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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
DOE OSTI2026

Soil microbiome resilience to short-term (30 days, 90 days) and long-term (1000 days) drought

This dataset contains data used for the paper "Drought duration does not impact soil microbiome resilience". The Related References will be updated with a full citation when available. Increasing global droughts exert large but poorly understood effects on the microbial communities and ecology of soil. Microbial communities generally show resilience and return to pre-drought conditions when short-term droughted soils are rewet; soils exposed to long-term drought, however, often show a lag upon rewetting, after which microbial communities may or may not return to their pre-stressed conditions. Though short-term droughts have been widely studied, long-term drought manipulation experiments remain rare, especially those that compare microbial response to short-term and long-term drought in tandem. We conducted a 1000-day drought simulation in controlled laboratory conditions with soil cores collected from a tidal freshwater ecosystem in Washington state, USA, and subsequently exposed them to rewetting for two weeks. We also included short-term (30-day and 90-day) drought and rewet treatments to directly compare microbial community and organic matter responses across drought durations. We found distinct microbial taxa belonging to Firmicutes and Actinobacteria enriched after the 1000-day drought, but not after the short-term droughts. While we hypothesized that the microbial community would recover from a short-term drought after rewetting to resemble pre-drought conditions, our results revealed community dissimilarities between rewet and pre-drought conditions across all drought durations. These findings suggest unique microbial life history strategies within certain microbial phyla that make them successful colonizers during an extended drought period, and the influence of environmental and physiological context on microbial responses to rewetting. The 16SrRNA gene amplicon dataset contains processed DNA sequences in the form of an ASV table with raw unrarefied read counts and representative sequences in .fasta format as described in the ESS-DIVE amplicon sequence reporting format (https://ess-dive.gitbook.io/amplicon-sequencing-reporting-format/instructions). The Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR-MS) dataset consists of processed files containing presence absence data of molecular formulae and molecular characterization of FTICR resolved peaks. The Nuclear Magnetic Resonance (NMR) dataset contains files relevant to NMR spectra and peaks. A sample key file and a sample metadata file is included for the FTICR/NMR and 16S dataset respectively.

1000-day drought↗
DOE OSTI2025

Optimizing Hydronic Heating for Comfort and Performance in Multifamily Housing

Inefficient control settings in multifamily boilers often lead to substantial energy and cost penalties. To address this, a Fault Detection and Diagnostic (FDD) tool was developed to automate data analysis and identify operational faults such as suboptimal outdoor temperature sensor placement, misconfigured outdoor air reset (OAR) curves, excess boiler cycling, and domestic hot water (DHW) setpoint errors. By comparing pre- and post-implementation periods and applying engineering models, the tool quantifies energy savings and reduces manual analysis time by over 90%. Testing on over 100 monitored sites and a targeted subset of 12 buildings showed an average 11% energy savings from remote optimization; further validation across 19 OAR curve changes confirmed the tool’s accuracy, predicting actual savings within ±5% for most cases. Simple payback can be under three years for many multifamily buildings, though rising hardware, labor, and fuel costs create uncertainties, and decarbonization goals increasingly shift focus to electrification. The FDD tool remains invaluable for optimizing existing boilers, enhancing future electrification measures, and adapting to new technologies by refining building load estimates. In doing so, it supports both near-term efficiency and long-term transitions to low-carbon alternatives, ensuring buildings achieve substantial cost and energy benefits throughout their system lifecycles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Plutonium Oxidation State Distribution in the Presence of WIPP-Relevant Organics and Iron Corrosion Products

