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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Cloud Physics Lidar Optical Measurements During the SAFARI-2000 Field Campaign

In this presentation, we will show new optical data processing results from the Cloud Physics War during SAFARI-2000. Retrieved products include aerosol and cloud layer location and identification, layer optical depths, vertical extinction profiles, and extinction-to-backscatter (S) ratios for 532 and 1064 nm. The retrievals will focus on the persistent and smoky planetary boundary layer and occasional elevated aerosol layers found in southern Africa during August and September 2000.

Hlavka, Dennis L.↗
NASA NTRS1993

Next generation initiation techniques

Four-dimensional data assimilation strategies can generally be classified as either current or next generation, depending upon whether they are used operationally or not. Current-generation data-assimilation techniques are those that are presently used routinely in operational-forecasting or research applications. They can be classified into the following categories: intermittent assimilation, Newtonian relaxation, and physical initialization. It should be noted that these techniques are the subject of continued research, and their improvement will parallel the development of next generation techniques described by the other speakers. Next generation assimilation techniques are those that are under development but are not yet used operationally. Most of these procedures are derived from control theory or variational methods and primarily represent continuous assimilation approaches, in which the data and model dynamics are 'fitted' to each other in an optimal way. Another 'next generation' category is the initialization of convective-scale models. Intermittent assimilation systems use an objective analysis to combine all observations within a time window that is centered on the analysis time. Continuous first-generation assimilation systems are usually based on the Newtonian-relaxation or 'nudging' techniques. Physical initialization procedures generally involve the use of standard or nonstandard data to force some physical process in the model during an assimilation period. Under the topic of next-generation assimilation techniques, variational approaches are currently being actively developed. Variational approaches seek to minimize a cost or penalty function which measures a model's fit to observations, background fields and other imposed constraints. Alternatively, the Kalman filter technique, which is also under investigation as a data assimilation procedure for numerical weather prediction, can yield acceptable initial conditions for mesoscale models. The third kind of next-generation technique involves strategies to initialize convective scale (non-hydrostatic) models.

Warner, Tom↗
NASA NTRS1989

Representation and display of vector field topology in fluid flow data sets

The visualization of physical processes in general and of vector fields in particular is discussed. An approach to visualizing flow topology that is based on the physics and mathematics underlying the physical phenomenon is presented. It involves determining critical points in the flow where the velocity vector vanishes. The critical points, connected by principal lines or planes, determine the topology of the flow. The complexity of the data is reduced without sacrificing the quantitative nature of the data set. By reducing the original vector field to a set of critical points and their connections, a representation of the topology of a two-dimensional vector field that is much smaller than the original data set but retains with full precision the information pertinent to the flow topology is obtained. This representation can be displayed as a set of points and tangent curves or as a graph. Analysis (including algorithms), display, interaction, and implementation aspects are discussed.

Helman, James↗
NASA NTRSDate not supplied

Data Science and the Knowledge Discovery Adventure

This talk will cover the important steps involved in the data science and knowledge discovery process: • Initial fact gathering (interview domain experts, review reports, articles, state-of-the-art) • Identify the problem (prediction, classification, statistical analysis, etc.) • Survey supporting data sources • Understand the data (numerical, categorical, text, sampling rate, data quality issues, etc.) • Selecting relevant features and sources • Acquire the data (set up agreements with the data stewards, APIs to download, etc.) • Merge data sources (temporal, spatial, common key, other ontologies...) • Feature Engineering (non linear domain knowledge or physics-based relationships) • Build data processing pipeline (may need to tap into data stream, develop parallel processing algorithm, federated learning etc.) • Build model and test (tune hyper-parameters, cross validation.) • Analyze/Validate results (do the results make sense. Does it answer the original question). • Deploy/Publish (Monitor and assess benefits)

Data science↗
NASA NTRS1983

Radial diffusion in Saturn's radiation belts - A modeling analysis assuming satellite and ring E absorption

A modeling analysis is carried out of six experimental phase space density profiles for nearly equatorially mirroring protons using methods based on the approach of Thomsen et al. (1977). The form of the time-averaged radial diffusion coefficient D(L) that gives an optimal fit to the experimental profiles is determined under the assumption that simple satellite plus Ring E absorption of inwardly diffusing particles and steady-state radial diffusion are the dominant physical processes affecting the proton data in the L range that is modeled. An extension of the single-satellite model employed by Thomsen et al. to a model that includes multisatellite and ring absorption is described, and the procedures adopted for estimating characteristic satellite and ring absorption times are defined. The results obtained in applying three representative solid-body absorption models to evaluate D(L) in the range where L is between 4 and 16 are reported, and a study is made of the sensitivity of the preferred amplitude and L dependence for D(L) to the assumed model parameters. The inferred form of D(L) is then compared with that which would be predicted if various proposed physical mechanisms for driving magnetospheric radial diffusion are operative at Saturn.

