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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Applicability of Satellite Freeze Forecasting and Cold Climate Mapping to the Other Parts of the United States

Tasks performed to determine the value of using GOES satellite thermal imagery to enhance fruit crop production in Michigan are described. An overview is presented of the system developed for image processing and thermal image and surface environmental data bases prepared to assess the physical models developed in Florida. These data bases were used to identify correlations between satellite apparent temperatures patterns and Earth surface factors. Significant freeze events in 1981 and the physical models used to provide a perspective on how Florida models can be applied in the context of the Michigan environment are discussed.

Source record↗
NASA NTRS1986

The need for isotopic data on refractory elements in the solar wind

The Sun accounts for the bulk of material in the solar system. Information on the isotopic composition of elements in the solar wind is therefore essential for an understanding of the contribution made by each nucleogenetic component that has been identified in meteorites. Recent work suggests that isotopic data on the solar wind may also help us to understand the physical process that is concentrating light elements at the solar surface. Refractory and volatile elements would behave alike under the conditions of solar fractionation. Prolonged exposure of foils at 1AU from the sun would be a relatively inexpensively way to collect the quantity of solar wind implanted refractory elements needed to test this hypothesis.

Manuel, O. K.↗
NASA NTRS2002

Relationships Between the Bulk-Skin Sea Surface Temperature Difference, Wind, and Net Air-Sea Heat Flux

The primary purpose of this project was to evaluate and improve models for the bulk-skin temperature difference to the point where they could accurately and reliably apply under a wide variety of environmental conditions. To accomplish this goal, work was conducted in three primary areas. These included production of an archive of available data sets containing measurements of the skin and bulk temperatures and associated environmental conditions, evaluation of existing skin layer models using the compiled data archive, and additional theoretical work on the development of an improved model using the data collected under diverse environmental conditions. In this work we set the basis for a new physical model of renewal type, and propose a parameterization for the temperature difference across the cool skin of the ocean in which the effects of thermal buoyancy, wind stress, and microscale breaking are all integrated by means of the appropriate renewal time scales. Ideally, we seek to obtain a model that will accurately apply under a wide variety of environmental conditions. A summary of the work in each of these areas is included in this report. A large amount of work was accomplished under the support of this grant. The grant supported the graduate studies of Sandra Castro and the preparation of her thesis which will be completed later this year. This work led to poster presentations at the 1999 American Geophysical Union Fall Meeting and 2000 IGARSS meeting. Additional work will be presented in a talk at this year's American Meteorological Society Air-Sea Interaction Meeting this May. The grant also supported Sandra Castro during a two week experiment aboard the R/P Flip (led by Dr. Andrew Jessup of the Applied Physics Laboratory) to help obtain additional shared data sets and to provide Sandra with a fundamental understanding of the physical processes needed in the models. In a related area, the funding also partially supported Dr. William Emery and Daniel Baldwin in the preparation of their publication "Accuracy of in situ sea surface temperatures used to calibrate infrared satellite measurements". The remainder of this report is drawn from these publications and presentations.

Emery, William J.↗
NASA NTRS1993

(abstract) Supernova Remnant and Molecular Clouds

Upon impact of the shockwaves generated by a supernova remnant, molecular gas and the associated dust grains are substantially excited and become prominent sources of infrared emission. Recent studies of such interactions, utilizing the infrared data and information from other wavelengths, have revealed many details of the physical processes in the interstellar medium. In particluar, the understanding of the temperature and ionization structures in the postshock material is helpful in modeling the star-gas cycles in the Galaxy, and probing the circumstances of star formation.

medium molecular gas↗
NASA NTRS2004

Discrepancy Reporting Management System

Discrepancy Reporting Management System (DRMS) is a computer program designed for use in the stations of NASA's Deep Space Network (DSN) to help establish the operational history of equipment items; acquire data on the quality of service provided to DSN customers; enable measurement of service performance; provide early insight into the need to improve processes, procedures, and interfaces; and enable the tracing of a data outage to a change in software or hardware. DRMS is a Web-based software system designed to include a distributed database and replication feature to achieve location-specific autonomy while maintaining a consistent high quality of data. DRMS incorporates commercial Web and database software. DRMS collects, processes, replicates, communicates, and manages information on spacecraft data discrepancies, equipment resets, and physical equipment status, and maintains an internal station log. All discrepancy reports (DRs), Master discrepancy reports (MDRs), and Reset data are replicated to a master server at NASA's Jet Propulsion Laboratory; Master DR data are replicated to all the DSN sites; and Station Logs are internal to each of the DSN sites and are not replicated. Data are validated according to several logical mathematical criteria. Queries can be performed on any combination of data.