The oxidation state and solubility of plutonium (Pu) in high ionic strength synthetic WIPP (Waste Isolation Pilot Plant) brines as a function of pC H+ in the presence and absence of WIPP-relevant organic ligands (EDTA [Ethylenediaminetetraacetic acid], oxalate, citrate, acetate) and iron corrosion products (magnetite and metallic iron) at 𝑇 = 23 ± 2 ∘C was thoroughly studied by long-term batch solubility experiments (between approximately 800-1,100 days) from an undersaturation approach. The oxidation state of Pu in the WIPP environment has been a topic of interest since the initial Compliance Certification Application (CCA). This study aims to investigate the solubility of Pu under the expected WIPP conditions and to determine the oxidation state of the solid phase that will control the solubility. One of the most important results of this study is that the Pu oxidation state was analyzed both from the surface area of the corrosion products and in the precipitated solid. The analysis of the Pu oxidation state on the surface of the iron mineral from the ongoing undersaturated experiments is more relevant to the performance assessment of nuclear waste disposal than short term batch (plutonium-iron phase) experiments. The X-ray Absorption Near-Edge Spectroscopy (XANES) analysis showed that Pu oxidation state is different on the metal surface and in the precipitated solid. Pu(III) is the dominant oxidation state in the metallic iron (Fe 0 ) system in the presence and absence of organics. Pu(IV) is the dominant oxidation state in the magnetite system in the presence and absence of organics. Also, organics stabilize Pu(IV) in the magnetite system. Analysis of Pu in the precipitated solid by Extended X-ray Absorption Fine Structure (EXAFS) analysis showed that Pu formed an inner-sphere complex with iron with minor amounts of PuO 2 present. X-ray diffraction (XRD) results indicate that metallic iron and magnetite did not oxidize in three years in the alkaline and high ionic strength system. Under these conditions (8 < pC H+ < 10 at T = (22 ± 2) °C under nitrogen atmosphere, the solubility of Pu changes by up to three orders of magnitude (10 -5 and 10 -8 M). The spread in solubility is highest at pC H+ = 9. Pu(III) and Pu(IV) showed different solubility behavior in the synthetic WIPP brine. A summary of the data collected in this report will be submitted to Sandia National Laboratories as part of a parameter update report which will outline the changes to the OXSTAT parameter for the 2026 Compliance Recertification Application (CRA-2026). The experiments performed were done according to the U.S. Department of Energy (DOE) approved Test Plan entitled “Effects of Radiolysis, Organic Complexation, and Redox Conditions on the Speciation and Oxidation State Distribution of Pu(III/IV)” (LCO-ACP-25). All data reported were obtained under the Los Alamos National Laboratory-Carlsbad Office (LANL-CO) Quality Assurance Program, which is compliant with the DOE Carlsbad Field Office, Quality Assurance Program Document (CBFO/QAPD).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Data-driven Community-centered Resilient Assessment and Planning Toolkit for Nexus of Energy and Water (DCRAPT-NEW)

Urban areas, including Detroit and Pittsburgh, have suffered significant dual outages of the electrical and water infrastructure in the past decade due, in part, to the increasing number of extreme weather events. With increasing temperatures and rainfall intensity, these regions need to prepare for increasing extreme events through community-based energy and water resilience analysis, planning, and enhancement. This project developed a suite of open-source, open-access, community-centered, data-driven assessment and distributed energy resource (DER) and planning tools for energy and water resilience enhancement in urban areas. Through establishing a multi-level community awareness and engagement mechanism and a comprehensive collection of power outage and flooding data, an innovative group of community energy and water resilience assessment and planning tools have been developed for a wide range of users with differing and variable sets of data available to them. The developed tools include (1) DOE EAGLE-I data-driven, deep-learning assisted resilience assessment and DER planning tools at the county level with socioeconomic factors incorporated; (2) Utility annual power outage data-driven tools for long term resilience assessment and DER planning and 15-min power outage data-driven tools for short term resilience assessment and planning; (3) Detailed engineering tools for energy and water systems resilience assessment and planning when the system topology and component fragility curves are available; (4) Alternative Resiliency Metric Calculation that extracts and separates outage and restoration processes; and (5) Co-optimization tools that evaluate the resilience of the power and sewage system and allow users to conduct joint planning with energy and wastewater systems. The developed tools provide planners, decision-makers, and stakeholders with powerful capabilities to systematically evaluate system/community resilience and optimal and actionable guidance for enhancing resilience while prioritizing DER investments. The tools have been used and validated in Detroit and Pittsburgh and can be used in other areas of the nation. In addition, this project will (1) advance the knowledge and applications of machine-learning methods in analyzing and fusing different layers of information and generating meaningful data points such as generating rare weather events; (2) significantly improve the energy and water resilience of the identified communities in Detroit and Pittsburgh and prepare for more frequent and severe weather conditions; (3) help communities assess extreme weather event impacts and address short-term and long-term resilience-related issues The developed tools have been made public via GitHub and demonstrated to community stakeholders and utility companies via the two annual workshops and numerous community engagement meetings. The project outcomes are also disseminated through publications in various journals and conference proceedings, and presentations at top conferences.

