SEARCH · Search NASA
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.
Computational aero-acoustics (CAA) is concerned with calculations of the aerodynamically generated sound source and its propagation. This paper presents a background to CAA, prediction of the time-dependent sound source using large-eddy simulations, and sound propagation to the far-field.
Employing quantum mechanical resources in computing, information processing, and networking opens the door to potential exponential advantages over classical counterparts. However, quantifying and realizing such advantages poses extensive scientific and engineering challenges. Department of Energy (DOE) investments have driven steady progress in addressing such challenges. Recently developed quantum algorithms offer asymptotic exponential advantages in speed or accuracy for fundamental scientific problems. These problems include simulating physical systems, solving systems of linear equations, differential equations, and optimization problems. Empirical demonstrations on nascent quantum hardware suggest better performance on contrived computational tasks than classical analogs. However, the requirements for a quantum computer or network to demonstrate an end-to-end rigorously quantifiable performance improvement over classical analogs remains a grand challenge, especially for problems of practical value. In particular, what will be required for quantum technology to ultimately exhibit scalable, rigorous, and transformative performance advantages for practical applications? In July 2023, DOE’s Advanced Scientific Computing Research program in the Office of Science convened the Workshop on Basic Research Needs in Quantum Computing and Networking, where major opportunities and grand challenges were identified. The following five priority research directions (PRDs) were identified as a result of the workshop.
Computer research at University of Maryland - computer programming and simulation in space science
Historically, scientific computing efforts have demonstrated the clear need for, and effective use of, supercomputing with traditional time-stepped simulations. Nevertheless, there are several areas in the mission spaces of the U.S. Department of Energy and other agencies waiting to tap advanced computing research using a different, discrete event style of modeling, simulation, and analysis. These span a wide spectrum of applications including energy grid resilience, urban planning and policy, transportation science, building technologies, emergency response and planning, environmental impact analysis, computational epidemiology, Internet communications, cyber security, and cyber-physical systems, to name only a few. Even within traditional scientific applications, the role of discrete event modes of execution is increasing in the form of new event-based mathematical solvers such as quantized state integration methods and discrete-continuous hybrid system solvers. Co-design of advanced supercomputing hardware systems is another area that exploits discrete event simulation at its core for effective analyses. Complex systems, entity behaviors and interconnections play a significant role in all these applications, which are mapped to large-scale models with discrete event formulations.
Various graduate research activities in the field of computer science are reported. Among the topics discussed are: (1) failure probabilities in multi-version software; (2) Gaussian Elimination on parallel computers; (3) three dimensional Poisson solvers on parallel/vector computers; (4) automated task decomposition for multiple robot arms; (5) multi-color incomplete cholesky conjugate gradient methods on the Cyber 205; and (6) parallel implementation of iterative methods for solving linear equations.
Symbolic programming digital computer techniques, hardware interfacing and system errors in simulation of lunar midcourse guidance and navigation systems
Advanced application of computers for biomedical research in manned space flight
Following a major assessment of NASA's computing technology needs, a new program of computer science research has been initiated by the Agency. The program includes work in concurrent processing, management of large scale scientific databases, software engineering, reliable computing, and artificial intelligence. The program is driven by applications requirements in computational fluid dynamics, image processing, sensor data management, real-time mission control and autonomous systems. It consists of university research, in-house NASA research, and NASA's Research Institute for Advanced Computer Science (RIACS) and Institute for Computer Applications in Science and Engineering (ICASE). The overall goal is to provide the technical foundation within NASA to exploit advancing computing technology in aerospace applications.
Researchers explore an approach to using general purpose parallel computers which involves mapping hardware resources onto computations instead of mapping computations onto hardware. Problems such as processor allocation, task scheduling and load balancing, which have traditionally proven to be challenging, change significantly under this approach and may become amenable to new attacks. Researchers describe the implementation of this approach used by the FFP Machine whose computation and communication resources are repeatedly partitioned into disjoint groups that match the needs of available tasks from moment to moment. Several consequences of this system are examined.
Over five years of research in Computational Fluid Dynamics and its applications are covered in this report. Using CFD as an established tool, aerodynamic optimization on parallel architectures is explored. The objective of this work is to provide better tools to vehicle designers. Submarine design requires accurate force and moment calculations in flow with thick boundary layers and large separated vortices. Low noise production is critical, so flow into the propulsor region must be predicted accurately. The High Speed Civil Transport (HSCT) has been the subject of recent work. This vehicle is to be a passenger vehicle with the capability of cutting overseas flight times by more than half. A successful design must surpass the performance of comparable planes. Fuel economy, other operational costs, environmental impact, and range must all be improved substantially. For all these reasons, improved design tools are required, and these tools must eventually integrate optimization, external aerodynamics, propulsion, structures, heat transfer and other disciplines.
Research in high energy physics (HEP) requires huge amounts of computing and storage, putting strong constraints on the code speed and resource usage. To meet these requirements, a compiled high-performance language is typically used; while for physicists, who focus on the application when developing the code, better research productivity pleads for a high-level programming language. A popular approach consists of combining Python, used for the high-level interface, and C++, used for the computing intensive part of the code. A more convenient and efficient approach would be to use a language that provides both high-level programming and high-performance. The Julia programming language, developed at MIT especially to allow the use of a single language in research activities, has followed this path. In this paper the applicability of using the Julia language for HEP research is explored, covering the different aspects that are important for HEP code development: runtime performance, handling of large projects, interface with legacy code, distributed computing, training, and ease of programming. The study shows that the HEP community would benefit from a large scale adoption of this programming language. The HEP-specific foundation libraries that would need to be consolidated are identified.
