Search NASA⌕ Search

SEARCH · Search NASA

Results for “Computer Science - Operating Systems”

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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Red Storm Case Study

Sandia and Cray Inc. co-developed Red Storm, a distributed memory, massively paralleled highperformance supercomputer modeled on ASCI Redl, to run computer codes used for conducting materials science simulations for national security. Supercomputers have some of the fastest highperformance systems available and are used primarily for scientific and engineering work requiring exceedingly high-speed computations. Unlike conventional computers, supercomputers have large storage capacity; more than one central processing unit to rapidly retrieve stored data and program instructions; and input/output capability.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1984

Transportable, university-level educational programs in interactive information storage and retrieval systems

Pursuant to the specifications of a research contract entered into in December, 1983 with NASA, the Computer Science Departments of the University of Southwestern Louisiana and Southern University will be working jointly to address a variety of research and educational issues relating to the use, by non-computer professionals, of some of the largest and most sophiticated interactive information storage and retrieval systems available. Over the projected 6 to 8 year life of the project, in addition to NASA/RECON, the following systems will be examined: Lockheed DIALOG, DOE/RECON, DOD/DTIC, EPA/CSIN, and LLNL/TIS.

Dominick, Wayne D.↗
NASA NTRS2013

SPICE: A Geometry Information System Supporting Planetary Mapping, Remote Sensing and Data Mining

SPICE is an information system providing space scientists ready access to a wide assortment of space geometry useful in planning science observations and analyzing the instrument data returned therefrom. The system includes software used to compute many derived parameters such as altitude, LAT/LON and lighting angles, and software able to find when user-specified geometric conditions are obtained. While not a formal standard, it has achieved widespread use in the worldwide planetary science community

planetary science investigations↗
NASA NTRS2017

An Automaton Rover Enabling Long Duration In-Situ Science in Extreme Environments

The Automaton Rover for Extreme Environments (AREE) is a NASA Innovative Advanced Concept (NIAC) funded study focused on enabling long duration science on Venus by replacing vulnerable electronics with an entirely mechanical design. By utilizing high temperature alloys, the rover would survive on the surface of Venus for weeks if not months. The rover concept harvests wind energy using a turbine and stores it in a constant force spring. The mobility system would be guided by a mechanical computer and logic system, programed to carry out the mission. It would collect basic science data such as wind speed, temperature, and seismic events. Communicating the data back to Earth is the most challenging aspect of the system design with multiple options being explored in a trade: a simple electronic high temperature transponder, a retroreflector target or inscribing phonograph style records to be launched via a balloon to a high altitude drone capable of transmitting the data back to Earth. AREE is not only a new exciting in-situ rover concept, but also a paradigm shift to conducting in-situ science in extreme environments. Traditional extreme environment vehicles collect as many diverse data points as possible in the short period of time before system failure. AREE breaks that trend by exploring what can be done with only a few basic scientific measurements, but recorded over long periods of time. In addition to Venus, the concept can be useful in other extreme environments in the solar system including Mercury, Jupiter's radiation belts, the interiors of gas giants, the mantle of the Earth and volcanoes throughout the solar system.

Parness, Aaron↗
DOE OSTI2021

Learning from learning machines: improving the predictive power of energy-water-land nexus models with insights from complex measured and simulated data

Focal Area(s): Insights gleaned from complex data (observed and simulated) using AI; Predictive modeling through the use of AI techniques and AI-derived model components, including physics- and knowledge-informed models; Energy-water-land nexus and integrated energy systems – models of MultiSector dynamics. Science Challenge: Scientific communities are in need of tools for the computational integration of physics-based models, experimental data, and empirical/observational studies across a broad range of temporal and spatial scales to explore and meet EESSD grand challenges. We require AI algorithms for the discovery of process-drivers in the Earth-energy-human system and to link them with complex measured and simulated data. We aim to understand the energy-water-land nexus, particularly under extreme forcing scenarios and rare events, leveraging scale-aware AI process models, including probabilistic uncertainties, and benefiting from the emerging 5G-enabled landscape-scale sensing and edge computing capabilities. These models are needed to identify instabilities and tipping points that manifest extreme system behaviors with consequences for integrated energy systems and the environment. This work requires fundamental advances in uncertainty quantification, in particular, to identify and model unlikely but catastrophic outliers. Specifically, we need models that are interpretable to domain scientists and, essentially, explainable to public and private stake holders.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

