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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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Ada to X-Window Bindings computer program developed to provide Ada programmers with complete interfaces to Xt Intrinsics and OSF Motif toolkits. Provides "Ada view" of some mostly C-language programming libraries. Package of software written in Ada and C languages.
CESDIS, the Center of Excellence in Space Data and Information Sciences was developed jointly by NASA, Universities Space Research Association (USRA), and the University of Maryland in 1988 to focus on the design of advanced computing techniques and data systems to support NASA Earth and space science research programs. CESDIS is operated by USRA under contract to NASA. The Director, Associate Director, Staff Scientists, and administrative staff are located on-site at NASA's Goddard Space Flight Center in Greenbelt, Maryland. The primary CESDIS mission is to increase the connection between computer science and engineering research programs at colleges and universities and NASA groups working with computer applications in Earth and space science. Research areas of primary interest at CESDIS include: 1) High performance computing, especially software design and performance evaluation for massively parallel machines; 2) Parallel input/output and data storage systems for high performance parallel computers; 3) Data base and intelligent data management systems for parallel computers; 4) Image processing; 5) Digital libraries; and 6) Data compression. CESDIS funds multiyear projects at U. S. universities and colleges. Proposals are accepted in response to calls for proposals and are selected on the basis of peer reviews. Funds are provided to support faculty and graduate students working at their home institutions. Project personnel visit Goddard during academic recess periods to attend workshops, present seminars, and collaborate with NASA scientists on research projects. Additionally, CESDIS takes on specific research tasks of shorter duration for computer science research requested by NASA Goddard scientists.
The Numerical Propulsion System Simulation (NPSS) is a full propulsion system simulation tool used by aerospace engineers to predict and analyze the aerothermodynamic behavior of commercial jet aircraft, military applications, and space transportation. The NPSS framework was developed to support aerospace, but other applications are already leveraging the initial capabilities, such as aviation safety, ground-based power, and alternative energy conversion devices such as fuel cells. By using the framework and developing the necessary components, future applications that NPSS could support include nuclear power, water treatment, biomedicine, chemical processing, and marine propulsion. NPSS will dramatically reduce the time, effort, and expense necessary to design and test jet engines. It accomplishes that by generating sophisticated computer simulations of an aerospace object or system, thus enabling engineers to "test" various design options without having to conduct costly, time-consuming real-life tests. The ultimate goal of NPSS is to create a numerical "test cell" that enables engineers to create complete engine simulations overnight on cost-effective computing platforms. Using NPSS, engine designers will be able to analyze different parts of the engine simultaneously, perform different types of analysis simultaneously (e.g., aerodynamic and structural), and perform analysis in a more efficient and less costly manner. NPSS will cut the development time of a new engine in half, from 10 years to 5 years. And NPSS will have a similar effect on the cost of development: new jet engines will cost about a billion dollars to develop rather than two billion. NPSS is also being applied to the development of space transportation technologies, and it is expected that similar efficiencies and cost savings will result. Advancements of NPSS in fiscal year 2001 included enhancing the NPSS Developer's Kit to easily integrate external components of varying fidelities, providing the initial Visual-Based Syntax (VBS) capability, and developing additional capabilities to support space transportation. NPSS was supported under NASA's High Performance Computing and Communications Program. Through the NASA/Industry Cooperative Effort agreement, NASA Glenn and its industry and Government partners are developing NPSS. The NPSS team consists of propulsion experts and software engineers from GE Aircraft Engines, Pratt & Whitney, The Boeing Company, Honeywell, Rolls-Royce Corporation, Williams International, Teledyne Continental Motors, Arnold Engineering Development Center, Wright Patterson Air Force Base, and the NASA Glenn Research Center. Glenn is leading the way in developing NPSS--a method for solving complex design problems that's faster, better, and cheaper.
A computer program has been developed to provide a common interface for all space mission data, and allows different types of data to be displayed in the same context. This software provides an infrastructure for representing any type of mission data.
ThreeDView is a computer program that enables high-performance interactive display of real-world scenes with annotations. ThreeDView was developed primarily as a component of the Science Activity Planner (SAP) software, wherein it is to be used to display annotated images of terrain acquired by exploratory robots on Mars and possibly other remote planets. The images can be generated from sets of multiple-texture image data in the Visible Scalable Terrain (ViSTa) format, which was described in "Format for Interchange and Display of 3D Terrain Data" (NPO-30600) NASA Tech Briefs, Vol. 28, No. 12 (December 2004), page 25. In ThreeDView, terrain data can be loaded rapidly, the geometric level of detail and texture resolution can be selected, false colors can be used to represent scientific data mapped onto terrain, and the user can select among navigation modes. ThreeDView consists largely of modular Java software components that can easily be reused and extended to produce new high-performance, application-specific software systems for displaying images of three-dimensional real-world scenes.
