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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2010

Computed Analysis of Three-Dimensional Cone-Beam Computed Tomography Angiography for Determination of Tumor-Feeding Vessels During Chemoembolization of Liver Tumor: A Pilot Study

The purpose of this study was to evaluate computed analysis of three-dimensional (3D) cone-beam computed tomography angiography (CTA) of the liver for determination of subsegmental tumor-feeding vessels (FVs). Eighteen consecutive patients underwent transarterial chemoembolization (TACE) from January to October 2008 for 25 liver tumors (15 hepatocellular carcinomas [HCCs] and 10 neuroendocrine metastases). Anteroposterior projection angiogram (two-dimensional [2D]) and 3D cone-beam CTA images were acquired by injection of the common hepatic artery. Retrospectively, FVs were independently identified by three radiology technologists using a software package (S) that automatically determines FVs by analysis of 3D images. Subsequently, three interventional radiologists (IRs) independently identified FVs by reviewing the 2D images followed by examination of the 3D images. Finally, the 'ground truth' for the number and location of FVs was obtained by consensus among the IRs, who were allowed to use any imaging-including 2D, 3D, and all oblique or selective angiograms-for such determination. Sensitivities, durations, and degrees of agreement for review of 2D, 3D, and S results were evaluated. Sensitivity of 3D (73%) was higher than 2D (64%) images for identification of FVs (P = 0.036). The sensitivity of S (93%) was higher than 2D (P = 0.02) and 3D (P = 0.005) imaging. The duration for review of 3D imaging was longer than that for 2D imaging (187 vs. 94 s, P = 0.0001) or for S (135 s, P = 0.0001). The degree of agreement between the IRs using 2D and 3D imaging were 54% and 62%, respectively, whereas it was 82% between the three radiology technologists using S. These preliminary data show that computed determination of FVs is both accurate and sensitive.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Noise analysis for the Sorkin and Peres tests performed on a quantum computer

We use quantum computers to test the foundations of quantum mechanics through quantum algorithms that implement some of the experimental tests as the basis of the theory's postulates. These algorithms can be used as a test of the physical theory under the premise of a perfect hardware or as a test of the hardware under the premise that quantum theory is correct. In this paper, we show how the algorithms can be used to test the efficacy of a quantum computer in obeying the postulates of quantum mechanics. We study the effect of different types of errors on the results of experimental tests of the postulates. A salient feature of this error analysis is that it is deeply rooted in the fundamentals of quantum mechanics as it highlights how systematic errors affect the quantumness of the quantum computer.

Sadana, Simanraj↗
DOE OSTI2016

Computational approaches to detect allosteric pathways in transmembrane molecular machines

Many of the functions of transmembrane proteins involved in signal processing and transduction across the cell membrane are determined by allosteric couplings that propagate the functional effects well beyond the original site of activation. Data gathered from breakthroughs in biochemistry, crystallography, and single molecule fluorescence have established a rich basis of information for the study of molecular mechanisms in the allosteric couplings of such transmembrane proteins. The mechanistic details of these couplings, many of which have therapeutic implications, however, have only become accessible in synergy with molecular modeling and simulations. Here, we review some recent computational approaches that analyze allosteric coupling networks (ACNs) in transmembrane proteins, and in particular the recently developed Protein Interaction Analyzer (PIA) designed to study ACNs in the structural ensembles sampled by molecular dynamics simulations. The power of these computational approaches in interrogating the functional mechanisms of transmembrane proteins is illustrated with selected examples of recent experimental and computational studies pursued synergistically in the investigation of secondary active transporters and GPCRs. This article is part of a Special Issue entitled: Membrane Proteins edited by J.C. Gumbart and Sergei Noskov.

Biochemistry & Molecular Biology↗
DOE OSTI2024

Accelerating Multivariate Functional Approximation Computation with Domain Decomposition Techniques⋆

Modeling large datasets through Multivariate Functional Approximations (MFA) provide an elegant way to handle many visualization and scientific analysis workflows. The process necessitates scalable data partitioning methods to compute MFA representations efficiently without compromising the accuracy or continuity of the reconstructed solution. We propose a domain -decomposed method for computing the MFA with B -spline bases, which reduces the total work per task and uses a restricted Additive Schwarz (RAS) method to converge the control point data degrees -of -freedom along subdomain boundaries. We provide an in-depth analysis of the parallel approach with domain decomposition solvers, aiming to minimize local subdomain error residuals and recover high -order continuity at subdomain interfaces with appropriate choices of knot overlaps. The communication cost, determined by the overlap regions in the RAS implementation, is optimized to recover the numerical error profile of the single subdomain case. Our proposed method stands in contrast to previous methods, which typically only recover either C 0 or at best C 1 continuity for arbitrary B -spline degree expansions, or those that require post -processing to blend discontinuities in the reconstructed data. We demonstrate the effectiveness of our approach using analytical and real -world datasets in 1D, 2D, and 3D through both strong and weak scaling studies. The performance results indicate that the overall cost of computing the approximation is directly proportional to the underlying nearest -neighbor communication implementation, and is only weakly dependent on the overlap region size that determines the size of the messages. This finding underscores the efficiency and scalability of our proposed method, making it a promising solution for handling large datasets in scientific workflows.

