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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1977

Mechanical properties of several nickel alloys in hydrogen at elevated temperatures

Tests were performed to determine low cycle fatigue and crack growth rate properties of one iron-base and two forms of one cast nickel-base alloy. The alloys were tested in various forms and/or heat-treat conditions that are proposed for use in a high-pressure hydrogen or a hydrogen-water vapor environment. Some general conclusions can be made comparing the results of tests in a hydrogen environment with those in a hydrogen-water vapor environment. The hydrogen-water vapor environment caused a 50 percent average reduction in fatigue life, indicating extreme degradation when compared with tests conducted in air, for Incoloy 903 at 1033 K (1400 F). Crack growth rates increased significantly for all materials with increasing test temperature. A very significant increase (three orders of magnitude) in crack growth rate occurred for Incoloy 903 tested in the hydrogen-water vapor environment when compared with testing done in hydrogen along at 922 K (1200 F).

Warren, J. R.↗
NASA NTRS1983

Design, fabrication and testing of porous tungsten vaporizers for mercury ion thrusters

The dispersions in the characteristics, performance and reliability of vaporizers for early model 30-cm thrusters were investigated. The purpose of the paper is to explore the findings and to discuss the approaches that were taken to reduce the observed dispersion and present the results of a program which validated those approaches. The information that is presented includes porous tungsten materials specifications, a discussion of assembly procedures, and a description of a test program which screens both material and fabrication processes. There are five appendices providing additional detail in the areas of vaporizer contamination, nitrogen flow testing, bubble testing, porosimeter testing, and mercury purity. Four neutralizers, seven cathodes and five main vaporizers were successfully fabricated, tested, and operated on thrusters. Performance data from those devices is presented and indicates extremely repeatable results from using the design and fabrication procedures.

Zavesky, R.↗
NASA NTRS1985

Conjecture about a hurricane system in the Jovian atmosphere

Arguments are presented in support of the hypothesis that the Great Red Spot (GRS) of Jupiter is a giant hurricane, and that the same decription might apply to the smaller vortices such as the white and brown ovals (barges) on the surface of Jupiter. Estimates of the spin-down times constants for the white and brown oval vortices, indicate that the motions must be sustained by the continued release of internal energy. In analogy with the CISK mechanism for terrestrial hurricanes, transport of water vapor is identified as a possible latent energy source. On the basis of the large size and long life time of the GRS, (indicating extreme depth), it is suggested that the hurricane GRS hurricane may have been induced by meteor impact. Voyager 1 images of the GRS are provided.

Mayr, H. G.↗
NASA NTRS1991

Oscillating airfoil velocity field during large amplitude dynamic stall

The leading edge flowfield of an airfoil executing a sinusoidal oscillatory motion and experiencing dynamic stall under compressibility conditions has been studied using a two component LDV system. Phase averaged mean velocity measurements and some flow quantities derived from it are presented and discussed. The results indicate extremely large accelerations of the flow are present around the leading edge with mean velocity values 60 percent higher than and instantaneous velocities as large as 1.75 times the free stream velocity. The velocity profiles at certain locations over the airfoil resemble that of a wake.

Chandrasekhara, M. S.↗
NASA NTRS1997

Evaluation of a Neural Adaptive Flight Controller

The objective of this paper is to present results from the evaluation of a direct adaptive tracking controller. The control architecture employs both pre-trained and an on-line neural networks to represent the non-linear aircraft dynamics in the model inversion portion of the controller. The aircraft model used for this evaluation is representative of the F-15 Advanced Control Technology for Integrated Vehicles (ACTIVE) aircraft. The controller was evaluated for three cases: (1) nominal conditions; (2) loss of control power; and (3) loss of control power in the presence of atmospheric turbulence. The results were compared with the existing F-15 ACTIVE conventional mode controller in all cases. The results indicate extremely desirable airframe stabilization characteristics for case (1) that do not degrade significantly for case (2) or (3) as does the conventional mode controller. It was concluded that this controller exhibits both stable and robust adaptive characteristics when subjected to mild and extreme loss of control power conditions. Integration of this neural adaptive flight controller into the full non-linear six degree-of-freedom F-15 ACTIVE simulation is recommended for evaluation in a real-time high fidelity piloted simulation environment.

