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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Optimizing Irregular Applications for Energy and Performance on the Tilera Many-core Architecture

Optimizing applications simultaneously for energy and performance is a complex problem. High performance, parallel, irregular applications are notoriously hard to optimize due to their data-dependent memory accesses, lack of structured locality and complex data structures and code patterns. Irregular kernels are growing in importance in applications such as machine learning, graph analytics and combinatorial scientific computing. Performance- and energy-efficient implementation of these kernels on modern, energy efficient, multicore and many-core platforms is therefore an important and challenging problem. We present results from optimizing two irregular applications { the Louvain method for community detection (Grappolo), and high-performance conjugate gradient (HPCCG) { on the Tilera many-core system. We have significantly extended MIT's OpenTuner auto-tuning framework to conduct a detailed study of platform-independent and platform-specific optimizations to improve performance as well as reduce total energy consumption. We explore the optimization design space along three dimensions: memory layout schemes, compiler-based code transformations, and optimization of parallel loop schedules. Using auto-tuning, we demonstrate whole node energy savings of up to 41% relative to a baseline instantiation, and up to 31% relative to manually optimized variants.

irregular applications, energy opttimization, many↗
DOE OSTI2012

FITTING THE UNION2.1 SUPERNOVA SAMPLE WITH THE R{sub h} = ct UNIVERSE

The analysis of Type Ia supernova data over the past decade has been a notable success story in cosmology. These standard candles offer us an unparalleled opportunity to study the cosmological expansion out to a redshift of {approx}1.5. The consensus today appears to be that {Lambda}CDM offers the best explanation for the luminosity-distance relationship seen in these events. However, a significant incompatibility is now emerging between the standard model and other equally important observations, such as those of the cosmic microwave background. {Lambda}CDM does not provide an accurate representation of the cosmological expansion at high redshifts (z >> 2). It is therefore essential to re-analyze the Type Ia supernova data in light of the cosmology (the R{sub h} = ct universe) that best represents the universe's dynamical evolution at early times. In this paper, we directly compare the distance relationship in {Lambda}CDM with that predicted by R{sub h} = ct, and each with the Union2.1 sample, and show that the two theories produce virtually indistinguishable profiles, though the fit with R{sub h} = ct has not yet been optimized. This is because the data cannot be determined independently of the assumed cosmology-the supernova luminosities must be evaluated by optimizing four parameters simultaneously with those in the adopted model. This renders the data compliant to the underlying theory, so the model-dependent data reduction should not be ignored in any comparative analysis between competing cosmologies. In this paper, we use R{sub h} = ct to fit the data reduced with {Lambda}CDM, and though quite promising, the match is not perfect. An even better fit would result with an optimization of the data using R{sub h} = ct from the beginning.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1992

The use of expert systems in combination with active and passive microwave data to classify sea ice

The authors report on a system for sea ice classification which combines passive and active microwave data with algorithmic and heuristic classification techniques in order to classify sea ice unambiguously. They describe initial algorithmic classification, extraction of features and symbolic information, the application of data-dependent expert systems, and the establishment of feature correspondence between data sets.

Haverkamp, Donna↗
DOE OSTI2020

Accelerating quantum optics experiments with statistical learning

Quantum optics experiments, involving the measurement of low-probability photon events, are known to be extremely time-consuming. We present a methodology for accelerating such experiments using physically motivated ansatzes together with simple statistical learning techniques such as Bayesian maximum a posteriori estimation based on few-shot data. We show that it is possible to reconstruct time-dependent data using a small number of detected photons, allowing for fast estimates in under a minute and providing a one-to-two order of magnitude speed-up in data acquisition time. We test our approach using real experimental data to retrieve the second order intensity correlation function, G (2) ($τ$), as a function of time delay τ between detector counts, for thermal light as well as anti-bunched light emitted by a quantum dot driven by periodic laser pulses. The proposed methodology has a wide range of applicability and has the potential to impact the scientific discovery process across a multitude of domains.

