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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

The X-Ray Core of the Low-Luminosity Radio Galaxy 3C346 and ASCA Spectroscopy to Test BL LAC/Radio Galaxy Unification

Radio galaxies are relatively faint sources for Advanced Spacecraft for Cosmology Astrophysics (ASCA), and so in order to get the best possible results from the observations two things have been necessary, both of which delayed the fast preparation of papers. Firstly, the best possible data screening and background subtraction were necessary to improve the signal-to-noise, and all our several initial analysis trials were discarded in favor of using FTOOLS versions 4.1 and above. Secondly, we found that the ASCA spectra were statistically too poor to discriminate well between non-thermal and thermal models, never mind the mixture of the two which we expected on the basis of our ROSAT spatial separation of components in radio galaxies. This means that in each case we have needed to combine the ASCA spectroscopy with analysis of data from other X-ray or radio observations in order to exploit the ASCA data to the full. Our analysis for 3C 346 has yielded the cleanest final result. This powerful radio galaxy at a redshift of 0.161, lies in a poor cluster, which we have separated well from the dominant X-ray component of unresolved emission using a spatial analysis of archival ROSAT data. We were then able to fix the thermal component in our ASCA spectral analysis, and have found evidence that the unresolved emission varied by 32 +/- 13% over the 18 months between the ROSAT and ASCA observations. The unresolved X-ray emission does not suffer from intrinsic absorption, and we have related it to radio structures on both milliarcsecond scales and the arcsecond scales which Chandra can resolve. The source is a target of a Chandra AO2 proposal which we have recently submitted to follow up on our ASCA (and ROSAT) work. 3C 346's orientation to the line of sight is uncertain. However, the absence of X-ray absorption, and the radio/optical/X-ray colors, when combined with with previous radio evidence that the source is a foreshortened radio galaxy of the FRII class, suggest that the radio jets are seen at an angle to the line of sight of about 30 deg, intermediate between the radio-galaxy and quasar classes. The relatively hard ASCA response has allowed us to place an upper limit of 5.6 x 10(exp 43) ergs/ s on the 2-10 keV luminosity of any central X-ray component absorbed bN, gas which might be obscuring the broad-line emission region. Attached to this report is an almost final draft of a paper which we have prepared for submission to the Astrophysical Journal. Our combined ASCA and ROSAT results for NGC 6251 rule out our previously preferred flat-spectrum model and inverse-Compton interpretation for the source based on ROSAT data alone, but a softer X-ray spectrum and moderate absorption bring all the available data (including our early VLA HI measurements) into consistency, and we are reasonably confident that we understand the processes responsible for the X-ray emission. We have made some more sensitive HI absorption measurements which are currently being analyzed, and our plans are to publish our ASCA analysis in conjunction with the new HI results. The ASCA data for NGC 4261 have been difficult to interpret. A re-analysis of our ROSAT data with a wider range of physical parameters brings the ROSAT and ASCA results into reasonable agreement only if the emission from hot gas dominates more than suggested by our earlier work, which is itself unexpected since the radio core is bright and a large jet-related X-ray component would bring the source into agreement with results for others of its type. However, we have recently received our Chandra A01 data for this source, with the spatial resolution which allows us to separate thermal and non-thermal emission components. Our ASCA results will be re-interpreted once the analysis of our Chandra data is complete. The interpretation of the ASCA data for BL Lac object 3C 371 is ongoing, in conjunction with analysis of archival multifrequency data. Radio galaxies are complex in their X-ray properties, and hindsight has shown that the spatial resolution of ASCA is too poor for a reliable interpretation of the data without drawing on other observations. However, the ASCA spectra have made a useful contribution to the interpretation of these sources, and the groundwork is now there for more sensitive work using Chandra and XMM-Newton.

Worrall, Diana↗
NASA NTRS1993

The Spectral Image Processing System (SIPS) - Interactive visualization and analysis of imaging spectrometer data

The Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, has developed a prototype interactive software system called the Spectral Image Processing System (SIPS) using IDL (the Interactive Data Language) on UNIX-based workstations. SIPS is designed to take advantage of the combination of high spectral resolution and spatial data presentation unique to imaging spectrometers. It streamlines analysis of these data by allowing scientists to rapidly interact with entire datasets. SIPS provides visualization tools for rapid exploratory analysis and numerical tools for quantitative modeling. The user interface is X-Windows-based, user friendly, and provides 'point and click' operation. SIPS is being used for multidisciplinary research concentrating on use of physically based analysis methods to enhance scientific results from imaging spectrometer data. The objective of this continuing effort is to develop operational techniques for quantitative analysis of imaging spectrometer data and to make them available to the scientific community prior to the launch of imaging spectrometer satellite systems such as the Earth Observing System (EOS) High Resolution Imaging Spectrometer (HIRIS).

