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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 NTRS1995

A General Purpose Sparse Matrix Parallel Solvers Package

A general purpose solver package for constructing and solving a range of sparse linear systems arising from discretization of PDEs on unstructured meshes is developed. Once the sparse symmetric complex matrix is constructed, it can be solved by either a preconditioned bi-conjugate gradient solver, a two-stage Cholesky LDLT factorization solver, or a hybrid solver combining the above two methods. (More detailed than 95-0127).

solver sparse matrix solvers package PDE↗
NASA NTRS1989

A one- and two-layer model for estimating evapotranspiration with remotely sensed surface temperature and ground-based meteorological data over partial canopy cover

Surface-air temperature differences are commonly used in a bulk resistance equation for estimating sensible heat flux (H), which is inserted in the one-dimensional energy balance equation to solve for the latent heat flux (LE) as a residual. Serious discrepancies between estimated and measured LE have been observed for partial-canopy-cover conditions, which are mainly attributed to inappropriate estimates of H. To improve the estimates of H over sparse canopies, one- and two-layer resistance models that account for some of the factors causing poor agreement are developed. The utility of the two models is tested with remotely sensed and micrometeorological data for a furrowed cotton field with 20 percent cover and a dry soil surface. It is found that the one-layer model performs better than the two-layer model when a theoretical bluff-body correction for heat transfer is used instead of an empirical adjustment; otherwise, the two-layer model is better.

Kustas, William P.↗
NASA NTRS2010

Analysis of Snow Bidirectional Reflectance from ARCTAS Spring-2008 Campaign

The spring 2008 Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) experiment was one of major intensive field campaigns of the International Polar Year aimed at detailed characterization of atmospheric physical and chemical processes in the Arctic region. A part of this campaign was a unique snow bidirectional reflectance experiment on the NASA P-3B aircraft conducted on 7 and 15 April by the Cloud Absorption Radiometer (CAR) jointly with airborne Ames Airborne Tracking Sunphotometer (AATS) and ground-based Aerosol Robotic Network (AERONET) sunphotometers. The CAR data were atmospherically corrected to derive snow bidirectional reflectance at high 1 degree angular resolution in view zenith and azimuthal angles along with surface albedo. The derived albedo was generally in good agreement with ground albedo measurements collected on 15 April. The CAR snow bidirectional reflectance factor (BRF) was used to study the accuracy of analytical Ross-Thick Li-Sparse (RTLS), Modified Rahman-Pinty-Verstraete (MRPV) and Asymptotic Analytical Radiative Transfer (AART) BRF models. Except for the glint region (azimuthal angles phi less than 40 degrees), the best fit MRPV and RTLS models fit snow BRF to within 0.05. The plane-parallel radiative transfer (PPRT) solution was also analyzed with the models of spheres, spheroids, randomly oriented fractal crystals, and with a synthetic phase function. The latter merged the model of spheroids for the forward scattering angles with the fractal model in the backscattering direction. The PPRT solution with synthetic phase function provided the best fit to measured BRF in the full range of angles. Regardless of the snow grain shape, the PPRT model significantly over-/underestimated snow BRF in the glint/backscattering regions, respectively, which agrees with other studies. To improve agreement with experiment, we introduced a model of macroscopic snow surface roughness by averaging the PPRT solution over the slope distribution function and by adding a simple model of shadows. With macroscopic roughness described by two parameters, the AART model achieved an accuracy of about plus or minus 0.05 with a possible bias of plus or minus 0.03 in the spectral range 0.4-2.2 micrometers. This high accuracy holds at view zenith angles below 55-60 degrees covering the practically important range for remote sensing applications, and includes both glint and backscattering directions.

Lyapustin, A.↗
NASA NTRS2012

Multi-Angle Implementation of Atmospheric Correction for MODIS (MAIAC). Part 3: Atmospheric Correction