36 MATERIALS SCIENCE↗
NASA NTRS2003

Fractals and Spatial Methods for Mining Remote Sensing Imagery

The rapid increase in digital remote sensing and GIS data raises a critical problem -- how can such an enormous amount of data be handled and analyzed so that useful information can be derived quickly? Efficient handling and analysis of large spatial data sets is central to environmental research, particularly in global change studies that employ time series. Advances in large-scale environmental monitoring and modeling require not only high-quality data, but also reliable tools to analyze the various types of data. A major difficulty facing geographers and environmental scientists in environmental assessment and monitoring is that spatial analytical tools are not easily accessible. Although many spatial techniques have been described recently in the literature, they are typically presented in an analytical form and are difficult to transform to a numerical algorithm. Moreover, these spatial techniques are not necessarily designed for remote sensing and GIS applications, and research must be conducted to examine their applicability and effectiveness in different types of environmental applications. This poses a chicken-and-egg problem: on one hand we need more research to examine the usability of the newer techniques and tools, yet on the other hand, this type of research is difficult to conduct if the tools to be explored are not accessible. Another problem that is fundamental to environmental research are issues related to spatial scale. The scale issue is especially acute in the context of global change studies because of the need to integrate remote-sensing and other spatial data that are collected at different scales and resolutions. Extrapolation of results across broad spatial scales remains the most difficult problem in global environmental research. There is a need for basic characterization of the effects of scale on image data, and the techniques used to measure these effects must be developed and implemented to allow for a multiple scale assessment of the data before any useful process-oriented modeling involving scale-dependent data can be conducted. Through the support of research grants from NASA, we have developed a software module called ICAMS (Image Characterization And Modeling System) to address the need to develop innovative spatial techniques and make them available to the broader scientific communities. ICAMS provides new spatial techniques, such as fractal analysis, geostatistical functions, and multiscale analysis that are not easily available in commercial GIS/image processing software. By bundling newer spatial methods in a user-friendly software module, researchers can begin to test and experiment with the new spatial analysis methods and they can gauge scale effects using a variety of remote sensing imagery. In the following, we describe briefly the development of ICAMS and present application examples.

Lam, Nina↗
DOE OSTI2011

Extraction of $P_{11}$ Resonance from piN Data and Its Stability

We address a question about how much resonance poles and residues extracted from data depend on a model used for the extraction, and on the precision of data. We focus on the P{sub 11} {pi}-N scattering and use the dynamical coupled-channel (DCC) model developed in Excited Baryon Analysis Center (EBAC) at JLab. We examine the model-dependence of the poles by varying parameters largely within the EBAC-DCC model. We find that two poles associated with the Roper resonance are fairly stable against the variation. We also study the stability of the Roper poles against different analytic structure of the P{sub 11} amplitude below {pi}-N threshold by using a bare nucleon model. We again find a good stability of the Roper poles.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2011

Extraction of P11 Resonance from pi-N Data and Its Stability

An important question about resonance extraction is how much resonance poles and residues extracted from data depend on a model used for the extraction, and on the precision of data. We address this question with the dynamical coupled-channel (DCC) model developed in Excited Baryon Analysis Center (EBAC) at JLab. We focus on the P11 pi-N scattering. We examine the model-dependence of the poles by varying parameters to a large extent within the EBAC-DCC model. We find that two poles associated with the Roper resonance are fairly stable against the variation. We also develop a model with a bare nucleon, thereby examining the stability of the Roper poles against different analytic structure of the P11 amplitude below pi-N threshold. We again find a good stability of the Roper poles.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Anomalous Hall Effect in Kagome Ferrimagnet GdMn 6 Sn 6

We present magnetotransport data on the ferrimagnet GdMn 6 Sn 6 . From the temperature-dependent data we are able to extract a large intrinsic contribution to the anomalous Hall effect σ int xz ~ 32 Ω –1 cm –1 and σ int xz ~ 223 Ω –1 cm –1 , which is comparable to values found in other systems also containing kagome nets of transition metals. From our transport anisotropy, as well as our density functional theory calculations, we argue that the system is electronically best described as a three-dimensional system. Furthermore we show that reduced dimensionality is not a strong requirement for obtaining large Berry phase contributions to transport properties. In addition, the coexistence of rare-earth and transition-metal magnetism makes the hexagonal MgFe 6 Ge 6 structure type a promising system to tune the electronic and magnetic properties in future studies.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Quantifying uncertainty in regional-scale seismic moment tensors

