to evaluate representations of the AO, and to examine skill scores for the deterministic and probabilistic forecast of the AO index. The reforecasts reproduce the observed changes in the large-scale patterns of the Northern Hemispheric surface temperature, upper-level wind, and precipitation according to the AO phase. Results demonstrate that all EPSs have better prediction skill than the persistence prediction for lead times up to 3-month, suggesting a great potential for skillful prediction of the AO and the associated climate anomalies in seasonal time scale. It is also found that the deterministic and probabilistic forecast skill of the AO in the recent period (1997-2010) is higher than that in the earlier period (1983-1996).

Coupled Models↗
DOE OSTI2022

Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic Approach

False alerts due to misconfigured or compromised intrusion detection systems (IDS) in industrial control system (ICS) networks can lead to severe economic and operational damage. However, research using deep learning to reduce false alerts often requires the physical and cyber sensor data to be trustworthy. Implicit trust is a major problem for artificial intelligence or machine learning (AI/ML) in cyber-physical system (CPS) security, because when these solutions are most urgently needed is also when they are most at risk (e.g., during an attack). To address this, the Inter-Domain Evidence theoretic Approach for Inference (IDEA-I) is proposed that reframes the detection problem as how to make good decisions given uncertainty. Specifically, an evidence theoretic approach leveraging Dempster–Shafer (DS) combination rules and their variants is proposed for reducing false alerts. A multi-hypothesis mass function model is designed that leverages probability scores obtained from supervised-learning classifiers. Using this model, a location-cum-domain-based fusion framework is proposed to evaluate the detector’s performance using disjunctive, conjunctive, and cautious conjunctive rules. The approach is demonstrated in a cyber-physical power system testbed, and the classifiers are trained with datasets from Man-In-The-Middle attack emulation in a large-scale synthetic electric grid. For evaluating the performance, we consider plausibility, belief, pignistic, and general Bayesian theorem-based metrics as decision functions. To improve the performance, a multi-objective-based genetic algorithm is proposed for feature selection considering the decision metrics as the fitness function. Finally, we present a software application to evaluate the DS fusion approaches with different parameters and architectures.

42 ENGINEERING↗
DOE OSTI2021

Analysis of power conversion technology options for a self-powered furnace

A self-powered furnace is defined as one that imports no electricity: a power cycle integrated into the furnace generates all the electrical power needed, and the heat rejected by the power cycle contributes to space heating. This paper presents criteria for selection of suitable power generation technology for such a furnace. A weighting system was presented to assign weight to each criterion based on its importance to the success of a self-powered furnace implementation. Power generation candidates were reviewed and scored based on the selection criteria. The top five candidates were analyzed to quantitatively compare the additional heat exchange requirements they impose on a baseline furnace. Air-cooled internal combustion engines and microturbine generators had negligible impact on the heat exchange requirement compared to a baseline furnace. Liquid-cooled internal combustion engines increased the heat exchange requirement by a factor of 1.5. Thermoelectric generators and thermophotovoltaic increased the heat exchange requirement by a factor of roughly 2.5. Organic Rankine cycle increased the heat exchange requirement by a factor of 5.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Dynamics-Based Peptide–MHC Binding Optimization by a Convolutional Variational Autoencoder: A Use-Case Model for CASTELO

An unsolved challenge in the development of antigen-specific immunotherapies is determining the optimal antigens to target. Comprehension of antigen–major histocompatibility complex (MHC) binding is paramount toward achieving this goal. Here, we apply CASTELO, a combined machine learning-molecular dynamics (ML-MD) approach, to identify per-residue antigen binding contributions and then design novel antigens of increased MHC-II binding affinity for a type 1 diabetes-implicated system. We build upon a small-molecule lead optimization algorithm by training a convolutional variational autoencoder (CVAE) on MD trajectories of 48 different systems across four antigens and four HLA serotypes. We develop several new machine learning metrics including a structure-based anchor residue classification model as well as cluster comparison scores. ML-MD predictions agree well with experimental binding results and free energy perturbation-predicted binding affinities. Moreover, ML-MD metrics are independent of traditional MD stability metrics such as contact area and root-mean-square fluctuations (RMSF), which do not reflect binding affinity data. Finally, our work supports the role of structure-based deep learning techniques in antigen-specific immunotherapy design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

