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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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NASA NTRS2010

Software Development to Assist in the Processing and Analysis of Data Obtained Using Fiber Bragg Grating Interrogation Systems

A fiber Bragg grating is a portion of a core of a fiber optic stand that has been treated to affect the way light travels through the strand. Light within a certain narrow range of wavelengths will be reflected along the fiber by the grating, while light outside that range will pass through the grating mostly undisturbed. Since the range of wavelengths that can penetrate the grating depends on the grating itself as well as temperature and mechanical strain, fiber Bragg gratings can be used as temperature and strain sensors. This capability, along with the light-weight nature of the fiber optic strands in which the gratings reside, make fiber optic sensors an ideal candidate for flight testing and monitoring in which temperature and wing strain are factors. A team of NASA Dryden engineers has been working to advance the fiber optic sensor technology since the mid 1990 s. The team has been able to improve the dependability and sample rate of fiber optic sensor systems, making them more suitable for real-time wing shape and strain monitoring and capable of rivaling traditional strain gauge sensors in accuracy. The sensor system was recently tested on the Ikhana unmanned aircraft and will be used on the Global Observer unmanned aircraft. Since a fiber Bragg grating sensor can be placed every halfinch on each optic fiber, and since fibers of approximately 40 feet in length each are to be used on the Global Observer, each of these fibers will have approximately 1,000 sensors. A total of 32 fibers are to be placed on the Global Observer aircraft, to be sampled at a rate of about 50 Hz, meaning about 1.6 million data points will be taken every second. The fiber optic sensors system is capable of producing massive amounts of potentially useful data; however, methods to capture, record, and analyze all of this data in a way that makes the information useful to flight test engineers are currently limited. The purpose of this project is to research the availability of software capable of processing massive amounts of data in both real-time and post-flight settings, and to produce software segments that can be integrated to assist in the task as well. The selected software must be able to: (1) process massive amounts of data (up to 4GB) at a speed useful in a real-time settings (small fractions of a second); (2) process data in post-flight settings to allow test reproduction or further data analysis, inclusive; (3) produce, or make easier to produce, three-dimensional plots/graphs to make the data accessible to flight test engineers; and (4) be customized to allow users to use their own processing formulas or functions and display the data in formats they prefer. Several software programs were evaluated to determine their utility in completing the research objectives. These programs include: OriginLab, Graphis, 3D Grapher, Visualization Sciences Group (VSG) Avizo Wind, Interactive Analysis and Display System (IADS), SigmaPlot, and MATLAB.

Hicks, Rebecca↗
NASA NTRS2016

Decision Manifold Approximation for Physics-Based Simulations

With the recent surge of success in big-data driven deep learning problems, many of these frameworks focus on the notion of architecture design and utilizing massive databases. However, in some scenarios massive sets of data may be difficult, and in some cases infeasible, to acquire. In this paper we discuss a trajectory-based framework that quickly learns the underlying decision manifold of binary simulation classifications while judiciously selecting exploratory target states to minimize the number of required simulations. Furthermore, we draw particular attention to the simulation prediction application idealized to the case where failures in simulations can be predicted and avoided, providing machine intelligence to novice analysts. We demonstrate this framework in various forms of simulations and discuss its efficacy.

Wong, Jay Ming↗
NASA NTRS2006

A Concept for Run-Time Support of the Chapel Language

A document presents a concept for run-time implementation of other concepts embodied in the Chapel programming language. (Now undergoing development, Chapel is intended to become a standard language for parallel computing that would surpass older such languages in both computational performance in the efficiency with which pre-existing code can be reused and new code written.) The aforementioned other concepts are those of distributions, domains, allocations, and access, as defined in a separate document called "A Semantic Framework for Domains and Distributions in Chapel" and linked to a language specification defined in another separate document called "Chapel Specification 0.3." The concept presented in the instant report is recognition that a data domain that was invented for Chapel offers a novel approach to distributing and processing data in a massively parallel environment. The concept is offered as a starting point for development of working descriptions of functions and data structures that would be necessary to implement interfaces to a compiler for transforming the aforementioned other concepts from their representations in Chapel source code to their run-time implementations.

