Search NASA⌕ Search

SEARCH · Search NASA

Results for “computational capability”

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.

At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Bosonic field digitization for quantum computers

Quantum simulation of quantum field theory is a flagship application of quantum computers that promises to deliver capabilities beyond classical computing. The realization of quantum advantage will require methods that can accurately predict error scaling as a function of the resolution and parameters of the model and that can be implemented efficiently on quantum hardware. In this paper, we address the representation of lattice bosonic fields in a discretized field amplitude basis, develop methods to predict error scaling, and present efficient qubit implementation strategies. A low-energy subspace of the bosonic Hilbert space, defined by a boson occupation number cutoff, can be represented with exponentially good accuracy by a low-energy subspace of a finite-size Hilbert space. The finite representation construction and the associated errors are directly related to the accuracy of the Nyquist-Shannon sampling and the finite Fourier transforms of the boson number states in the field and the conjugate-field bases. We analyze the relation between the boson mass, the discretization parameters used for wave function sampling, and the finite representation size. Numerical simulations of small size Φ 4 problems demonstrate that the boson mass optimizing the sampling of the ground state wave function is a good approximation to the optimal boson mass yielding the minimum low-energy subspace size. However, we find that accurate sampling of general wave functions does not necessarily result in accurate representation. Finally, we develop methods for validating and adjusting the discretization parameters to achieve more accurate simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

2024 Atmospheric Radiation Measurement (ARM) Annual Report

This report provides an overview of the Atmospheric Radiation Measurement (ARM) user facility and a sample of achievements for fiscal year 2024 (FY2024). Strong collaborations between nine DOE national laboratories enable ARM to successfully operate in remote locations around the world. This unique partnership supports the DOE mission to provide for the energy security of the nation. Without the support of the following laboratories, ARM would not be the state-of-the-art facility that it is today. ARM is a multi-laboratory, U.S. Department of Energy (DOE) Office of Science user facility and a key contributor to national and international atmospheric and climate research efforts. ARM offers scientists cutting-edge, ground-based observatories, aerial observation capabilities, and high-performance computing. ARM’s capabilities have enabled more than 30 years of continuous measurements of cloud and aerosol properties and their effects on Earth’s energy balance.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2004

Benchmarking Memory Performance with the Data Cube Operator

Data movement across a computer memory hierarchy and across computational grids is known to be a limiting factor for applications processing large data sets. We use the Data Cube Operator on an Arithmetic Data Set, called ADC, to benchmark capabilities of computers and of computational grids to handle large distributed data sets. We present a prototype implementation of a parallel algorithm for computation of the operatol: The algorithm follows a known approach for computing views from the smallest parent. The ADC stresses all levels of grid memory and storage by producing some of 2d views of an Arithmetic Data Set of d-tuples described by a small number of integers. We control data intensity of the ADC by selecting the tuple parameters, the sizes of the views, and the number of realized views. Benchmarking results of memory performance of a number of computer architectures and of a small computational grid are presented.

Frumkin, Michael A.↗
NASA NTRS2013

Ultrascale Visualization of Climate Data

Fueled by exponential increases in the computational and storage capabilities of high-performance computing platforms, climate simulations are evolving toward higher numerical fidelity, complexity, volume, and dimensionality. These technological breakthroughs are coming at a time of exponential growth in climate data, with estimates of hundreds of exabytes by 2020. To meet the challenges and exploit the opportunities that such explosive growth affords, a consortium of four national laboratories, two universities, a government agency, and two private companies formed to explore the next wave in climate science. Working in close collaboration with domain experts, the Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT) project aims to provide high-level solutions to a variety of climate data analysis and visualization problems.

