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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 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Calculations of hot gas ingestion for a STOVL aircraft model

Hot gas ingestion problems for STOVL (Short Take-Off, Vertical Landing) aircraft are typically approached with empirical methods and experience. In this study, the hot gas environment around a STOVL aircraft was modeled as multiple jets in crossflow with inlet suction. The flow field was calculated with a Navier-Stokes, Reynolds-averaged, turbulent, 3D CFD code using a multigrid technique. A simple model of a STOVL aircraft with four choked jets at 1000 K was studied at various heights, headwind speeds, and thrust splay angles in a modest parametric study. Scientific visualization of the computed flow field shows a pair of vortices in front of the inlet. This and other qualitative aspects of the flow field agree well with experimental data.

Fricker, D. M.↗
NASA NTRS1991

6.7 radio sky mapping from satellites at very low frequencies

Wave Distribution Function (WDF) analysis is a procedure for making sky maps of the sources of natural electromagnetic waves in space plasmas, given local measurements of some or all of the three magnetic and three electric field components. The work that still needs to be done on this subject includes solving basic methodological problems, translating the solution into efficient algorithms, and embodying the algorithms in computer software. One important scientific use of WDF analysis is to identify the mode of origin of plasmaspheric hiss. Some of the data from the Japanese satellite Akebono (EXOS D) are likely to be suitable for this purpose.

Storey, L. R. O.↗
NASA NTRS1990

Virtual workstations and telepresence interfaces: Design accommodations and prototypes for Space Station Freedom evolution

An advanced human-system interface is being developed for evolutionary Space Station Freedom as part of the NASA Office of Space Station (OSS) Advanced Development Program. The human-system interface is based on body-pointed display and control devices. The project will identify and document the design accommodations ('hooks and scars') required to support virtual workstations and telepresence interfaces, and prototype interface systems will be built, evaluated, and refined. The project is a joint enterprise of Marquette University, Astronautics Corporation of America (ACA), and NASA's ARC. The project team is working with NASA's JSC and McDonnell Douglas Astronautics Company (the Work Package contractor) to ensure that the project is consistent with space station user requirements and program constraints. Documentation describing design accommodations and tradeoffs will be provided to OSS, JSC, and McDonnell Douglas, and prototype interface devices will be delivered to ARC and JSC. ACA intends to commercialize derivatives of the interface for use with computer systems developed for scientific visualization and system simulation.

Mcgreevy, Michael W.↗
NASA NTRS1993

VPU(tm) demonstration mission vicinity processor unit(tm) overview

Evolving Technologies Corporation is designing a new type of computer called a Vicinity Processor Unit (VPU). The VPU is designed to monitor and control payloads using an operating system, control process and software environment that specifically takes into account the time delay associated with remotely controlling and monitoring a group of payloads in real time. This is a fundamentally different way of computing which has vast scientific implications for the design and implementation of data processing systems for future space flight missions. As a demonstration mission, the VPU is expected to be flown aboard the Space Shuttle (Payload Identification No. G-700) as a part of NASA's Small Self-Contained Payloads (SSCP) Program. We believe this mission can greatly facilitate the development and validation of technology which may prove to be mission critical for many future space flight missions.

Abdulezer, Loren↗
NASA NTRS1998

Investigation of Coronal Large Scale Structures Utilizing Spartan 201 Data

Two telescopes aboard Spartan 201, a small satellite has been launched from the Space Shuttles, on April 8th, 1993, September 8th, 1994, September 7th, 1995 and November 20th, 1997. The main objective of the mission was to answer some of the most fundamental unanswered questions of solar physics-What accelerates the solar wind and what heats the corona? The two telescopes are 1) Ultraviolet Coronal Spectrometer (UVCS) provided by the Smithsonian Astrophysical Observatory which uses ultraviolet emissions from neutral hydrogen and ions in the corona to determine velocities of the coronal plasma within the solar wind source region, and the temperature and density distributions of protons and 2) White Light Coronagraph (WLC) provided by NASA's Goddard Space Flight Center which measures visible light to determine the density distribution of coronal electrons within the same region. The PI has had the primary responsibility in the development and application of computer codes necessary for scientific data analysis activities, end instrument calibration for the white-light coronagraph for the entire Spartan mission. The PI was responsible for the science output from the WLC instrument. PI has also been involved in the investigation of coronal density distributions in large-scale structures by use of numerical models which are (mathematically) sufficient to reproduce the details of the observed brightness and polarized brightness distributions found in SPARTAN 201 data.