An Integrated Framework for Effective Management of Delivery Risk in Electricity Markets: From Batteries to Insurance and Beyond

Net load imbalances due to imperfect day-ahead forecasts can cause variability in real-time electricity prices and higher system operations costs. We propose a novel market product called Flexibility Options that allow participants to hedge uncertainty by buying flexibility from flexible resources. Simulations show that flexibility options can reduce total system operating costs by up to 15% and can reduce variability in market participant revenues. To better quantify the flexibility that DER aggregators can provide, we develop DER flexibility scores that account for asset flexibility and uncertainty from occupant behavior and weather. Preliminary results show that realistic sets of DERs have significant variability in flexibility and uncertainty metrics.

delivery risk↗
DOE OSTI2022

Feedstock-Conversion Interface Consortium: Failure Mode and Effects Analysis Summary Report (FY2022)

This report provides an overview of the development of failure modes and effects analysis (FMEA) and its implementation as a systematic criticality and risk assessment tool supporting a quality by design (QbD) approach for FCIC research. This report also provides a high-level overview of the results for the FMEA evaluation of two feedstock preprocessing system configurations: (1) generation of pine residue materials for high-temperature pyrolysis conversion and (2) generation of corn stover materials for low-temperature conversion using deacetylation and disc mechanical refining pretreatment for fermentation to hydrocarbons. For the results presented in this report, our FMEA interviews included two approaches. The first approach was to perform FMEA interviews for the entire system of unit operations giving a wholistic system level view. The second approach consisted of detailed interviews for each individual unit operation within the system allowing for a “deep dive” into the specific failures for the individual components within the configuration. These two approaches provide different resolutions of information. The FMEA results of this report were focused on failures associated with meeting critical quality attributes (CQAs) identified for the target conversion processes for each processed feedstock type. The information gathered through the FMEA interviews include estimations of risk scores for meeting each given CQA specification, identification of the impacts for not meeting a CQA specification, capturing causes associated with material attributes and process parameters for each failure, identification of current detection methods, and speculation of potential mitigation strategies for decreasing a failure’s risk score. The complete results of all FMEA interviews are provided in the Appendices of this report.

09 BIOMASS FUELS↗
DOE OSTI2021

Anomaly detection in Hyper Suprime-Cam galaxy images with generative adversarial networks

ABSTRACT The problem of anomaly detection in astronomical surveys is becoming increasingly important as data sets grow in size. We present the results of an unsupervised anomaly detection method using a Wasserstein generative adversarial network (WGAN) on nearly one million optical galaxy images in the Hyper Suprime-Cam (HSC) survey. The WGAN learns to generate realistic HSC-like galaxies that follow the distribution of the data set; anomalous images are defined based on a poor reconstruction by the generator and outlying features learned by the discriminator. We find that the discriminator is more attuned to potentially interesting anomalies compared to the generator, and compared to a simpler autoencoder-based anomaly detection approach, so we use the discriminator-selected images to construct a high-anomaly sample of ∼13 000 objects. We propose a new approach to further characterize these anomalous images: we use a convolutional autoencoder to reduce the dimensionality of the residual differences between the real and WGAN-reconstructed images and perform UMAP clustering on these. We report detected anomalies of interest including galaxy mergers, tidal features, and extreme star-forming galaxies. A follow-up spectroscopic analysis of one of these anomalies is detailed in the Appendix; we find that it is an unusual system most likely to be a metal-poor dwarf galaxy with an extremely blue, higher-metallicity H ii region. We have released a catalogue with the WGAN anomaly scores; the code and catalogue are available at https://github.com/kstoreyf/anomalies-GAN-HSC; and our interactive visualization tool for exploring the clustered data is at https://weirdgalaxi.es.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1981