We examine the ability of three different inversion methods: 1) first motion (FM) inversions, 2) amplitude inversions, and 3) full waveform (FW) time variable moment tensor (TVMT) inversions, to recover an accurate source mechanism for simulated data for an earthquake as well as an explosion. The ability of inversion methods, such as the ones described above, to recover accurate models representative of the data depends on both a priori information as well as the quality of data being inverted. Therefore, we examine the effect that station geometry, geologic model, and noise level has on the inversion and estimated source mechanism. We find that FM data can provide more robust solutions than amplitude data and are not as sensitive to inaccurate earth models, especially in low-noise cases, and overall FW inversions are the most accurate out of the methods examined, but can still be biased by inaccurate earth models.

58 GEOSCIENCES↗
NASA NTRS1992

Deriving inertial wave characteristics from surface drifter velocities - Frequency variability in the tropical Pacific

Two techniques were developed for estimating statistics of inertial oscillations from satellite-tracked drifters that overcome the difficulties inherent in estimating such statistics from data dependent upon space coordinates that are a function of time. Application of these techniques to tropical surface drifter data collected during the NORPAX, EPOCS, and TOGA programs reveals a latitude-dependent, statistically significant 'blue shift' of inertial wave frequency. The latitudinal dependence of the blue shift is similar to predictions based on 'global' internal-wave spectral models, with a superposition of frequency shifting due to modification of the effective local inertial frequency by the presence of strongly sheared zonal mean currents within 12 deg of the equator.

Poulain, Pierre-Marie↗
NASA NTRS1999

Sensitivity of Temperature Profiles Retrieved from Mars Global Surveyor Thermal Emission Spectrometer (MGS/TES) Observations to the GSFC Synthetic Mars Model Atmosphere

Part of the task of interpreting IR spectral features observed by MGS/TES due to surface minerals requires distinguishing those IR signatures from atmospheric signatures of gas and dust. Surface-atmosphere separation for MGS/TES depends on knowledge of the retrieved temperature profile. In turn, the temperature retrieval Erom the observed data depends on molecular parameters including 15 micron CO2 line shape or line intensities which contribute to defining the Mars synthetic radiative transfer model. Using a simple isothermal, homogeneous single layer model of Pinnock and Shine, we find the ratio of (the error in degrees Kelvin of the retrieved temperature profile) to (the percentage error in the absorption coefficient) (deg K/percent) to be 0.4 at 200K. This ratio at 150K and 250K is 0.2 and 0.6, respectively. A more refined model, incorporating observed MGS/TES retrieved temperature profiles, the TES instrumental resolution and the most recent molecular modelling, will yield an improved knowledge of this error sensitivity. We present results of such a sensitivity study to determine the dependence of temperature profiles inverted from MGS/TES on these and other molecular parameters. This work was supported in part by NASA's Mars Data Analysis Program.

Maguire, William C.↗
DOE OSTI2011

Effects of voids on the reconstruction of the equation of state of dark energy

We quantify the effects of the voids known to exist in the Universe upon the reconstruction of the dark energy equation of state w. We show that the effect can start to be comparable with some of the other errors taken into account when analyzing supernova data, depending strongly upon the low redshift cutoff used in the sample. For the supernova data alone, the error induced in the reconstruction of w is much larger than the percent level. When the Baryonic Acoustic Oscillations and the Cosmic Microwave Background data are included in the fit, the effect of the voids upon the determination of w is much lessened but is not much smaller than some of the other errors taken into consideration when performing such fits. We also look at the effect of voids upon the estimation of the equation of state when we allow w to vary over time and show that even when supernova, Cosmic Microwave Background, and Baryonic Acoustic Oscillations data are used to constrain the equation of state, the best fit points in parameter space can change at the 10% level due to the presence of voids, and error-bars increase significantly.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Robotic Mapping and Monitoring of Nuclear Infrastructure

Routine inspection of nuclear infrastructure is currently expensive and slow. To remedy this the Applied Research Center (ARC) at Florida International University (FIU) is developing a field robot capable of gathering valuable data quickly, safely, and cheaply. Motivation: Nuclear infrastructure should be inspected routinely to ensure early detection of problems. This process is expensive, hazardous, and slow. Objective: Develop a platform capable of surveying nuclear infrastructure autonomously. Discussion: The quality of the captured data depends on the success of every component of the robot. Good sensors, hardware, localization, data collection, and autonomy are required. Conclusion: This is a promising approach for surveying nuclear infrastructure. The captured data is rich with information and can be evaluated by both people and algorithms.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2000