additive Schwarz solvers↗
DOE OSTI2017

Computational material design for Q&P steels with plastic instability theory

In this paper, the deformation limits of Quenching and Partitioning (Q&P) steels are examined with the plastic instability theory. For this purpose, the constituent phase properties of various Q&P steels were first experimentally obtained, and used to estimate the overall tensile stress-strain curves based on the simple rule of mixture (ROM) with the iso-strain and iso-stress assumptions. Plastic instability theory was then applied to the obtained overall stress-strain curves in order to estimate the deformation limits of the Q&P steels. A parametric study was also performed to examine the effects of various material parameters on the deformation limits of Q&P steels. Computational material design was subsequently carried out based on the information obtained from the parametric study. The results show that the plastic instability theory with iso-stress-based stress-strain curve may be used to provide the lower bound estimate of the uniform elongation (UE) for the various Q&P steels considered. The results also indicate that higher austenite stability/volume fractions, less strength difference between the primary phases, higher hardening exponents of the constituent phases are generally beneficial for the performance improvement of Q&P steels, and that various material parameters may be concurrently adjusted in a cohesive way in order to improve the performance of Q&P steel. The information from this study may be used to devise new heat treatment parameters and alloying elements to produce Q&P steels with the improved performance.

Cheng, G.↗
DOE OSTI2018

Comprehensive Experimental and Computational Spectroscopic Study of Hexacyanoferrate Complexes in Water: From Infrared to X-ray Wavelengths

We present a joint experimental and computational study of the hexacyanoferrate aqueous complexes at equilibrium in the 250 meV to 7.15 keV regime. The experiments include the vibrational spectroscopy of the cyanide ligands, the valence electronic absorption spectra and Fe 1s core hole spectra using X-ray absorption and emission techniques. These experiments are accompanied by density functional theory (DFT) based quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations and spectroscopy calculations of the experiments spanning the IR to X-ray wavelengths. Our simulations, including explicit solvent effects, across this large energy window allows for a systematic comparison of vibrational, valence electronic and core-level spectra. The analysis reveals how the local solvation environment influences the geometric and electronic structure and impacts the line shape and intensity of the observed spectral features in the infrared, optical and X-ray wavelengths.

Ross, Matthew↗
DOE OSTI2020

Physics for neuromorphic computing

Neuromorphic computing takes inspiration from the brain to create energy-efficient hardware for information processing, capable of highly sophisticated tasks. Systems built with standard electronics achieve gains in speed and energy by mimicking the distributed topology of the brain. Scaling-up such systems and improving their energy usage, speed and performance by several orders of magnitude requires a revolution in hardware. Here, we discuss how including more physics in the algorithms and nanoscale materials used for data processing could have a major impact in the field of neuromorphic computing. We review striking results that leverage physics to enhance the computing capabilities of artificial neural networks, using resistive switching materials, photonics, spintronics and other technologies. We discuss the paths that could lead these approaches to maturity, towards low-power, miniaturized chips that could infer and learn in real time.

device physics↗
DOE OSTI2014

Computational quantum chemistry for single Heisenberg spin couplings made simple: Just one spin flip required

We highlight a simple strategy for computing the magnetic coupling constants, J, for a complex containing two multiradical centers. On the assumption that the system follows Heisenberg Hamiltonian physics, J is obtained from a spin-flip electronic structure calculation where only a single electron is excited (and spin-flipped), from the single reference with maximum S{sup ^}{sub z}, M, to the M − 1 manifold, regardless of the number of unpaired electrons, 2M, on the radical centers. In an active space picture involving 2M orbitals, only one β electron is required, together with only one α hole. While this observation is extremely simple, the reduction in the number of essential configurations from exponential in M to only linear provides dramatic computational benefits. This (M, M − 1) strategy for evaluating J is an unambiguous, spin-pure, wave function theory counterpart of the various projected broken symmetry density functional theory schemes, and likewise gives explicit energies for each possible spin-state that enable evaluation of properties. The approach is illustrated on five complexes with varying numbers of unpaired electrons, for which one spin-flip calculations are used to compute J. Some implications for further development of spin-flip methods are discussed.

