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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Performance Analysis of Scientific and Engineering Applications Using MPInside and TAU

In this paper, we present performance analysis of two NASA applications using performance tools like Tuning and Analysis Utilities (TAU) and SGI MPInside. MITgcmUV and OVERFLOW are two production-quality applications used extensively by scientists and engineers at NASA. MITgcmUV is a global ocean simulation model, developed by the Estimating the Circulation and Climate of the Ocean (ECCO) Consortium, for solving the fluid equations of motion using the hydrostatic approximation. OVERFLOW is a general-purpose Navier-Stokes solver for computational fluid dynamics (CFD) problems. Using these tools, we analyze the MPI functions (MPI_Sendrecv, MPI_Bcast, MPI_Reduce, MPI_Allreduce, MPI_Barrier, etc.) with respect to message size of each rank, time consumed by each function, and how ranks communicate. MPI communication is further analyzed by studying the performance of MPI functions used in these two applications as a function of message size and number of cores. Finally, we present the compute time, communication time, and I/O time as a function of the number of cores.

Saini, Subhash↗
NASA NTRS2019

The RELAMPAGO Lightning Mapping Array: Preliminary Scientific Results and Application to GLM Calibration and Validation

During November 2018 through April 2019, an 11-station NASA lightning mapping array (LMA) was installed in the Cordoba region of Argentina, in support of GOES-16 Geostationary Lightning Mapper (GLM) calibration and validation, as well as the Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign. This region of Argentina is well known for frequent, intense thunderstorms and severe weather. The LMA was monitored remotely via the Internet throughout its deployment, but due to bandwidth limitations no real-time data were available. Custom GOES-16 imagery provided by NASA SPoRT assisted with monitoring of thunderstorm cases. Occasional site visits were done to obtain data disks, perform routine maintenance, and troubleshoot problems. During the deployment the network captured lightning in a variety of storm modes, including ordinary and severe multicells, supercells, and mesoscale convective systems. Many examples of normal-polarity thunderstorms, as well as a few examples of anomalously charged thunderstorms, were observed. Long (100+ km) horizontally stratified lightning flashes, as well as lightning in overshooting tops, also were frequently observed. Supporting research radar observations were available through January 2019, with operational radar coverage available after that time. Some cases featured supporting ABI meso scanning. This presentation will report on the LMA deployment in context with the RELAMPAGO field campaign, show results from some representative case studies, and will provide initial comparisons to GLM observations.

Lang, Timothy J.↗
NASA NTRS1979

The cost benefits of the Spacelab system to scientific investigations and space applications

Cost advantages of the Spacelab system to scientific experiments in space and space operations are presented. The payload integration concepts developed for Spacelab are examined, and it is shown that Spacelab and Shuttle integration, communications and data processing, launch support requirements and flight operations will provide a considerable savings in the costs of space research relative to previous space systems. Spacelab modular design, incorporating features such as standard payload interfaces, optional mission dependent equipment and standard services, such as the Experiment Computer Operating System, is shown to offer the user a wider range of services than previous programs, also at significantly lower costs. It is concluded that the Spacelab system will greatly reduce the costs and broaden the opportunities for scientific investigation in space.

Jean, O. C.↗
NASA NTRSDate not supplied

Creating Benchmark Data for Artificial Intelligence and Machine Learning Space Biology Research