Cooper, Tonja M.↗
NASA NTRS2011

Vostok Subglacial Lake: A Review of Geophysical Data Regarding Its Discovery and Topographic Setting

Vostok Subglacial Lake is the largest and best known sub-ice lake in Antarctica. The establishment of its water depth (>500 m) led to an appreciation that such environments may be habitats for life and could contain ancient records of ice sheet change, which catalyzed plans for exploration and research. Here we discuss geophysical data used to identify the lake and the likely physical, chemical, and biological processes that occur in it. The lake is more than 250 km long and around 80 km wide in one place. It lies beneath 4.2 to 3.7 km of ice and exists because background levels of geothermal heating are sufficient to warm the ice base to the pressure melting value. Seismic and gravity measurements show the lake has two distinct basins. The Vostok ice core extracted >200 m of ice accreted from the lake to the ice sheet base. Analysis of this ice has given valuable insights into the lake s biological and chemical setting. The inclination of the ice-water interface leads to differential basal melting in the north versus freezing in the south, which excites circulation and potential mixing of the water. The exact nature of circulation depends on hydrochemical properties, which are not known at this stage. The age of the subglacial lake is likely to be as old as the ice sheet (approx.14 Ma). The age of the water within the lake will be related to the age of the ice melting into it and the level of mixing. Rough estimates put that combined age as approx.1 Ma.

Siegert, Martin J.↗
NASA NTRSDate not supplied

Single Field-of-View Sounding Atmospheric Products

The Single Field-of-view Sounder Atmospheric Products (SiFSAP) answer the need for a novel high spatial resolution atmospheric data product for major hyper-spectral infrared (IR) sounder missions. SiFSAP include a complete set of atmospheric vertical profiles, cloud, and surface properties, which are physically retrieved from top-of-atmosphere (TOA) spectral radiances under all-sky conditions via a rigorously defined radiative transfer relationship. By using a state-of-art fast radiative transfer model and a carefully designed optimal estimation based physical retrieval scheme, the SiFSAP algorithm ensures both an ultra-fast data processing speed needed for operational weather applications and the radiometric consistency desired by long-term climate studies. SiFSAP supplement existing operational products by providing data at the native resolution of the sounder instruments, the direct and accurate retrieval of cloud scattering properties, and the establishment of ‘radiance closure.’

Wan Wu↗
NASA NTRSDate not supplied

Single Field-of-View Sounding Atmospheric Products

The Single Field-of-view Sounder Atmospheric Products (SiFSAP) answer the need for a novel high spatial resolution atmospheric data product for major hyper-spectral infrared (IR) sounder missions. SiFSAP include a complete set of atmospheric vertical profiles, cloud, and surface properties, which are physically retrieved from top-of-atmosphere (TOA) spectral radiances under all-sky conditions via a rigorously defined radiative transfer relationship. By using a state-of-art fast radiative transfer model and a carefully designed optimal estimation based physical retrieval scheme, the SiFSAP algorithm ensures both an ultra-fast data processing speed needed for operational weather applications and the radiometric consistency desired by long-term climate studies. SiFSAP supplement existing operational products by providing data at the native spatial resolution of the sounder instruments, the direct and accurate retrieval of cloud scattering properties, and the establishment of ‘radiance closure.’

single-field-of-view, retrieval, hyperspectral sou↗
NASA NTRSDate not supplied

Maturation of In-space Welding in Reduced Gravity and Reduced Pressure Environments Through Progression to Suborbital Flight Experiments

Development of in-space welding (ISW) is a key and enabling manufacturing technology as welding is currently involved in producing approximately 90% of durable goods, and this trend is expected to hold for in-space manufacturing as well. ISW is critical to a sustainable space economy. To mature in-space welding, numerous gaps must be closed since NASA has not made a weld in space in 50 years. Advancing laser beam welding (LBW) towards a suborbital flight demonstration will help mature the process for in-space assembly & manufacturing (ISAM) through demonstration in relevant environments and achieving technology readiness milestones. LBW technologies and science improved by this work will benefit in-space manufacturing, long-term sustainability of space structures & operations, and the overall space economy. Ground-based welding of aerospace hardware requires an Edisonian approach to qualify a welding process; however, this approach is infeasible for ISW qualification due to mass, volume, logistic, and cost challenges inherent to the space environment. To address this challenge, we are conducting ground and suborbital tests with partners performing parabolic flights to gather data on LBW processes and infuse them into NASA & partner missions. This progression from ground to flight experiments will elucidate relevant physics during LBW: 1) reduced gravity, 2) reduced pressure, and 3) extreme temperature. Data from post-flight evaluation and in situ instrumentation will feed integrated computational materials engineering (ICME) tools to reduce the burden of ISW infusion by establishing a link between space and terrestrial environments, ultimately accelerating the qualification of LBW in space.

in-space welding↗
NASA NTRS1997

Penetration Electric Fields and Inner Magnetosphere Dynamics: A Model and Data Comparison

The initial substorm interval to be studied has been selected to be the magnetic storm of June 4, 1991, and following. The CRRES electric and magnetic field data has been processed. We have added DMSP ion drift and energetic particle data and further refined the CRRES data for intercomparison. The DMSP data increase the frequency of monitoring of the temporal response of the penetration electric fields to every 100 min. Energy is seen to flow between the ionosphere and magnetosphere at low L values during the main phase of the magnetic storm in the form of field-aligned Poynting flux. This indicates electrodynamic coupling of the regions with Alfven waves. The first comparisons of the data with outputs of the Rice Magnetospheric Specification Model (MSM) were made. Both positive and negative correlation were seen as might be expected. Differences were especially evident in the time constants of the processes. Comparisons with the more physically self-consistent Rice Convection Model (RCM) with both electric fields and particle data are in progress to suggest physical constraints for our understanding of the phenomena.

