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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

A COMPARATIVE STUDY OF DENSITY FIELD ESTIMATION FOR GALAXIES: NEW INSIGHTS INTO THE EVOLUTION OF GALAXIES WITH ENVIRONMENT IN COSMOS OUT TO z ∼ 3

It is well-known that a galaxy’s environment has a fundamental influence in shaping its properties. We study the environmental effects on galaxy evolution, with an emphasis on the environment defined as the local number density of galaxies. The density field is estimated with different estimators (weighted adaptive kernel smoothing, 10th and 5th nearest neighbors, Voronoi and Delaunay tessellation) for a K{sub s} < 24 sample of ∼190,000 galaxies in the COSMOS field at 0.1 < z < 3.1. The performance of each estimator is evaluated with extensive simulations. We show that overall there is a good agreement between the estimated density fields using different methods over ∼2 dex in overdensity values. However, our simulations show that adaptive kernel and Voronoi tessellation outperform other methods. Using the Voronoi tessellation method, we assign surface densities to a mass complete sample of quiescent and star-forming galaxies out to z ∼ 3. We show that at a fixed stellar mass, the median color of quiescent galaxies does not depend on their host environment out to z ∼ 3. We find that the number and stellar mass density of massive (>10{sup 11} M{sub ⊙}) star-forming galaxies have not significantly changed since z ∼ 3, regardless of their environment. However, for massive quiescent systems at lower redshifts (z ≲ 1.3), we find a significant evolution in the number and stellar mass densities in denser environments compared to lower density regions. Our results suggest that the relation between stellar mass and local density is more fundamental than the color–density relation and that environment plays a significant role in quenching star-formation activity in galaxies at z ≲ 1.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2011

Second-order response Bethe-Salpeter equation

A Bethe-Salpeter equation for the three-particle correlation function is derived, representing a second-order response generalization of the usual Bethe-Salpeter equation. The equation can be solved formally, giving the three-particle correlation function in terms of the two-particle correlation function and many-body interaction kernels. The similarity to second-order response time-dependent density-functional theory can be used to improve the understanding of the higher-order exchange and correlation kernels of this theory. An exact expression for such a kernel is derived.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Affine Approximation of Parametrized Kernels and Model Order Reduction for Nonlocal and Fractional Laplace Models

In this work, we consider parametrized problems driven by spatially nonlocal integral operators with parameter-dependent kernels. In particular, kernels with varying nonlocal interaction radius $\delta > 0$ and fractional Laplace kernels, parametrized by the fractional power $s\in(0,1)$, are studied. Furthermore, in order to provide an efficient and reliable approximation of the solution for different values of the parameters, we develop the reduced basis method as a parametric model order reduction approach. Major difficulties arise since the kernels are not affine in the parameters, singular, and discontinuous. Moreover, the spatial regularity of the solutions depends on the varying fractional power $s$. To address this, we derive regularity and differentiability results with respect to $\delta$ and $s$, which are of independent interest for other applications such as optimization and parameter identification. We then use these results to construct affine approximations of the kernels by local polynomials. Finally, we certify the method by providing reliable a posteriori error estimators, which account for all approximation errors, and support the theoretical findings by numerical experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

