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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

Differentiating matrices for arbitrarily spaced grid points

Differentiating matrices allow the numerical differentiation of functions defined at points of a discrete grid. Previous derivations of these matrices have been restricted to grids with uniformly spaced points, and the resulting derivative approximations have lacked precision, especially at endpoints. The present work derives differentiating matrices on grids with arbitrarily spaced points. It is shown that high accuracy can be achieved through use of differentiating matrices on non-uniform grids through the expedient of including 'near boundary' points. Use of the differentiating matrix as an operator in the solution of problems involving ordinary differential equations is also considered.

Lakin, W. D.↗
NASA NTRS1988

Preparation and evaluation of silicon nitride matrices for silicon nitride-SiC fiber composites

Continuous silicon carbide (SiC) fiber was added to three types of silicon nitride (Si3N4) matrices. Efforts were aimed at producing a dense Si3N4 matrix from reaction-bonded silicon nitride (RBSN) by hot-isostatic-pressing (HIP) and pressureless sintering, and from Si3N4 powder by hot-pressing. The sintering additives utilized were chosen to allow for densification, while not causing severe degradation of the fiber. The ceramic microstructures were evaluated using scanning optical microscopy. Vickers indentation was used to determine the microhardness and fracture toughness values of the matrices. The RBSN matrices in this study did not reach more than 80 percent of theoretical density after sintering at various temperatures, pressures, and additive levels. Hot-pressing Si3N4 powder produced the highest density matrices; hardness and toughness values were within an order of magnitude of the best literature values. The best sintering aid composition chosen included Y2O3, SiO2, and Al2O3 or AlN. Photomicrographs demonstrate a significant reduction of fiber attack by this additive composition.

Axelson, Scott R.↗
NASA NTRS1980

Performance Properties of Graphite Reinforced Composites with Advanced Resin Matrices

This article looks at the effect of different resin matrices on thermal and mechanical properties of graphite composites, and relates the thermal and flammability properties to the anaerobic char yield of the resins. The processing parameters of graphite composites utilizing graphite fabric and epoxy or other advanced resins as matrices are presented. Thermoset resin matrices studied were: aminecured polyfunctional glycidyl aminetype epoxy (baseline), phenolicnovolac resin based on condensation of dihydroxymethyl-xylene and phenol cured with hexamine, two types of polydismaleimide resins, phenolic resin, and benzyl resin. The thermoplastic matrices studied were polyethersulfone and polyphenylenesulfone. Properties evaluated in the study included anaerobic char yield, limiting oxygen index, smoke evolution, moisture absorption, and mechanical properties at elevated temperatures including tensile, compressive, and short-beam shear strengths. Generally, it was determined that graphite composites with the highest char yield exhibited optimum fire-resistant properties.

Kourtides, Demetrius A.↗
DOE OSTI2022

Impact of fission yield covariance matrices on decay heat uncertainty quantification with the DARWIN2 package

Although fission yields are strongly correlated, correlations between them are usually not taken into account when performing decay heat uncertainty calculations with the CEA DARWIN2 package due to the lack of reference covariance matrices associated to the JEFF-3.1.1 evaluation. However, covariance matrices for {sup 235}U and {sup 239}Pu thermal fission yields have been produced recently by the subgroup 37 of OECD/NEA Working Party on International Nuclear Data Evaluation Cooperation (WPEC). The study presented in this paper evaluates the effect of those covariances on the decay heat uncertainty calculation, both on fission burst experiments and on integral decay heat measurements that are part of the DARWIN2 experimental validation database. Although some differences are observed, partly due to the variety of models used to produce the matrices, the propagation of fission yield covariances always leads to a reduction of the decay heat uncertainty for cooling time above 10 seconds, indicating that not taking them into account is a conservative hypothesis from a safety point of view. Nevertheless, the strong impact of those covariances on the decay heat uncertainty also highlights the need of documented and consistent fission yield uncertainties and correlation data. On top of that, all the covariance matrices used for this study represent the correlation between the physical model parameters, but none of them includes the experimental correlations. An evaluation of the experimental correlations would also be of strong interest for decay heat uncertainty calculations. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Improved Evaluation of Large Network Matrices for Linear Power Flow Within Optimization Problems: Preprint

This work discusses methods for evaluating the Power Transfer Distribution Factor (PTDF) and Line Outage Distribution Factor (LODF) matrices by employing sparse linear algebra for large-scale computing applications. These matrices are critical in many power systems applications, such as the Unit Commitment Problem (UC), pre- and post-contingency power flow analysis, and transmission expansion. These matrices are typically dense, which means they require a significant amount of time and memory to be computed for large networks. However, by analyzing the structure of the matrices and their computation method, it is possible to use reduced memory methods based on sparse matrix operations. This paper shows that sparse linear algebra algorithms are faster and require less memory and time than traditional dense approaches. Additionally, we explore the effect of matrix sparsification by eliminating trailing digits on power flow calculations.

ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Structure of Rank-One Matrices Over the Domain of Principal Ideals Relative to Similarity Transformations

We study the structure of rank-one matrices over the domain of principal ideals relative to equivalence and similarity transformations. The canonical form of rank-one matrices relative to similarity transformations is established. We propose conditions under which a pair of rank-one matrices is reduced to the triangular form by a similarity transformation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Controlling matric potential in microfluidics to examine microbial dynamics in unsaturated porous media

The use of microfluidics for the study of soil microbial ecology is an emerging field. Most microfluidic studies of biological systems, however, have been performed under fully saturated conditions that are not representative of natural soil. Therefore, while microfluidics offer many unique capabilities that other methodologies cannot, they are not currently suited to address the effects of matric potential, an important variable defining the microbial moisture niche. Here, a methodology is presented that allows the user to control the aqueous conditions within microfluidic networks by manipulating matric potential using a hanging water column. The method relies on hydrophilic surface treatment of the microfluidic device using polyvinyl alcohol (PVA) and incorporating a bed of small pores at the network boundaries, which serve as a porous ceramic plate analogue (PPA). The method was validated on a simple capillary bundle and then on a more complex pore network. A water retention curve, exhibiting hysteresis, was generated for the pore network over a narrow matric potential range of 0 to – 5 kPa. Both the drainage and wetting curves were reproducible, as were the spatial configuration and the number of fragmented moisture niches in the pore network, particularly on the drainage curve. In contrast, the wetting curve exhibited greater variability in spatial configuration due to the “ink bottle effect,” where capillarity was interrupted by wider pore bodies. Ultimately, the methodology provides realistic pore-scale moisture conditions that can be easily manipulated and maintained, enabling new opportunities to explore soil biophysics and microbial biogeography in unsaturated porous media. As a brief example, images showing the localization of fluorescently tagged Pantoea sp. YR343 at −4.3 kPa are presented, highlighting bacterial distributions in water films and air-water interfaces.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

Kerov's interlacing sequences and random matrices

To a N × N real symmetric matrix Kerov assigns a piecewise linear function whose local minima are the eigenvalues of this matrix and whose local maxima are the eigenvalues of its (N − 1) × (N − 1) submatrix. We study the scaling limit of Kerov's piecewise linear functions for Wigner and Wishart matrices. For Wigner matrices the scaling limit is given by the Verhik-Kerov-Logan-Shepp curve which is known from asymptotic representation theory. For Wishart matrices the scaling limit is also explicitly found, and we explain its relation to the Marchenko-Pastur limit spectral law.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

On the asymptotic distribution of block-modified random matrices

We study random matrices acting on tensor product spaces which have been transformed by a linear block operation. Using operator-valued free probability theory, under some mild assumptions on the linear map acting on the blocks, we compute the asymptotic eigenvalue distribution of the modified matrices in terms of the initial asymptotic distribution. Moreover, using recent results on operator-valued subordination, we present an algorithm that computes, numerically but in full generality, the limiting eigenvalue distribution of the modified matrices. Our analytical results cover many cases of interest in quantum information theory: we unify some known results and we obtain new distributions and various generalizations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2011

Random matrices with external source and the asymptotic behaviour of multiple orthogonal polynomials

Ensembles of random Hermitian matrices with a distribution measure defined by an anharmonic potential perturbed by an external source are considered. The limiting characteristics of the eigenvalue distribution of the matrices in these ensembles are related to the asymptotic behaviour of a certain system of multiple orthogonal polynomials. Strong asymptotic formulae are derived for this system. As a consequence, for matrices in this ensemble the limit mean eigenvalue density is found, and a variational principle is proposed to characterize this density. Bibliography: 35 titles.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Universal Polarization Transformations: Spatial Programming of Polarization Scattering Matrices Using a Deep Learning‐Designed Diffractive Polarization Transformer

Abstract Controlled synthesis of optical fields having nonuniform polarization distributions presents a challenging task. Here, a universal polarization transformer is demonstrated that can synthesize a large set of arbitrarily‐selected, complex‐valued polarization scattering matrices between the polarization states at different positions within its input and output field‐of‐views (FOVs). This framework comprises 2D arrays of linear polarizers positioned between isotropic diffractive layers, each containing tens of thousands of diffractive features with optimizable transmission coefficients. After its deep learning‐based training, this diffractive polarization transformer can successfully implement N i N o = 10 000 different spatially‐encoded polarization scattering matrices with negligible error, where N i and N o represent the number of pixels in the input and output FOVs, respectively. This universal polarization transformation framework is experimentally validated in the terahertz spectrum by fabricating wire‐grid polarizers and integrating them with 3D‐printed diffractive layers to form a physical polarization transformer. Through this set‐up, an all‐optical polarization permutation operation of spatially‐varying polarization fields is demonstrated, and distinct spatially‐encoded polarization scattering matrices are simultaneously implemented between the input and output FOVs of a compact diffractive processor. This framework opens up new avenues for developing novel devices for universal polarization control and may find applications in, e.g., remote sensing, medical imaging, security, material inspection, and machine vision.