Totah, Joseph J.↗
NASA NTRS2008

Phoenix - the First Mars Scout Mission

As the first of the new Mars Scouts missions, the Phoenix project was selected by NASA in August of 2003. Four years later, almost to the day, Phoenix was launched from Cape Canaveral Air Station and successfully injected into an interplanetary trajectory on its way to Mars. This paper will highlight some of the key changes since the 2006 IEEE paper of the same name, as well as activities, challenges and problems encountered on the way to the launch pad. Phoenix Follows the water responding directly to the recently published data from Dr. William Boynton, PI (and Phoenix co-I) of the Mars Odyssey Gamma Ray Spectrometer (GRS). GRS data indicate extremely large quantities of water ice (up to 50% by mass) within the upper 50 cm of the northern polar regolith. Phoenix will land within the north polar region at 68.2 N, 233.4 W identified by GRS to harbor near surface water ice and provide in-situ confirmation of this extraordinary find. Our mission will investigate water in all its phases, and will investigate the history of water as evidenced in the soil characteristics that will be carefully examined by the powerful suite of onboard instrumentation. Access to the critical subsurface region expected to contain this information is made possible by a third generation robotic arm capable of excavating the expected Martian regolith to a depth of 1m. Phoenix has four primary science objectives: 1) Determine the polar climate and weather, interaction with the surface, and composition of the lower atmosphere around 70 N for at least 90 sols focusing on water, ice, dust, noble gases, and CO2. Determine the atmospheric characteristics during descent through the atmosphere. 2) Characterize the geomorphology and active processes shaping the northern plains and the physical properties of the near surface regolith focusing on the role of water. 3) Determine the aqueous mineralogy and chemistry as well as the adsorbed gases and organic content of the regolith. Verify the Odyssey discovery of near-surface ice. 4) Characterize the history of water, ice, and the polar 1 1-4244-1488-1/08/$25.00 2008 IEEE 2 IEEEAC paper#1579, Version 1, Updated 2008:01:09 climate. Determine the past and present biological potential of the surface and subsurface environments.

Mars exploration↗
DOE OSTI2022

Metagenomic features of bioburden serve as outcome indicators in combat extremity wounds

Abstract Battlefield injury management requires specialized care, and wound infection is a frequent complication. Challenges related to characterizing relevant pathogens further complicates treatment. Applying metagenomics to wounds offers a comprehensive path toward assessing microbial genomic fingerprints and could indicate prognostic variables for future decision support tools. Wound specimens from combat-injured U.S. service members, obtained during surgical debridements before delayed wound closure, were subjected to whole metagenome analysis and targeted enrichment of antimicrobial resistance genes. Results did not indicate a singular, common microbial metagenomic profile for wound failure, instead reflecting a complex microenvironment with varying bioburden diversity across outcomes. Genus-level Pseudomonas detection was associated with wound failure at all surgeries. A logistic regression model was fit to the presence and absence of antimicrobial resistance classes to assess associations with nosocomial pathogens. A. baumannii detection was associated with detection of genomic signatures for resistance to trimethoprim, aminoglycosides, bacitracin, and polymyxin. Machine learning classifiers were applied to identify wound and microbial variables associated with outcome. Feature importance rankings averaged across models indicated the variables with the largest effects on predicting wound outcome, including an increase in P. putida sequence reads. These results describe the microbial genomic determinants in combat wound bioburden and demonstrate metagenomic investigation as a comprehensive tool for providing information toward aiding treatment of combat-related injuries.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Evaluation of CMIP6 models in simulating the statistics of extreme precipitation over Eastern Africa