CUDA Computation of the Feynman Distribution

In 2006, NVIDIA Corporation introduced the Computer Unified Device Architecture (CUDA) for C, C++, and Fortran programming languages. The CUDA parallel computing platform uses the Graphics Processing Unit (GPU) of NVIDIA graphics card, and it is designed for parallel computations. In these computations, the same algorithms are executed on the data in parallel. The GPU contains many Streaming Multiprocessors (SM) that can independently execute blocks of threads. A thread is a sequence of serial programmed instructions. The SM executes threads in groups of 32 and a group of 32 threads is called warp. The threads within a block can be synchronized and have access to a shared memory that is visible only to them. Conversely, blocks of threads cannot be synchronized and have access only to the global memory of the graphics card. This memory is called device memory and is independent of the host memory controlled by the Central Processing Unit (CPU). A single thread has access to a local memory (register) that is not visible to any other thread. The part of the program that is executed on the graphics card is called kernel. Each thread of the kernel has a unique identification index that is accessible by the in-built variable threadIdx; the latter is a 3-component vector. A threads block can contain up to 1024 threads. Blocks of threads have a unique identification index that is accessible by the in-built variable blockIdx; the latter is a 3-component vector. The in-built variable blockDim contains the number of threads per block; this variable is a 3-component vector. Threads and blocks represent different levels of parallelism; a kernel is executed as a grid of blocks of threads. Figure 1 illustrates a grid of thread blocks for a GPU with 2 SM. A SM can execute multiple blocks of threads in parallel; a block of threads is executed on a single SM. A GPU can execute multiple kernels concurrently. This work has used the Quadro M5000 graphics card with the following technical specifications: - 2048 cores with 861 MHz frequency, - 16 streaming multiprocessors, - 5.2 trillion transistors, - 8.2 Gb device memory, - 64 kb register memory (per thread), - 48 kb shared memory (per block) - 211.6 GB/s memory bandwidth, and - 3.5 GFLOPS floating point performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Determining the memory kernel from a fixed point measurement data for a parabolic equation with memory effect

We consider an inverse problem for a parabolic equation with memory effect. This inverse problem aims to identify the memory kernel function from a fixed point measurement data. Based on the fixed point arguments, we derive the global in time existence and uniqueness of our inverse problem. Moreover, we present a numerical algorithm to reconstruct the memory kernel function. Numerical simulations show the effectiveness of the proposed method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

A real-space Green’s function method for thenumerical solution of Maxwell’s equations

A new method for solving the transverse part of the free-space Maxwell equations in three dimensions is presented. By taking the Helmholtz decomposition of the electric field and current sources and considering only the divergence-free parts, we obtain an explicit real-space representation for the transverse propagator that explicitly respects finite speed of propagation. Because the propagator involves convolution against a singular distribution, we regularize via convolution with smoothing kernels (B-splines) prior to sampling based on a method due to Beyer and LeVeque (1992). We show that the ultimate discrete convolutional propagator can be constructed to attain an arbitrarily high order of accuracy by using higher-order regularizing kernels and finite difference stencils and that it satisfies von Neumann’s stability condition. Furthermore, the propagator is compactly supported and can be applied using Hockney’s method (1970) and parallelized using the same observation as made by Vay, Haber, and Godfrey (2013), leading to a method that is computationally efficient.

Lo, Boris↗
DOE OSTI2013

Parallel 3-D method of characteristics in MPACT

A new parallel 3-D MOC kernel has been developed and implemented in MPACT which makes use of the modular ray tracing technique to reduce computational requirements and to facilitate parallel decomposition. The parallel model makes use of both distributed and shared memory parallelism which are implemented with the MPI and OpenMP standards, respectively. The kernel is capable of parallel decomposition of problems in space, angle, and by characteristic rays up to 0(104) processors. Initial verification of the parallel 3-D MOC kernel was performed using the Takeda 3-D transport benchmark problems. The eigenvalues computed by MPACT are within the statistical uncertainty of the benchmark reference and agree well with the averages of other participants. The MPACT k{sub eff} differs from the benchmark results for rodded and un-rodded cases by 11 and -40 pcm, respectively. The calculations were performed for various numbers of processors and parallel decompositions up to 15625 processors; all producing the same result at convergence. The parallel efficiency of the worst case was 60%, while very good efficiency (>95%) was observed for cases using 500 processors. The overall run time for the 500 processor case was 231 seconds and 19 seconds for the case with 15625 processors. Ongoing work is focused on developing theoretical performance models and the implementation of acceleration techniques to minimize the number of iterations to converge. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

The impact of new Geant4-DNA cross section models on electron track structure simulations in liquid water