A comparison of head and manual control for a position-control pursuit tracking task

Head control was compared with manual control in a pursuit tracking task involving proportional controlled-element dynamics. An integrated control/display system was used to explore tracking effectiveness in horizontal and vertical axes tracked singly and concurrently. Compared with manual tracking, head tracking resulted in a 50 percent greater rms error score, lower pilot gain, greater high-frequency phase lag and greater low-frequency remnant. These differences were statistically significant, but differences between horizontal- and vertical-axis tracking and between 1- and 2-axis tracking were generally small and not highly significant. Manual tracking results were matched with the optimal control model using pilot-related parameters typical of those found in previous manual control studies. Head tracking performance was predicted with good accuracy using the manual tracking model plus a model for head/neck response dynamics obtained from the literature.

Levison, W. H.↗
DOE OSTI2026

New framework for benchmarking decadal predictions leveraging the PCMDI Metric Package with interactive visualization

Reliable climate predictions across multiple timescales are increasingly critical as climate-related risks continue to rise. With the growing number and diversity of climate prediction systems, systematic intercomparison has become essential. Here, we present a comprehensive evaluation framework based on the PCMDI Metric Package to assess the performance of multiple decadal climate prediction systems. Unlike uninitialized simulations, initialized predictions exhibit bias and predictive skill that evolve with forecast lead time. To address this, we introduce (1) model-by-lead-time portrait plots, which efficiently summarize metrics of global temperature, precipitation, and Arctic/Antarctic sea-ice extent, and (2) an HTML-based interactive visualization platform that provides detailed regional and seasonal diagnostics of model bias, skill scores, and ensemble spread for each model and lead time. Comparisons with uninitialized simulations further quantify the relative impacts of initialization and external forcing on prediction skill. The proposed framework provides a scalable and transparent approach for multi-model climate prediction assessments and can be readily extended to a wide range of operational and research forecasting systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Geo-Data Science Method for Assessing Unconventional Rare-Earth Element Resources in Sedimentary Systems

Abstract Rare-earth elements (REEs) supply raw materials that constitute many of our modern critical infrastructure, defense, technology, and electrification needs. Despite REE accumulations occurring in conventional bedrock and ion-adsorption deposits sourced from weathering of igneous rocks, unconventional host materials such as coal and related sedimentary strata have been identified as promising sources of REEs to meet growing demand. To maximize the potential of unconventional resources such as REE-coal systems, new approaches are needed overcome challenges from mineral systems with no known deposits and areas with sparse geochemical data. This article presents a systematic knowledge-data resource assessment method for predicting and identifying REE resource potential and occurrence in these unconventional systems. The method utilizes a geologic and geospatial knowledge-data approach informed and guided by REE accumulation mechanisms to systematically assess and identify areas of higher enrichment. An assessment of the Powder River Basin is presented as a test case to demonstrate the method workflow and results. The key output is a potential enrichment score map reported with varying confidence levels based on the amount of supporting evidence. Results from the test case indicate several locations with promising potential for different types of coal-REE deposits, demonstrating the viability of the method for exploration and assessment of unconventional REE resources. The method is flexible by design and, with sufficient applicable knowledge and data, can be adapted for assessing critical mineral systems in other sedimentary systems as well.