This paper describes the atmospheric correction (AC) component of the Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) which introduces a new way to compute parameters of the Ross-Thick Li-Sparse (RTLS) Bi-directional reflectance distribution function (BRDF), spectral surface albedo and bidirectional reflectance factors (BRF) from satellite measurements obtained by the Moderate Resolution Imaging Spectroradiometer (MODIS). MAIAC uses a time series and spatial analysis for cloud detection, aerosol retrievals and atmospheric correction. It implements a moving window of up to 16 days of MODIS data gridded to 1 km resolution in a selected projection. The RTLS parameters are computed directly by fitting the cloud-free MODIS top of atmosphere (TOA) reflectance data stored in the processing queue. The RTLS retrieval is applied when the land surface is stable or changes slowly. In case of rapid or large magnitude change (as for instance caused by disturbance), MAIAC follows the MODIS operational BRDF/albedo algorithm and uses a scaling approach where the BRDF shape is assumed stable but its magnitude is adjusted based on the latest single measurement. To assess the stability of the surface, MAIAC features a change detection algorithm which analyzes relative change of reflectance in the Red and NIR bands during the accumulation period. To adjust for the reflectance variability with the sun-observer geometry and allow comparison among different days (view geometries), the BRFs are normalized to the fixed view geometry using the RTLS model. An empirical analysis of MODIS data suggests that the RTLS inversion remains robust when the relative change of geometry-normalized reflectance stays below 15%. This first of two papers introduces the algorithm, a second, companion paper illustrates its potential by analyzing MODIS data over a tropical rainforest and assessing errors and uncertainties of MAIAC compared to conventional MODIS products.

Lyapustin, A.↗
NASA NTRS2001

Crystallization of bFGF-DNA Aptamer Complexes Using a Sparse Matrix Designed for Protein-Nucleic Acid Complexes

The Sparse Matrix approach for obtaining lead crystallization conditions has proven to be very fruitful for the crystallization of proteins and nucleic acids. Here we report a Sparse Matrix developed specifically for the crystallization of protein-DNA complexes. This method is rapid and economical, typically requiring 2.5 mg of complex to test 48 conditions. The method was originally developed to crystallize basic fibroblast growth factor (bFGF) complexed with DNA sequences identified through in vitro selection, or SELEX, methods. Two DNA aptamers that bind with approximately nanomolar affinity and inhibit the angiogenic properties of bFGF were selected for co-crystallization. The Sparse Matrix produced lead crystallization conditions for both bFGF-DNA complexes.

Cannone, Jaime J.↗
NASA NTRS2015

Towards Characterizing the Variability in the Loading Demands of an Unmanned Aerial Vehicle

This paper presents a computational methodology to characterize and quantify the variability in the power demands during the take-off of an unmanned aerial vehicle (UAV). A lithium-ion battery-based power system is used to power the unmanned aerial vehicle, and the capabilities of the unmanned aerial vehicle are driven by the amount of charge in this battery. In order to design the power system, it is necessary to analyze the power and charge requirements of the UAV. This paper focuses on the take-off segment, and aims to quantify the amount of charge that is required for this particular segment. Sparse data is available through different flight tests and this data is used to analyze the flight profile and the charge requirement during take-off. The amount of charge required for take-off depends on several factors that are not only variable but cannot be controlled in reality, and hence, the entire flight profile and the corresponding charge requirement are variable in nature. The information available through flight tests is converted into multi-dimensional sparse data and a new method is developed in this paper for variability characterization using multi-dimensional sparse data. This analysis is useful for prognostics and health management where it is necessary to anticipate future charge requirements in order to compute the end-of-discharge of the battery, and hence, the remaining useful life of the power system.

unmanned aerial vehicle↗
NASA NTRS2016

An Optimized Multicolor Point-Implicit Solver for Unstructured Grid Applications on Graphics Processing Units

In the field of computational fluid dynamics, the Navier-Stokes equations are often solved using an unstructuredgrid approach to accommodate geometric complexity. Implicit solution methodologies for such spatial discretizations generally require frequent solution of large tightly-coupled systems of block-sparse linear equations. The multicolor point-implicit solver used in the current work typically requires a significant fraction of the overall application run time. In this work, an efficient implementation of the solver for graphics processing units is proposed. Several factors present unique challenges to achieving an efficient implementation in this environment. These include the variable amount of parallelism available in different kernel calls, indirect memory access patterns, low arithmetic intensity, and the requirement to support variable block sizes. In this work, the solver is reformulated to use standard sparse and dense Basic Linear Algebra Subprograms (BLAS) functions. However, numerical experiments show that the performance of the BLAS functions available in existing CUDA libraries is suboptimal for matrices representative of those encountered in actual simulations. Instead, optimized versions of these functions are developed. Depending on block size, the new implementations show performance gains of up to 7x over the existing CUDA library functions.

