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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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Computer vision research at Marshall Space Flight Center

Orbital docking, inspection, and sevicing are operations which have the potential for capability enhancement as well as cost reduction for space operations by the application of computer vision technology. Research at MSFC has been a natural outgrowth of orbital docking simulations for remote manually controlled vehicles such as the Teleoperator Retrieval System and the Orbital Maneuvering Vehicle (OMV). Baseline design of the OMV dictates teleoperator control from a ground station. This necessitates a high data-rate communication network and results in several seconds of time delay. Operational costs and vehicle control difficulties could be alleviated by an autonomous or semi-autonomous control system onboard the OMV which would be based on a computer vision system having capability to recognize video images in real time. A concept under development at MSFC with these attributes is based on syntactic pattern recognition. It uses tree graphs for rapid recognition of binary images of known orbiting target vehicles. This technique and others being investigated at MSFC will be evaluated in realistic conditions by the use of MSFC orbital docking simulators. Computer vision is also being applied at MSFC as part of the supporting development for Work Package One of Space Station Freedom.

Vinz, Frank L.↗
DOE OSTI2024

Solution of the Schrödinger equation for quasi-one-dimensional materials using helical waves

We formulate and implement a spectral method for solving the Schrödinger equation, as it applies to quasi-one-dimensional materials and structures. This allows for computation of the electronic structure of important technological materials such as nanotubes (of arbitrary chirality), nanowires, nanoribbons, chiral nanoassemblies, nanosprings and nanocoils, in an accurate, efficient and systematic manner. Our work is motivated by the observation that one of the most successful methods for carrying out electronic structure calculations of bulk/crystalline systems — the plane-wave method — is a spectral method based on eigenfunction expansion. Our scheme avoids computationally onerous approximations involving periodic supercells often employed in conventional plane-wave calculations of quasi-one-dimensional materials, and also overcomes several limitations of other discretization strategies, e.g., those based on finite differences and atomic orbitals. The basis functions in our method — called helical waves (or twisted waves) — are eigenfunctions of the Laplacian with symmetry adapted boundary conditions, and are expressible in terms of plane waves and Bessel functions in helical coordinates. We describe the setup of fast transforms to carry out discretization of the governing equations using our basis set, and the use of matrix-free iterative diagonalization to obtain the electronic eigenstates. Miscellaneous computational details, including the choice of eigensolvers, use of a preconditioning scheme, evaluation of oscillatory radial integrals and the imposition of a kinetic energy cutoff are discussed. We have implemented these strategies into a computational package called HelicES (Helical Electronic Structure). We demonstrate the utility of our method in carrying out systematic electronic structure calculations of various quasi-one-dimensional materials through numerous examples involving nanotubes, nanoribbons and nanowires. We also explore the convergence properties of our method, and assess its accuracy and computational efficiency by comparison against reference finite difference, transfer matrix method and plane-wave results. We anticipate that our method will find applications in computational nanomechanics and multiscale modeling, for carrying out transport calculations of interest to the field of semiconductor devices, and for the discovery of novel chiral phases of matter that are of relevance to the burgeoning quantum hardware industry.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1982

Computational fluid dynamics - The coming revolution

The development of aerodynamic theory is traced from the days of Aristotle to the present, with the next stage in computational fluid dynamics dependent on superspeed computers for flow calculations. Additional attention is given to the history of numerical methods inherent in writing computer codes applicable to viscous and inviscid analyses for complex configurations. The advent of the superconducting Josephson junction is noted to place configurational demands on computer design to avoid limitations imposed by the speed of light, and a Japanese projection of a computer capable of several hundred billion operations/sec is mentioned. The NASA Numerical Aerodynamic Simulator is described, showing capabilities of a billion operations/sec with a memory of 240 million words using existing technology. Near-term advances in fluid dynamics are discussed.

Graves, R. A., Jr.↗
NASA NTRS1988

Spacecraft applications of advanced global positioning system technology

This is the final report on the Texas Instruments Incorporated (TI) simulations study of Spacecraft Application of Advanced Global Positioning System (GPS) Technology. This work was conducted for the NASA Johnson Space Center (JSC) under contract NAS9-17781. GPS, in addition to its baselined capability as a highly accurate spacecraft navigation system, can provide traffic control, attitude control, structural control, and uniform time base. In Phase 1 of this program, another contractor investigated the potential of GPS in these four areas and compared GPS to other techniques. This contract was for the Phase 2 effort, to study the performance of GPS for these spacecraft applications through computer simulations. TI had previously developed simulation programs for GPS differential navigation and attitude measurement. These programs were adapted for these specific spacecraft applications. In addition, TI has extensive expertise in the design and production of advanced GPS receivers, including space-qualified GPS receivers. We have drawn on this background to augment the simulation results in the system level overview, which is Section 2 of this report.