Guhathakurta, Madhulika↗
NASA NTRS2001

Design of Unstructured Adaptive (UA) NAS Parallel Benchmark Featuring Irregular, Dynamic Memory Accesses

We describe the design of a new method for the measurement of the performance of modern computer systems when solving scientific problems featuring irregular, dynamic memory accesses. The method involves the solution of a stylized heat transfer problem on an unstructured, adaptive grid. A Spectral Element Method (SEM) with an adaptive, nonconforming mesh is selected to discretize the transport equation. The relatively high order of the SEM lowers the fraction of wall clock time spent on inter-processor communication, which eases the load balancing task and allows us to concentrate on the memory accesses. The benchmark is designed to be three-dimensional. Parallelization and load balance issues of a reference implementation will be described in detail in future reports.

Feng, Hui-Yu↗
NASA NTRS2004

Unstructured Adaptive (UA) NAS Parallel Benchmark

We present a complete specification of a new benchmark for measuring the performance of modern computer systems when solving scientific problems featuring irregular, dynamic memory accesses. It complements the existing NAS Parallel Benchmark suite. The benchmark involves the solution of a stylized heat transfer problem in a cubic domain, discretized on an adaptively refined, unstructured mesh.

Feng, Huiyu↗
NASA NTRS2004

Parallel 3D Mortar Element Method for Adaptive Nonconforming Meshes

High order methods are frequently used in computational simulation for their high accuracy. An efficient way to avoid unnecessary computation in smooth regions of the solution is to use adaptive meshes which employ fine grids only in areas where they are needed. Nonconforming spectral elements allow the grid to be flexibly adjusted to satisfy the computational accuracy requirements. The method is suitable for computational simulations of unsteady problems with very disparate length scales or unsteady moving features, such as heat transfer, fluid dynamics or flame combustion. In this work, we select the Mark Element Method (MEM) to handle the non-conforming interfaces between elements. A new technique is introduced to efficiently implement MEM in 3-D nonconforming meshes. By introducing an "intermediate mortar", the proposed method decomposes the projection between 3-D elements and mortars into two steps. In each step, projection matrices derived in 2-D are used. The two-step method avoids explicitly forming/deriving large projection matrices for 3-D meshes, and also helps to simplify the implementation. This new technique can be used for both h- and p-type adaptation. This method is applied to an unsteady 3-D moving heat source problem. With our new MEM implementation, mesh adaptation is able to efficiently refine the grid near the heat source and coarsen the grid once the heat source passes. The savings in computational work resulting from the dynamic mesh adaptation is demonstrated by the reduction of the the number of elements used and CPU time spent. MEM and mesh adaptation, respectively, bring irregularity and dynamics to the computer memory access pattern. Hence, they provide a good way to gauge the performance of computer systems when running scientific applications whose memory access patterns are irregular and unpredictable. We select a 3-D moving heat source problem as the Unstructured Adaptive (UA) grid benchmark, a new component of the NAS Parallel Benchmarks (NPB). In this paper, we present some interesting performance results of ow OpenMP parallel implementation on different architectures such as the SGI Origin2000, SGI Altix, and Cray MTA-2.

Feng, Huiyu↗
NASA NTRS2012

Algorithm for Compressing Time-Series Data

An algorithm based on Chebyshev polynomials effects lossy compression of time-series data or other one-dimensional data streams (e.g., spectral data) that are arranged in blocks for sequential transmission. The algorithm was developed for use in transmitting data from spacecraft scientific instruments to Earth stations. In spite of its lossy nature, the algorithm preserves the information needed for scientific analysis. The algorithm is computationally simple, yet compresses data streams by factors much greater than two. The algorithm is not restricted to spacecraft or scientific uses: it is applicable to time-series data in general. The algorithm can also be applied to general multidimensional data that have been converted to time-series data, a typical example being image data acquired by raster scanning. However, unlike most prior image-data-compression algorithms, this algorithm neither depends on nor exploits the two-dimensional spatial correlations that are generally present in images. In order to understand the essence of this compression algorithm, it is necessary to understand that the net effect of this algorithm and the associated decompression algorithm is to approximate the original stream of data as a sequence of finite series of Chebyshev polynomials. For the purpose of this algorithm, a block of data or interval of time for which a Chebyshev polynomial series is fitted to the original data is denoted a fitting interval. Chebyshev approximation has two properties that make it particularly effective for compressing serial data streams with minimal loss of scientific information: The errors associated with a Chebyshev approximation are nearly uniformly distributed over the fitting interval (this is known in the art as the "equal error property"); and the maximum deviations of the fitted Chebyshev polynomial from the original data have the smallest possible values (this is known in the art as the "min-max property").