Comparison of MSL RCS Jet Computations With Flow Visualization and Velocimetry

Numerical predictions of the Mars Science Laboratory (MSL) reaction control system (RCS) jets interacting with a Mach 10 hypersonic flow are compared to experimental nitric oxide (NO) planar laser-induced fluorescence (PLIF) data. The steady Reynolds Averaged Navier Stokes (RANS) equations using the Baldwin-Barth one-equation turbulence model were solved using the OVERFLOW code. The experimental PLIF data used for comparison consists of qualitative two-dimensional visualization images, qualitative reconstructed three-dimensional flow structures, and quantitative two-dimensional distributions of streamwise velocity. Through modeling of the PLIF signal equation, computational flow images (CFI) were produced and directly compared to the qualitative PLIF data. Post processing of the experimental and simulation data enabled the jet trajectory to be extracted for a more quantitative comparison. The two-dimensional velocity fields were reconstructed through interpolation of a series of single-component velocity profiles. Each distribution of single-component velocity was obtained using molecular tagging velocimetry (MTV). After validating the numerical model, the numerical solution was further examined to gain insight into hypersonic jet-in-crossflow interaction. Future NO-PLIF experiments are proposed based on this analysis.

Johansen, Craig T.↗
NASA NTRS1990

Improving a data-acquisition software system with abstract data type components

Abstract data types and object-oriented design are active research areas in computer science and software engineering. Much of the interest is aimed at new software development. Abstract data type packages developed for a discontinued software project were used to improve a real-time data-acquisition system under maintenance. The result saved effort and contributed to a significant improvement in the performance, maintainability, and reliability of the Goldstone Solar System Radar Data Acquisition System.

Howard, S. D.↗
DOE OSTI2023

Ultra-fast interpretable machine-learning potentials

Abstract All-atom dynamics simulations are an indispensable quantitative tool in physics, chemistry, and materials science, but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and predictive accuracy. To address this challenge, we combine effective two- and three-body potentials in a cubic B-spline basis with regularized linear regression to obtain machine-learning potentials that are physically interpretable, sufficiently accurate for applications, as fast as the fastest traditional empirical potentials, and two to four orders of magnitude faster than state-of-the-art machine-learning potentials. For data from empirical potentials, we demonstrate the exact retrieval of the potential. For data from density functional theory, the predicted energies, forces, and derived properties, including phonon spectra, elastic constants, and melting points, closely match those of the reference method. The introduced potentials might contribute towards accurate all-atom dynamics simulations of large atomistic systems over long-time scales.

36 MATERIALS SCIENCE↗
DOE OSTI2017

SymPLe 1131: A novel architectural solution for the realization of verifiable digital I and C systems and embedded digital devices - 387

One of the main reasons for the increasing popularity of FPGAs in embedded systems and digital I and C systems is that they provide many of the advantages from both hardware and software components. FPGAs provide configurability approaching that of SW, while at the same time delivering deterministic behavior of pure hardware solutions. However, with regard to Software Common Cause Failures (CCF), the use of FPGAs in digital I and C platforms should not be seen as plug-in remedy for reduction or elimination of CCF. To address CCF, we argue that it is not enough to manage the complexity of an I and C device or system, but we must also have the ability to constrain complexity (as needed) and rigorously reason about execution and device functionality. This paper presents the work done regarding an FPGA overlay architecture called SymPLe. SymPLe is designed to reduce the gap between software and hardware approaches for digital I and C systems and increases deterministic behavior and verifiability of the application, architecture and safety-critical system while reasoning about CCF. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Utilizing genome-scale models to optimize nutrient supply for sustained algal growth and lipid productivity

Nutrient availability is critical for growth of algae and other microbes used for generating valuable biochemical products. Determining the optimal levels of nutrient supplies to cultures can eliminate feeding of excess nutrients, lowering production costs and reducing nutrient pollution into the environment. With the advent of omics and bioinformatics methods, it is now possible to construct genome-scale models that accurately describe the metabolism of microorganisms. In this study, a genome-scale model of the green alga Chlorella vulgaris (iCZ946) was applied to predict feeding of multiple nutrients, including nitrate and glucose, under both autotrophic and heterotrophic conditions. The objective function was changed from optimizing growth to instead minimizing nitrate and glucose uptake rates, enabling predictions of feed rates for these nutrients. The metabolic model control (MMC) algorithm was validated for autotrophic growth, saving 18% nitrate while sustaining algal growth. Additionally, we obtained similar growth profiles by simultaneously controlling glucose and nitrate supplies under heterotrophic conditions for both high and low levels of glucose and nitrate. Finally, the nitrate supply was controlled in order to retain protein and chlorophyll synthesis, albeit at a lower rate, under nitrogen-limiting conditions. This model-driven cultivation strategy doubled the total volumetric yield of biomass, increased fatty acid methyl ester (FAME) yield by 61%, and enhanced lutein yield nearly 3 fold compared to nitrogen starvation. This study introduces a control methodology that integrates omics data and genome-scale models in order to optimize nutrient supplies based on the metabolic state of algal cells in different nutrient environments. This approach could transform bioprocessing control into a systems biology-based paradigm suitable for a wide range of species in order to limit nutrient inputs, reduce processing costs, and optimize biomanufacturing for the next generation of desirable biotechnology products.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Multiscale Methods for Accurate, Efficient, and Scale-Aware Models of the Earth System