To identify an appropriate AI/ML approach for a specific problem, the best practice is to measure algorithm performance through the benchmarking process. A scientific benchmark consists of an AI-ready dataset and a reference implementation on a specific scientific question. The NASA Science Mission Directorate (SMD) has started the “Benchmark Initiative for AI/ML to create scientific benchmark datasets in three applications: 1) scientific benchmarking, which finds the best algorithm for a specific problem; 2) application benchmarking, which measures algorithm performance against a set of parameters; and 3) system benchmarking, which evaluates performance of hardware and software architecture. Currently, there are no standardized datasets available to benchmark AI/ML algorithms in the domain of space biology. In this work, we constructed two AI/ML-ready biological datasets from experiments in space-flown mice: cellular imaging and RNA-seq. First, radiation-exposed immune cells harbor DNA damage foci that can be fluorescently marked to visualize the amount of damage following exposure to ionizing radiation. However, such large datasets are difficult to analyze visually, due to imaging inconsistencies and human bias, and classical image processing approaches can fail on imaging artifacts. AI/ML are therefore exciting alternative, providing the speed of machines and the accuracy of humans. We have made this dataset available at https://registry.opendata.aws/bps_microscopy/. Second, high-throughput nucleic acid sequencing (DNA-seq, RNA-seq) has become widespread in biomedical research due to the growing availability and affordability of these assays. However, most sequencing datasets suffer from high dimensionality and low sample count. In this work, we used a generative adversarial network to synthesize a standardized, AI-ready, publicly available benchmark dataset for space biology RNA-seq data with sufficient space-flown and ground control mouse liver samples from NASA GeneLab. This dataset is available at https://registry.opendata.aws/bps_rnaseq/. These datasets are now fully open the Space Biology community to test their favorite AI/ML approaches.

James Casaletto↗
NASA NTRS1993

A Microseismometer for Terrestrial and Extraterrestrial Applications

The scientific and technical requirements of extraterrestrial seismology place severe demands on instrumentation. Performance in terms of sensitivity, stability, and frequency band must match that of the best terrestrial instruments, at a fraction of the size, mass, and power. In addition, this performance must be realized without operator intervention in harsh temperature, shock, and radiation environments. These constraints have forced us to examine some fundamental limits of accelerometer design in order to produce a small, rugged, sensitive seismometer. Silicon micromachined sensor technology offers techniques for the fabrication of monolithic, robust, compact, low-power and -mass accelerometers. However, currently available sensors offer inadequate sensitivity and bandwidth. Our implementation of an advanced silicon micro machined seismometer is based on principles developed at JPL for high-sensitivity position sensor technology. The use of silicon micro machining technology with these new principles should enable the fabrication of a 10(exp -11) g sensitivity seismometer with a bandwidth of at least 0.01 to 20 Hz. The low Q properties of pure single-crystal silicon are essential in order to minimize the Brownian thermal noise limitations generally characteristic of seismometers with small proof masses. A seismometer consists of a spring-supported proof mass and a transducer for measuring its motion. For long period motion a position sensor is generally used, for which the displacement is proportional to the ground acceleration. The mechanical sensitivity can be increased either by increasing the proof mass or decreasing the spring stiffness, neither of which is desirable for planetary applications. Our approach has been to use an ultra sensitive capacitive position sensor with a sensitivity of better than 10(exp -13) m/Hz(exp 1/2). This allows the use of a stiffer suspension and a smaller proof mass. We have built several prototypes using these principles, and tests show that these devices can exhibit performance comparable to state-of-the-art instruments.

Banerdt, W.↗
NASA NTRS1992

Concept for obtaining geostationary limb infrared measurements (GEOLIM) and their application

The scientific value and utility of including the capability for spectral earthlimb emission measurements in an advanced geostationary mission is discussed. These measurements are expected to support improved scientific understanding of the atmospheric region extending from the tropospheric cloud tops up to the thermosphere. Specific topics in the science of this region that are addressed by geostationary observations of spectral earthlimb emission are discussed. For many of these topics, nadir sounding measurements are either at great disadvantage or completely inappropriate. A promising approach for acquiring the earthlimb measurements, and a preliminary instrument concept are discussed.

Kumer, J. B.↗
NASA NTRS1998

Measurement of Damping of Composite Materials for Turbomachinery Applications

The scientific community has felt that ceramic matrix composite (CMC) materials possess more material damping than the superalloys used in the production of rocket engine turbomachinery turbine-end components. The purpose of this NASA/MFSC study is to quantify the damping in CMC's as compared to a typical super-alloy, Inconel 718. It was observed through testing of beam coupons and disk specimens that the CMC's do indeed possess more material damping than the baselined alloy Inconel 718.