Kruse, F. A.↗
DOE OSTI2024

Nucleus++ : a new tool bridging AME and NUBASE for advancing nuclear data analysis

The newly developed software, Nucleus++ , is an advanced tool for displaying basic nuclear physics properties from NUBASE and integrating comprehensive mass information for each nuclide from Atomic Mass Evaluation. Additionally, it allows users to compare experimental nuclear masses with predictions from different mass models. Building on the success and learning experiences of its predecessor, Nucleus , this enhanced tool introduces improved functionality and compatibility. With its user-friendly interface, Nucleus++ was designed as a valuable tool for scholars and practitioners in the field of nuclear science. Finally, this article offers an in-depth description of Nucleus++ , highlighting its main features and anticipated impacts on nuclear science research.

AME↗
NASA NTRS1986

Pseudo-prototyping of aerospace mechanical dynamic systems with a generalized computer program

The ADAMS computer program for automated analysis of mechanisms and machines is described. The program automatically formulates mathematical models for prototype or existing mechanisms with the minimum necessary physical and geometric data. The model can then be analyzed in various modes of analysis. The outputs (displacements, velocities, acceleration and forces) can be produced in tabular and graphical (plots, wire frame graphics) form. The application of this computer program to simulating satellite docking maneuvers is illustrated.

Sohoni, Vikram N.↗
NASA NTRS2005

Giovanni: A System for Rapid Access, Visualization and Analysis of Earth Science Data Online

Collecting data and understanding data structures traditionally are the first steps that a user must take, before the core investigation can begin. This is a time-consuming and challenging task, especially when science objectives require users to deal with large multi-sensor data that are usually in different formats and internal structures. The Goddard Earth Sciences Data and Information Services Center (GES DISC) has created the GES DISC Interactive Online Visualization and ANalysis Infrastructure, Giovanni, to enable Web-based visualization and analysis of satellite remotely sensed meteorological, oceanographic, and hydrologic data sets, without users having to download data. The current operational Giovanni interfaces provide the capability to process a number of important satellite measurements, such as (1) ozone and other trace gases from TOMS, OMI, HALOE, and MLS; (2) air temperature, water vapor, and geopotential height from AIRS; (3) aerosols from MODIS TerrdAqua, and GOCART model; (4) precipitation from TRMM and ground measurements; (5) chlorophyll and other ocean color products from SeaWiFS and MODIS Aqua; and (6) sea surface temperature from MODIS Aqua. Depending on the input data structure, the system provides simple statistical analysis and creates time-averaged area plot, area-averaged time series, animations, Hovmoller latitude vs. time and longitude vs. time plots, as well as vertical profiles. The inter-comparison interfaces allow a user to compare observations from different instruments, to conduct anomaly analysis, and to study basic relationships between physical parameters. Giovanni handles data with different temporal and spatial resolutions and, thus, enables both regional and global long-term climate research and short-term special events investigation, as well as data validations and assessments. Because of its simplicity of usage, Giovanni is powerful and versatile, able to assist a wide range of users, from the discipline scientists conducting preliminary research in various fields, to students in the classroom learning about weather, climate, and other natural phenomena. Giovanni can be accessed from: http://disc. esfc.nasa.gov/techlab/giovanni/index.shtml

Shen, S.↗
NASA NTRSDate not supplied

Principles of Fluid Dynamic Similarity Analysis for Slosh Experiments

Most tank slosh test programs are not conducted with flight hardware in a flight-like environment due to cost and schedule constraints. Instead, subscale tanks are typically tested on the ground. If the “real”, flight liquid in the tank is a cryogenic propellant, sometimes a cryogen is used during testing, but more often a simulant liquid is used. Fluid dynamic similarity analysis, or “scaling analysis” as it is sometimes called, for slosh examines nondimensional numbers to determine which fluid dynamic regime(s) a tank is operating in. If the relevant nondimensional numbers of a scale tank test are similar enough to the real tank’s nondimensional numbers, then it is likely that the scale test data will be similar to what would be obtained with the real tank. This type of analysis is useful for guiding experiment design, test liquid selection, and for understanding the physical relevance of prior test data to a specific application. The authors have performed scaling analyses for many slosh test programs conducted in ground, parabolic flight, suborbital, and in-orbit environments. Basics of how to perform the analysis, along with select examples from ground and microgravity environments, are presented. Suggestions for various assumptions and compromises to make are discussed. Limitations and challenges are also discussed.