The major goal of this project was to contribute improvements to the infrastructure of an Earth System Model in order to support research in the Multiscale Methods for Accurate, Efficient, and Scale-Aware models of the Earth System project. In support of this, the NCAR team accomplished two main tasks: improving input/output performance of the model and improving atmospheric model simulation quality. Improvement of the performance and scalability of data input and diagnostic output within the model required a new infrastructure which can efficiently handle the unstructured grids common in multiscale simulations. This allows for a more computationally efficient model, enabling more years of Earth System simulation. The quality of the model simulations was improved by reducing grid-point noise in the spectral element version of the Community Atmosphere Model (CAM-SE). This was achieved by running the physics of the model using grid-cell data on a finite-volume grid.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1984

The Pilot Land Data System: Report of the Program Planning Workshops

An advisory report to be used by NASA in developing a program plan for a Pilot Land Data System (PLDS) was developed. The purpose of the PLDS is to improve the ability of NASA and NASA sponsored researchers to conduct land-related research. The goal of the planning workshops was to provide and coordinate planning and concept development between the land related science and computer science disciplines, to discuss the architecture of the PLDs, requirements for information science technology, and system evaluation. The findings and recommendations of the Working Group are presented. The pilot program establishes a limited scale distributed information system to explore scientific, technical, and management approaches to satisfying the needs of the land science community. The PLDS paves the way for a land data system to improve data access, processing, transfer, and analysis, which land sciences information synthesis occurs on a scale not previously permitted because of limits to data assembly and access.

Source record↗
DOE OSTI2016

Software Build and Delivery Systems

This presentation deals with the hierarchy of software build and delivery systems. One of the goals is to maximize the success rate of new users and developers when first trying your software. First impressions are important. Early successes are important. This also reduces critical documentation costs. This is a presentation focused on computer science and goes into detail about code documentation.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1996

High Performance Input/Output for Parallel Computer Systems

The goal of our project is to study the I/O characteristics of parallel applications used in Earth Science data processing systems such as Regional Data Centers (RDCs) or EOSDIS. Our approach is to study the runtime behavior of typical programs and the effect of key parameters of the I/O subsystem both under simulation and with direct experimentation on parallel systems. Our three year activity has focused on two items: developing a test bed that facilitates experimentation with parallel I/O, and studying representative programs from the Earth science data processing application domain. The Parallel Virtual File System (PVFS) has been developed for use on a number of platforms including the Tiger Parallel Architecture Workbench (TPAW) simulator, The Intel Paragon, a cluster of DEC Alpha workstations, and the Beowulf system (at CESDIS). PVFS provides considerable flexibility in configuring I/O in a UNIX- like environment. Access to key performance parameters facilitates experimentation. We have studied several key applications fiom levels 1,2 and 3 of the typical RDC processing scenario including instrument calibration and navigation, image classification, and numerical modeling codes. We have also considered large-scale scientific database codes used to organize image data.

Ligon, W. B.↗
DOE OSTI2025

Privacy Preservation from High-Performance Computing to Autonomous Science [Industrial and Governmental Activities]

High-Performance Computing (HPC) and Leadership-Class Supercomputing are driving forces behind scientific advancements, enabling researchers to tackle complex challenges in physics, chemistry, biology, and engineering. These systems power vast simulations and data analyses, fueling discoveries in fields ranging from materials science to climate modeling. However, their use often involves processing sensitive data—such as proprietary industry simulations, biomedical records, and national security computations—posing significant privacy concerns. In conclusion, this issue is amplified in collaborative environments like Department of Energy (DOE) user facilities, where HPC resources are shared across institutions to foster innovation.