Source record↗
DOE OSTI2017

Release Notes for Whisper-1.1

Whisper is a statistical analysis package developed in 2014 to support nuclear criticality safety (NCS) validation [1-3]. It uses the sensitivity profile data for an application as computed by MCNP6 [4-6] along with covariance files [7,8] for the nuclear data to determine a baseline upper-subcritical-limit (USL) for the application. Whisper version 1.0 was first developed and used at LANL in 2014 [3]. During 2015-2016, Whisper was updated to version 1.1 [9] and is to be included with the upcoming release of MCNP6.2. This document describes the Whisper-1.1 package that will be included with the MCNP6.2 release during 2017. Specific details are provided on the computer systems supported, the software quality assurance (SQA) procedures, installation, and testing. This document does not address other important topics, such as the importance of sensitivity-uncertainty (SU) methods to NCS validation, the theory underlying SU methodology, tutorials on the usage of MCNP-Whisper, practical approaches to using SU methodology to support and extend traditional validation, etc. There are over 50 documents included with Whisper-1.1 and available in the MCNP Reference Collection on the MCNP website (mcnp.lanl.gov) that address all of those topics and more. In this document, however, a complete bibliography of relevant MCNP-Whisper references is provided.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

DuoModel: Leveraging Reduced Model for Data Reduction and Re-Computation on HPC Storage

High-performance computing (HPC) applications generate large amounts of floating-point data that need to be stored and analyzed efficiently to extract the insights and advance knowledge discovery. With the growing disparities between compute and I/O, optimizing the storage stack alone may not suffice to cure the I/O problem. There has been a strong push in the HPC communities to perform data reduction before data is transmitted to storage in order to lower the I/O cost. However, as of now, neither lossless nor lossy compressors can achieve the adequate reduction ratio that is desired by applications. This paper proposes DuoModel, a new approach that leverages the similarity between the full and reduced application models, and further improve the data reduction ratio. DouModel further improves the compression ratio of state-of-the-art compressors via compressing the differences (termed as delta) between the data products of the two models. For data analytics, the high fidelity data can be re-computed by launching the reduced model and applying the compressed delta. Our evaluations confirm that DuoModel can further push the limit of data reduction while the high fidelity of data is maintained.

Luo, Huizhang (ORCID:0000000323920267)↗
DOE OSTI2021

Considerations regarding the Use of Computer Vision Machine Learning in Safety-Related or Risk-Significant Applications in Nuclear Power Plants

With the advancements made to date in the field of artificial intelligence (AI), significant potential exists to utilize AI capabilities for nuclear power plant (NPP) applications. AI can replicate human decision making and it is usually faster and more accurate than humans. For implementations that impact critical NPP applications (e.g., safety-related or non-safety systems that potentially affect overall plant risk), a deeper safety analysis of the AI methods is necessary. AI applied to NPP operations could resemble the use of digital I&C (DI&C) because such applications involve digital computer hardware and custom-designed software that input plant data, execute complex software algorithms, and output the results to a system or licensed human operator to potentially provoke an action. For AI methods to be compliant with current safety requirements for DI&C, AI compatibility must be evaluated, and AI-related gaps may exist that prevent the prompt deployment of AI in NPPs. This effort aims to evaluate how example AI technologies align with the DI&C safety framework, and discusses how they could be analyzed, modeled, tested, and validated in a manner similar to typical DI&C technologies. Because AI is a broad field that encompasses areas such as machine learning (ML), natural language processing, and computer vision, this research focused on a subset of methods categorized as the computer vision ML (CVML) methods. This report explores two CVML use cases, gauge reading and fire watch, considered relevant to the DI&C standards, as they could play a safety-critical role. For the gauge reading use case, a CVML-enabled technology that can read gauges at oblique angles is utilized. For the fire watch use case, a CVML-enabled technology is utilized that migrates fire watch from a manual (human) approach to automated fire detection. These use cases are mainly intended to give context to the CVML system discussion. This effort assumes the worst-case scenario, with the CVML system being used to replace a safety-related or risk-significant system, thus requiring evaluation. Evaluating CVML against most of the relevant safety requirements for DI&C yielded several CVML-specific considerations due to the uniqueness of its characteristics in comparison with typical DI&C systems. For example, CVML models often employ commonly used (open-source) datasets, and it is not always possible to determine the level of overlap among open-source datasets. Therefore, the independence of the developed CVML models when demonstrating diversity is questionable, therefore creating vulnerability to common cause failure (CCF). The design verification process is also impacted since the data overlap could result in overestimation of the software validation and verification (V&V) performance results. Section 2 of this report evaluates a list of the identified CVML-specific characteristics and discusses the resulting considerations and potential solutions in the context of each referenced requirement. A summation is provided in Section 3. This report is not to be used as a guideline. It was developed to identify and consider issues in the implementation of ML technologies used to augment activities that may have a bearing on plant operation. The report draws parallels to the use of DI&C technologies, for which many standards are available to guide their use in nuclear plant operation. It considers the technologies and some of the potential implications of their use in safety-related applications but is not intended to address regulatory or licensing related issues.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2002