13 HYDRO ENERGY↗
DOE OSTI2026

Monitoring of Liquid Metal Reactor Heater Zones with Recurrent Neural Network Learning of Temperature Time Series

Advanced high-temperature fluid reactors (ARs), such as sodium fast reactors (SFRs) and molten salt cooled reactors (MSCRs) utilize high-temperature fluids at ambient pressure. To melt the fluid during reactor startup and prevent fluid freezing during cooldown, the thermal–hydraulic systems of such ARs include heater zones consisting of specific heaters with controllers, temperature sensors, and thermal insulation. The failure of heater zones due to insulation material degradation or improper installation, resulting in parasitic heat losses, can lead to fluid freezing. The detection of faults using a heat-transfer model is difficult because of a lack of knowledge of the experimental details. Data-driven machine learning of heater zone temperature time series offers a viable alternative. In this study, we benchmarked the performance of recurrent neural networks (RNNs) in an analysis of heat-up transient temperature time series of heater zones installed on a liquid sodium vessel. The RNN models include long short-term memory (LSTM) and gated recurrent unit (GRU) networks, as well as their bi-directional variants, BiLSTM and BiGRU. Anomalous temperature points were designated using a percentile-based threshold applied to residual fluctuations in the detrended temperature time series. Additionally, the impact of the exponentially weighted moving average (EWMA) method on detection accuracy was examined. The RNN models’ performance was assessed using precision, recall, and F 1 score metrics. Results demonstrated that RNN models effectively detect anomalies in temperature time series with the best models for each heater zone achieving F 1 scores of over 93%. To explain the variations in RNN model performance across different heater zones, we used Kullback–Leibler (KL) divergence to quantify the relative entropy between training and testing data, and the Detrended Fluctuation Analysis (DFA) to assess long-range temporal correlations. For datasets with strong long-range correlations and minimal relative entropy between training and testing data, GRU is the best-performing model. When the data exhibits weaker long-term correlations and a significant relative entropy between training and testing distributions, BiGRU shows the best performance. For the data sets with intermediate values of both KL divergence and DFA, the best performance is obtained with LSTM and BiLSTM, respectively.

gated recurrent unit↗
DOE OSTI2026

NASA’s Pandora SmallSat Mission: Simulated Modeling and Retrieval of Near-infrared Exoplanet Transmission Spectra

Pandora is a SmallSat mission dedicated to understanding exoplanets and their host stars by disentangling the impact of stellar heterogeneity on exoplanet transmission spectra. Selected as a NASA Astrophysics Pioneers mission in 2021, Pandora will provide simultaneous long-term visible photometric monitoring (0.4–0.7 μm) and low-resolution near-infrared (NIR) spectroscopy (0.9–1.6 μm) of transiting systems for the purposes of monitoring host star variability and characterizing exoplanetary atmospheres. Pandora’s year-long prime mission from 2026 to 2027 coincides with the middle of a decade defined by targeted efforts for atmospheric characterization of exoplanets, offering a key opportunity to leverage this new resource to maximize science with JWST and other observatories. Here we investigate Pandora’s anticipated performance for the general exoplanet population accessible to transit spectroscopy, from hot Jupiters to temperate sub-Neptunes. By modeling the atmospheres of five test cases broadly consistent with the bulk properties of HD 209458b, HD 189733b, WASP-80 b, HAT-P-18 b, and K2-18 b, we find that Pandora may provide abundance constraints as precise as ∼1.0 dex for main atmospheric absorbers such as H 2 O and CH 4 . Then, we explore the synergies between Pandora and JWST. Our results suggest that targets with JWST data in the NIR can benefit from the addition of Pandora observations and result in more reliable abundance estimates than with JWST data alone. Moreover, Pandora can serve the community by providing precursory observations of targets of interest for JWST atmospheric characterization. We conclude by outlining strategies for the use of Pandora as a standalone observatory and in synergy with JWST.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Observational ozone datasets over the global oceans and polar regions (version 2024)