The most recent release of the open source and general purpose Geant4 Monte Carlo simulation toolkit (Geant4 10.2 release) contains a new set of physics models in the Geant4-DNA extension for improving the modelling of low-energy electron transport in liquid water (<10 keV). This includes updated electron cross sections for excitation, ionization, and elastic scattering. In the present work, the impact of these developments to track-structure calculations is examined for providing the first comprehensive comparison against the default physics models of Geant4-DNA. Significant differences with the default models are found for the average path length and penetration distance, as well as for dose-point-kernels for electron energies below a few hundred eV. On the other hand, self-irradiation absorbed fractions for tissue-like volumes and low-energy electron sources (including some Auger emitters) reveal rather small differences (up to 15%) between these new and default Geant4-DNA models. The above findings indicate that the impact of the new developments will mainly affect those applications where the spatial pattern of interactions and energy deposition of very-low energy electrons play an important role such as, for example, the modelling of the chemical and biophysical stage of radiation damage to cells.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Local and linear chemical reactivity response functions at finite temperature in density functional theory

We explore the local and nonlocal response functions of the grand canonical potential density functional at nonzero temperature. In analogy to the zero-temperature treatment, local (e.g., the average electron density and the local softness) and nonlocal (e.g., the softness kernel) intrinsic response functions are defined as partial derivatives of the grand canonical potential with respect to its thermodynamic variables (i.e., the chemical potential of the electron reservoir and the external potential generated by the atomic nuclei). To define the local and nonlocal response functions of the electron density (e.g., the Fukui function, the linear density response function, and the dual descriptor), we differentiate with respect to the average electron number and the external potential. The well-known mathematical relationships between the intrinsic response functions and the electron-density responses are generalized to nonzero temperature, and we prove that in the zero-temperature limit, our results recover well-known identities from the density functional theory of chemical reactivity. Specific working equations and numerical results are provided for the 3-state ensemble model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2010

Computerized image analysis: Texture-field orientation method for pectoral muscle identification on MLO-view mammograms

Purpose: To develop a new texture-field orientation (TFO) method that combines a priori knowledge, local and global information for the automated identification of pectoral muscle on mammograms. Methods: The authors designed a gradient-based directional kernel (GDK) filter to enhance the linear texture structures, and a gradient-based texture analysis to extract a texture orientation image that represented the dominant texture orientation at each pixel. The texture orientation image was enhanced by a second GDK filter for ridge point extraction. The extracted ridge points were validated and the ridges that were less likely to lie on the pectoral boundary were removed automatically. A shortest-path finding method was used to generate a probability image that represented the likelihood that each remaining ridge point lay on the true pectoral boundary. Finally, the pectoral boundary was tracked by searching for the ridge points with the highest probability lying on the pectoral boundary. A data set of 130 MLO-view digitized film mammograms (DFMs) from 65 patients was used to train the TFO algorithm. An independent data set of 637 MLO-view DFMs from 562 patients was used to evaluate its performance. Another independent data set of 92 MLO-view full field digital mammograms (FFDMs) from 92 patients was used to assess the adaptability of the TFO algorithm to FFDMs. The pectoral boundary detection accuracy of the TFO method was quantified by comparison with an experienced radiologist's manually drawn pectoral boundary using three performance metrics: The percent overlap area (POA), the Hausdorff distance (Hdist), and the average distance (AvgDist). Results: The mean and standard deviation of POA, Hdist, and AvgDist were 95.0{+-}3.6%, 3.45{+-}2.16 mm, and 1.12{+-}0.82 mm, respectively. For the POA measure, 91.5%, 97.3%, and 98.9% of the computer detected pectoral muscles had POA larger than 90%, 85%, and 80%, respectively. For the distance measures, 85.4% and 98.0% of the computer detected pectoral boundaries had Hdist within 5 and 10 mm, respectively, and 99.4% of computer detected pectoral muscle boundaries had AvgDist within 5 mm from the radiologist's manually drawn boundaries. Conclusions: The pectoral muscle on DFMs can be detected accurately by the automated TFO method. The preliminary study of applying the same pectoral muscle identification algorithm to FFDMs without retraining demonstrates that the TFO method is reasonably robust against the differences in the image properties between the digitized and digital mammograms.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2013