A workshop was held at Langley Research Center, November 2-5, 1981, to highlight ongoing computer science research at Langley and to identify additional areas of research based upon the computer user requirements. A panel discussion was held in each of nine application areas, and these are summarized in the proceedings. Slides presented by the invited speakers are also included. A survey of scientific, business, data reduction, and microprocessor computer users helped identify areas of focus for the workshop. Several areas of computer science which are of most concern to the Langley computer users were identified during the workshop discussions. These include graphics, distributed processing, programmer support systems and tools, database management, and numerical methods.
Research on the CHREC Space Processor (CSP) takes a multifaceted hybrid approach to embedded space computing. Working closely with the NASA Goddard SpaceCube team, researchers at the National Science Foundation (NSF) Center for High-Performance Reconfigurable Computing (CHREC) at the University of Florida and Brigham Young University are developing hybrid space computers that feature an innovative combination of three technologies: commercial-off-the-shelf (COTS) devices, radiation-hardened (RadHard) devices, and fault-tolerant computing. Modern COTS processors provide the utmost in performance and energy-efficiency but are susceptible to ionizing radiation in space, whereas RadHard processors are virtually immune to this radiation but are more expensive, larger, less energy-efficient, and generations behind in speed and functionality. By featuring COTS devices to perform the critical data processing, supported by simpler RadHard devices that monitor and manage the COTS devices, and augmented with novel uses of fault-tolerant hardware, software, information, and networking within and between COTS devices, the resulting system can maximize performance and reliability while minimizing energy consumption and cost. NASA Goddard has adopted the CSP concept and technology with plans underway to feature flight-ready CSP boards on two upcoming space missions.
NASA Ames Research Center is one of NASA s oldest centers, having started out as part of the National Advisory Committee on Aeronautics, (NACA). The site, about 40 miles south of San Francisco, still houses many wind tunnels and other aviation related departments. In recent years, with the growing realization that space exploration is heavily dependent on computing and data analysis, its focus has turned more towards Information Technology. The Computational Sciences Division has expanded rapidly as a result. In this article, I will give a brief overview of some of the past and present projects with a Bayesian content. Much more than is described here goes on with the Division. The web pages at http://ic.arc. nasa.gov give more information on these, and the other Division projects.
Research performed over the past 10 years in engineering data base management and parallel computing is discussed, and certain opportunities for research toward the next generation of structural analysis capability are proposed. Particular attention is given to data base management associated with the IPAD project and parallel processing associated with the Finite Element Machine project, both sponsored by NASA, and a near term strategy for a distributed structural analysis capability based on relational data base management software and parallel computers for a future structural analysis system.
This grant addresses the topic of research on computer systems benchmarking and is more generally concerned with performance issues in computer systems. This report reviews work in those areas during the period of NASA support under this grant. The bulk of the work performed concerned benchmarking and analysis of CPUs, compilers, caches, and benchmark programs. The first part of this work concerned the issue of benchmark performance prediction. A new approach to benchmarking and machine characterization was reported, using a machine characterizer that measures the performance of a given system in terms of a Fortran abstract machine. Another report focused on analyzing compiler performance. The performance impact of optimization in the context of our methodology for CPU performance characterization was based on the abstract machine model. Benchmark programs are analyzed in another paper. A machine-independent model of program execution was developed to characterize both machine performance and program execution. By merging these machine and program characterizations, execution time can be estimated for arbitrary machine/program combinations. The work was continued into the domain of parallel and vector machines, including the issue of caches in vector processors and multiprocessors. All of the afore-mentioned accomplishments are more specifically summarized in this report, as well as those smaller in magnitude supported by this grant.
It is essential to Sandia National Laboratory’s continued success in scientific and technological advances and mission delivery to embrace a hybrid workforce culture under which current and future employees can thrive. This report focuses on the findings of the Hybrid Work Team for the Center for Computing Research, which met weekly from March to June 2023 and conducted a survey across the Center at Sandia. Conclusions in this report are drawn from the 9 authors of this report, which comprises the Hybrid Work Team, and 15 responses to a center-wide survey, as well as numerous conversations with colleagues. A major finding was widespread dissatisfaction with the quantity, execution, and tooling surrounding formal meetings with remote participants. While there was consensus that remote work enables people to produce high quality individual and technical work, there was also consensus that there was widespread social disconnect, with particular concern about hires that were made after the onset of the Covid-19 pandemic. There were many concerns about tooling and policy to facilitate remote collaboration both within Sandia and with its external collaborators. This report includes recommendations for mitigating these problems. For problems for which obvious recommendations cannot be made, ideas of what a successful solution might look like are presented.
The mesoscale analysis and space sensor (MASS) data management and analysis system on the research computer system is presented. The MASS data base management and analysis system was implemented on the research computer system which provides a wide range of capabilities for processing and displaying large volumes of conventional and satellite derived meteorological data. The research computer system consists of three primary computers (HP-1000F, Harris/6, and Perkin-Elmer 3250), each of which performs a specific function according to its unique capabilities. The overall tasks performed concerning the software, data base management and display capabilities of the research computer system in terms of providing a very effective interactive research tool for the digital processing of mesoscale analysis and space sensor data is described.