Harris, D. L.↗
NASA NTRS1996

Ra: The Sun for Science and Humanity

To guide the development of the Ra Strategic Framework, we defined scientific and applications objectives. For our primary areas of scientific interest, we choose the corona, the solar wind, the Sun's effect on the Earth, and solar theory and model development. For secondary areas of scientific interest, we selected sunspots, the solar constant, the Sun's gravitational field, helioseismology and the galactic cosmic rays. We stress the importance of stereoscopic imaging, observations at high spatial, spectral, and temporal resolutions, as well as of long duration measurements. Further exploration of the Sun's polar regions is also important, as shown already by the Ulysses mission. From an applications perspective, we adopted three broad objectives that would derive complementary inputs for the Strategic Framework. These were to identify and investigate: possible application spin-offs from science missions, possible solar-terrestrial missions dedicated to a particular application, and possible future applications that require technology development. The Sun can be viewed as both a source of resources and of threats. Our principal applications focus was that of threat mitigation, by examining ways to improve solar threat monitoring and early warning systems. We compared these objectives to the mission objectives of past, current, and planned international solar missions. Past missions (1962-1980) seem to have been focused on improvement of scientific knowledge, using multiple instrument spacecraft. A ten year gap followed this period, during which the results from previous missions were analyzed and solar study programmes were prepared in international organizations. Current missions (1990-1996) focus on particular topics such as the corona, solar flares, and coronal mass ejections. In planned missions, Sun/Earth interactions and environmental effects of solar activity are becoming more important. The corona is the centre of interest of almost all planned missions. It seems that no international long-term strategy has yet been adopted. For these plans the number of necessary future missions can be reduced and the onboard instrumentation can be optimized by performing a comparative analysis. The study of the corona must be done from different observing locations, orbits closer to the Sun, and by different means. The Cluster mission replacement is in progress; however, if the replacement is not implemented, the ISTP programme will fade after 1998. Furthermore, the physics of the Sun's interior should be emphasized more in the Mid- and Far-Term programmes. Finally, more emphasis should be placed on monitoring space weather and forecasting Sun/Earth interactions.

Source record↗
NASA NTRS2008

Ares V: Application to Solar System Scientific Exploration

The development of the Ares V launch vehicle will provide levels of performance unseen since the days of Apollo. This capability, like the Saturn V before it, is being developed primarily for crewed lunar missions. However, the tremendous jump in performance offered by the Ares V launch system has tremendous potential for the furtherance of robotic solar system exploration missions as well. Preliminary performance assessments indicate that Ares V could deliver 5 times the payload to Mars as compared to the most capable US expendable launch vehicle available today. Beyond Mars, the outer planets offer a number of high-priority investigations with compelling science. Presently, missions to these destinations are only achievable using indirect flights with gravity assist trajectories and, in many cases, suffer from long flight times. An Ares V with an upper stage could capture these missions using direct flights with shorter interplanetary transfer times that would enable extensive in situ investigations and possibly the return of samples to Earth. This paper lays out an estimate of Ares V performance for moderate and high C3 missions, and goes on to discuss a range of revolutionary mission concepts that could be enabled by this significant in-crease in launch capability.

Elliott, John↗
NASA NTRS2008

Ares V: Application to Solar System Scientific Exploration

Ares V would revolutionize the way we accomplish solar system exploration -Shift to a better balance of field data acquisition and laboratory analysis -Greatly improve field data acquisition Better instruments, better instrument complements. Larger data volumes returned. Would require a new fiscal paradigm. "Ares V Solar System Science" workshop helped expand the set of possibly-enabled missions.