Optical neural networks↗
DOE OSTI2016

A fast, memory efficient and robust sparse preconditioner based on a multifrontal approach with applications to finite‐element matrices

Summary In this article, we introduce a fast, memory efficient and robust sparse preconditioner that is based on a direct factorization scheme for sparse matrices arising from the finite‐element discretization of elliptic partial differential equations. We use a fast (but approximate) multifrontal approach as a preconditioner and use an iterative scheme to achieve a desired accuracy. This approach combines the advantages of direct and iterative schemes to arrive at a fast, robust, and accurate preconditioner. We will show that this approach is faster (∼2×) and more memory efficient (∼2–3×) than a conventional direct multifrontal approach. Furthermore, we will demonstrate that this preconditioner is both faster and more effective than other preconditioners such as the incomplete LU preconditioner. Specific speedups depend on the matrix size and improve as the size of the matrix increases. The preconditioner can be applied to both structured and unstructured meshes in a similar manner. We build on our previous work and utilize the fact that dense frontal and update matrices, in the multifrontal algorithm, can be represented as hierarchically off‐diagonal low‐rank matrices. Using this idea, we replace all large dense matrix operations in the multifrontal elimination process with O ( N ) hierarchically off‐diagonal low‐rank operations to arrive at a faster and more memory efficient factorization scheme. We then use this direct factorization method at low accuracies as a preconditioner and apply it to various real‐life engineering test cases. Copyright © 2016 John Wiley & Sons, Ltd.

Aminfar, AmirHossein↗
DOE OSTI2018

Carbon Nanoparticles and Graphene Nanosheets as MALDI Matrices in Glycomics: a New Approach to Improve Glycan Profiling in Biological Samples

Glycomics continues to be a highly dynamic and interesting research area due to the need to comprehensively understand the biological attributes of glycosylation in many important biological functions such as the immune response, cell development, cell differentiation/adhesion, and host-pathogen interactions. Although matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS) has proven to be suitable for glycomic profiling studies, there is a need for improved sensitivity in the detection of native glycans, which ionize inefficiently. In this study, we investigated the efficiencies of graphene nanosheets (GNs) and carbon nanoparticles (CNPs) as MALDI matrices and co-matrices in glycan profiling. Our results indicated an enhancement of signal intensity by several orders of magnitude upon using GNs and CNPs in MALDI analysis of N-glycans derived from a variety of biological samples. Interestingly, increasing the amounts of CNPs and GNs improved not only the signal intensities but also prompted in-source decay (ISD) fragmentations, which produced extensive glycosidic and cross-ring cleavages. Our results indicated that the extent of ISD fragmentation could be modulated by CNP and GN concentrations, to obtain MS{sup 2} and pseudo-MS{sup 3} spectra. The results for glycan profiling in high salt solutions confirmed high salt-tolerance capacities for both CNPs and GNs. Finally, the results showed that by using CNPs and GNs as co-matrices, DHB crystal formation was more homogeneous which improved shot-to-shot reproducibility and sensitivity. .

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2013

Environmental effects on noble-gas hydrides: HXeBr, HXeCCH, and HXeH in noble-gas and molecular matrices

Noble-gas hydrides HNgY (Ng is a noble-gas atom and Y is an electronegative group) are sensitive probes of local environment due to their relatively weak bonding and large dipole moments. We experimentally studied HXeBr in Ar, Kr, and N{sub 2} matrices, HXeCCH in Ne and N{sub 2} matrices, and HXeH in an N{sub 2} matrix. These are the first observations of noble-gas hydrides in an N{sub 2} matrix. An N{sub 2} matrix strongly increases the H–Xe stretching frequency of HXeBr and HXeCCH with respect to a Ne matrix, which is presumably due to a strong interaction between the HNgY dipole moment and quadrupole moments of the surrounding lattice N{sub 2} molecules. The spectral shift of HXeBr in an N{sub 2} matrix is similar to that in a CO{sub 2} matrix, which is a rather unexpected result because the quadrupole moment of CO{sub 2} is about three times as large as that of N{sub 2}. The H–Xe stretching frequencies of HXeBr and HXeCCH in noble-gas matrices show a trend of ν(Ne) < ν(Xe) < ν(Kr) < ν(Ar), which is a non-monotonous function of the dielectric constants of the noble-gas solids. The MP2(full) calculations of HXeBr and HXeCCH with the polarizable continuum model as well as the CCSD(T) calculations of the HXeBr···Ng and HXeCCH···Ng (Ng = Ne, Ar, Kr, and Xe) complexes cannot fully explain the experimental observations. It is concluded that more sophisticated computational models should be used to describe these experimental findings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Unbiased reduced density matrices and electronic properties from full configuration interaction quantum Monte Carlo