Hood, L. L.↗
NASA NTRS1963

S-6, AN AERONOMY SATELLITE

Mission and design requirements for the nasa s-6 satellite including instrumentation necessary to obtain data on physical and chemical processes in the upper-atmosphere between 250 and 900 kilometers

EXPLORER XVII SATELLITE↗
NASA NTRS1972

Considerations for an earth physics information-management service.

In a preliminary investigation into the feasibility of establishing a data center for earth physics, 12 disciplines were considered for inclusion. Estimation of the size of the data base for each indicated a need for storage of approximately 10 to the 10th power characters. The computer-based system deemed most worthy of further investigation was the interactive concept with remote-terminal access. Users are divided into three classes according to how they would access information: with no terminal, with an interactive terminal, and with a multidevice terminal. All these users can be served by the same center without any particular difficulty, but the real benefactor is the user with an interactive terminal, because he can compile, debug, and run programs in one continuous session. Final points stressed are multiprogramming for dynamic resource sharing, hardware modularity for future expansion, and information protection for such a large community of users. It is concluded that a survey should be conducted to gather more information from the potential users of such a system, and that a pilot project should be developed at some location where both earth-physics research and data-processing capabilities already exist.

Martin, R. W.↗
NASA NTRS1987

The First ISLSCP Field Experiment (FIFE)

In 1987, the International Satellite Land Surface Climatology Project (ISLSCP) will conduct a major field experiment aimed at the development of superior methods for relating satellite remote sensing to biological and physical parameter and exchange process data obtained during simultaneous ground truth measurements. This, the First ISLSCP Field Experiment, will attempt to arrive at a better understanding of the role of the land surface in the behavior of the global climate system, as required by next-generation global circulation models and general biospheric models. The field experiment will be conducted in the Konza Prairie of Kansas.

Murphy, Robert E.↗
NASA NTRS2005

Mission leverage education: NSU/NASA innovative undergraduate model

The BEST Lab (Center for Excellence in Science Education), the Center for Materials Research (CMR), and the Chemistry, Mathematics, Physics, and Computer Science (CS) Departments at Norfolk State University (NSU) joined forces to implement MiLEN(2) IUM - an innovative approach tu integrate current and emerging research into the undergraduate curricula and train students on NASA-related fields. An Earth Observing System (EOS) mission was simulated where students are educated and trained in many aspects of Remote Sensing: detector physics and spectroscopy; signal processing; data conditioning, analysis, visualization; and atmospheric science. This model and its continued impact is expected to significantly enhance the quality of the Mathematics, Science, Engineering and Technology (MSET or SMET) educational experience and to inspire students from historically underrepresented groups to pursue careers in NASA-related fields. MiLEN(2) IUM will be applicable to other higher education institutions that are willing to make the commitment to this endeavor in terms of faculty interest and space.

Chaudhury, S. Raj↗
NASA NTRSDate not supplied

Role of NDE and In-Situ Process Monitoring in Managing Risk of AM Space Hardware

The recently published NASA-STD-6030 defines the Additive Manufacturing (AM) Requirements for Spaceflight Systems. Key aspects of the certification approach include the development of a qualified material process (QMP) and material characterization determined by part classification. Nondestructive evaluation (NDE) of the full surface and volume is required for all part classifications except those with negligible risk. NASA is exploring the use of in-process monitoring data to improve risk posture and supplement post-build inspection for complex parts. Currently, the most challenging obstacle to overcome is linking the indications in the monitoring data to the physics of the process and the final material state of the finished part. NASA is undertaking studies to understand and quantify this relationship for various monitoring methods. The desired goal is to develop a protocol to establish this correlation for any monitoring method. Once this correlation is known, the critical defect size can be linked to a representative indication in the monitoring data, and the capability of the monitoring system can be tested using the 90/95 probability of detection requirement for NDE methods. This would enable the use of in-process monitoring as a defect screening activity for AM part certification. Many high-criticality components built with AM have high complexity and therefore limited inspectability, so using in-process monitoring can help address this certification gap. The use of adaptive, closed-loop monitoring systems that alter the locked process will require a new approach to the QMP.