Kotevska, Olivera [Oak Ridge National Laboratory (↗
DOE OSTI2022

Pseudonymization at Scale: OLCF’s Summit Usage Data Case Study

The analysis of vast amounts of data and the processing of complex computational jobs have traditionally relied upon high performance computing (HPC) systems, which offer reliable and efficient management of large-scale computational and data resources. Understanding these analyses’ needs is paramount for designing solutions that can lead to better science, and similarly, understanding the characteristics of the user behavior on those systems is important for improving user experiences on HPC systems. A common approach to gathering data about user behavior is to extract workload characteristics from system log data available only to system administrators. Recently at Oak Ridge Leadership Computing Facility (OLCF), however, we unveiled user behavior about the Summit supercomputer by collecting data from a user’s point of view with ordinary Unix commands.In this paper, we discuss the process, challenges, and lessons learned while preparing this dataset for publication and submission to an open data challenge. The original dataset contains personal identifiable information (PII) about the users of OLCF which needed be masked prior to publication, and we determined that anonymization, which scrubs PII completely, destroyed too much of the structure of the data to be interesting for the data challenge. We instead chose to pseudonymize the dataset, which reduced the linkability of the dataset to the users’ identities. Pseudonymization is significantly more computationally expensive than anonymization, and the size of our dataset, which is approximately 175 million lines of raw text, necessitated the development of a parallelized workflow that could be reused on different HPC machines. We demonstrate the scaling behavior of the workflow on two leadership class HPC systems at OLCF, and we show that we were able to bring the overall makespan time from an impractical 20+ hours on a single node down to around 2 hours. As a result of this work, we release the entire pseudonymized dataset and make the workflows and source code publicly available.

Maheshwari, Ketan↗
NASA NTRS2002

Satellites as Shared Resources for Caribbean Climate and Health Studies

Remotely-sensed data and observations are providing powerful new tools for addressing climate and environment-related human health problems through increased capabilities for monitoring, risk mapping, and surveillance of parameters useful to such problems as vector-borne and infectious diseases, air and water quality, harmful algal blooms, UV (ultraviolet) radiation, contaminant and pathogen transport in air and water, and thermal stress. Remote sensing, geographic information systems (GIS), global positioning systems (GPS), improved computational capabilities, and interdisciplinary research between the Earth and health science communities are being combined in rich collaborative efforts resulting in more rapid problem-solving, early warning, and prevention in global health issues. Collaborative efforts among scientists from health and Earth sciences together with local decision-makers are enabling increased understanding of the relationships between changes in temperature, rainfall, wind, soil moisture, solar radiation, vegetation, and the patterns of extreme weather events and the occurrence and patterns of diseases (especially, infectious and vector-borne diseases) and other health problems. This increased understanding through improved information and data sharing, in turn, empowers local health and environmental officials to better predict health problems, take preventive measure, and improve response actions. This paper summarizes the remote sensing systems most useful for climate, environment and health studies of the Caribbean region and provides several examples of interdisciplinary research projects in the Caribbean currently using remote sensing technologies. These summaries include the use of remote sensing of algal blooms, pollution transport, coral reef monitoring, vectorborne disease studies, and potential health effects of African dust on Trinidad and Barbados.

Maynard, Nancy G.↗
DOE OSTI2019

Thin Film Synthesis of Semiconductors in the Mg–Sb–N Materials System

Nitrides feature many interesting properties, such as a wide range of bandgaps suitable for optoelectronic devices including light-emitting diodes (LEDs), and piezoelectric response used in microelectromechanical systems (MEMS). Nitrides are also significantly underexplored compared to oxides and other chemistries, with many being thermochemically metastable, sparking interest from a basic science point of view. This paper reports on experimental and computational exploration of the Mg-Sb-N material system, featuring both metastable materials and semiconducting properties. Using sputter deposition, we discovered a new Mg 2 SbN 3 nitride with a wurtzite-derived crystal structure, and synthesized the antimonide-nitride Mg 3 SbN with an antiperovskite crystal structure for the first time in thin film form. Theoretical calculations indicate that Mg 2 SbN 3 is metastable and has properties relevant to LEDs and MEMS, whereas Mg 3 SbN has a large dielectric constant (28ε o ) and low hole effective masses (0.9m o ), of interest for photovoltaic solar cell absorbers. The experimental solar-matched 1.3 eV optical absorption onset of the Mg 3 SbN antiperovskite agrees with the theoretical prediction (1.3 eV direct, 1.1 eV indirect), and with the measurements of room-temperature near-bandgap photoluminescence. Furthermore, these results make an important contribution towards understanding semiconductor properties and chemical trends in the Mg-Sb-N materials system, paving the way to future practical applications of these novel materials.

14 SOLAR ENERGY↗