A generalized 2D pencil beam scaling algorithm for proton dose calculation in heterogeneous slab geometries

Purpose: Pencil beam algorithms are commonly used for proton therapy dose calculations. Szymanowski and Oelfke ['Two-dimensional pencil beam scaling: An improved proton dose algorithm for heterogeneous media,' Phys. Med. Biol. 47, 3313-3330 (2002)] developed a two-dimensional (2D) scaling algorithm which accurately models the radial pencil beam width as a function of depth in heterogeneous slab geometries using a scaled expression for the radial kernel width in water as a function of depth and kinetic energy. However, an assumption made in the derivation of the technique limits its range of validity to cases where the input expression for the radial kernel width in water is derived from a local scattering power model. The goal of this work is to derive a generalized form of 2D pencil beam scaling that is independent of the scattering power model and appropriate for use with any expression for the radial kernel width in water as a function of depth. Methods: Using Fermi-Eyges transport theory, the authors derive an expression for the radial pencil beam width in heterogeneous slab geometries which is independent of the proton scattering power and related quantities. The authors then perform test calculations in homogeneous and heterogeneous slab phantoms using both the original 2D scaling model and the new model with expressions for the radial kernel width in water computed from both local and nonlocal scattering power models, as well as a nonlocal parameterization of Moliere scattering theory. In addition to kernel width calculations, dose calculations are also performed for a narrow Gaussian proton beam. Results: Pencil beam width calculations indicate that both 2D scaling formalisms perform well when the radial kernel width in water is derived from a local scattering power model. Computing the radial kernel width from a nonlocal scattering model results in the local 2D scaling formula under-predicting the pencil beam width by as much as 1.4 mm (21%) at the depth of the Bragg peak for a 220 MeV proton beam in homogeneous water. This translates into a 32% dose discrepancy for a 5 mm Gaussian proton beam. Similar trends were observed for calculations made in heterogeneous slab phantoms where it was also noted that errors tend to increase with greater beam penetration. The generalized 2D scaling model performs well in all situations, with a maximum dose error of 0.3% at the Bragg peak in a heterogeneous phantom containing 3 cm of hard bone. Conclusions: The authors have derived a generalized form of 2D pencil beam scaling which is independent of the proton scattering power model and robust to the functional form of the radial kernel width in water used for the calculations. Sample calculations made with this model show excellent agreement with expected values in both homogeneous water and heterogeneous phantoms.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2017

Acquisition of STEM Images by Adaptive Compressive Sensing

Compressive Sensing (CS) allows a signal to be sparsely measured first and accurately recovered later in software [1]. In scanning transmission electron microscopy (STEM), it is possible to compress an image spatially by reducing the number of measured pixels, which decreases electron dose and increases sensing speed [2,3,4]. The two requirements for CS to work are: (1) sparsity of basis coefficients and (2) incoherence of the sensing system and the representation system. However, when pixels are missing from the image, it is difficult to have an incoherent sensing matrix. Nevertheless, dictionary learning techniques such as Beta-Process Factor Analysis (BPFA) [5] are able to simultaneously discover a basis and the sparse coefficients in the case of missing pixels. On top of CS, we would like to apply active learning [6,7] to further reduce the proportion of pixels being measured, while maintaining image reconstruction quality. Suppose we initially sample 10% of random pixels. We wish to select the next 1% of pixels that are most useful in recovering the image. Now, we have 11% of pixels, and we want to decide the next 1% of “most informative” pixels. Active learning methods are online and sequential in nature. Our goal is to adaptively discover the best sensing mask during acquisition using feedback about the structures in the image. In the end, we hope to recover a high quality reconstruction with a dose reduction relative to the non-adaptive (random) sensing scheme. In doing this, we try three metrics applied to the partial reconstructions for selecting the new set of pixels: (1) variance, (2) Kullback-Leibler (KL) divergence using a Radial Basis Function (RBF) kernel, and (3) entropy. Figs. 1 and 2 display the comparison of Peak Signal-to-Noise (PSNR) using these three different active learning methods at different percentages of sampled pixels. At 20% level, all the three active learning methods underperform the original CS without active learning. However, they all beat the original CS as more of the “most informative” pixels are sampled. One can also argue that CS equipped with active learning requires less sampled pixels to achieve the same value of PSNR than CS with pixels randomly sampled, since all the three PSNR curves with active learning grow at a faster pace than that without active learning. For this particular STEM image, by observing the reconstructed images and the sensing masks, we find that while the method based on RBF kernel acquires samples more uniformly, the one on entropy samples more areas of significant change, thus less uniformly. The KL-divergence method performs the best in terms of reconstruction error (PSNR) for this example [8].