analysis tools↗
DOE OSTI2021

Editorial: Quantum Information and Quantum Computing for Chemical Systems

Quantum computing has emerged as an exciting inter-disciplinary research topic that cuts across the traditional fields of physics, computer science, and engineering. It is a revolutionary model of computation that has offered new insights into methods for modeling and simulation of chemical systems. Applications of quantum computing to chemistry have demonstrated rapid progress on both theoretical and experimental fronts. Past theoretical efforts have shown how to adapt quantum computation to a variety of problems including electronic structure and molecular dynamics. In addition, the development of quantum algorithms for quantum chemistry has been stimulated by the greater availability of more capable quantum computing devices. Advances in the number and quality of qubits continues to enable remarkable proof-of-concept demonstrations working towards a milestone of quantum computational advantage.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2012

High Fidelity Simulations of Plume Impingement to the International Space Station

With the retirement of the Space Shuttle, the United States now depends on recently developed commercial spacecraft to supply the International Space Station (ISS) with cargo. These new vehicles supplement ones from international partners including the Russian Progress, the European Autonomous Transfer Vehicle (ATV), and the Japanese H-II Transfer Vehicle (HTV). Furthermore, to carry crew to the ISS and supplement the capability currently provided exclusively by the Russian Soyuz, new designs and a refinement to a cargo vehicle design are in work. Many of these designs include features such as nozzle scarfing or simultaneous firing of multiple thrusters resulting in complex plumes. This results in a wide variety of complex plumes impinging upon the ISS. Therefore, to ensure safe "proximity operations" near the ISS, the need for accurate and efficient high fidelity simulation of plume impingement to the ISS is as high as ever. A capability combining computational fluid dynamics (CFD) and the Direct Simulation Monte Carlo (DSMC) techniques has been developed to properly model the large density variations encountered as the plume expands from the high pressure in the combustion chamber to the near vacuum conditions at the orbiting altitude of the ISS. Details of the computational tools employed by this method, including recent software enhancements and the best practices needed to achieve accurate simulations, are discussed. Several recent examples of the application of this high fidelity capability are presented. These examples highlight many of the real world, complex features of plume impingement that occur when "visiting vehicles" operate in the vicinity of the ISS.

Lumpkin, Forrest E., III↗
DOE OSTI2018

Advancing Science Through Computation at NDSU (Final Report)

The Center for Computationally Assisted Science and Technology (CCAST), a research unit within the Office for the Vice President for Research and Creative Activity, is in its build-out phase at North Dakota State University (NDSU). CCAST is developing high-performance computing (HPC) capabilities, designed to support existing HPC-oriented NDSU programs as well as to support its own research program which will initially concentrate, but not be limited to, application of theory and computation toward development of high-value functional materials and systems for renewable energy and energy efficiency. The following initial priorities for the Center were established: (1) reconstruct the NDSU HPC hardware and software infrastructure, (2) define CCAST research focus, (3) hire research and HPC support staff, (4) fit-out CCAST computer rooms to ensure continuation and growth of the Center’s HPC activities and, (5) support the NDSU existing thrust in chemo- and bio-informatics (as applied to the high-throughput combinatorial chemistry, biology, and genomics). These priorities are subject to change as the Center grows and evolves.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Hypothesis testing via AI: Generating physically interpretable models of scientific data with machine learning (Full Technical Report)

Deep learning has demonstrated an exceptional ability to solve complex tasks (an engineering success); however, it has done so at the expense of the ability to generate new knowledge (a scientific failure). We propose an alternative framework—entitled Deep Symbolic Regression (DSR)—in which artificial neural networks (NNs) rapidly generate hypotheses about physical relationships among inputs. This framework bypasses the need to interpret an NN altogether, while still leveraging the representational power of deep learning. The resulting models are tractable mathematical expressions, which are inherently and readily human interpretable and can provide insights into underlying physical phenomena. Further, we fold this methodology into the scientific process by allowing the scientist to directly integrate a priori knowledge and beliefs to accelerate learning. We demonstrate this methodology on symbolic regression—the problem of rediscovering underlying expressions describing a dataset—and achieve state-of-the-art performance across a wide variety of symbolic regression problems. Further, we generalize our DSR framework to apply to the more general class of symbolic optimization problems, in which one seeks to optimize a sequence of symbols or “tokens” under a black-box reward function. Examples of other symbolic optimization problems include neural architecture search and computational antibody design. Our generalized tool, Deep Symbolic Optimization (DSO), has been demonstrated on the task of learning symbolic control policies for reinforcement learning environments, and has been adopted as an enabling capability for computational antibody design.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1985