Studying tropospheric ozone over the remote areas of the planet, such as the open oceans and the polar regions, is crucial to understand the role of ozone as a global climate forcer and regulator of atmospheric oxidative capacity. A focus on the pristine oceanic and polar regions complements the available land-based datasets and provides insights into key photochemical and depositional loss processes that control the concentrations and spatiotemporal variability in ozone as well as the physicochemical mechanisms driving these patterns. However, an assessment of the role of ozone over the oceanic and polar regions has been hampered by a lack of comprehensive observational datasets. Here, we present the first comprehensive collection of ozone data over the oceans and the polar regions. The overall dataset consists of 77 ship cruises/buoy-based observations and 48 aircraft-based campaigns. The dataset, consisting of more than 630 000 independent ozone measurement data points covering the period from 1977 to 2022 and an altitude range from the surface to 5000 m (with a focus on the lowest 2000 m), allows systematic analyses of the spatiotemporal distribution and long-term trends over the 11 defined ocean/polar regions. The datasets from ships, buoys, and aircraft are complemented by ozonesonde data from 29 launch sites or field campaigns and by 21 non-polar and 17 polar ground-based station datasets. The datasets contain information on how long the observed air masses were isolated from land, as estimated by backward trajectories from the individual observation points. To extract observations representative of oceanic conditions, we recommend using a subset of the data with an isolation time of 72 h or longer, from the analysis with coincident radon observations. These filtered oceanic and polar data showed typically flat diurnal cycles at high latitudes, whereas daytime decreases in ozone (11 %–16 %) were observed at lower latitudes. The ship/buoy- and aircraft-based datasets presented here will supplement the land-based ones in the TOAR-II (Tropospheric Ozone Assessment Report Phase II) database to provide a fully global assessment of tropospheric ozone. The described dataset is available at https://doi.org/10.17596/0004044 (Kanaya et al., 2025).

Kanaya, Yugo [Japan Agency for Marine-Earth Scienc↗
DOE OSTI2025

Evaluating the potential of short-term instrument deployment to improve distributed wind resource assessment

Distributed wind projects, which are connected at the distribution level of an electricity system or in off-grid applications to serve specific or local energy needs, often rely solely on wind resource models to establish wind speed and energy generation expectations. Historically, anemometer loan programs have provided an affordable avenue for more accurate onsite wind resource assessment, and the lowering cost of lidar systems has shown similar advantages for more recent assessments. While a full 12 months of onsite wind measurement is the standard for correcting model-based long-term wind speed estimates for utility-scale wind farms, the time and capital investment involved in gathering onsite measurements must be reconciled with the energy needs and funding opportunities that drive expedient deployment of distributed wind projects. Much literature exists to quantify the performance of correcting long-term wind speed estimates with 1 or more years of observational data, but few studies explore the impacts of correcting with months-long observational periods. This study aims to answer the question of how short you can go in terms of the observational time period needed to make impactful improvements to model-based long-term wind speed estimates. Three algorithms, multivariable linear regression, adaptive regression splines, and regression trees, are evaluated for their skill at correcting long-term wind resource estimates from the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) using months-long periods of observational data from 66 locations across the US. On average, correction with even 1 month of observations provides significant improvement over the baseline ERA5 wind speed estimates and produces median bias magnitudes and relative errors within 0.22 m s −1 and 4 percentage points of the median bias magnitudes and relative errors achieved using the standard 12 months of data for correction. However, in cases when the shortest observational periods (1 to 2 months) used for correction are not well correlated with the overlapping ERA5 reference, the resultant long-term wind speed errors are worse than those produced using ERA5 without correction. Summer months, which are characterized by weaker relative wind speeds and standard deviations for most of the evaluation sites, tend to produce the worst results for long-term correction using months-long observations. The three tested algorithms perform similarly for long-term wind speed bias; however, regression trees perform notably worse than multivariable linear regression and adaptive regression splines in terms of correlation when using 6 months or less of observational data for correction. Translating the analysis to wind energy, median relative errors in the capacity factor are on average within 10 % using 1 month of training. If the observation period used for correction is not well correlated with the reference data, however, misrepresentation of the observed capacity factor can be substantial. The risk associated with poor correlation between the observed and reference datasets decreases with increasing training period length. In the worst-correlation scenarios, the median capacity factor relative errors from using 1, 3, and 6 months are within 47 %, 26 %, and 16 %, respectively.