Zubair, Mohammad↗
NASA NTRS1992

Monitoring Global Temperatures From Satellites

Report provides evidence that passive microwave radiometry from satellites provides more-precise information on atmospheric temperatures than sparse distribution of thermometers. Accurate temperatures needed for detection of "greenhouse" warming, evaluation of computer models of change in climate, and for understanding important factors in climate system.

Spencer, Roy W.↗
NASA NTRS1990

Estimation and filtering for high-precision GPS positioning applications

Estimation of GPS satellite orbits and other parameters for high-accuracy geodetic and tracking applications is carried out with a multi-satellite batch sequential pseudo-epoch state process noise filter. Both square-root information filtering and UD factorized covariance filtering and smoothing formulations are implemented in the software to enhance numerical stability. The filtering algorithms have been arranged to take advantage of sparse matrices and other characteristics of the GPS measurement scenarios. The filter includes unique error evaluation capabilities to assess effects from mismodeling. Process noise plays a key role in the orbit determination for stochastic behavior of transmitter/receiver clocks, atmospheric-induced delay fluctuations, and unmodeled spacecraft accelerations. Present GPS orbit accuracy is better than 1 m, with ground baselines determined simultaneously to relative accuracy of 1.5 parts in 10 to the 8th over several thousand km distance.

Lichten, Stephen M.↗
NASA NTRS1990

High-performance equation solvers and their impact on finite element analysis

The role of equation solvers in modern structural analysis software is described. Direct and iterative equation solvers which exploit vectorization on modern high-performance computer systems are described and compared. The direct solvers are two Cholesky factorization methods. The first method utilizes a novel variable-band data storage format to achieve very high computation rates and the second method uses a sparse data storage format designed to reduce the number of operations. The iterative solvers are preconditioned conjugate gradient methods. Two different preconditioners are included; the first uses a diagonal matrix storage scheme to achieve high computation rates and the second requires a sparse data storage scheme and converges to the solution in fewer iterations that the first. The impact of using all of the equation solvers in a common structural analysis software system is demonstrated by solving several representative structural analysis problems.

Poole, Eugene L.↗
NASA NTRS1992

High-performance equation solvers and their impact on finite element analysis

The role of equation solvers in modern structural analysis software is described. Direct and iterative equation solvers which exploit vectorization on modern high-performance computer systems are described and compared. The direct solvers are two Cholesky factorization methods. The first method utilizes a novel variable-band data storage format to achieve very high computation rates and the second method uses a sparse data storage format designed to reduce the number od operations. The iterative solvers are preconditioned conjugate gradient methods. Two different preconditioners are included; the first uses a diagonal matrix storage scheme to achieve high computation rates and the second requires a sparse data storage scheme and converges to the solution in fewer iterations that the first. The impact of using all of the equation solvers in a common structural analysis software system is demonstrated by solving several representative structural analysis problems.

Poole, Eugene L.↗
NASA NTRS1989

A Cerebellar-model Associative Memory as a Generalized Random-access Memory

A versatile neural-net model is explained in terms familiar to computer scientists and engineers. It is called the sparse distributed memory, and it is a random-access memory for very long words (for patterns with thousands of bits). Its potential utility is the result of several factors: (1) a large pattern representing an object or a scene or a moment can encode a large amount of information about what it represents; (2) this information can serve as an address to the memory, and it can also serve as data; (3) the memory is noise tolerant--the information need not be exact; (4) the memory can be made arbitrarily large and hence an arbitrary amount of information can be stored in it; and (5) the architecture is inherently parallel, allowing large memories to be fast. Such memories can become important components of future computers.

Kanerva, Pentti↗
NASA NTRS1994

Globular cluster photometry with the Hubble Space Telescope. 3: Blue stragglers and variable stars in the core of M3