Analysis of Atmospheric Aerosol Data Sets and Application of Radiative Transfer Models to Compute Aerosol Effects

This report is the final report for "Analysis of Atmospheric Aerosol Data Sets and Application of Radiative Transfer Models to Compute Aerosol Effects". It is a bibliographic compilation of 29 peer-reviewed publications (published, in press or submitted) produced under this Cooperative Agreement and 30 first-authored conference presentations. The tasks outlined in the various proposals are listed below with a brief comment as to the research performed. Copies of title/abstract pages of peer-reviewed publications are attached.

Schmid, Beat↗
DOE OSTI2025

MaTableGPT: GPT‐Based Table Data Extractor from Materials Science Literature

Abstract Efficiently extracting data from tables in the scientific literature is pivotal for building large‐scale databases. However, the tables reported in materials science papers exist in highly diverse forms; thus, rule‐based extractions are an ineffective approach. To overcome this challenge, the study presents MaTableGPT, which is a GPT‐based table data extractor from the materials science literature. MaTableGPT features key strategies of table data representation and table splitting for better GPT comprehension and filtering hallucinated information through follow‐up questions. When applied to a vast volume of water splitting catalysis literature, MaTableGPT achieves an extraction accuracy (total F1 score) of up to 96.8%. Through comprehensive evaluations of the GPT usage cost, labeling cost, and extraction accuracy for the learning methods of zero‐shot, few‐shot, and fine‐tuning, the study presents a Pareto‐front mapping where the few‐shot learning method is found to be the most balanced solution owing to both its high extraction accuracy (total F1 score >95%) and low cost (GPT usage cost of 5.97 US dollars and labeling cost of 10 I/O paired examples). The statistical analyses conducted on the database generated by MaTableGPT revealed valuable insights into the distribution of the overpotential and elemental utilization across the reported catalysts in the water splitting literature.

Yi, Gyeong Hoon [Computational Science Research Ce↗
DOE OSTI2017

Evaluating the networking characteristics of the Cray XC-40 Intel Knights Landing-based Cori supercomputer at NERSC

There are many potential issues associated with deploying the Intel Xeon Phi™ (code named Knights Landing [KNL]) manycore processor in a large-scale supercomputer. One in particular is the ability to fully utilize the high-speed communications network, given that the serial performance of a Xeon Phi TM core is a fraction of a Xeon®core. In this paper, we take a look at the trade-offs associated with allocating enough cores to fully utilize the Aries high-speed network versus cores dedicated to computation, e.g., the trade-off between MPI and OpenMP. In addition, we evaluate new features of Cray MPI in support of KNL, such as internode optimizations. We also evaluate one-sided programming models such as Unified Parallel C. We quantify the impact of the above trade-offs and features using a suite of National Energy Research Scientific Computing Center applications.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1975

Computer-aided analysis of Skylab scanner data for land use mapping, forestry and water resource applications

Skylab data were obtained over a mountainous test site containing a complex association of cover types and rugged topography. The application of computer-aided analysis techniques to the multispectral scanner data produced a number of significant results. Techniques were developed to digitally overlay topographic data (elevation, slope, and aspect) onto the S-192 MSS data to provide a method for increasing the effectiveness and accuracy of computer-aided analysis techniques for cover type mapping. The S-192 MSS data were analyzed using computer techniques developed at Laboratory for Applications of Remote Sensing (LARS), Purdue University. Land use maps, forest cover type maps, snow cover maps, and area tabulations were obtained and evaluated. These results compared very well with information obtained by conventional techniques. Analysis of the spectral characteristics of Skylab data has conclusively proven the value of the middle infrared portion of the spectrum (about 1.3-3.0 micrometers), a wavelength region not previously available in multispectral satellite data.