17 WIND ENERGY↗
DOE OSTI2025

TRAILS Output Files

Overview This data repository contains ZIP files that store compressed versions of the output of running the WaterPaths utility planning and management tool in the DU Re-Evaluation mode (to download the tool, please see this GitHub repository). The tool was used to simulate the six-utility North Carolina Research Triangle problem. Details on the contents of each ZIP file can be seen below. Data details Temporal range: Weekly data for 2,344 weeks from 2015 to 2060 (45 years). Spatial range: Six water utilities in the North Carolina Research Triangle region (0: Chapel Hil/OWASA, 1: Durham, 2: Cary, 3: Raleigh, 4: Pittsboro, and 5: Chatham) File types: CSV and OUT Different solutions available The solution numbers correspond to the different pathway strategies (henceforth referred to as "solutions") discussed in paper's main and supporting text (abstract and link to the paper here). They are as follows: Sol92: The Durham-focused pathway strategy Sol132: The Raleigh-focused pathway strategy Sol140: The regionally-robust pathway strategy Objectives files These files can be accessed by unzipping solXX_objectives_pathways.zip that contains 1,000 Objectives_RDMXX_solsXX_to_XX.csv files. Each CSV file will consist of a row representing all the objective values for that specific solution, while every six columns represents the reliability, restriction frequency, infrastructure net present value ($ mil), peak financial cost, worst-case cost, and unit cost ($ per MG; in that order) for each of the six utilities. There will be 1,000 such files, denoting the performance of the six utilities across the 1,000 deeply uncertain states of the world (DU SOWs). Pathway files These files can be accessed by unzipping solXX_objectives_pathways.zip that contains 1,000 Pathways_sXX_RDMXX.out file. Each OUT corresponds to the set of infrastructure being triggered in a specific DU SOW, and each file will have the name file will consist of four tab-delimited columns that are described as follows: Realization: The realization in which an infrastructure options being triggered utility: The utility currently triggering infrastructure week: The week in which a specific infrastructure option is being triggered infra.: The infrastructure option being triggered If the OUT file contains only the header line, no infrastructure was triggered for that specific DU SOW. Policies files These files can be obtained by unzipping Policies.zip. Each of the 1,000 CSV files within the unzipped folder will contain weekly water use restriction policies for all 1,000 hydroclimatic realizations within a specific DU SOW. The column structure is as follows: 0rest_m: restriction multiplier for utility 0 (values between 0 and 1) 1rest_m: restriction multiplier for utility 1 (values between 0 and 1) 2rest_m: restriction multiplier for utility 2 (values between 0 and 1) 3rest_m: restriction multiplier for utility 3 (values between 0 and 1) 4rest_m: restriction multiplier for utility 4 (values between 0 and 1) 5rest_m: restriction multiplier for utility 5 (values between 0 and 1) 0transf: transfer volume for utility 0 (in MGD) 1transf: transfer volume for utility 1 (in MGD) 2transf: transfer volume for utility 2 (in MGD) 3transf: transfer volume for utility 3 (in MGD) 4transf: transfer volume for utility 4 (in MGD) 5transf: transfer volume for utility 5 (in MGD) Water Sources files These files can be obtained by unzipping WaterSources_subset.zip. Each of the 100 CSV files within the unzipped folder will contain weekly state variables at each water source for all 1,000 hydroclimatic realizations within a specific DU SOW. The column structure is as follows: Xvolume: available water volume from source X (in MGD) Xs_area: surface area of source X (in ACF) Xdemand: demand drawn from a water source from source X (in MGD) Xup_spill: upstream spillage from source X (in MGD) Xww_inflow: wastewater inflow from source X (in MGD) Xcatch_inflow: upstream catchment inflow to source X (in MGD) Xevap: evaporation multiplier for source X (values between 0 and 1) Xds_spill: downstream spillage from source X (in MGD) X_Y_alloc_cap: the allocated capacity from source X to utility Y (values between 0 and 1) X_Y_alloc_dem: the allocated demand from source X to utility Y (values between 0 and 1) Xtrmt_alloc_Y: the allocated treatment capacity from source X to utility Y (values between 0 and 1) Utilities files These files can be obtained by unzipping Utilities_subset.zip. Each of the 100 CSV files within the unzipped folder will contain weekly state variables at each utility for all 1,000 hydroclimatic realizations within a specific DU SOW. The column structure is as follows: Xst_vol: total available storage volume of utility X (in MG) Xcapacity: total storage capacity of utility X (in MG) Xnet_inf: : net inflow for all storage infrastructure for utility X (in MGD) Xst_rof: short term ROF for utility X (values between 0 and 1) Xst_stor_rof: short-term storage ROF for utility X (values between 0 and 1) Xst_trmt_rof: short-term treatment ROF for utility X (values between 0 and 1) Xlt_rof: long-term ROF for utility X (values between 0 and 1) Xlt_stor_rof: long-term storage ROF for utility X (values between 0 and 1) Xlt_trmt_rof: long-term treatment ROF for utility X (values between 0 and 1) Xrest_demand: restricted demand for utility X (in MGD) Xunrest_demand: unrestricted demand for utility X (in MGD) Xunfulf_demand: unfulfilled demand for utility X (in MGD) Xwastewater: wastewater return for utility X (in MGD) Xtreat_capacity: total treatment capacity for utility X (in MG) Xcont_fund: reserve (contingency) fund balance for utility X Xins_pout: insurance payout for utility X (% annual volumetric revenue) Xins_price: insurance price for utility X (% annual volumetric revenue) Xinfra_npv: infrastructure net present value for utility ($mil) Xst_vol: total available storage volume of utility X (in MG) Xdebt_serv: debt service for utility X (usually once per year if the infrastructure is triggered; % annual volumetric revenue) Xstor_vol: total stored volume (in MGD) Xobs_ann_dem: observed annual demand for utility X (in MGD) Xproj_dem: projected annual demand for utility X (in MGD) Xpv_debt_serv: present value of debt service payments for utility X (% annual volumetric revenue) Xgross_rev: gross revenue for utility X ($mil) Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program.