The slab geometry laser. II - Thermal effects in a finite slab

This paper presents two methods for calculating the thermally induced stress, focusing, and depolarization in a pumped zigzag-slab solid-state laser. A computer program capable of detailed calculations of thermal effects in the general case is described. An approximate analysis of slab thermal effects in many cases allows calculation of these effects without use of the computer model directly. The analysis predicts that slabs of square cross section can be designed to have low depolarization and thermal focusing compared to Nd:YAG laser rods.

Kane, T. J.↗
DOE OSTI2020

(U) Energy-Dependent Perturbations of Scattering Cross Sections Using the PERT Card in MCNP6.2

The PERT card is used for first- and second-order perturbations of material densities, isotope densities, and isotope cross sections in the MCNP6.2 Monte Carlo code. Using the PERT card for firstand second-order sensitivities is straightforward. Reference 2 only looked at density perturbations with no energy dependence. This report looks at the sensitivity of a response to scattering cross sections as a function of energy and finds that there is a bug in the PERT capability. The SENSMG multigroup neutron sensitivity code was recently given the capability of computing sensitivities to user-specified reactions whose cross sections are available in a user-supplied NJOY output file. SENSMG uses the PARTISN multigroup discrete ordinates code for the transport. The NJOY-reaction capability is similar to that of SUSD3D, which also uses PARTISN for the multigroup transport. The PERT bug was identified by comparing MCNP6.2 outputs with SENSMG outputs. The next section of this report describes the test problem. Section III presents the MCNP6.2 PERT bug and proposes a solution. Section IV is a summary and conclusions. The SENSMG and MCNP input files are listed in the appendix.

07 ISOTOPE AND RADIATION SOURCES↗
NASA NTRS2013

Advances in SPICE Support of Planetary Science

SPICE is the de facto international standard for determining the geometric conditions-parameters such as altitude, lighting angles, and LAT/LON coverage of an instrument footprint-pertaining to scientific observations acquired by instruments on board robotic spacecraft. This system, comprised of data and allied software, is used for planning science observations and for analyzing the data returned from those observations. Use of SPICE is not a NASA requirement but is recommended by NASA's Planetary Data System and by the International Planetary Data Alliance. Owing in part to its reliability, stability, portability and user support, the use of SPICE has spread to many national space agencies, including those of the U.S., Europe (ESA), Japan, Russia and India. SPICE has been in use since the Magellan mission to Venus and so has many well-known capabilities. But the NAIF Team responsible for implementing SPICE continues to add new features; this presentation describes a number of these.

computational capability↗
DOE OSTI2023

Predicting Sintering Window of Binder Jet Additively Manufactured Parts Using a Coupled Data Analytics and CALPHAD Approach

Batch-to-batch variation in powder compositions for binder jet additive manufacturing (BJAM) can significantly deter defining an “ideal” sintering window for a given alloy. One way to overcome the problem is by running sintering experiments at various temperatures for each batch of the powder. However, such an approach increases the time required to achieve large-scale production of parts. The predictive capabilities of computational thermodynamic tools like CALPHAD can be leveraged to overcome the challenge, especially for binder jet additive manufacturing, since the process occurs under near-equilibrium conditions. However, calculating the sintering window using CALPHAD can be computationally expensive, considering many possible feedstock compositions within “specification”. Here, we generate high throughput CALPHAD data for nickel-based superalloys to develop machine learning models to predict the sintering window rapidly. The predictive capability of the models has been validated using published results on BJAM of Inconel 718 and 625. Further, validated models are lightweight and can be deployed in an industrial setting to get sintering window in an accelerated manner.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The Virtual Blast Furnace - An Integrated High Performance Computing Modeling, Simulation, and Visualization Capability for Steel Manufacturing (Final Report)