Determining a Prony Series for a Viscoelastic Material From Time Varying Strain Data

In this study a method of determining the coefficients in a Prony series representation of a viscoelastic modulus from rate dependent data is presented. Load versus time test data for a sequence of different rate loading segments is least-squares fitted to a Prony series hereditary integral model of the material tested. A nonlinear least squares regression algorithm is employed. The measured data includes ramp loading, relaxation, and unloading stress-strain data. The resulting Prony series which captures strain rate loading and unloading effects, produces an excellent fit to the complex loading sequence.

Tzikang, Chen↗
DOE OSTI2019

Scalable Extended Dynamic Mode Decomposition Using Random Kernel Approximation

The Koopman operator is a linear, infinite-dimensional operator that governs the dynamics of system observables; extended dynamic mode decomposition (EDMD) is a data-driven method for approximating the Koopman operator using functions (features) of the system state snapshots. This paper investigates an approach to EDMD in which the features used provide random approximations to a particular kernel function. The objective of this is computational economy for large data sets: EDMD is generally ill-suited for problems with large state dimension, and its dual kernel formulation (KDMD) is well-suited for such problems only if the number of data snapshots is relatively small. We discuss two specific methods for generating features: random Fourier features and the Nÿstrom method. The first method is a data-independent method for translation-invariant kernels only and involves random sampling in feature space; the second method is a data-dependent empirical method that may be used for any kernel and involves random sampling of data. We first discuss how these ideas may be applied in an EDMD context, as well as a means for adaptively adding random Fourier features. As a result, we demonstrate these methods on two example problems and conclude with an analysis of the relative benefits and drawbacks of each method.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Facilitating Data Collection of Maintenance Events to Populate the Hydrogen Component Reliability Database (HyCReD)

The Hydrogen Component Reliability Database (HyCReD) is a collaborative project between the National Renewable Energy Laboratory, the University of Maryland, and hydrogen stakeholders to improve safety and reliability for hydrogen facilities by implementing component reliability data taxonomies that support hydrogen infrastructure failure rate analysis. The project aims to quantify failure rates of hydrogen components through high-quality data collection and analysis on root causes and maintenance needed. HyCReD provides a common database for cataloging hydrogen component failures which exists for reliability research in many other mature industries [2]. The database fills a gap for the hydrogen community by providing a scientifically rigorous approach to quantitative risk assessment (QRA), prognostic health management (PHM), and reliability-centered maintenance (RCM) analysis. High level results will be aggregated and anonymized to protect company sensitive information; detailed results will be used to help address issues of hydrogen components. These advanced analytics will support accelerated deployment of hydrogen infrastructure by enabling better: design and safety of projects (safety codes and standards development), infrastructure reliability and cost (component failure rates, maintenance protocols), and component R&D needs (robust supply chain). A key to a successful HyCReD implementation is facilitating the ease of reporting and data quality in the database that can be used for analysis. Maintenance data was a previously identified gap in initial efforts to populate and validate the database taxonomies [3]. Collection of maintenance data will be instrumental in identifying failure modes and rates, identifying incipient component failures or reduced performance, cataloging best practices for maintenance routines and methods for prognostic health management, and quantifying the risk and effect of different failure modes. Several key priorities are identified for streamlined data collection to achieve quality and detailed failure data: Applicability, Ease of Use, Accessibility, and Information Security. The HyCReD team has now begun deployment of the database to several companies and groups that have signed non-disclosure agreements to facilitate the data collection of failures in industry hydrogen refueling station infrastructure. This paper will provide an update into the process of HyCReD deployment including the development of a coding guide for facility personnel to reference and ensure data quality and consistency from one station to another as well as implementation of contextually dependent data fields of system taxonomy and formatted entries to provide ease of use. The goal is to communicate the lessons learned from the roll-out to technicians and engineers in the field, and the addition of need for high level of security to protect all stakeholders.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