James, Mark↗
NASA NTRS2006

Simulated convective systems using a cloud resolving model: Impact of large-scale temperature and moisture forcing using observations and GEOS-3 reanalysis

The GCE (Goddard Cumulus Ensemble) model, which has been developed and improved at NASA Goddard Space Flight Center over the past two decades, is considered as one of the finer and state-of-the-art CRMs (Cloud Resolving Models) in the research community. As the chosen CRM for a NASA Interdisciplinary Science (IDS) Project, GCE has recently been successfully upgraded into an MPI (Message Passing Interface) version with which great improvement has been achieved in computational efficiency, scalability, and portability. By basically using the large-scale temperature and moisture advective forcing, as well as the temperature, water vapor and wind fields obtained from TRMM (Tropical Rainfall Measuring Mission) field experiments such as SCSMEX (South China Sea Monsoon Experiment) and KWAJEX (Kwajalein Experiment), our recent 2-D and 3-D GCE simulations were able to capture detailed convective systems typical of the targeted (simulated) regions. The GEOS-3 [Goddard EOS (Earth Observing System) Version-3] reanalysis data have also been proposed and successfully implemented for usage in the proposed/performed GCE long-term simulations (i.e., aiming at producing massive simulated cloud data -- Cloud Library) in compensating the scarcity of real field experimental data in both time and space (location). Preliminary 2-D or 3-D pilot results using GEOS-3 data have generally showed good qualitative agreement (yet some quantitative difference) with the respective numerical results using the SCSMEX observations. The first objective of this paper is to ensure the GEOS-3 data quality by comparing the model results obtained from several pairs of simulations using the real observations and GEOS-3 reanalysis data. The different large-scale advective forcing obtained from these two kinds of resources (i.e., sounding observations and GEOS-3 reanalysis) has been considered as a major critical factor in producing various model results. The second objective of this paper is therefore to investigate and present such an impact of large-scale forcing on various modeled quantities (such as hydrometeors, rainfall, and etc.). A third objective is to validate the overall GCE 3-D model performance by comparing the numerical results with sounding observations, as well as available satellite retrievals.

Shie, C.-L.↗
NASA NTRSDate not supplied

The Future of NASA Earth Science in the Commercial Cloud: Challenges and Opportunities

NASA produces a large volume and variety of data products that are used every day to support research, decision making, and education. The widespread use of NASA’s Earth Science data is enabled by NASA’s Earth Science Data System (ESDS) program, which oversees the archiving and distribution of these data and invests in the development of new data systems and tools. However, NASA’s current approach to Earth Science data distribution — based on distributed institutional archives with individual on-premises high-performance computing capabilities — faces some significant challenges, including massive increases in data volume from upcoming missions, a greater need for transdisciplinary science that synthesizes many different kinds of observations, and a push to make science more open, inclusive, and accessible. To address these challenges, NASA is aggressively migrating its Earth Science data and related tools and services into the commercial cloud. Migration of data into the commercial cloud can significantly improve NASA’s existing data system capabilities by (1) providing more flexible options for storage and compute (including rapid, as-needed access to state-of-the-art capabilities); (2) by centralizing and standardizing data access, which gives all of NASA’s institutional data centers access to all of each other’s datasets; and (3) by facilitating “analysis-in-place”, whereby users can bring their own computational workflows and tools to the data rather than having to maintain their own copies of NASA datasets. However, migration to the commercial cloud also poses some significant challenges, including (1) managing costs under a “pay-as-you-go” model; (2) incompatibility with existing tools and data formats with object-based storage and network access; (3) vendor lock-in; (4) challenges with data access for workflows that mix on-premise and cloud computing; and (5) standardization for highly diverse data as is present in NASA’s data archive. I conclude with two examples of recent NASA activities showcasing capabilities enabled by the commercial cloud: An interactive analysis and development platform for analyzing airborne imaging spectroscopy data, and a new collection of tools and services for data discovery, analysis, publication, and data-driven storytelling (Visualization, Exploration, and Data Analysis, VEDA).