Xie, Weiyi↗
DOE OSTI2010

A Novel Multigrid Method for Sn Discretizations of the Mono-Energetic Boltzmann Transport Equation in the Optically Thick and Thin Regimes with Anisotropic Scattering, Part I

This paper presents a new multigrid method applied to the most common Sn discretizations (Petrov-Galerkin, diamond-differenced, corner-balanced, and discontinuous Galerkin) of the mono-energetic Boltzmann transport equation in the optically thick and thin regimes, and with strong anisotropic scattering. Unlike methods that use scalar DSA diffusion preconditioners for the source iteration, this multigrid method is applied directly to an integral equation for the scalar flux. Thus, unlike the former methods that apply a multigrid strategy to the scalar DSA diffusion operator, this method applies a multigrid strategy to the integral source iteration operator, which is an operator for 5 independent variables in spatial 3-d (3 in space and 2 in angle) and 4 independent variables in spatial 2-d (2 in space and 2 in angle). The core smoother of this multigrid method involves applications of the integral operator. Since the kernel of this integral operator involves the transport sweeps, applying this integral operator requires a transport sweep (an inversion of an upper triagular matrix) for each of the angles used. As the equation is in 5-space or 4-space, the multigrid approach in this paper coarsens in both angle and space, effecting efficient applications of the coarse integral operators. Although each V-cycle of this method is more expensive than a V-cycle for the DSA preconditioner, since the DSA equation does not have angular dependence, the overall computational efficiency is about the same for problems where DSA preconditioning {\it is} effective. This new method also appears to be more robust over all parameter regimes than DSA approaches. Moreover, this new method is applicable to a variety of Sn spatial discretizations, to problems involving a combination of optically thick and thin regimes, and more importantly, to problems with anisotropic scattering cross-sections, all of which DSA approaches perform poorly or not applicable at all. This multigrid approach is most effective in neutron scattering applications, where the total cross-section coefficient $\sigma_t$ and spatial meshsize $h$ satisfies $\sigma_t h\approx 1.$ For this case, coarsening can be done aggressively. For problems with $\sigma_t h\approx 10,$ this multigrid scheme requires a moderately coarsened multiple-coarsening scheme. An even slow coarsening, an angle semi-coarsening, is required for problems with $\sigma_t h$ ranges between 100 and 1000, which occur in high-energy photon applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

αAMG based on Weighted Matching for Systems of Elliptic PDEs Arising From Displacement and Mixed Methods

Adaptive Algebraic Multigrid (or Multilevel) Methods (αAMG) are introduced to improve robustness and efficiency of classical algebraic multigrid methods in dealing with problems where no a-priori knowledge or assumptions on the near-null kernel of the underlined matrix are available. Recently we proposed an adaptive (bootstrap) AMG method, αAMG, aimed to obtain a composite solver with a desired convergence rate. Each new multigrid component relies on a current (general) smooth vector and exploits pairwise aggregation based on weighted matching in a matrix graph to define a new automatic, general-purpose coarsening process, which we refer to as “the compatible weighted matching”. In this work, we present results that broaden the applicability of our method to different finite element discretizations of elliptic PDEs. In particular, we consider systems arising from displacement methods in linear elasticity problems and saddle-point systems that appear in the application of the mixed method to Darcy problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

SU-E-I-17: Comparison of Two Novel Algorithms for the Modulation Transfer Function of CT Using a Simple Cylindrical Phantom