Hoffer, R. M.↗
DOE OSTI2017

User Manual for Whisper-1.1

Whisper is a statistical analysis package developed in 2014 to support nuclear criticality safety (NCS) validation [1-3]. It uses the sensitivity profile data for an application as computed by MCNP6 [4-6] along with covariance files [7,8] for the nuclear data to determine a baseline upper-subcritical-limit (USL) for the application. Whisper version 1.0 was first developed and used at LANL in 2014 [3]. During 2015- 2016, Whisper was updated to version 1.1 and is to be included with the upcoming release of MCNP6.2. This document describes the user input and options for running whisper-1.1, including 2 perl utility scripts that simplify ordinary NCS work, whisper_mcnp.pl and whisper_usl.pl. For many detailed references on the theory, applications, nuclear data & covariances, SQA, verification-validation, adjointbased methods for sensitivity-uncertainty analysis, and more – see the Whisper – NCS Validation section of the MCNP Reference Collection at mcnp.lanl.gov. There are currently over 50 Whisper reference documents available.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1976

Heat pipe radiators for space

Analysis of the data heat pipe radiator systems tested in both vacuum and ambient environments was continued. The systems included (1) a feasibility VCHP header heat-pipe panel, (2) the same panel reworked to eliminate the VCHP feature and referred to as the feasibility fluid header panel, and (3) an optimized flight-weight fluid header panel termed the 'prototype.' A description of freeze-thaw thermal vacuum tests conducted on the feasibility VCHP was included. In addition, the results of ambient tests made on the feasibility fluid header are presented, including a comparison with analytical results. A thermal model of a fluid header heat pipe radiator was constructed and a computer program written. The program was used to make a comparison of the VCHP and fluid-header concepts for both single and multiple panel applications. The computer program was also employed for a parametric study, including optimum feeder heat pipe spacing, of the prototype fluid header.

Sellers, J. P.↗
NASA NTRS1981

Iterative optical vector-matrix processors (survey of selected achievable operations)

An iterative optical vector-matrix multiplier with a microprocessor-controlled feedback loop capable of performing a wealth of diverse operations was described. A survey and description of many of its operations demonstrates the versatility and flexibility of this class of optical processor and its use in diverse applications. General operations described include: linear difference and differential equations, linear algebraic equations, matrix equations, matrix inversion, nonlinear matrix equations, deconvolution and eigenvalue and eigenvector computations. Engineering applications being addressed for these different operations and for the IOP are: adaptive phased-array radar, time-dependent system modeling, deconvolution and optimal control.

Casasent, D.↗
DOE OSTI2020

Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

From PeleC to PeleACC, to PeleC++

PeleC is an Exascale Computing Project application for simulating compressible combustion in complex geometries. It has been built on top of the popular AMReX library. In the beginning of the Exascale Computing Project, PeleC was focused on KNL. It uses a mixture of C++, C, and kernels written in Fortran to obtain performance by focusing on vectorization. Recently we have taken two approaches in deciding PeleC's future for obtaining performance on exascale GPU machines. In the first programming model, we decorated the Fortran kernels with OpenACC directives. This expedited our ability to run at large scales on Summit's GPUs, where we achieved a significant speedup over the CPUs on Summit. The second programming model involved rewriting the Fortran kernels in C++ and using AMReX's Kokkos-like lambda abstractions for running on the GPU. This resulted in similar speedups on Summit's GPUs over merely utilizing the CPUs. Both approaches involved AMReX's management of memory transfers between the device and host. In this work, we compare and contrast the benefits and pitfalls to both programming approaches regarding performance, performance portability, and productivity. We also discuss advantages we have found in taking the time to modernize our code and why have chosen a specific pathway to prepare our code for the future DOE exascale machines.

exascale computing↗