Alexey N Shiklomanov↗
NASA NTRS2001

Compact Holographic Data Storage

NASA's future missions would require massive high-speed onboard data storage capability to Space Science missions. For Space Science, such as the Europa Lander mission, the onboard data storage requirements would be focused on maximizing the spacecraft's ability to survive fault conditions (i.e., no loss in stored science data when spacecraft enters the 'safe mode') and autonomously recover from them during NASA's long-life and deep space missions. This would require the development of non-volatile memory. In order to survive in the stringent environment during space exploration missions, onboard memory requirements would also include: (1) survive a high radiation environment (1 Mrad), (2) operate effectively and efficiently for a very long time (10 years), and (3) sustain at least a billion write cycles. Therefore, memory technologies requirements of NASA's Earth Science and Space Science missions are large capacity, non-volatility, high-transfer rate, high radiation resistance, high storage density, and high power efficiency. JPL, under current sponsorship from NASA Space Science and Earth Science Programs, is developing a high-density, nonvolatile and rad-hard Compact Holographic Data Storage (CHDS) system to enable large-capacity, high-speed, low power consumption, and read/write of data in a space environment. The entire read/write operation will be controlled with electrooptic mechanism without any moving parts. This CHDS will consist of laser diodes, photorefractive crystal, spatial light modulator, photodetector array, and I/O electronic interface. In operation, pages of information would be recorded and retrieved with random access and high-speed. The nonvolatile, rad-hard characteristics of the holographic memory will provide a revolutionary memory technology meeting the high radiation challenge facing the Europa Lander mission. Additional information is contained in the original extended abstract.

Chao, T. H.↗
NASA NTRS2011

Space-Time Data fusion for Remote Sensing Applications

NASA has been collecting massive amounts of remote sensing data about Earth's systems for more than a decade. Missions are selected to be complementary in quantities measured, retrieval techniques, and sampling characteristics, so these datasets are highly synergistic. To fully exploit this, a rigorous methodology for combining data with heterogeneous sampling characteristics is required. For scientific purposes, the methodology must also provide quantitative measures of uncertainty that propagate input-data uncertainty appropriately. We view this as a statistical inference problem. The true but notdirectly- observed quantities form a vector-valued field continuous in space and time. Our goal is to infer those true values or some function of them, and provide to uncertainty quantification for those inferences. We use a spatiotemporal statistical model that relates the unobserved quantities of interest at point-level to the spatially aggregated, observed data. We describe and illustrate our method using CO2 data from two NASA data sets.

carbon dioxide↗
NASA NTRS2012

Content Platforms Meet Data Storage, Retrieval Needs

Earth is under a constant barrage of information from space. Whether from satellites orbiting our planet, spacecraft circling Mars, or probes streaking toward the far reaches of the Solar System, NASA collects massive amounts of data from its spacefaring missions each day. NASA s Earth Observing System (EOS) satellites, for example, provide daily imagery and measurements of Earth s atmosphere, oceans, vegetation, and more. The Earth Observing System Data and Information System (EOSDIS) collects all of that science data and processes, archives, and distributes it to researchers around the globe; EOSDIS recently reached a total archive volume of 4.5 petabytes. Try to store that amount of information in your standard, four-drawer file cabinet, and you would need 90 million to get the job done. To manage the flood of information, NASA has explored technologies to efficiently collect, archive, and provide access to EOS data for scientists today and for years to come. One such technology is now providing similar capabilities to businesses and organizations worldwide.

Source record↗
NASA NTRS1993

The contribution of disks and envelopes to the millimeter continuum emission from very young low-mass stars

We investigate the question of disk formation during the protostar phase. We model the dust continuum emission from the dense cloud core using the cloud-collapse models of Terebey et al. (1984) and show that dust emission from the dense core is important when measured with large single-dish telescopes at 1.3 mm, but nearly negligible with interferometers at 2.7 mm. From published and new data, we conclude that massive disks are also seen toward a number of other sources including L1448 IRS 3, whose disk mass is estimated to be 0.5 solar mass. However, 1.3 mm data show that massive disks are relatively rare, occurring around perhaps 5 percent of young embedded stars. This implies that either massive disks occur briefly during the embedded phase or that relatively few young stars form massive disks. The median 1.3 mm flux density of IRAS-Dense cores in our sample is nearly the same as T Tauri stars in the sample of Beckwith et al. (1990). We conclude that the typical disk mass is not significantly higher during the embedded phase than during the later T Tauri phase.