Artificial Intelligence↗
NASA NTRS1972

Study of advanced techniques for determining the long-term performance of components

A study was conducted of techniques having the capability of determining the performance and reliability of components for spacecraft liquid propulsion applications for long term missions. The study utilized two major approaches; improvement in the existing technology, and the evolution of new technology. The criteria established and methods evolved are applicable to valve components. Primary emphasis was placed on the propellants oxygen difluoride and diborane combination. The investigation included analysis, fabrication, and tests of experimental equipment to provide data and performance criteria.

Source record↗
NASA NTRS1975

Main principles used for building up a data bank for the long-term storage and retrieval of scientific space data

Methodological, technical, and practical organizational problems are considered of constructing a long term data bank. Data processing flowcharts are presented which are based on an analysis of the tasks which the data bank must fulfill, an estimate of the existing and predicted data flows in the processing center, and the selection of the data storage media and the forms in which the data are presented. Questions related to the use of standard and special hardware and software in the data bank are discussed. Reasons are presented for the adopted structure of the document-fact type information retrieval system which permits the mechanization and automation of various stages in the retrieval and selection of the data.

Zlotin, G. N.↗
NASA NTRS1976

The long-term intensity behavior of Centaurus X-3

In three years of observation, the X-ray source Cen X-3 appears to alternate between 'high states', with an intensity of 150 counts/s (2-6 keV) or greater, and 'low states', where the source is barely detectable. The time scale of this behavior is of the order of months, and no apparent periodicity has been observed. Analysis of two transitions between these states is reported. During two weeks in July 1972, the source increased from about 20 counts/s to 150 counts/s. The detailed nature of this turn-on is interpreted in terms of a model in which the supergiant's stellar wind decreases in density. A second transition, a turnoff in February 1973, is similarly analyzed and found to be consistent with a simple decrease in accretion rate. The presence of absorption dips during transitions at orbital phases 0.4-0.5 as well as at phase 0.75 is discussed. The data are consistent with a stellar-wind accretion model and with different kinds of extended lows caused by increased wind density masking the X-ray emission or by decreased wind density lowering the accretion rate.

Schreier, E. J.↗
NASA NTRS1976

The computer simulation of automobile use patterns for defining battery requirements for electric cars

The modeling process of a complex system, based on the calculation and optimization of the system parameters, is complicated in that some parameters can be expressed only as probability distributions. In the present paper, a Monte Carlo technique was used to determine the daily range requirements of an electric road vehicle in the United States from probability distributions of trip lengths, frequencies, and average annual mileage data. The analysis shows that a daily range of 82 miles meets to 95% of the car-owner requirements at all times with the exception of long vacation trips. Further, it is shown that the requirement of a daily range of 82 miles can be met by a (intermediate-level) battery technology characterized by an energy density of 30 to 50 Watt-hours per pound. Candidate batteries in this class are nickel-zinc, nickel-iron, and iron-air. These results imply that long-term research goals for battery systems should be focused on lower cost and longer service life, rather than on higher energy densities