Purpose: To compare and analyze two novel algorithms for the assessment of modulation transfer functions (MTFs) of computed tomography (CT) systems using a simple acrylic cylindrical phantom Method and Materials: Images of the acrylic cylindrical phantom were acquired by a GE LightSpeed 16 RT (GE Healthcare, Milwaukee, WI) using 120 kVp, 330 mA, 2.5 mm slice thickness, 10 cm field-of view (FOV), four reconstruction kernels (e.g. standard, soft, detail, bone, and lung). Two different algorithms were used to analyze images for MTF assessment. First, Richard et al. suggested a task-based MTF assessment method through an edge spread function (ESF) which described pixel intensities as a function of distance from the center. The MTF was obtained as the absolute value of Fourier transform of the differentiated ESF. Second, Ohkubo et al. devised an effective method to determine the point spread function (PSF) of CT system accompanied with verification. The line spread function (LSF), which was the one-dimensional integration of the PSF, was used to obtain the MTF. We validated the reliability of two above-mentioned methods through the comparison with a conventional method using a thin tungsten wire phantom. Results: The measured MTFs by two methods were mostly similar each other for standard, soft, and detail kernels. In 0.6 lp/mm, the MTF difference between two methods were 0.012(standard), 0.004(soft), and 0.037(detail). They also coincided with the MTF by the conventional method well. However, there were considerable distinctions for bone and lung kernels containing edge enhancement that might cause undershoots near the peak of the LSF. Conclusions: We compared two novel methods to assess task-based MTFs for clinical CT systems especially using a simple acrylic cylindrical phantom with high-convenience and low-cost, and validated them against a conventional method. This work can provide a practical solution to users for the quality assurance of CT.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Symmetry-preserving contact interaction model for heavy-light mesons

We use a symmetry-preserving regularization method of ultraviolet divergences in a vector-vector contact interaction model for low-energy QCD. The contact interaction is a representation of nonperturbative kernels used Dyson-Schwinger and Bethe-Salpeter equations. The regularization method is based on a subtraction scheme that avoids standard steps in the evaluation of divergent integrals that invariably lead to symmetry violation. Aiming at the study of heavy-light mesons, we have implemented the method to the pseudoscalar π and K mesons. We have solved the Dyson-Schwinger equation for the u, d and s quark propagators, and obtained the bound-state Bethe-Salpeter amplitudes in a way that the Ward-Green-Takahashi identities reflecting global symmetries of the model are satisfied for arbitrary routing of the momenta running in loop integrals.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

Transport-constrained extensions of collision and track length estimators for solutions of radiative transport problems

In this paper we develop novel extensions of collision and track length estimators for the complete space-angle solutions of radiative transport problems. We derive the relevant equations, prove that our new estimators are unbiased, and compare their performance with that of more conventional estimators. Such comparisons based on numerical solutions of simple one dimensional slab problems indicate the the potential superiority of the new estimators for a wide variety of more general transport problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2010

KERNEL PHASE IN FIZEAU INTERFEROMETRY

The detection of high contrast companions at small angular separation appears feasible in conventional direct images using the self-calibration properties of interferometric observable quantities. The friendly notion of closure phase, which is key to the recent observational successes of non-redundant aperture masking interferometry used with adaptive optics, appears to be one example of a wide family of observable quantities that are not contaminated by phase noise. In the high-Strehl regime, soon to be available thanks to the coming generation of extreme adaptive optics systems on ground-based telescopes, and already available from space, closure phase like information can be extracted from any direct image, even taken with a redundant aperture. These new phase-noise immune observable quantities, called kernel phases, are determined a priori from the knowledge of the geometry of the pupil only. Re-analysis of archive data acquired with the Hubble Space Telescope NICMOS instrument using this new kernel-phase algorithm demonstrates the power of the method as it clearly detects and locates with milliarcsecond precision a known companion to a star at angular separation less than the diffraction limit.

79 ASTRONOMY AND ASTROPHYSICS↗