This paper describes Hubble Space Telescope (HST)/Planetary Camera-I images of the core of the dense globular cluster M3 (NGC 5272). Stellar photometry in the F555W (V) and F785LP (I) bands, with a 1-sigma photometric accuracy of about 0.1 mag, has been used to construct color-magnitude diagrams of about 4700 stars above the main-sequence turnoff within r less than or approximately equal to 1 min of the cluster center. We have also analyzed archival HST F336W (U) images of M3 obtained by the Wide Field/Planetary Camera-I Instrument Definition Team. The UVI data are used to identify 28 blue straggler (BS) stars within the central 0.29 sq. arcmin. The specific frequency of BSs in this region of M3, N(sub BS)/N(sub V less than (V(HB)+2)) = 0.094 +/- 0.019, is about a factor of 2 - 3 higher than that found by Bolte et al. in a recent ground-based study of the same region, but comparable to that seen in the sparse outer parts of the same cluster and in HST observations of the core of the higher density cluster 47 Tuc. The BSs in M3 are slightly more centrally concentrated than red giant branch stars while horizontal branch stars are somewhat less concentrated red giants. The radial distribution of V-selected subgiant and turnoff stars is well fit by a King model with a core radius r(sub core) = 28 arcmin +/- 2 arcmin (90% confidence limits), which corresponds to 1.4 pc. Red giant and horizontal branch stars selected in the ultraviolet data (U less than 18) have a somewhat more compact distribution (r(sub core) = 22.5 arcmin). The HST U data consist of 17 exposures acquired over a span of three days. We have used these data to isolate 40 variable stars for which relative astrometry, brightnesses, colors, and light curves are presented. A Kolmogorov-Smirnov test indicates that, typically, the variability for each star is significant at the 95% level. We identify two variable BS candidates (probably of the SX Phe type), out of a sample of approximately 25 BSs in which variability could have been detected. Most of the variables are RR Lyrae stars on the horizontal branch. All of them have periods P greater than or approximately equal 8 h.

Guhathakurta, Puragra↗
NASA NTRS1989

Krylov methods preconditioned with incompletely factored matrices on the CM-2

The performance is measured of the components of the key interative kernel of a preconditioned Krylov space interative linear system solver. In some sense, these numbers can be regarded as best case timings for these kernels. Sweeps were timed over meshes, sparse triangular solves, and inner products on a large 3-D model problem over a cube shaped domain discretized with a seven point template. The performance of the CM-2 is highly dependent on the use of very specialized programs. These programs mapped a regular problem domain onto the processor topology in a careful manner and used the optimized local NEWS communications network. The rather dramatic deterioration in performance was documented when these ideal conditions no longer apply. A synthetic workload generator was developed to produce and solve a parameterized family of increasingly irregular problems.

Berryman, Harry↗
NASA NTRS1990

Precise monitoring of global temperature trends from satellites

Passive microwave radiometry from satellites provides more precise atmospheric temperature information than that obtained from the relatively sparse distribution of thermometers over the earth's surface. Accurate global atmospheric temperature estimates are needed for detection of possible greenhouse warming, evaluation of computer models of climate change, and for understanding important factors in the climate system. Analysis of the first 10 years (1979 to 1988) of satellite measurements of lower atmospheric temperature changes reveals a monthly precision of 0.01 C, large temperature variability on time scales from weeks to several years, but no obvious trend for the 10-year period. The warmest years, in descending order, were 1987, 1988, 1983, and 1980. The years 1984, 1985, and 1986 were the coolest.

Spencer, Roy W.↗
NASA NTRS1984

Algorithms for solving large sparse systems of simultaneous linear equations on vector processors

Very efficient algorithms for solving large sparse systems of simultaneous linear equations have been developed for serial processing computers. These involve a reordering of matrix rows and columns in order to obtain a near triangular pattern of nonzero elements. Then an LU factorization is developed to represent the matrix inverse in terms of a sequence of elementary Gaussian eliminations, or pivots. In this paper it is shown how these algorithms are adapted for efficient implementation on vector processors. Results obtained on the CYBER 200 Model 205 are presented for a series of large test problems which show the comparative advantages of the triangularization and vector processing algorithms.

David, R. E.↗
NASA NTRS1991

The fractional Fourier transform and applications

This paper describes the 'fractional Fourier transform', which admits computation by an algorithm that has complexity proportional to the fast Fourier transform algorithm. Whereas the discrete Fourier transform (DFT) is based on integral roots of unity e exp -2(pi)i/n, the fractional Fourier transform is based on fractional roots of unity e exp -2(pi)i(alpha), where alpha is arbitrary. The fractional Fourier transform and the corresponding fast algorithm are useful for such applications as computing DFTs of sequences with prime lengths, computing DFTs of sparse sequences, analyzing sequences with noninteger periodicities, performing high-resolution trigonometric interpolation, detecting lines in noisy images, and detecting signals with linearly drifting frequencies. In many cases, the resulting algorithms are faster by arbitrarily large factors than conventional techniques.

Bailey, David H.↗