Schwartz, H.-J.↗
NASA NTRS1977

Lifetimes of fiber composites under sustained tensile loading

Results are presented for a study intended to summarize lifetime data on several fiber/epoxy composite materials subjected to sustained uniaxial tensile loading, to report preliminary results of an accelerated test method for predicting the life of simple composites, and to describe related work in progress on pressure vessels and other filament-wound structures. The lifetime performance of the tested composites was compared by plotting the percent of ultimate strength (applied fiber stress normalized with respect to fiber failure stress in a composite) versus lifetime. In terms of performance in long-term tensile applications, the tested composites are ranked in the following order: graphite/epoxy, Be wire/epoxy, Aramid/epoxy, and S-glass/epoxy. The accelerated test using temperature and stress to simulate the passage of time proves to be encouraging, at least in the case of the Aramid/epoxy composite. The potential of a statistical analysis based on Weibull distribution analyses or a power law relationship is demonstrated.

Chiao, T. T.↗
NASA NTRS1975

Water vapor weathering of Taurus-Littrow orange soil - A pore-structure analysis

A pore-volume analysis was performed on water vapor adsorption data previously obtained on a fresh sample of Taurus-Littrow orange soil, and the analysis was repeated on the same sample after its exposure to moist air for a period of approximately six months. The results indicate that exposure of an outgassed sample to high relative pressures of water vapor can result in the formation of substantial micropore structure, the precise amount being dependent on the sample pretreatment, particularly the outgassing temperature. Micropore formation is explained in terms of water penetration into surface defects. In contrast, long-term exposure to moist air at low relative pressures appears to reverse the process with the elimination of micropores and enlargement of mesopores possibly through surface diffusion of metastable adsorbent material. The results are considered with reference to the storage of lunar samples.

Cadenhead, D. A.↗
NASA NTRS1979

Long-term risk analysis associated with nuclear waste disposal in space

An assessment and verification of previous analytic results on the long term risk of earth reentry for hazardous payloads is presented. The two areas were studied: (1) stability of nominal, near-circular storage orbits in the regions between Venus and earth and between earth and Mars, and (2) probability of earth reentry for off-nominal planet-crossing orbits resulting from deployment system failures. In the first area, numerical integrations of the equations of motion are compared with stability predications based on secular perturbation theory. The agreement is good in terms of the heliocentric distances covered and the general behavior of the orbital history, although certain near-resonance situations can lead to difficulty. In the second area, a Monte Carlo simulation of orbital evolution is used and the results compared with Opik's analytic theory of planetary encounters and collision statistics, with data verified to within a close order-of-magnitude.

Friedlander, A. L.↗
NASA NTRS1979

On the design of a postprocessor for a search for extraterrestrial intelligence /SETI/ system

The design of an on-line postprocessor for a search for extraterrestrial intelligence (SETI) system is described. Signal processing tasks of the postprocessor include: (1) analysis of power level, phase coherence, and state of polarization of single-channel signals in a search for significant signals; (2) grouping or aggregation of adjacent channel data, time averaging of data; and (3) the detection of drifting and modulated signals. Control functions include multichannel spectrum analyzer frequency and clock control, system calibration and selfdiagnostic, control of data flow to and from short-term and long-term (archival) memories, and operation of detection subsystems, such as a visual display and a tunable receiver.

Healy, T. J.↗
NASA NTRS1980

Automatic control of NASA Langley's 0.3-meter cryogenic test facility

Experience during the past 6 years of operation of the 0.3-meter transonic cryogenic tunnel at the NASA Langley Research Center has shown that there are problems associated with efficient operation and control of cryogenic tunnels using manual control schemes. This is due to the high degree of process crosscoupling between the independent control variables (temperature, pressure, and fan drive speed) and the desired test condition (Mach number and Reynolds number). One problem has been the inability to maintain long-term accurate control of the test parameters. Additionally, the time required to change from one test condition to another has proven to be excessively long and much less efficient than desirable in terms of liquid nitrogen and electrical power usage. For these reasons, studies have been undertaken to: (1) develop and validate a mathematical model of the 0.3-meter cryogenic tunnel process, (2) utilize this model in a hybrid computer simulation to design temperature and pressure feedback control laws, and (3) evaluate the adequacy of these control schemes by analysis of closed-loop experimental data. This paper will present the results of these studies.

Thibodeaux, J. J.↗