58 GEOSCIENCES↗
NASA NTRSDate not supplied

Evaluation of Planetary Extravehicular Activity Prebreathe Protocols using a 56.5 kPa, 34% O2, 66% N2 Saturation Cabin Atmosphere in an 11-day Hypobaric Hypoxia Study

INTRODUCTION: Apollo missions used 100% O2 cabin atmospheres which effectively eliminated the risk of decompression sickness (DCS) during Lunar extravehicular activities (EVAs, ‘spacewalks’); however, this atmosphere presented a flammability risk that is no longer acceptable to NASA. Denitrogenation prebreathe protocols used to mitigate DCS risk for Space Shuttle and International Space Station EVAs are validated for the microgravity environment, but the significantly increased risk of DCS during equivalent ambulatory surface EVAs make these protocols inapplicable to planetary/Lunar missions. A cabin/vehicle “Exploration Atmosphere” of 56.5 kPa (8.2 psia), 34% O2, 66% N2 has been recommended by NASA for future Moon and Mars missions as a compromise that balances subsequent pre-EVA prebreathe duration, hypoxia, and flammability risk, assuming a 29.6 kPa (4.3 psi) spacesuit. Prebreathe validation studies were initiated utilizing a three-story 6m diameter hypobaric chamber at NASA’s Johnson Space Center. Here, we report the results of a 11-day human-in-the-loop system checkout. METHODS: Six volunteers lived in a hyboparic chamber for 11 days with an ‘exploration atmosphere’ of 56.6kPa/34% O2 66% N2. Subjects acclimated to the exploration atmosphere for 48hrs and thereafter participated in five 6-hour simulated EVAs at 34kPa/85% O2 / 15% N2 over the course of 11 days. Prior to each simulated EVA, subjects underwent a 20-minute prebreathe at 85% O2. The EVA simulation was designed to include tasks that are physically and ergonomically representative of future planetary EVAs, proportionate to the subject’s VO2max. Decompression stress was evaluated during the simulated EVA by serial doppler and echocardiographs alternating every 15min, as well as clinical monitoring for DCS signs/symptoms. RESULTS AND DISCUSSION: Venous gas emboli (VGE) were present in 3 of 6 subjects during EVAs, with peak Grade II VGE as evaluated by Doppler and a peak Eftedal-Brubakk score of 5 by cardiac ultrasonography. Two cases of DCS were diagnosed during the 11-day test chamber. No acute hypoxic symptoms were noted. Musculoskeletal and gastrointestinal complaints were noted, likely associated with the exercise load and the food system. Two cases of DCS (8%) does not cross either accept or reject pre-test criterion, so an additional study is planned for 2023 to meet our pre-test thresholds.

Alejandro Garbino↗
DOE OSTI2024

Effects of Temporal Light Modulation on Individuals Sensitive to Pattern Glare

Solid-state lighting systems can vary widely in the degree of temporal light modulation (TLM) of their light output. TLM is known to have visual, cognitive, and behavioral effects but there are few recommendations for limits on the acceptable TLM in everyday lighting systems and there is little information concerning individual differences in sensitivity. This paper is a re-analysis of previously presented data, focusing on two subgroups in a larger sample: those scoring low or high on the Wilkins Pattern Glare Sensitivity (PGS) test, which is a validated test that identifies people at high risk of visual stress. In conclusion, the results show that the PGS groups differed in their sensitivity to TLM conditions, despite short exposures and a restricted field of view.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2019

NOAA-20 ATMS Status: ATMS SDR Team

Data quality looks better than Suomi-NPP (Suomi National Polar-orbiting Partnership): similar biases. Smaller standard deviation of first guess departures and diagnosed observation errors. Weaker striping signal than Suomi-NPP ATMS (Advanced Technology Microwave Sounder). Improved first guess fits to: temperature observations (AMSU-A (Advanced Microwave Sounding Unit-A), CrIS (Cross-track Infrared Sounder), GPSRO (Global Positioning System Radio Occultation)); Humidity observations (MHS (Microwave Humidity Sounder), GEO CSRs (Geostationary Orbit Clear Sky Radiances)). Indicates improved accuracy of short range temperature and humidity forecasts. Neutral to slightly positive forecast scores.

Sensor Data Record (SDR)↗