SEARCH · Engineering Papers
Results for “matrices”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Extraction of Spectra in the Shell Model Monte Carlo Method Using Imaginary-Time Correlation Matrices
Not Available
Ground-State Preparation and Energy Estimation on Early Fault-Tolerant Quantum Computers via Quantum Eigenvalue Transformation of Unitary Matrices
Not Available
Random matrices and chaos in nuclear physics: Nuclear reactions
The application of random-matrix theory (RMT) to compound-nucleus (CN) reactions is reviewed. An introduction into the basic concepts of nuclear scattering theory is followed by a survey of phenomenological approaches to CN scattering. The implementation of a random-matrix approach into scattering theory leads to a statistical theory of CN reactions. Since RMT applies generically to chaotic quantum systems, that theory is, at the same time, a generic theory of quantum chaotic scattering. It uses a minimum of input parameters (average S matrix and mean level spacing of the CN). Predictions of the theory are derived with the help of field-theoretical methods adapted from condensed-matter physics and compared with those of phenomenological approaches. Thorough tests of the theory are reviewed, as are applications in nuclear physics, with special attention given to violation of symmetries (isospin and parity) and time-reversal invariance.
A Communication-Avoiding 3D LU Factorization Algorithm for Sparse Matrices
We propose a new algorithm to improve the strong scalability of right-looking sparse LU factorization on distributed memory systems. Our 3D sparse LU algorithm uses a three-dimensional MPI process grid, aggressively exploits elimination tree parallelism and trades off increased memory for reduced per-process communication. We also analyze the asymptotic improvements for planar graphs (e.g., from 2D grid or mesh domains) and certain non-planar graphs (specifically for 3D grids and meshes). For planar graphs with n vertices, our algorithm reduces communication volume asymptotically in n by a factor of O{log n} and latency by a factor of O{log n}. For non-planar cases, our algorithm can reduce the per-process communication volume by 3× and latency by O{n^1/3} times. In all cases, the memory needed to achieve these gains is a constant factor. We implemented our algorithm by extending the 2D data structure used in superLU. Our new 3D code achieves speedups up to 27× for planar graphs and up to 3.3× for non-planar graphs over the baseline 2D superLU when run on 24,000 cores of a Cray XC30.
Assessing the Feasibility of Bordered Block Diagonal Reordering in Power System Matrices using Fully Convolutional Network
In electromagnetic transient (EMT) simulations for power systems and inverter-based resources (IBRs), the arrangement of states within the system's linear equations, represented by matrix A in Ax=b, is critical. The state ordering in matrix A can highlight distinct characteristics of the system's graph, and identifying an optimal state ordering is crucial for efficient computation. The choice of state ordering, however, is dependent on the solver used, as each solver may perform optimally with different matrix patterns. With a wide array of matrix reordering algorithms available, selecting the most suitable one becomes challenging without insights into the matrix's ideal configuration. To address this, the paper proposes a fully convolutional network (FCN) to evaluate the reordering potential of the A matrix into a bordered block diagonal (BBD) pattern, which is commonly observed in power system and IBR modeling. The FCN's assessment aims to streamline the solver's operation, which in turn could substantially reduce the computational time required to find a solution.
Data-driven Performance Modeling of Linear Solvers for Sparse Matrices
Abstract not provided
True Load Balancing for Matricized Tensor Times Khatri-Rao Product
MTTKRP is the bottleneck operation in algorithms used to compute the CP tensor decomposition. For sparse tensors, utilizing the compressed sparse fibers (CSF) storage format and the CSF-oriented MTTKRP algorithms is important for both memory and computational efficiency on distributed-memory architectures. Existing intelligent tensor partitioning models assume the computational cost of MTTKRP to be proportional to the total number of nonzeros in the tensor. However, this is not the case for the CSF-oriented MTTKRP on distributed-memory architectures. We outline two deficiencies of nonzero-based intelligent partitioning models when CSF-oriented MTTKRP operations are performed locally: failure to encode processors' computational loads and increase in total computation due to fiber fragmentation. We focus on existing fine-grain hypergraph model and propose a novel vertex weighting scheme that enables this model encode correct computational loads of processors. We also propose to augment the fine-grain model by fiber nets for reducing the increase in total computational load via minimizing fiber fragmentation. In this way, the proposed model encodes minimizing the load of the bottleneck processor. In conclusion, parallel experiments with real-world sparse tensors on up to 1024 processors prove the validity of the outlined deficiencies and demonstrate the merit of our proposed improvements in terms of parallel runtimes.