We report the Eastern Africa region experiences frequent extreme precipitation events that can cause destruction of property and environment, and loss of lives. Thus, there is a need to understand how these events may change in the future and how well the global climate models that are used to make projections can simulate precipitation extremes in this region before they can be used in downscaling or flood and drought impact assessment studies. In this work, we evaluated the ability of sixteen Coupled Model Intercomparison Project Phase 6 (CMIP6) models to simulate present-day precipitation extremes over the Eastern Africa region during the two rainy seasons (March–May and September–November). We used nine extreme precipitation indices (including seven (one) indices of wet (dry) extremes) defined by the Expert Team on Climate Change Detection and Indices. The CMIP6 models were evaluated against two gridded observation datasets: Global Precipitation Climatology Project One-Degree Daily Dataset and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis 3B42. Three model performance metrics (percentage bias, normalized root-mean-square error, and pattern correlation coefficient) were employed to further assess the strengths and weakness of the models. Our results show that the multi-model ensemble mean generally provides a better representation of observed precipitation and related extremes compared to individual models when considering all metrics and seasons. Several consistent biases are evident across CMIP6 models, which tend to overestimate the total-wet day precipitation and consecutive wet days, and underestimate very wet days and maximum 5-day precipitation in both seasons. Furthermore, no single model consistently performs best, model performance varies with the season and index under consideration and is generally independent of horizontal resolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2002

Extreme Lightning Flash Rates as an Early Indicator of Severe Storms

Extreme lightning flash rates are proving to be an early indicator of intensifying storms capable of producing tornadoes, damaging winds and hail. Most of this lightning is in the cloud, where the naked eye can not see it. Recent global observations of thunderstorms from space indicate that giant electrical storms (supercells and convective complexes) with flash rates on the order of 1 flash per second are most common over the land masses of the America sub-tropics and equatorial Congo Basin. Within the United States, the average tornado warning lead time on a national basis is about 11 min. The real-time observation of extreme flash rates and the rapid increase in the in-cloud flash rate, signalling the intensification of the storm updraft, may provide as much as a 50% increase in severe storm warning lead time.

Goodman, Steven J.↗
DOE OSTI2025

Implications of Changes in Water Stress and Precipitation Extremes for Cocoa Production in Côte D'ivoire and Ghana

ABSTRACT Climate change induces high variability in drought patterns and extreme precipitation indices in rainfed cocoa farming, impacting cocoa production. This study evaluated water stress, meteorological and agricultural drought conditions, and critical extreme precipitation indices in the world's two largest cocoa‐producing nations from 1981 to 2022. The results revealed a significant reduction in total annual precipitation (PRCPTOT), in the last three decades, with the greatest decline in the 1991–2000 and 2011–2022 periods. Ghana experienced the most significant reduction up to 15% (200 mm/year) in the last decade, attributed to a substantial decrease in wet days number (RR1) up to 25 days per year, a reduction in maximum consecutive wet days (CWD) up to 6 days per year, and an increase in maximum consecutive dry days (CDD) up to 15 days per year. Moreover, there was a notable decline in the Simple Daily Intensity Index (SDII), with reductions of up to 4 mm/day in certain areas, contributing to increased drought frequency, severity, and duration. In the most recent decade (2011–2022), particularly during the extremely dry years of 2013 and 2015, cocoa‐growing regions in Ghana (GHA) and eastern Côte d'Ivoire (CIV) experienced prolonged agricultural drought expressed by soil moisture deficit, typically extending from May to September. Additionally, large portions of central and eastern Ghana, as well as northeastern Côte d'Ivoire experienced sustained water stress, with over three consecutive months of total monthly precipitation falling below 100 mm, negatively impacting cocoa productivity. The decrease in the yield in the range of 2.5% to 37% was noted in the dry years and the following years, varying according to the country depending on the severity of the drought. Sensitivity analysis highlights cocoa yield's responsiveness to drought and water stress, particularly in specific years when water stress occurred, such as 1984,1985, 1989, 1995, 1999, 2000, and 2008. Considering the observed trends in precipitation patterns and their impact on cocoa production, it is crucial to acknowledge the inherent uncertainty of future precipitation patterns due to climate change. To address this challenge effectively, our study underscores the importance of identifying and closely monitoring regions currently facing water stress, as determined by precipitation and drought indicators. Over the analysed period (1981–2022), we have noted shifts in the distribution of water‐stressed areas, highlighting the dynamic nature of this issue. Consequently, we advocate for a targeted approach to implement cocoa supplementary irrigation in consistently water‐stressed regions.