Elliott, John O.↗
NASA NTRS2015

Landsat-8 Sensor Characterization and Calibration

Landsat-8 was launched on 11 February 2013 with two new Earth Imaging sensors to provide a continued data record with the previous Landsats. For Landsat-8, pushbroom technology was adopted, and the reflective bands and thermal bands were split into two instruments. The Operational Land Imager (OLI) is the reflective band sensor and the Thermal Infrared Sensor (TIRS), the thermal. In addition to these fundamental changes, bands were added, spectral bandpasses were refined, dynamic range and data quantization were improved, and numerous other enhancements were implemented. As in previous Landsat missions, the National Aeronautics and Space Administration (NASA) and United States Geological Survey (USGS) cooperated in the development, launch and operation of the Landsat- 8 mission. One key aspect of this cooperation was in the characterization and calibration of the instruments and their data. This Special Issue documents the efforts of the joint USGS and NASA calibration team and affiliates to characterize the new sensors and their data for the benefit of the scientific and application users of the Landsat archive. A key scientific use of Landsat data is to assess changes in the land-use and land cover of the Earth's surface over the now 43-year record. In order to perform these analyses and avoid confusing sensor changes with Earth surface changes, a solid understanding of the sensors' performance, consistent geolocation and radiometry are essential. Particularly with the significant changes in the Landsat-8 sensors relative to previous Landsat missions, this characterization becomes all the more important.

Radiometry↗
NASA NTRS1993

Imaging Spectrometry-Concepts and System Tradeoffs

The concept of imaging spectrometry is finding broad application in scientific instrumentation for earth-based, airborne, and space applications. An imaging spectrometer is characterized by the combination of imaging with complete sampling in the spectral domain. In so doing, material identification can be accomplished and displayed in conjunction with the conventional recognizable image. An image spectrometer incorporates a wide variety of techniques, including focal plane arrays, imaging and spectrometer optics, and spectral dispersing devices. The design of a successful system involves a complex set of tradeoffs incorporating the properties and limitations of the various technologies. For applications in the infrared, additional technologies such as focal plane cooling are required, and the other technologies present more limitations and constraints.

Herring, M.↗
NASA NTRSDate not supplied

Space Flown Rodent Liver RNA Sequencing Data for Machine Learning in Space Biology Research

High-throughput nucleic acid sequencing (DNA-seq, RNA-seq) has become widespread in biomedical research due to the growing availability and affordability of these assays. Data analysis has been accelerated in recent years by the adoption of artificial intelligence (AI) and machine learning (ML) techniques by biomedical researchers. In space biology research, RNAseq datasets from space-flown experimental samples are critical for characterizing the gene expression aberrations associated with exposure to spaceflight stressors. However, space biological experiments tend to be very low sample size, so identifying proper AI/ML algorithms for sequencing data analysis is an ongoing challenge since these algorithms typically require large sample size. The NASA Science Mission Directorate (SMD) has started the “Benchmark Initiative for AI/ML”, focused on creating datasets meant for three main applications: 1) scientific benchmarking, which finds the best algorithm for a specific problem; 2) application benchmarking, which measures algorithm performance against a set of parameters; and 3) system benchmarking, which evaluates performance of hardware and software architecture. These scientific benchmarks consist of an AI-ready dataset and a reference implementation on a specific scientific question. In this work, we focused on generating standardized datasets to allow the scientific community to benchmark AI/ML algorithms in the domain of space biology. We present here a standardized, AI-ready, publicly available benchmark dataset for space biology RNA-seq data as a collaboration between the NASA AI4LS (Artificial Intelligence for Life Sciences) working group. and NASA’s SMD. This dataset consists of space-flown and ground control mouse liver found in the NASA GeneLab omics database. However, to amplify the small sample number (n=112 samples) for ML purposes, we employ Gaussian noise and a generative adversarial network to extend this dataset to 6,000 synthetic samples, matching the original gene expression characteristics.

James Casaletto↗