Use of carbonyldiimidazole as a derivatization agent for the detection of pinacolyl alcohol, a forensic marker for Soman, by EI-GC–MS and LC-HRMS in official OPCW proficiency test matrices
Pinacolyl alcohol (PA), a key forensic marker for the nerve agent Soman (GD), is a particularly difficult analyte to detect by various analytical methods. In this work, we have explored the reaction between PA and 1,1'-carbonyldiimidazole (CDI) to yield pinacolyl 1H-imidazole-1-carboxylate (PIC), a product that can be conveniently detected by gas chromatography–mass spectrometry (GC–MS) and liquid chromatography-high-resolution mass spectrometry (LC-HRMS). Regarding its GC–MS profile, this new carbamate derivative of PA possesses favorable chromatographic features such as a sharp peak and a longer retention time (RT = 16.62 min) relative to PA (broad peak and short retention time, RT = 4.1 min). Additionally, the derivative can also be detected by LC-HRMS, providing an avenue for the analysis of this chemical using this technique where PA is virtually undetectable unless present in large concentrations. From a forensic science standpoint, detection of this low molecular weight alcohol signals the past or latent presence of the nerve agent Soman (GD) in a given matrix (i.e., environmental or biological). The efficiency of the protocol was tested separately in the analysis and detection of PA by EI-GC–MS and LC-HRMS when present at a 10 μg/mL in a soil matrix featured in the 44th PT and in a glycerol-rich liquid matrix featured in the 48th Official Organization for the Prohibition of Chemical Weapons (OPCW) Proficiency Test when present at a 5 μg/mL concentration. In both scenarios, PA was successfully transformed into PIC, establishing the protocol as an additional tool for the analysis of this unnatural and unique nerve agent marker by GC–MS and LC-HRMS.
XRD studies of cement embedding matrices containing complexing reagents from decontamination practices
The study of embedding matrix of radioactive waste is an important issue concerning the need to assure the radiological safety requirements for final disposal. The conditioning of the radioactive wastes by cementation process involves also a structural investigation by X-ray diffraction (XRD) of the samples prepared with cement and complexing agents at different concentration. This paper gives useful information about the influence of complexing agents related to damages produced in concrete microstructures. The experimental data obtained on samples kept in real repository environment for a period up to 15 years, offer structural information regarding the interaction between the different decontamination agents with the major components of the matrix. (authors)
Effect of Bi isovalent dopants on the formation of homogeneous coherently strained InAs quantum dots in GaAs matrices
The distribution of hydrostatic strains in Bi{sup 3+}-doped InAs quantum dots embedded in a GaAs matrix are calculated in the context of the deformation-potential model. The dependences of strains in the material of spherical InAs quantum dots with substitutional (Bi {yields} As) and interstitial (Bi) impurities on the quantum-dot size are derived. The qualitative correlation of the model with the experiment is discussed. The data on the effect of doping on the morphology of self-assembled InAs:Bi quantum dots in a GaAs matrix are obtained.
Photo- and electroluminescence from semiconductor colloidal quantum dots in organic matrices: QD-OLED
The results are reported of an experimental study of samples of organic light-emitting diodes (OLEDs) with luminescent layers fabricated on the basis of two types of CdSe/CdS/ZnS semiconductor quantum dots (QDs) with average CdSe core diameters of 3.2 and 4.1 nm and the same overall diameters of 6.5 nm. The dependences of the LED efficiency on the applied voltage are determined. Assumptions are made about ways of optimizing the design of high-efficiency LEDs.
Effect of the morphology of CNT arrays on the current density of field-emitter matrices
Structured carbon-nanotube arrays synthesized in topological regions formed by electron-beam lithography are studied. The effect of the morphology and topology of the carbon-nanotube arrays on the emission characteristics of the structures being formed are considered.
SiC-Based Composite Materials Obtained by Siliconizing Carbon Matrices
We have developed a method for fabrication of parts of complicated configuration from composite materials based on SiC ceramics, which employs the interaction of silicon melt with the carbon matrix having a certain composition and porosity. For elevating the operating temperatures of ceramic components, we have developed a method for depositing protective silicon-carbide coatings that is based on the interaction of the silicon melt and vapor with carbon obtained during thermal splitting of hydrocarbon molecules. The new structural ceramics are characterized by higher operating temperatures; chemical stability; mechanical strength; thermal shock, wear and radiation resistance; and parameters stability.
Autoemission of Multipointed Cathode Matrices Based on p-Type Silicon in Strong Pulsed Electric Fields
In this paper, we experimentally studied the dynamic properties of autoelectron emission in strong pulsed electric fields of microsecond duration for multipointed cathode arrays based on surface-modified silicon crystals of the hole type. A decrease in the thresholds for the autoemission onset with an increase in the pulse duration is shown to increase the transparency of potential barriers due to an increase in the electron energy. Autoemission parameters are determined by surface dipole moments and embedded surface potentials that are formed during plasma etching of silicon in various chemically active media.
An Algebraic Multilevel Preconditioner with Low-Rank Corrections for Sparse Symmetric Matrices
In this work, we describe a multilevel preconditioning technique for solving sparse symmetric linear systems of equations. This “Multilevel Schur Low-Rank” (MSLR) preconditioner first builds a tree structure $\mathcal{T}$ based on a hierarchical decomposition of the matrix and then computes an approximate inverse of the original matrix level by level. Unlike classical direct solvers, the construction of the MSLR preconditioner follows a top-down traversal of $\mathcal{T}$ and exploits a low-rank property that is satisfied by the difference between the inverses of the local Schur complements and specific blocks of the original matrix. A few steps of the generalized Lanczos tridiagonalization procedure are applied to capture most of this difference. Numerical results are reported to illustrate the efficiency and robustness of the MSLR preconditioner with both two- and three-dimensional discretized PDE problems and with publicly available test problems.