Terebey, S.↗
NASA NTRS2019

New Era, New Opportunity, Is GES DISC Ready for Big Data Challenge?

The new era of Big Data has opened doors for many new opportunities, as well as new challenges, for both Earth science research/application and data communities. As one of the twelve NASA data centers - Goddard Earth Sciences Data and Information Services Center (GES DISC), one of our great challenges has been how to help research/application community efficiently (quickly and properly) accessing, visualizing and analyzing the massive and diverse data in natural hazard research, management, or even prediction. GES DISC has archived over 2000 TB data on premises and distributed over 23,000 TB of data since 2010. Our data has been widely used in every phase of natural hazard management and research, i.e. long term risk assessment and reduction, forecasting and predicting, monitoring and detection, early warning, damage assessment and response. The big data challenge is not just about data storage, but also about data discoverability and accessibility, and even more, about data migration/mirroring in the cloud. This paper is going to demonstrate GES DISC’s efforts and approaches of evolving our overall Web services and powerful Giovanni (Geospatial Interactive Online Visualization ANd aNalysis Infrastructure) tool into further improving data discoverability and accessibility. Prototype works will also be presented.

Li, A.↗
NASA NTRS1994

Performance results of cooperating expert systems in a distributed real-time monitoring system

There are numerous definitions for real-time systems, the most stringent of which involve guaranteeing correct system response within a domain-dependent or situationally defined period of time. For applications such as diagnosis, in which the time required to produce a solution can be non-deterministic, this requirement poses a unique set of challenges in dynamic modification of solution strategy that conforms with maximum possible latencies. However, another definition of real time is relevant in the case of monitoring systems where failure to supply a response in the proper (and often infinitesimal) amount of time allowed does not make the solution less useful (or, in the extreme example of a monitoring system responsible for detecting and deflecting enemy missiles, completely irrelevant). This more casual definition involves responding to data at the same rate at which it is produced, and is more appropriate for monitoring applications with softer real-time constraints, such as interplanetary exploration, which results in massive quantities of data transmitted at the speed of light for a number of hours before it even reaches the monitoring system. The latter definition of real time has been applied to the MARVEL system for automated monitoring and diagnosis of spacecraft telemetry. An early version of this system has been in continuous operational use since it was first deployed in 1989 for the Voyager encounter with Neptune. This system remained under incremental development until 1991 and has been under routine maintenance in operations since then, while continuing to serve as an artificial intelligence (AI) testbed in the laboratory. The system architecture has been designed to facilitate concurrent and cooperative processing by multiple diagnostic expert systems in a hierarchical organization. The diagnostic modules adhere to concepts of data-driven reasoning, constrained but complete nonoverlapping domains, metaknowledge of global consequences of anomalous data, hierarchical reporting of problems that extend beyond a single domain, and shared responsibility for problems that overlap domains. The system enables efficient diagnosis of complex system failures in real-time environments with high data volumes and moderate failure rates, as indicated by extensive performance measurements.

Schwuttke, U. M.↗
NASA NTRS1985

Visualizing flow in the Space Shuttle Main Engine

Ongoing work is described aimed at applying advanced post-processing techniques to interpret and understand the complex flow behavior of the Space Shuttle Main Engine. The use of color graphics and powerful new engineering work stations to effectively present massive amounts of numerical data is emphasized. This combination of tools can result in better designs, faster turnaround, and more complete understanding of complex engineering systems like the Shuttle. Furthermore, it allows engineers access to the massive amounts of data generated by supercomputers and provides a more efficient coupling of man and supercomputer.