Obahoundje, Salomon [International Water Managemen↗
DOE OSTI2023

Influence of data uncertainty on cold season threshold-based climate indices

Climate indices are used to reduce the complex climate system and its changes to simple measures. The data basis – whether observational data or climate model data – to which the climate indices are applied, is usually subject to uncertainties. For threshold-based climate indices, the data uncertainty influences the threshold value, and, hence, the uncertainty can influence the values for the climate index. What the actual impacts of these uncertainties are on threshold-based climate indices is examined in this paper. The focus is not only on the climate model uncertainty, but also on the observational data uncertainty. The general sensitivity of each of the chosen climate indices to arbitrary changes in the threshold is studied. This shows a higher sensitivity of indices assessing extremes (ice days, heavy precipitation days) to changes in the threshold than indices that integrate a quantity over a given time interval (coldsum, consecutive days). For assessing an ensemble of climate model data with respect to their ability to reproduce the index values for current climate, the reference data uncertainty is applied to the chosen threshold-based climate indices by changing their threshold value by its corresponding uncertainty. It is shown that the climate model uncertainty can be within the range of the reference data uncertainty. When using threshold-based climate indices to assess changes in future climate periods, uncertainties should always be taken into account and ideally corrected in an appropriate way. This is especially important for indices that assess extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Huge ensembles – Part 1: Design of ensemble weather forecasts using spherical Fourier neural operators

Abstract. Simulating low-likelihood high-impact extreme weather events in a warming world is a significant and challenging task for current ensemble forecasting systems. While these systems presently use up to 100 members, larger ensembles could enrich the sampling of internal variability. They may capture the long tails associated with climate hazards better than traditional ensemble sizes. Due to computational constraints, it is infeasible to generate huge ensembles (comprised of 1000–10 000 members) with traditional, physics-based numerical models. In this two-part paper, we replace traditional numerical simulations with machine learning (ML) to generate hindcasts of huge ensembles. In Part 1, we construct an ensemble weather forecasting system based on spherical Fourier neural operators (SFNOs), and we discuss important design decisions for constructing such an ensemble. The ensemble represents model uncertainty through perturbed-parameter techniques, and it represents initial condition uncertainty through bred vectors, which sample the fastest-growing modes of the forecast. Using the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS) as a baseline, we develop an evaluation pipeline composed of mean, spectral, and extreme diagnostics. With large-scale, distributed SFNOs with 1.1 billion learned parameters, we achieve calibrated probabilistic forecasts. As the trajectories of the individual members diverge, the ML ensemble mean spectra degrade with lead time, consistent with physical expectations. However, the individual ensemble members' spectra stay constant with lead time. Therefore, these members simulate realistic weather states during the rollout, and the ML ensemble passes a crucial spectral test in the literature. The IFS and ML ensembles have similar extreme forecast indices, and we show that the ML extreme weather forecasts are reliable and discriminating. These diagnostics ensure that the ensemble can reliably simulate the time evolution of the atmosphere, including low-likelihood high-impact extremes. In Part 2, we generate a huge ensemble initialized each day in summer 2023, and we characterize the simulations of extremes.

Mahesh, Ankur↗
DOE OSTI2020

The frequency of extreme X-ray variability for radio-quiet quasars

ABSTRACT We analyse 1598 serendipitous Chandra X-ray observations of 462 radio-quiet quasars to constrain the frequency of extreme amplitude X-ray variability that is intrinsic to the quasar corona and innermost accretion flow. The quasars in this investigation are all spectroscopically confirmed, optically bright (mi ≤ 20.2), and contain no identifiable broad absorption lines in their optical/ultraviolet spectra. This sample includes quasars spanning z ≈ 0.1–4 and probes X-ray variability on time-scales of up to ≈12 rest-frame years. Variability amplitudes are computed between every epoch of observation for each quasar and are analysed as a function of time-scale and luminosity. The tail-heavy distributions of variability amplitudes at all time-scales indicate that extreme X-ray variations are driven by an additional physical mechanism and not just typical random fluctuations of the coronal emission. Similarly, extreme X-ray variations of low-luminosity quasars seem to be driven by an additional physical mechanism, whereas high-luminosity quasars seem more consistent with random fluctuations. The amplitude at which an X-ray variability event can be considered extreme is quantified for different time-scales and luminosities. Extreme X-ray variations occur more frequently at long time-scales (Δt ≳ 300 d) than at shorter time-scales and in low-luminosity quasars compared to high-luminosity quasars over a similar time-scale. A binomial analysis indicates that extreme intrinsic X-ray variations are rare, with a maximum occurrence rate of $\lt 2.4{{\ \rm per\ cent}}$ of observations. Finally, we present X-ray variability and basic optical emission-line properties of three archival quasars that have been newly discovered to exhibit extreme X-ray variability.