Hawkins, S. Edward, III↗
NASA NTRS2018

Climate Lecture 20: The Educational Global Climate Model (EdGCM)

Climate change will profoundly impact Earth's environmental health as well as the world's economic and geopolitical landscape over the coming decades. The impacts of climate change are, in fact, already beginning to be experienced and have the potential to affect every living plant and animal on Earth within decades. Given this reality, every citizen of this planet should have the right to knowledge about the Earth's climate system and have the option to adapt to, or help mitigate the profound changes that are coming. In addition, a portion of the workforce needs to be capable of interpreting and analyzing climate information because, since the impacts of climate change will be widespread, pervasive, and continue to change over time, more professions will be interacting with climate data. We are already at, or past, the point where educators and their students require access to the scientific and technological resources - computer models, data, and visualization tools - that scientists use daily in the study of climate change. Although scientists use many methods to study Earth's climate system, global climate models (GCMs) have become the primary tools for exploring the complex interactions between components of the entire system: atmosphere, oceans, and land. GCMs are used to make projections of future climate change, to simulate climates of the past, and even to help scientists look for life on other planets. Like any model, a GCM can help people evaluate actions before they are taken. Like Business Intelligence software, they are Climate Intelligence tools. Unfortunately, GCMs are black boxes to most people. A previous chapter in this book by Gary Russell, entitled Building a Climate Model, is one example of the growing body of literature aimed at the general public describing the inner workings of global climate models. This literature goes a long way toward explaining climate model fundamentals. However, it will not be enough to alleviate their black-box nature unless people are afforded hands-on, authentic learning experiences as well.

Chandler, Mark A.↗
DOE OSTI2017

Hybrid Parallel Contour Trees, Version 1.0

A common operation in scientific visualization is to compute and render a contour of a data set. Given a function of the form f : R^d -> R, a level set is defined as an inverse image f^-1(h) for an isovalue h, and a contour is a single connected component of a level set. The Reeb graph can then be defined to be the result of contracting each contour to a single point, and is well defined for Euclidean spaces or for general manifolds. For simple domains, the graph is guaranteed to be a tree, and is called the contour tree. Analysis can then be performed on the contour tree in order to identify isovalues of particular interest, based on various metrics, and render the corresponding contours, without having to know such isovalues a priori. This code is intended to be the first data-parallel algorithm for computing contour trees. Our implementation will use the portable data-parallel primitives provided by Nvidia’s Thrust library, allowing us to compile our same code for both GPUs and multi-core CPUs. Native OpenMP and purely serial versions of the code will likely also be included. It will also be extended to provide a hybrid data-parallel / distributed algorithm, allowing scaling beyond a single GPU or CPU.

Sewell, Christopher↗
DOE OSTI2018

Deep Learning for Scientific Discovery

Deep learning has positively impacted fields were perceptive tasks are paramount, such as computer vision and natural language processing. Tremendous strides have also been made in deep reinforcement learning towards robotics and gaming. Given the success in these fields, it is apparent that deep learning may also accelerate scientific discovery. Herein, we discuss several topic areas where modern representation learning is driving innovation. This article presents a) learning non-linear models from data, b) learning scientific laws from models, c) deep learning on graphs, and d) approaches to scalable deep learning.

deep learning, scientific computing, koopman, mate↗
DOE OSTI2023

symPACK: A GPU-Capable Fan-Out Sparse Cholesky Solver

Sparse symmetric positive definite systems of equations are ubiquitous in scientific workloads and applications. Parallel sparse Cholesky factorization is the method of choice for solving such linear systems. Therefore, the development of parallel sparse Cholesky codes that can efficiently run on today’s large-scale heterogeneous distributed-memory platforms is of vital importance. Modern supercomputers offer nodes that contain a mix of CPUs and GPUs. To fully utilize the computing power of these nodes, scientific codes must be adapted to offload expensive computations to GPUs. We present symPACK, a GPU-capable parallel sparse Cholesky solver that uses one-sided communication primitives and remote procedure calls provided by the UPC++ library. We also utilize the UPC++ "memory kinds" feature to enable efficient communication of GPU-resident data. We show that on a number of large problems, symPACK outperforms comparable state-of-the-art GPU-capable Cholesky factorization codes by up to 14x on the NERSC Perlmutter supercomputer.