slosh↗
DOE OSTI2025

Plasma proteomic biomarkers of physical frailty in heart failure: a propensity score matched discovery-based pilot study

Background: Physical frailty is highly prevalent in heart failure (HF), but we lack an understanding of the underlying pathophysiology. Proteomics evaluation of plasma samples may elucidate potential mechanisms and biomarkers of physical frailty in HF. We aimed to identify plasma proteomic biomarkers that are differentially expressed between physically frail and non physically frail adults with HF. Methods: This was a secondary analysis of a subset of data and plasma samples from a study of frailty among patients with New York Heart Association (NYHA) Functional Classification I-IV HF. Physical frailty was measured using the Frailty Phenotype Criteria. Propensity score matching was used to match pairs of physically frail (n = 20) vs. non-physically frail (n = 20) patients on clinical characteristics. Plasma samples were processed using a sensitive liquid chromatography mass spectrometry platform, utilizing a multiplexed tandem mass tag-labeled quantitative proteomics approach. Differentially expressed proteins were quantified individually using paired t tests with associated log fold change of 0.3 and Fisher’s combined p values. Results: The sample (n = 40) was 62.8±16.9 years old, 58% female, and 55% NYHA Class III/IV. Proteomics analysis revealed 7 proteins differentially expressed using full differential criteria: matrix metalloproteinase-14 was downregulated in frailty, and copine-1, low affinity immunoglobulin gamma Fc region receptor III-A and III-B, probable non-functional immunoglobulin kappa variable 2D-24, glutathione S-transferase Mu 1, and argininosuccinate lyase were upregulated in frailty. Conclusions: Proteomic biomarkers related to the immune system, stress response, and detoxification were differentially expressed between physically frail and non-physically frail adults with HF.

Biomarkers↗
NASA NTRS1985

Interpolation Methods for GTD Analysis of Shaped Reflectors

The finding of smooth analytic representations for antenna reflector surfaces which are prescribed only by discretized data obtained by various synthesis methods is examined. Frequently the data are distributed in a nonuniform grid and contain noise. The smoothness required is to C sub 1 for physical optics diffraction analysis and to C sub 2 for geometrical theory of diffraction (GTD) analysis. The GTD analysis approach requires a surface description which returns data very rapidly. Two methods of interpolation, the global and the local methods, are discussed. They each have advantages and disadvantages; characteristics are discussed and examples are presented.

Galindo-Israel, V.↗
NASA NTRS1987

An overview of the mathematical and statistical analysis component of RICIS

Mathematical and statistical analysis components of RICIS (Research Institute for Computing and Information Systems) can be used in the following problem areas: (1) quantification and measurement of software reliability; (2) assessment of changes in software reliability over time (reliability growth); (3) analysis of software-failure data; and (4) decision logic for whether to continue or stop testing software. Other areas of interest to NASA/JSC where mathematical and statistical analysis can be successfully employed include: math modeling of physical systems, simulation, statistical data reduction, evaluation methods, optimization, algorithm development, and mathematical methods in signal processing.

Hallum, Cecil R.↗
NASA NTRS1990

Acquisition and analysis of accelerometer data

Acceleration data reduction must be undertaken with a complete understanding of the physical process, the means by which the data are acquired, and finally, the calculations necessary to put the data into a meaningful format. Discussed here are the acceleration sensor requirements dictated by the measurements desired. Sensor noise, dynamic range, and linearity will be determined from the physical parameters of the experiment. The digitizer requirements are discussed. Here the system from sensor to digital storage medium will be integrated, and rules of thumb for experiment duration, filter response, and number of bits are explained. Data reduction techniques after storage are also discussed. Time domain operations including decimating, digital filtering, and averaging are covered, as well as frequency domain methods, including windowing and the difference between power and amplitude spectra, and simple noise determination via coherence analysis. Finally, an example experiment using the Teledyne Geotech Model 44000 Seismometer to measure from 1 Hz to 10(exp -6) Hz is discussed. The sensor, data acquisition system, and example spectra are presented.