Timlin, III, John D.↗
NASA NTRS2020

Data Science for Weather Impacts on Crop Yield

Private businesses in sectors, such as food, energy, and retail, as well as public sector and federal agencies are interested in the predictive understanding of weather impacts on crop yield, which is an important aspect of food security. Scientific literature has mainly examined how crop yield is impacted by growing season-averaged weather indices. Although a few studies did consider weather extremes in their analysis, their scope was either restricted to measuring their conditional relationship with yield or the extreme event types considered were limited. Selection of regression models, whether the more commonly used linear approaches or nonlinear methods, have not been appropriately justified in this context. Here, we develop data-driven methods to examine two inter-related hypotheses for improved scientific understanding and enhanced predictive modeling. The first hypothesis, that extreme weather indices have a statistically significant information content in them is found to be valid based on linear and nonlinear methods for pairwise dependence. The second hypothesis, examines the value addition of nonlinear regression methods, and suggests that linear approaches may not alone be adequate. The results of this study can inform scientific understanding, generation and relevance of indices and end-to-end risk assessment systems in the context of climate impacts on crop yield. An immediate application may be in the context of NASA Earth Exchange (NEX) which facilitates the generation and dissemination of impacts relevant weather data and indices using a multitude of satellite-derived data sets and model outputs.

Data mining, food security, weather impacts↗
NASA NTRS2002

Evolvable, reconfigurable hardware for future space systems

This paper overviews Evolvable Hardware (EHW) technology, examining its potential for enhancing survivability and flexibility of future space systems. EHW refers to selfconfiguration of electronic hardware by evolutionary/genetic search mechanisms. Evolvable Hardware can maintain existing functionality in the presence of faults and degradations due to aging, temperature and radiation. It can also configure itself for new functionality when required for mission changes or encountered opportunities. The paper illustrates hardware evolution in silicon using a JPL-designed programmable device reconfigurable at transistor level as the platform and a genetic algorithm running on a DSP as the reconfiguration mechanism. Rapid reconfiguration allows convergence to circuit solutions in the order of seconds. The experiments demonstrate functional recovery from faults as well as from degradation at extreme temperatures indicating the possibility of expanding the operational range of extreme electronics through evolved circuit solutions.

Evolvable Hardware EHW↗
NASA NTRSDate not supplied

Evaluating and Exploring Extreme Precipitation in CHIRPS3, CHIMES, and the Climate Hazards Station Database