Belie, R. G.↗
NASA NTRS2001

Legato: Personal Computer Software for Analyzing Pressure-Sensitive Paint Data

'Legato' is personal computer software for analyzing radiometric pressure-sensitive paint (PSP) data. The software is written in the C programming language and executes under Windows 95/98/NT operating systems. It includes all operations normally required to convert pressure-paint image intensities to normalized pressure distributions mapped to physical coordinates of the test article. The program can analyze data from both single- and bi-luminophore paints and provides for both in situ and a priori paint calibration. In addition, there are functions for determining paint calibration coefficients from calibration-chamber data. The software is designed as a self-contained, interactive research tool that requires as input only the bare minimum of information needed to accomplish each function, e.g., images, model geometry, and paint calibration coefficients (for a priori calibration) or pressure-tap data (for in situ calibration). The program includes functions that can be used to generate needed model geometry files for simple model geometries (e.g., airfoils, trapezoidal wings, rotor blades) based on the model planform and airfoil section. All data files except images are in ASCII format and thus are easily created, read, and edited. The program does not use database files. This simplifies setup but makes the program inappropriate for analyzing massive amounts of data from production wind tunnels. Program output consists of Cartesian plots, false-colored real and virtual images, pressure distributions mapped to the surface of the model, assorted ASCII data files, and a text file of tabulated results. Graphical output is displayed on the computer screen and can be saved as publication-quality (PostScript) files.

Schairer, Edward T.↗
NASA NTRS2017

An Experimental Study and Database for Tip Vortex Flow From an Airfoil

An experimental investigation of tip vortices from a NACA0012 airfoil is conducted in a low-speed wind tunnel at a chord Reynolds number (Rc) of 4×10(exp 4 ). Data for the stationary airfoil at various angles of attack (alpha) are first discussed. Detailed flow-field surveys are done for two cases: alpha = 10deg with attached flow and alpha = 25deg with massive flow separation. Data include mean velocity, streamwise vorticity, and turbulent stresses at various streamwise locations. For all cases, the vortex core is seen to involve a mean velocity deficit. The deficits in these cases trace to the airfoil wake, part of which gets wrapped up by the tip vortex. Comparison with data from the literature suggests that with increasing Rc, the deficit turns into an excess, with the transition occurring in the approximate Rc range of 2×10(exp 5) to 5×10(exp 5). Survey results for various shapes of the airfoil wingtip are then presented. The shapes include square and rounded ends and a number of winglet designs. Finally, data under sinusoidal pitching condition, for the airfoil with square ends, are documented. All pitching cases pertain to a mean alpha = 15deg, while the amplitude and frequency are varied. Amplitudes of +/-5deg, +/-10deg, and +/-15deg and reduced frequencies k = 0.08, 0.2, and 0.33 are covered. Digital records of all data and some of the hardware design are made available on a supplemental CD with the electronic version of the paper for those interested in numerical simulation.

turbulence↗
NASA NTRS2019

Using Science Operations Tools and Methods from NASA for Remote Presence Ocean Exploration on Earth: E/V Nautilus Cruise NA108

NASA Ames' Exploration Ground Data Systems (xGDS) supports rapid scientific decision making by synchronizing information in time and space, including video and still images, scientific instrument data, and science and operations notes in geographic and temporal context. We have deployed xGDS at multiple NASA field analog missions over the past decade.In the last two years, we have participated in SUBSEA, a multi-institution collaborative project. SUBSEA used the research ship E/V Nautilus along with its two remotely operated vehicles (ROVs), Hercules and Argus, to explore deep ocean volcanic vents as an analog for ocean worlds (e.g. Enceladus). This work allowed us to compare the existing oceanographic operations methods and technologies used for ocean exploration with corresponding tools and approaches developed and used at NASA. In the first year of SUBSEA we observed existing remote science operations from the Inner Space Center (ISC). In the second year, we deployed xGDS at ISC to complement existing capabilities with xGDS tools designed to support remote Nautilus science operations from the ISC. During operations, video, ROV position and instrument telemetry were streamed from the ship to the ISC. As the science team watched dive operations, they could annotate the data with observations that were relevant to their work domain. Later, the team members could review the data at their own pace to collaboratively develop a dive plan for the next day, which had to be delivered on a fixed daily schedule.The opportunity to compare operations under different conditions enabled us to make several key observations about conducting remote science and planning operations efficiently: (i) Reviewing data collaboratively and interactively with temporal and spatial context was critical for the remote science team's ability to plan dive operations on the Nautilus. (ii) Science team members were actively engaged with the remote dive operations because they could interact with the collected data and visualize it as they desired. (iii) Being able to replay past events at accelerated speeds, and jump to points in time and spaced based on search results, provided efficient access to critical points of interest in a massive volume of data, so the remote science team could deliver plans on time. SUBSEA (Systematic Underwater Biogeochemical Science and Exploration Analog) is a multi-institution collaboration supported by NASA, NOAA's Office of Exploration Research (OER), the Ocean Exploration Trust (OET) and the University of Rhode Island's Graduate School of Oceanography (GSO). ISC is GSO's telepresence operations facility.