Properties that are necessarily formulated within pure (symmetric) expectation values are difficult to calculate for projector quantum Monte Carlo approaches, but are critical in order to compute many of the important observable properties of electronic systems. Here, we investigate an approach for the sampling of unbiased reduced density matrices within the full configuration interaction quantum Monte Carlo dynamic, which requires only small computational overheads. This is achieved via an independent replica population of walkers in the dynamic, sampled alongside the original population. The resulting reduced density matrices are free from systematic error (beyond those present via constraints on the dynamic itself) and can be used to compute a variety of expectation values and properties, with rapid convergence to an exact limit. A quasi-variational energy estimate derived from these density matrices is proposed as an accurate alternative to the projected estimator for multiconfigurational wavefunctions, while its variational property could potentially lend itself to accurate extrapolation approaches in larger systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

General integrable n-level, many-mode Janes-Cummings-Dicke models and classical r-matrices with spectral parameters

Using the technique of classical r-matrices and quantum Lax operators, we construct the most general form of the quantum integrable “n-level, many-mode” spin-boson Jaynes-Cummings-Dicke-type hamiltonians describing an interaction of a molecule of N n-level atoms with many modes of electromagnetic field and containing, in general, additional non-linear interaction terms. We explicitly obtain the corresponding quantum Lax operators and spin-boson analogs of the generalized Gaudin hamiltonians and prove their quantum commutativity. We investigate symmetries of the obtained models that are associated with the geometric symmetries of the classical r-matrices and construct the corresponding algebra of quantum integrals. We consider in detail three classes of non-skew-symmetric classical r-matrices with spectral parameters and explicitly obtain the corresponding quantum Lax operators and Jaynes-Cummings-Dicke-type hamiltonians depending on the considered r-matrix.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

One-electron reduced density matrices of strongly correlated harmonium atoms

Explicit asymptotic expressions are derived for the reduced one-electron density matrices (the 1-matrices) of strongly correlated two- and three-electron harmonium atoms in the ground and first excited states. These expressions, which are valid at the limit of small confinement strength ω, yield electron densities and kinetic energies in agreement with the published values. In addition, they reveal the ω{sup 5/6} asymptotic scaling of the exchange components of the electron-electron repulsion energies that differs from the ω{sup 2/3} scaling of their Coulomb and correlation counterparts. The natural orbitals of the totally symmetric ground state of the two-electron harmonium atom are found to possess collective occupancies that follow a mixed power/Gaussian dependence on the angular momentum in variance with the simple power-law prediction of Hill’s asymptotics. Providing rigorous constraints on energies as functionals of 1-matrices, these results are expected to facilitate development of approximate implementations of the density matrix functional theory and ensure their proper description of strongly correlated systems.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2016

Optical properties of graphene-based materials in transparent polymer matrices

Different aspects of graphene-based materials (GBMs) and GBM-nanocomposites have been investigated due to their intriguing features; one of these features is their transparency. Transparency of GBMs has been of an interest to scientists and engineers mainly with regard to electronic devices. In this study, optical transmittance of structural, purpose-made nanocomposites reinforced with GBMs was analyzed to lay a foundation for optical microstructural characterization of nanocomposites in future studies. Two main types of GBM reinforcements were studied, graphene oxide (GO) and graphite nanoplates (GNPs). The nanocomposites investigated are GO/poly(vinyl alcohol), GO/sodium alginate, and GNP/epoxy with different volume fractions of GBMs. Together with UV-visible spectrophotometry, image-processing-assisted micro and macro photography were used to assess the transparency of GBMs embedded in the matrices. The micro and macro photography methods developed were proven to be an alternative way of measuring light transmittance of semi-transparent materials. It was found that there existed a linear relationship between light absorbance and a volume fraction of GBMs embedded in the same type of polymer matrices, provided that the nanocomposites of interest had the same thicknesses. This suggests that the GBM dispersion characteristics in the same type of polymer are similar and any possible change in crystal structure of polymer due to different volumetric contents of GBM does not have an effect on light transmittance of the matrices. The study also showed that the same types of GBMs could display different optical properties in different matrix materials. The results of this study will help to develop practical microstructural characterization techniques for GBM-based nanocomposites.

36 MATERIALS SCIENCE↗