Between 2015 and 2019, the impacts and losses associated with droughts, floods, and hurricanes surged through the interaction of increasing exposure and more extreme weather and climate (Funk 2021). In 2020, the Aon-Benfield Reinsurance Company reported that cyclones, floods, and droughts resulted in $165 billion (USD) in losses. For 2020, EM-DAT lists 363 flood, landslide, storm, and drought events impacting some 98 million people. As hazards mount, models indicate more variable precipitation, and observations indicate increasing extremes in humid regions, yet the small number of available gauge observations is declining. In this talk we briefly introduce two new resources: version 3 of the 1981-near present Climate Hazards center Infrared Precipitation with Stations (CHIRPS3) archive, and the 2000-near present Climate Hazards IMErg with Stations archive (CHIMES). The satellite-only CHIRP3 and CHIME components are based, respectively, on geostationary thermal infrared Cold Cloud Duration (CCD) values and the NASA GPM IMERGlate product. These archives benefit from thousands of station observations. In addition to standard global sources, the Climate Hazards station database includes about 5,000 additional stations in humid tropical and sub-tropical areas associated with likely increases in extreme rainfall. While effectively monitoring extreme precipitation is important to humanitarian agencies like FEWS NET, and to hydrologic modeling applications (a, b), evaluations of the widely used CHIRPS2 product indicate a propensity to underestimate extremes. Here, we explain the source of this problem. Then, focusing on pentad totals in 12 well-gauged regions, we use the high-quality gauge-based REGEN dataset to show that CHIRP3 and CHIME perform substantially better than CHIRP2. We examine trends in extremes in these validation regions, comparing REGEN results with values from the satellite-only CHIRP2, CHIRP3, CHIME and the gauge-enhanced CHIRP2, CHIRP3, CHIME. Trends in dry and humid regions are contrasted and compared. We conclude with global evaluations of the pentad CHIRP3, CHIRPS3, CHIME, CHIMES, and Climate Hazards station database products. How does the performance of the shorter period of record CHIME compare with CHIRP3? We anticipate that the sophisticated microwave-based IMERGlate inputs into CHIME will perform better. Where do we see the largest increases in extremes in these products? How well might CHIRPS3 and CHIMES perform in the context of humanitarian assistance efforts?

C Funk↗
DOE OSTI2023

Evaluation of present-day extreme precipitation over the United States: an inter-comparison of convection and dynamic permitting configurations of E3SMv1

Abstract Accurate simulation of the present-day characteristics of mean and extreme precipitation at regional scales remains a challenge for Earth system models, which is due in part to deficiencies in model physics such as convective parameterization (CP), and coarse resolution. High horizontal resolution (HR, ∼25 km) and multiscale modeling framework (MMF, i.e. replacing conventional CP with embedded km-scale cloud-resolving models) are two promising directions that could help improve the interaction between subgrid-scale physical processes and large-scale climate. Here, we evaluate simulated extreme precipitation over the United States (US) across three configurations (i.e. low-resolution [LR], HR, and MMF) of the Energy Exascale Earth System Model (E3SMv1) and intercompare them against two gridded observation datasets (climate prediction center daily US precipitation and integrated multi-satellite retrievals for global precipitation measurement). We assess the model’s ability to simulate very heavy seasonal precipitation (illustrated by the difference between the 99th and 90th percentile values) as well as the spatial distributions of several extreme precipitation indices defined by the expert team on climate change detection and indices. Our results show that both the dry (i.e. consecutive dry days (CDD)) and wet (i.e. consecutive wet days, maximum 5 day precipitation, and very wet days) extremes evaluated herein show some improvement as well as degradation with MMF and HR relative to LR. These results vary across seasons and US subregions. For instance, only the very heavy precipitation of winter is improved with MMF and HR. Both configurations alleviate the well-known drizzling bias evident in LR across both winter and summer in many parts of the US, largely due to the overall improvement in intensity and frequency of precipitation. Additionally, our results suggest that while E3SMv1-MMF has higher intensity rates when it does rain, it has too many CDD during the summer, contributing to a low mean precipitation bias.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1976

Radar characteristics of Viking 1 landing sites

Radar observations of Mars at centimeter wavelengths in May, June, and July 1976 provided estimates of surface roughness and reflectivity in three potential landing areas for Viking 1. Surface roughness is characterized by the distribution of surface landing slopes or tilts on lateral scales of the order of 1 to 10 meters; measurements of surface reflectivity are indicators of bulk surface density in the uppermost few centimeters. By these measures, the landing site at 47.5 deg W, 22.4 deg N is rougher than the Martian average, although it may be near the average for elevations accessible to Viking, and is estimated to be near the Mars average in reflectivity. The site at the center of Chryse Planitia, 43.5 deg W, 23.4 deg N, may be an area of anomalous radar characteristics, indicative of extreme, small-scale roughness, very low surface density, or a combination of these two characteristics. Observations of the original Chryse site at 34 deg W, 19.5 deg N indicate that that area is at least twice as rough as the Mars average.

Tyler, G. L.↗