Bellavita, Julian↗
DOE OSTI2019

SUNDIALS Multiphysics+MPIManyVector Performance Testing

In this report we document performance test results on a SUNDIALS-based multiphysics demonstration application. We aim to assess the large-scale parallel performance of new capabilities that have been added to the SUNDIALS suite of time integrators and nonlinear solvers in recent years under funding from both the Exascale Computing Project (ECP) and the Scientific Discovery through Advanced Scientific (SciDAC) program, specifically: 1. SUNDIALS’ new MPIManyVector module, that allows extreme flexibility in how a solution "vector" is staged on computational resources. 2. ARKode’s new multirate integration module, MRIStep, allowing high-order accurate calculations that subcycle "fast" processes within "slow" ones. 3. SUNDIALS’ new flexible linear solver interfaces, that allow streamlined specification of problem-specific linear solvers. 4. SUNDIALS’ new N_Vector additions of "fused" vector operations (to increase arithmetic intensity) and separation of reduction operations into "local" and "global" versions (to reduce latency by combining multiple reductions into a single MPI_Allreduce call). We anticipate that subsequent reports will extend this work to investigate a variety of other new features, including SUNDIALS’ generic SUNNonlinearSolver interface and accelerator-enabled N Vector modules, and upcoming MRIStep extensions to support custom "fast" integrators (that leverage problem structure) and IMEX integration of the "slow" time scale (to add diffusion).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

The Road to Useful Quantum Computers

Building a useful quantum computer is a grand science and engineering challenge, currently pursued intensely by teams around the world. In the 1980s, Richard Feynman and Yuri Manin observed independently that computers based on quantum mechanics might enable better simulations of quantum phenomena. Their vision remained an intellectual curiosity until Peter Shor published his famous quantum algorithm for integer factoring, and shortly thereafter a proof that errors in quantum computations can be corrected. Since then, quantum computing R&D has progressed rapidly, from small-scale experiments in university physics laboratories to well-funded industrial efforts and prototypes. Hype notwithstanding, quantum computers have yet to solve scientifically or practically important problems -- a target often called quantum utility. In this article, we describe the capabilities of contemporary quantum computers, compare them to the requirements of quantum utility, and illustrate how to track progress from today to utility. We highlight key science and engineering challenges on the road to quantum utility, touching on relevant aspects of our own research.

Emerging Technologies (cs.ET)↗
DOE OSTI2022

Ion Coulomb Crystals in Storage Rings for Quantum Information Science

Quantum information science is a growing field that promises to take computing into a new age of higher performance and larger scale computing as well as being capable of solving problems classical computers are incapable of solving. The outstanding issue in practical quantum computing today is scaling up the system while maintaining interconnectivity of the qubits and low error rates in qubit operations to be able to implement error correction and fault-tolerant operations. Trapped ion qubits offer long coherence times that allow error correction. However, error correction algorithms require large numbers of qubits to work properly. We can potentially create many thousands (or more) of qubits with long coherence states in a storage ring. For example, a circular radio-frequency quadrupole, which acts as a large circular ion trap and could enable larger scale quantum computing. Such a Storage Ring Quantum Computer (SRQC) would be a scalable and fault tolerant quantum information system, composed of qubits with very long coherence lifetimes. With computing demands potentially outpacing the supply of high-performance systems, quantum computing could bring innovation and scientific advances to particle physics and other DOE supported programs. Increased support of R$\&$D in large scale ion trap quantum computers would allow the timely exploration of this exciting new scalable quantum computer. The R$\&$D program could start immediately at existing facilities and would include the design and construction of a prototype SRQC. We invite feedback from and collaboration with the particle physics and quantum information science communities.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Potential Applications of Quantum Computing at Los Alamos National Laboratory, v0.3.0

Since the scientific revolution in the 16th and 17th centuries, the process of scientific discovery has followed an iterative feedback process of observation, hypothesis development and testing with physical experiments, which is widely referred to as the scientific method. This process remained largely unchanged until the middle of the 20th century, when the emergence of digital computers empowered scientist to build and inspect detailed simulations of physical phenomena. Over the last century, computational tools have transformed modern approaches to scientific discovery by enabling fast and affordable hypothesis testing before physical experiments are conducted, shown in Figure 1-1. Some notable examples include: global climate forecasts to understand how the environment may change over decades [130]; modeling the behavior of plasma to design fusion reactors [59]; and understanding the behavior of molecules in biological processes [161, 223].

36 MATERIALS SCIENCE↗