Verges, Keith R.↗
NASA NTRS2015

The Investigation of Perchlorate/Iron Phase Mixtures as A Possible Source of Oxygen Detected by the Sample Analysis at Mars (SAM) Instrument in Gale Crater, Mars

The Sample Analysis at Mars (SAM) instrument onboard the Curiosity rover detected O2 and HCl gas releases from the Rocknest (RN) eolian bedform and the John Klein (JK) and Cumberland (CB) drill hole materials in Gale Crater. Chlorinated hydrocarbons have also been detected by the SAM quadrupole mass spectrometer (QMS) and gas chromatography/mass spectrometer (GCMS). These detections along with the detection of perchlorate (ClO4-) by the Mars Phoenix Lander's Wet Chemistry Laboratory (WCL) suggesting perchlorate is a possible candidate for evolved O2 and chlorine species. Laboratory thermal analysis of individual per-chlorates has yet to provide an unequivocal temperature match to the SAM O2 and HCl release data. These detections along with the detection of perchlorate (ClO4-) by the Mars Phoenix Lander's Wet Chemistry Laboratory suggested perchlorate is a possible candidate for evolved O2 and chlorine species. Laboratory thermal analysis of pure perchlorates has yet to provide an unequivocal temperature match to the SAM O2 and HCl release data. Analog laboratory analysis of iron mineralogy detected in Gale materials that was physically mixed with Ca- and Mg-perchlorate has been shown to catalyze lower O2 release temperatures and approach some SAM O2 release data. Instead of physical mixtures used in previous work, the work presented here utilized perchlorate solutions added to Fe phases. This technique allowed for perchlorate to come in closer contact with the Fe-phase and may more closely mimic Mars conditions where humidity can increase enough to cause deliquescence of the highly hygroscopic perchlorate phases. The objective of this work is to: 1) Utilize a laboratory SAM analog instrument to evaluate the O2 release temperatures from Mg- and Ca-perchlorates solutions applied to Fephases detetected in Gale Crate; and 2) Determine if perchlorate solutions can provide improved matches with the SAM O2 temperature release profiles.

Sutter, B.↗
NASA NTRS2001

The Computational Complexity, Parallel Scalability, and Performance of Atmospheric Data Assimilation Algorithms

The computational complexity of algorithms for Four Dimensional Data Assimilation (4DDA) at NASA's Data Assimilation Office (DAO) is discussed. In 4DDA, observations are assimilated with the output of a dynamical model to generate best-estimates of the states of the system. It is thus a mapping problem, whereby scattered observations are converted into regular accurate maps of wind, temperature, moisture and other variables. The DAO is developing and using 4DDA algorithms that provide these datasets, or analyses, in support of Earth System Science research. Two large-scale algorithms are discussed. The first approach, the Goddard Earth Observing System Data Assimilation System (GEOS DAS), uses an atmospheric general circulation model (GCM) and an observation-space based analysis system, the Physical-space Statistical Analysis System (PSAS). GEOS DAS is very similar to global meteorological weather forecasting data assimilation systems, but is used at NASA for climate research. Systems of this size typically run at between 1 and 20 gigaflop/s. The second approach, the Kalman filter, uses a more consistent algorithm to determine the forecast error covariance matrix than does GEOS DAS. For atmospheric assimilation, the gridded dynamical fields typically have More than 10(exp 6) variables, therefore the full error covariance matrix may be in excess of a teraword. For the Kalman filter this problem can easily scale to petaflop/s proportions. We discuss the computational complexity of GEOS DAS and our implementation of the Kalman filter. We also discuss and quantify some of the technical issues and limitations in developing efficient, in terms of wall clock time, and scalable parallel implementations of the algorithms.

Lyster, Peter M.↗
DOE OSTI2024

Numerical Investigation of Observational Flux Partitioning Methods for Water Vapor and Carbon Dioxide

Abstract While yearly budgets of CO 2 flux (F c ) and evapotranspiration (ET) above vegetation can be readily obtained from eddy‐covariance measurements, the separate quantification of their soil (respiration and evaporation) and canopy (photosynthesis and transpiration) components remains an elusive yet critical research objective. In this work, we investigate four methods to partition observed total fluxes into soil and plant sources: two new and two existing approaches that are based solely on analysis of conventional high frequency eddy‐covariance (EC) data. The physical validity of the assumptions of all four methods, as well as their performance under different scenarios, are tested with the aid of large‐eddy simulations, which are used to replicate eddy‐covariance field experiments. Our results indicate that canopies with large, exposed soil patches increase the mixing and correlation of scalars; this negatively impacts the performance of the partitioning methods, all of which require some degree of uncorrelatedness between CO 2 and water vapor. In addition, best performances for all partitioning methods were found when all four flux components are non‐negligible, and measurements are collected close to the canopy top. Methods relying on the water‐use efficiency (W) perform better whenWis known a priori, but are shown to be very sensitive to uncertainties in this input variable especially when canopy fluxes dominate. We conclude by showing how the correlation coefficient between CO 2 and water vapor can be used to infer the reliability of differentWparameterizations.

