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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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Accessing a P-27 Data Acquisition Computer from an Off-Site Computer Not Owned by LANL
The steps to access a LANL owned P-27 data acquisition (DAQ) computer from off site using a non-LANL computer are documented here. Two connection paths should be possible. The first uses an HTML5 connection directly from portal.lanl.gov, and the steps for this path follow some preliminaries. The second connection path again uses an HTML5 from connection to portal.lan.gov to a virtual Windows desktop to then access our DAQ computers. Instructions for this will be added later.
High Performance Computing and Quantum Computing Integration Framework Architecture and Requirements Document
The HPC/QC Requirements Document presents a comprehensive framework for integrating High Performance Computing (HPC) and Quantum Computing (QC). The framework proposed in this document supports a hybrid quantum-classical computing model that provides application flexibility, and resource abstraction and standardization.
Neutron and x-ray computed tomography of a natural uranium tristructural isotropic (TRISO) fuel compact
A natural uranium-based, unirradiated tristructural isotropic (TRISO) fuel compact was nondestructively imaged using both X-ray (XCT) and neutron computed tomography (nCT). While XCT of compacts can provide information on fuel kernels, imaging artifacts preclude examination of the graphite matrix. In this work, nCT was used for the first time on a TRISO compact to examine the graphite matrix. A crack was clearly resolved within the graphite matrix, proving that nCT is a viable tool for nondestructive volumetric examination of the matrix material in TRISO fuel compacts. The XCT and nCT data were then fused together to create a more comprehensive dataset containing both matrix and fuel kernels.
Computational Reproducibility of Scientific Workflows at Extreme Scales
We propose an approach for improved reproducibility that includes capturing and relating provenance characteristics and performance metrics, in a hybrid queriable system, the ProvEn server. The system capabilities are illustrated on two use cases: scientific reproducibility of results in the ACME climate simulations and performance reproducibility in molecular dynamics workflows on HPC computing platforms.
Computational design of mixed chirality peptide macrocycles with internal symmetry
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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.
X-ray computed tomography analysis of pore deformation in IN718 made with directed energy deposition
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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.
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.
Fast, scalable and accurate finite-element based ab initio calculations using mixed precision computing: 46 PFLOPS simulation of a metallic dislocation system
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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.
Digital computer technique for setup and checkout of an analog computer
Computer program technique, called Analog Computer Check-Out Routine Digitally /ACCORD/, generates complete setup and checkout data for an analog computer. In addition, the correctness of the analog program implementation is validated.
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.
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.
Computational techniques for solar wind flows past terrestrial planets: Theory and computer programs
The interaction of the solar wind with terrestrial planets can be predicted using a computer program based on a single fluid, steady, dissipationless, magnetohydrodynamic model to calculate the axisymmetric, supersonic, super-Alfvenic solar wind flow past both magnetic and nonmagnetic planets. The actual calculations are implemented by an assemblage of computer codes organized into one program. These include finite difference codes which determine the gas-dynamic solution, together with a variety of special purpose output codes for determining and automatically plotting both flow field and magnetic field results. Comparisons are made with previous results, and results are presented for a number of solar wind flows. The computational programs developed are documented and are presented in a general user's manual which is included.
Interpretive computer simulator for the NASA Standard Spacecraft Computer-2 (NSSC-2)
An Interpretive Computer Simulator (ICS) for the NASA Standard Spacecraft Computer-II (NSSC-II) was developed as a code verification and testing tool for the Annular Suspension and Pointing System (ASPS) project. The simulator is written in the higher level language PASCAL and implented on the CDC CYBER series computer system. It is supported by a metal assembler, a linkage loader for the NSSC-II, and a utility library to meet the application requirements. The architectural design of the NSSC-II is that of an IBM System/360 (S/360) and supports all but four instructions of the S/360 standard instruction set. The structural design of the ICS is described with emphasis on the design differences between it and the NSSC-II hardware. The program flow is diagrammed, with the function of each procedure being defined; the instruction implementation is discussed in broad terms; and the instruction timings used in the ICS are listed. An example of the steps required to process an assembly level language program on the ICS is included. The example illustrates the control cards necessary to assemble, load, and execute assembly language code; the sample program to to be executed; the executable load module produced by the loader; and the resulting output produced by the ICS.
Computing the Fast Fourier Transform on a vector computer
Two algorithms are presented for performing a Fast Fourier Transform on a vector computer and are compared on the Control Data Corporation STAR-100. The relative merits of the two algorithms are shown to depend upon whether only a few or many independent transforms are desired. A theorem is proved which shows that a set of independent transforms can be computed by performing a partial transformation on a single vector. The results of this theorem also apply to nonvector machines and have reduced the average time per transform by a factor of two on the CDC 6600 computer.