Maynard, Nelson C.↗
NASA NTRSDate not supplied

Hybrid Model Based Approaches for Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Hybrid Modeling↗
NASA NTRSDate not supplied

Hybrid Approaches to Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Systems Health Management↗
NASA NTRS1991

Lunar sourcebook - A user's guide to the moon

The present status of lunar knowledge based on U.S. and USSR lunar missions and the continuing analysis of lunar samples and data is reviewed. Particular attention given to exploration, samples, and recent concepts of the moon; the lunar environment; lunar surface processes; the lunar minerals, rocks, and regolith; chemical elements in the moon; physical properties of the lunar surface; and global and regional data about the moon.

Heiken, Grant H.↗
NASA NTRS2016

Persistence Mapping Using EUV Solar Imager Data

We describe a simple image processing technique that is useful for the visualization and depiction of gradually evolving or intermittent structures in solar physics extreme-ultraviolet imagery. The technique is an application of image segmentation, which we call "Persistence Mapping," to isolate extreme values in a data set, and is particularly useful for the problem of capturing phenomena that are evolving in both space and time. While integration or "time-lapse" imaging uses the full sample (of size N ), Persistence Mapping rejects (N - 1)/N of the data set and identifies the most relevant 1/N values using the following rule: if a pixel reaches an extreme value, it retains that value until that value is exceeded. The simplest examples isolate minima and maxima, but any quantile or statistic can be used. This paper demonstrates how the technique has been used to extract the dynamics in long-term evolution of comet tails, erupting material, and EUV dimming regions.

Thompson, B. J.↗
NASA NTRS2022

Hybrid Model Based Approaches for Systems Health Management and Prognostics

This is a previously approved and published presentation. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Present achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. In principle, data-driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data-driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety-critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Prognostics↗
NASA NTRSDate not supplied

Hyperspectral Sounder Spectral Fingerprinting: Using Machine Learning Techniques to Enhance Model-Based Physical Inversion

Different retrieval algorithms have been developed to process top-of-atmosphere (TOA) spectral radiance data provided by hyperspectral infrared sounder missions. Those algorithms are either optimal estimation method (OEM) based schemes with radiative transfer calculation involved in the retrieval process, or machine learning based methods that allow ultra-efficient data procession but lack of radiometric consistency validation based on the directly measured information. Combining both approaches leverages their respective technical advantages, leading to more accurate results. This study introduces a hyperspectral sounder fingerprinting algorithm to explore this hybrid approach. This approach involves the use of a spectral information-based classification method to identify an reference geophysical state and the corresponding radiative kernel. This enables the efficient retrieval of geophysical variables of interest through a radiative kernel-based linear inversion procedure. The fingerprinting method has been applied to analyze a decade-long hyperspectral sounder data record.

Wan Wu↗
NASA NTRS1990

Studying marine stratus with large eddy simulation

Data sets from field experiments over the stratocumulus regime may include complications from larger scale variations, decoupled cloud layers, diurnal cycle, or entrainment instability, etc. On top of the already complicated turbulence-radiation-condensation processes within the cloud-topped boundary layer (CTBL), these complexities may sometimes make interpretation of the data sets difficult. To study these processes, a better understanding is needed of the basic processes involved in the prototype CTBL. For example, is cloud top radiative cooling the primary source of the turbulent kinetic energy (TKE) within the CTBL. Historically, laboratory measurements have played an important role in addressing the turbulence problems. The CTBL is a turbulent field which is probably impossible to generate in laboratories. Large eddy simulation (LES) is an alternative way of 'measuring' the turbulent structure under controlled environments, which allows the systematic examination of the basic physical processes involved. However, there are problems with the LES approach for the CTBL. The LES data need to be consistent with the observed data. The LES approach is discussed, and results are given which provide some insights into the simulated turbulent flow field. Problems with this approach for the CTBL and information from the FIRE experiment needed to justify the LES results are discussed.

Moeng, Chin-Hoh↗
NASA NTRS1974

Satellite-tracking and earth-dynamics research programs

Observations and research progress of the Smithsonian Astrophysical Observatory are reported. Satellite tracking networks (ground stations) are discussed and equipment (Baker-Nunn cameras) used to observe the satellites is described. The improvement of the accuracy of a laser ranging system of the ground stations is discussed. Also, research efforts in satellite geodesy (tides, gravity anomalies, plate tectonics) is discussed. The use of data processing for geophysical data is examined, and a data base for the Earth and Ocean Physics Applications Program is proposed. Analytical models of the earth's motion (computerized simulation) are described and the computation (numerical integration and algorithms) of satellite orbits affected by the earth's albedo, using computer techniques, is also considered. Research efforts in the study of the atmosphere are examined (the effect of drag on satellite motion), and models of the atmosphere based on satellite data are described.

Source record↗