Environmental Sciences & Ecology↗
DOE OSTI2024

Digital Twin for Chemical Science (DTCS) v0.01

Directly visualizing the trajectories of chemistry can unravel novel insights into the behavior of catalysts, gas phase reactions, photo-induced dynamics, and building blocks for quantum information processing. The ability of explicitly identifying, tracking, and tagging the exchange of matter, hence the annihilation and creation of new chemical species, can be best realized through a close coupling of theory and experiment. While the synchrotron-based characterization facilities propelled rapidly in its hardware, providing higher brightness, better resolution, and more precision, the software infrastructure is lagging. We developed DTCS (Digital Twin for Chemical Science) v.01, a central platform that faithfully mimics advanced instrumentations in Scientific User Facilities, by solving a variety of technical challenges in data acquisition, analysis, and model-driven interpretation. Rooted in physics and accelerated by AI, we validated this concept by direct comparison with precise experimental X-ray Photoelectron Spectroscopy (XPS) observations using a ubiquitous metal-water interfacial scenario, i.e., Ag/H2O as our main narrative. The DTCS v.01 input mirrors how the bench chemists work, with the output directly linked to the end station computer, thereby providing a user-friendly, knowledge-driven, and accessible user experience with mechanistic insights standardized in a way that are ready to be published, versioned, and transferred flexibly.

Qian, Jin↗
DOE OSTI2024

Are Atmospheric Models Too Cold in the Mountains? The State of Science and Insights from the SAIL Field Campaign

Mountains play an outsized role in water resource availability, and the amount and timing of water they provide depend strongly on temperature. To that end, we ask the question: How well are atmospheric models capturing mountain temperatures? We synthesize results showing that high-resolution, regionally relevant climate models produce 2-m air temperature (T2m) measurements colder than what is observed (a “cold bias”), particularly in snow-covered midlatitude mountain ranges during winter. We find common cold biases in 44 studies across global mountain ranges, including single-model and multimodel ensembles. We explore the factors driving these biases and examine the physical mechanisms, data limitations, and observational uncertainties behind T2m. Our analysis suggests that the biases are genuine and not due to observation sparsity or resolution mismatches. Cold biases occur primarily on mountain peaks and ridges, whereas valleys are often warm biased. Our literature review suggests that increasing model resolution does not clearly mitigate the bias. By analyzing data from the Surface Atmosphere Integrated Field Laboratory (SAIL) field campaign in the Colorado Rocky Mountains, we test various hypotheses related to cold biases and find that local wind circulations, longwave (LW) radiation, and surface-layer parameterizations contribute to the T2m biases in this particular location. We conclude by emphasizing the value of coordinated model evaluation and development efforts in heavily instrumented mountain locations for addressing the root cause(s) of T2m biases and improving predictive understanding of mountain climates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Are Atmospheric Models Too Cold in the Mountains? The State of Science and Insights from the SAIL Field Campaign

Mountains play an outsized role in water resource availability, and the amount and timing of water they provide depend strongly on temperature. To that end, we ask the question: How well are atmospheric models capturing mountain temperatures? We synthesize results showing that high-resolution, regionally relevant climate models produce 2-m air temperature (T2m) measurements colder than what is observed (a “cold bias”), particularly in snow-covered midlatitude mountain ranges during winter. We find common cold biases in 44 studies across global mountain ranges, including single-model and multimodel ensembles. We explore the factors driving these biases and examine the physical mechanisms, data limitations, and observational uncertainties behind T2m. Our analysis suggests that the biases are genuine and not due to observation sparsity or resolution mismatches. Cold biases occur primarily on mountain peaks and ridges, whereas valleys are often warm biased. Our literature review suggests that increasing model resolution does not clearly mitigate the bias. By analyzing data from the Surface Atmosphere Integrated Field Laboratory (SAIL) field campaign in the Colorado Rocky Mountains, we test various hypotheses related to cold biases and find that local wind circulations, longwave (LW) radiation, and surface-layer parameterizations contribute to the T2m biases in this particular location. We conclude by emphasizing the value of coordinated model evaluation and development efforts in heavily instrumented mountain locations for addressing the root cause(s) of T2m biases and improving predictive understanding of mountain climates.

54 ENVIRONMENTAL SCIENCES↗