Many manufacturing industries require substantial capital and utilize energy intensive processes that involve complex phenomena. One example of such an industry is the steel industry, which is the fourth largest energy consuming industry in the U.S. By harnessing the power of High-Performance Computing (HPC) to enhance current simulation and visualization methods in the steel industry, it should be possible to increase resolution and/or decrease time of these methods by a factor of 1000. In this way, information can be obtained in a time frame that is useful for making business and engineering decisions, optimizing manufacturing processes and, ultimately, improving the completeness of U.S. industries. For example, if coke usage in blast furnaces were optimized such that the average coke rate was reduced from 797 lb/net tonne of hot metal (NTHM) to 604 lb/NTHM, costs could be reduced by $894 million/year. Additionally, members of the steel industry need the flexibility to efficiently operate blast furnaces at a range of production rates in order to meet fluctuating market demands. Large scale parameter studies can be utilized to discover workable operating parameters at a range of production rates.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1993

A Lanczos algorithm for vibration, suckling and termal analysis

This paper reviews an eigensolver algorithm based on the Lanczos Method for vibration, buckling and thermal analysis. The original code was written for inclusion in the Computational Mechanics Testbed (COMET), a general purpose finite element code. A portable version of the Lanczos code that is optimized for high-performance supercomputers has been developed. Special features of the algorithm include the capability to compute rigid body modes, thermal modes and Lanczos vectors that are derived from the applied load vector. The latter is necessary when using the Lanczos vectors as reduced-basis vectors in transient structural response and transient heat conduction calculations. The modularity of the code allows the user the option of including the most up-to-date utilities, such as the equation solver best suited for the application. The algorithm is discussed in detail and results of several applications are presented. Timing results for a vibration application indicate that the Lanczos algorithm is twenty times faster than the subspace iteration method which has been extensively used in the past.

Bostic, Susan W.↗
NASA NTRS1995

Design geometry and design/off-design performance computer codes for compressors and turbines

This report summarizes some NASA Lewis (i.e., government owned) computer codes capable of being used for airbreathing propulsion system studies to determine the design geometry and to predict the design/off-design performance of compressors and turbines. These are not CFD codes; velocity-diagram energy and continuity computations are performed fore and aft of the blade rows using meanline, spanline, or streamline analyses. Losses are provided by empirical methods. Both axial-flow and radial-flow configurations are included.

Glassman, Arthur J.↗
NASA NTRS1983

Development INTERDATA 8/32 computer system

The capabilities of the Interdata 8/32 minicomputer were examined regarding data and word processing, editing, retrieval, and budgeting as well as data management demands of the user groups in the network. Based on four projected needs: (1) a hands on (open shop) computer for data analysis with large core and disc capability; (2) the expected requirements of the NASA data networks; (3) the need for intermittent large core capacity for theoretical modeling; (4) the ability to access data rapidly either directly from tape or from core onto hard copy, the system proved useful and adequate for the planned requirements.

Sonett, C. P.↗
DOE OSTI2023

Experiences Detecting Defective Hardware in Exascale Supercomputers

In May 2022, the newest supercomputer to top the TOP 500 list was Frontier at Oak Ridge National Laboratory, demonstrating the capability of computing more than 1.1 quintillion (1018) floating-point calculations every second. Driving this ground-breaking rate of computing is Frontier’s more than 37,000 graphics processing units (GPUs) and 9,408 central processing units (CPUs). In total, Frontier contains more than 60 million parts. At this scale, the smallest margin of error may generate hundreds of hardware errors across the system. These errors are capable of directly hindering world-class science performed on Frontier if not found. In this work, we describe and evaluate two strategies for finding hardware-level faults in Frontier’s 9,408 compute nodes. There are two strategies developed: the first uses the Slurm scheduler to scavenge available compute time to run the node screen, the second builds upon the lessons learned in the first strategy and enforces a weekly screen of each node. Using June 2023 as a case study, we find that the first scheduling strategy consumed more than ten times the resources as the second scheduling strategy, but successfully detected five hardware defects in Frontier. We summarize the lessons learned while developing and running a node screen on the world’s first exascale supercomputer.

Hagerty, Nick↗