additive manufacturing↗
NASA NTRSDate not supplied

Role of NDE and In-Situ Process Monitoring in Managing Risk of AM Space Hardware

The recently published NASA-STD-6030 defines the Additive Manufacturing (AM) Requirements for Spaceflight Systems. Key aspects of the certification approach include the development of a qualified material process (QMP) and material characterization determined by part classification. Nondestructive evaluation (NDE) of the full surface and volume is required for all part classifications except those with negligible risk. NASA is exploring the use of in-process monitoring data to improve risk posture and supplement post-build inspection for complex parts. Currently, the most challenging obstacle to overcome is linking the indications in the monitoring data to the physics of the process and the final material state of the finished part. NASA is undertaking studies to understand and quantify this relationship for various monitoring methods. The desired goal is to develop a protocol to establish this correlation for any monitoring method. Once this correlation is known, the critical defect size can be linked to a representative indication in the monitoring data, and the capability of the monitoring system can be tested using the 90/95 probability of detection requirement for NDE methods. This would enable the use of in-process monitoring as a defect screening activity for AM part certification. Many high-criticality components built with AM have high complexity and therefore limited inspectability, so using in-process monitoring can help address this certification gap. The use of adaptive, closed-loop monitoring systems that alter the locked process will require a new approach to the QMP.

advanced manufacturing↗
NASA NTRS1980

Regenerator matrix physical property data

Among several cellular ceramic structures manufactured by various suppliers for regenerator application in a gas turbine engine, three have the best potential for achieving durability and performance objectives for use in gas turbines, Stirling engines, and waste heat recovery systems: (1) an aluminum-silicate sinusoidal flow passage made from a corrugated wate paper process; (2) an extruded isosceles triangle flow passage; and (3) a second generation matrix incorporating a square flow passage formed by an embossing process. Key physical and thermal property data for these configurations presented include: heat transfer and pressure drop characteristics, compressive strength, tensile strength and elasticity, thermal expansion characteristics, chanical attack, and thermal stability.

Fucinari, C. A.↗
NASA NTRS1990

Overview of the Astrophysics Data System

The Astrophysics Division of NASA has built a geographically- and logically-distributed heterogeneous information system for the dissemination and coordinated multispectral analysis of data from astrophysics missions. The Astrophysics Data System (ADS) is a truly distributed system in which the data and the required processing are physically distributed. To accommodate the anticipated growth and changes in both requirements and technology, the ADS employs a server/client architecture which allows services and data to be added or replaced without having to change the basic architecture or interfaces. Current datasets accessible through the system include all the tabular astronomical data available at each of six existing astrophysics data centers. Additional data nodes, at both NASA data centers and academic institutions, will be added shortly. The future evolution of the system will be driven in large part by user services mounted both by the ADS project itself and by members of the astrophysics community.

Good, John C.↗
NASA NTRS1991

Overview of the Astrophysics Data System

The Astrophysics Division of NASA has built a geographically and logically distributed heterogeneous information system for the dissemination and coordinated multispectral analysis of data from astrophysics missions. The Astrophysics Data System (ADS) is a truly distributed system in which the data and the required processing are physically distributed. To accommodate the anticipated growth and changes in both requirements and technology, the ADS employs a server/client architecture which allows services and data to be added or replaced without having to change the basic architecture or interfaces. Current datasets accessible through the system include all the tabular astronomical data available at each of six existing astrophysics data centers. Additional data nodes, at both NASA data centers and academic institutions, will be added shortly. The future evolution of the system will be driven in large part by user services mounted both by the ADS project itself and by members of the astrophysics community.