Lees, David S.↗
NASA NTRS2020

Conducting Efficient Remote Science and Planning Operations for Ocean Exploration Using Exploration Ground Data Systems (xGDS)

NASA Ames’ Exploration Ground Data Systems (xGDS) supports rapid scientific decision making by synchronizing information in time and space, including video and still images, scientific instrument data, and science and operations notes in geographic and temporal context. We have deployed xGDS at multiple NASA field analog missions over the past decade. In the last two years, we have participated in SUBSEA, a multi-institution collaborative project*. SUBSEA used the research ship E/V Nautilus along with its two remotely operated vehicles (ROVs), Hercules and Argus, to explore deep ocean volcanic vents as an analog for ocean worlds (e.g. Enceladus). This work allowed us to compare the existing oceanographic operations methods and technologies used for ocean exploration with corresponding tools and approaches developed and used at NASA. In the first year of SUBSEA we observed existing remote science operations from the Inner Space Center (ISC)**. In the second year, we deployed xGDS at ISC to complement existing capabilities with xGDS tools designed to support remote Nautilus science operations from the ISC. During operations, video, ROV position and instrument telemetry were streamed from the ship to the ISC. As the science team watched dive operations, they could annotate the data with observations that were relevant to their work domain. Later, the team members could review the data at their own pace to collaboratively develop a dive plan for the next day, which had to be delivered on a fixed daily schedule. The opportunity to compare operations under different conditions enabled us to make several key observations about conducting remote science and planning operations efficiently: (i) Reviewing data collaboratively and interactively with temporal and spatial context was critical for the remote science team’s ability to plan dive operations on the Nautilus. (ii) Science team members were actively engaged with the remote dive operations because they could interact with the collected data and visualize it as they desired. (iii) Being able to replay past events at accelerated speeds, and jump to points in time and spaced based on search results, provided efficient access to critical points of interest in a massive volume of data, so the remote science team could deliver plans on time. * SUBSEA (Systematic Underwater Biogeochemical Science and Exploration Analog) is a multi-institution collaboration supported by NASA, NOAA’s Office of Exploration Research (OER), the Ocean Exploration Trust (OET) and the University of Rhode Island’s Graduate School of Oceanography (GSO).

Cohen, Tamar E.↗
NASA NTRS1989

The crush of new earth science data knocking at our door

The reasons for collecting massive amounts of earth science data in the Earth Observing System (EOS) Project are discussed. A processing hierarchy for handling the data is described, and the prospects for adequate throughput and storage for operational data analysis in the EOS era are addressed. Needs for successful exploratory data analysis are examined, and the policy issues implicated by the large stream of EOS data are considered.

Kahn, Ralph↗
NASA NTRS1992

A distributed analysis and visualization system for model and observational data

The objective of this proposal is to develop an integrated and distributed analysis and display software system which can be applied to all areas of the Earth System Science to study numerical model and earth observational data from storm to global scale. This system will be designed to be easy to use, portable, flexible and easily extensible and to adhere to current and emerging standards whenever possible. It will provide an environment for visualization of the massive amounts of data generated from satellites and other observational field measurements and from model simulations during or after their execution. Two- and three-dimensional animation will also be provided. This system will be based on a widely used software package from NASA called GEMPAK and prototype software for three dimensional interactive displays built at NCSA. The underlying foundation of the system will be a set of software libraries which can be distributed across a UNIX based supercomputer and workstations.

Wilhelmson, Robert↗