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

Computational studies of adsorption in metal organic frameworks and interaction of nanoparticles in condensed phases

In this review, we describe recent efforts to systematically study nano-structured metal organic frameworks (MOFs), also known as metal organic heat carriers, with particular emphasis on their application in heating and cooling processes. We used both molecular dynamics and grand canonical Monte Carlo simulation techniques to gain a molecular-level understanding of the adsorption mechanism of gases in these porous materials. We investigated the uptake of various gases such as refrigerants R12 and R143a. We also evaluated the effects of temperature and pressure on the uptake mechanism. Our computed results compared reasonably well with available measurements from experiments, thus validating our potential models and approaches. In addition, we investigated the structural, diffusive and adsorption properties of different hydrocarbons in Ni-2(dhtp). Finally, to elucidate the mechanism of nanoparticle dispersion in condensed phases, we studied the interactions among nanoparticles in various liquids, such as n-hexane, water and methanol.

Annapureddy, HVR↗
DOE OSTI2012

Architecture-based multiscale computational modeling of plant cell wall mechanics to examine the hydrogen-bonding hypothesis of cell wall network structure model

A primary plant cell wall network was computationally modeled using the finite element approach to study the hypothesis of hemicellulose (HC) tethering with the cellulose microfibrils (CMFs) as one of the major load-bearing mechanisms of the growing cell wall. A computational primary cell wall network fragment (10 × 10 μm) comprising typical compositions and properties of CMFs and HC was modeled with well-aligned CMFs. The tethering of HC to CMFs is modeled in accordance with the strength of the hydrogen bonding by implementing a specific load-bearing connection (i.e. the joint element). The introduction of the CMF-HC interaction to the computational cell wall network model is a key to the quantitative examination of the mechanical consequences of cell wall structure models, including the tethering HC model. When the cell wall network models with and without joint elements were compared, the hydrogen bond exhibited a significant contribution to the overall stiffness of the cell wall network fragment. When the cell wall network model was stretched 1% in the transverse direction, the tethering of CMF-HC via hydrogen bonds was not strong enough to maintain its integrity. When the cell wall network model was stretched 1% in the longitudinal direction, the tethering provided comparable strength to maintain its integrity. This substantial anisotropy suggests that the HC tethering with hydrogen bonds alone does not manifest sufficient energy to maintain the integrity of the cell wall during its growth (i.e. other mechanisms are present to ensure the cell wall shape).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Application of Quantum Machine Learning to High Energy Physics Analysis at LHC using IBM Quantum Computer Simulators and IBM Quantum Computer Hardware

Our group pioneers the use of Quantum Machine Learning (QML) on High Energy Physics analysis at LHC. We have successfully employed several QML classification algorithms in the ttH (Higgs production in association with a top quark pair) and Higgs to two muons (Higgs coupling to second generation fermions), two recent LHC flagship physics analysis, on gate-model quantum computer simulators and hardware. The simulation studies have been performed with the IBM Quantum Framework, Google Tensorflow Quantum Framework, and Amazon Braket Framework, and we have achieved good classification performance that is similar to the performances of the classical machine learning methods currently used in LHC physics analyses, classical SVM, classical BDT, and classical deep neural network for example. We have also performed our studies using IBM superconducting quantum computer hardware and the performance is promising and is approaching the performance from IBM quantum simulators. Moreover, we extend our studies to other QML areas such as quantum anomaly detection and quantum generative adversarial, and some preliminary results have been obtained. Also, we have overcome the challenges of intensive computing resources in the cases of large qubits (25 qubits or more) and large numbers of events using NVIDIA cuQuantum with NERSC Perlmutter HPC. Our studies give an example that Quantum Machine Learning performs as well as its classical counterpart for realistic High Energy Physics analysis datasets. Furthermore, our result on noisy quantum hardware provides important validation for the result on noiseless quantum simulators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1971

Spaceborne computer executive routine functional design specification. Volume 2: Computer executive design for space station/base

The computer executive functional system design concepts derived from study of the Space Station/Base are presented. Information Management System hardware configuration as directly influencing the executive design is reviewed. The hardware configuration and generic executive design requirements are considered in detail in a previous report (System Configuration and Executive Requirements Specifications for Reusable Shuttle and Space Station/Base, 9/25/70). This report defines basic system primitives and delineates processes and process control. Supervisor states are considered for describing basic multiprogramming and multiprocessing systems. A high-level computer executive including control of scheduling, allocation of resources, system interactions, and real-time supervisory functions is defined. The description is oriented to provide a baseline for a functional simulation of the computer executive system.

Kennedy, J. R.↗
NASA NTRS1973

Universal computer test stand (recommended computer test requirements)

Techniques are considered which would be used to characterize areospace computers with the space shuttle application as end usage. The system level digital problems which have been encountered and documented are surveyed. From the large cross section of tests, an optimum set is recommended that has a high probability of discovering documented system level digital problems within laboratory environments. Defined is a baseline hardware, software system which is required as a laboratory tool to test aerospace computers. Hardware and software baselines and additions necessary to interface the UTE to aerospace computers for test purposes are outlined.

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