Good, John C.↗
NASA NTRS2024

Enabling Advanced Snow Physics within Land Surface Models Through an Interoperable Model-Physics Coupling Framework

Accurate estimation of snow accumulation and melt is a critical part of decision-making in snow-dominated watersheds. In this study, we demonstrate a flexible methodology to couple a detailed snow model, Crocus, separately to two different land surface models (LSMs), Noah-MP and Noah. The original LSMs and the coupled models (Noah-MP-Crocus and Noah-Crocus) are used to simulate snow depth, snow water equivalent, and other water and energy states and fluxes. The results of simulations are compared against a wide range of independent gridded and point scale reference datasets. Our results show that coupling the detailed snow model, Crocus, with the LSMs improves the snow depth and snow water equivalent relative to independent observations. Overall, larger improvements are obtained with coupling Crocus to the Noah LSM, with the coupled Noah-Crocus configuration reducing the RMSE and bias of snow depth from 2-12% and 57-75%, respectively, relative to Snow Data Assimilation System (SNODAS) and snow product from the University of Arizona. On the other hand, smaller improvements are obtained by coupling Crocus with Noah-MP. The Coupled Noah-MP-Crocus reduces the snow depth bias but slightly degrades the RMSE of snow depth and snow water equivalent. The corresponding impacts in other water budget terms such as evapotranspiration, soil moisture, and streamflow, however, are mixed, pointing to the significant need to improve the coupling assumptions of these processes within land models. Overall, the interoperable coupling framework demonstrated here offers the opportunity to include more detailed snow physics and processes, and to advance data assimilation systems through improved exploitation of information from snow remote sensing instruments.

Snow model↗
NASA NTRSDate not supplied

Development of Solar Flare and Energetic Particle Prediction Portal (SEP 3 )

Solar activity is a primary factor determining the state of the Earth’s space environment, geomagnetic and ionospheric disturbances, and radiation hazards. In the current state of knowledge, machine learning (ML) methods provide essential tools for processing data, investigating relationships among various physical properties and characteristics, uncovering hidden connections, and predicting hazardous solar events. The primary difficulty in developing and applying modern machine-learning tools in heliophysics is that the essential data are scattered among over a hundred data repositories developed by instrument teams of space missions and ground-based observatories. In addition, statistical and ML methods require long time series of homogeneous measurements. To facilitate ML-ready data preparation and access, we have developed an interactive database of solar flares integrating the most essential datasets (https://solarflare.njit.edu/). The database performs an initial data processing and is automatically updated. In addition, we are developing the Solar Energetic Particle Prediction Portal (SEP3, https://sun.njit.edu/SEP3), which hosts web applications that allow users to retrieve the database records. The Portal has a search page for browsing the events from the most widely used catalogs and a dedicated space to share the most recent achievements of the team. The interactive widget can display soft X-ray and proton flux time series from GOES satellites and the flare records. The data portal has been used to evaluate the forecasts of solar proton events and investigate machine-learning approaches to SEP prediction.

SMD↗
NASA NTRS1992

Methods of testing parameterizations: Vertical ocean mixing

The ocean's velocity field is characterized by an exceptional variety of scales. While the small-scale oceanic turbulence responsible for the vertical mixing in the ocean is of scales a few centimeters and smaller, the oceanic general circulation is characterized by horizontal scales of thousands of kilometers. In oceanic general circulation models that are typically run today, the vertical structure of the ocean is represented by a few tens of discrete grid points. Such models cannot explicitly model the small-scale mixing processes, and must, therefore, find ways to parameterize them in terms of the larger-scale fields. Finding a parameterization that is both reliable and plausible to use in ocean models is not a simple task. Vertical mixing in the ocean is the combined result of many complex processes, and, in fact, mixing is one of the less known and less understood aspects of the oceanic circulation. In present models of the oceanic circulation, the many complex processes responsible for vertical mixing are often parameterized in an oversimplified manner. Yet, finding an adequate parameterization of vertical ocean mixing is crucial to the successful application of ocean models to climate studies. The results of general circulation models for quantities that are of particular interest to climate studies, such as the meridional heat flux carried by the ocean, are quite sensitive to the strength of the vertical mixing. We try to examine the difficulties in choosing an appropriate vertical mixing parameterization, and the methods that are available for validating different parameterizations by comparing model results to oceanographic data. First, some of the physical processes responsible for vertically mixing the ocean are briefly mentioned, and some possible approaches to the parameterization of these processes in oceanographic general circulation models are described in the following section. We then discuss the role of the vertical mixing in the physics of the large-scale ocean circulation, and examine methods of validating mixing parameterizations using large-scale ocean models.

Tziperman, Eli↗