Future human missions to the Moon, Mars, and other destinations offer many new opportunities for exploration. But, astronaut time will always be limited and some work will not be feasible for humans to do manually. Robots, however, can complement human explorers, performing work autonomously or under remote supervision from Earth. Since 2004, the Intelligent Robotics Group has been working to make human-robot interaction efficient and effective for space exploration. A central focus of our research has been to develop and field test robots that benefit human exploration. Our approach is inspired by lessons learned from the Mars Exploration Rovers, as well as human spaceflight programs, including Apollo, the Space Shuttle, and the International Space Station. We conduct applied research in computer vision, geospatial data systems, human-robot interaction, planetary mapping and robot software. In planning for future exploration missions, architecture and study teams have made numerous assumptions about how crew can be telepresent on a planetary surface by remotely operating surface robots from space (i.e. from a flight vehicle or deep space habitat). These assumptions include estimates of technology maturity, existing technology gaps, and likely operational and functional risks. These assumptions, however, are not grounded by actual experimental data. Moreover, no crew-controlled surface telerobotic system has yet been fully tested, or rigorously validated, through flight testing. During Summer 2013, we conducted a series of tests to examine how astronauts in the International Space Station (ISS) can remotely operate a planetary rover across short time delays. The tests simulated portions of a proposed human-robotic Lunar Waypoint mission, in which astronauts in lunar orbit remotely operate a planetary rover on the lunar Farside to deploy a radio telescope array. We used these tests to obtain baseline-engineering data.
A research project is underway at NASA Glenn to produce computer software that can accurately predict ice growth for many meteorological conditions for any aircraft surface. This report will present results from the latest LEWICE release, version 3.5. This program differs from previous releases in its ability to model mixed phase and ice crystal conditions such as those encountered inside an engine. It also has expanded capability to use structured grids and a new capability to use results from unstructured grid flow solvers. An extensive comparison of the results in a quantifiable manner against the database of ice shapes that have been generated in the NASA Glenn Icing Research Tunnel (IRT) has also been performed. This paper will show the differences in ice shape between LEWICE 3.5 and experimental data. In addition, comparisons will be made between the lift and drag calculated on the ice shapes from experiment and those produced by LEWICE. This report will also provide a description of both programs. Quantitative geometric comparisons are shown for horn height, horn angle, icing limit, area and leading edge thickness. Quantitative comparisons of calculated lift and drag will also be shown. The results show that the predicted results are within the accuracy limits of the experimental data for the majority of cases.
Noise from aircraft in the airport vicinity is one of the leading aviation-induced environmental issues. The FAA developed the Integrated Noise Model (INM) and its replacement Aviation Environmental Design Tool (AEDT) software to assess noise impact resulting from all aviation activities. However, a software tool is needed that is simple to use for terminal route modification, quick and reasonably accurate for preliminary noise impact evaluation and flexible to be used for iterative design of optimal noise-abatement terminal routes. In this paper, we extend our previous work on developing a noise-abatement terminal approach route design tool, named AIRNOISE, to satisfy this criterion. First, software efficiency has been significantly increased by over tenfold using the C programming language instead of MATLAB. Moreover, a state-of-the-art high performance GPU-accelerated computing module is implemented that was tested to be hundreds time faster than the C implementation. Secondly, a Graphical User Interface (GUI) was developed allowing users to import current terminal approach routes and modify the routes interactively to design new terminal approach routes. The corresponding noise impacts are then calculated and displayed in the GUI in seconds. Finally, AIRNOISE was applied to Baltimore-Washington International Airport terminal approach route to demonstrate its usage.
At the Argonne Leadership Computing Facility, the process for standing up a new system is paired with The Early Science Program. This competitive program prepares a select group of applications science teams to use the new machine very readily by providing them staff support and early access to preproduction system hardware and software. As a result, this column describes three Theta Early Science projects: two science applications and one code development effort.
The number of NASA science missions per year is increasing from less than one to more than six. At the same time, individual mission budgets are smaller and cannot afford their own dedicated technology developments. In response to this, NASA has formed the X2000 Program. This program, which is divided into a set of subsequent "deliveries" will provide the basic avionics, power, communications, and software capability for future science missions. X2000 First Delivery, which will be completed in early 2001, will provide a full-functioned one MRAD tolerant flight computer, power switching electronics, a highly efficient radioisotope power source, and a transponder that provides high-level services at both 8.4 GHz and 32 GHz bands. The X2000 Second Delivery, which will be completed in the 2003 time frame, will enable complete spacecraft in the 10-50 kg class. All capabilities delivered by the X2000 program will be commercialized within the US and therefore will be available for others to use. Although the immediate customers for these technologies are deep space missions, most of the capabilities being delivered are generic in nature and will be equally applicable to Earth Observation missions.
The development of algorithms for machine learning and data analysis for the 3013 MIS corrosion surveillance program is a collaborative effort by SRNL, USC and GT. For corrosion detection, LCM image data is extracted from large binary files, with software written to convert the data to physical attributes (i.e. height, color and grayscale values; all as functions of a location in a plane projection). The user interface for the software permits selective downloading of binary data and interrogation of attributes. User input thresholds are used to flag attributes of interest. Machine learning algorithms, developed for this application, are used to determine whether the features are the result of corrosion. To address the fundamental mechanisms of corrosion, machine learning algorithms are being developed to derive interatomic potential force-fields from ab-initio DFT calculations. The goal is to apply molecular modeling on a large enough scale to guide the design of resistant materials.
The Automated Scheduling and Planning Environment (ASPEN) computer program has been updated to version 3.0. ASPEN as a whole (up to version 2.0) has been summarized, and selected aspects of ASPEN have been discussed in several previous NASA Tech Briefs articles. Restated briefly, ASPEN is a modular, reconfigurable, application software framework for solving batch problems that involve reasoning about time, activities, states, and resources. Applications of ASPEN can include planning spacecraft missions, scheduling of personnel, and managing supply chains, inventories, and production lines. ASPEN 3.0 can be customized for a wide range of applications and for a variety of computing environments that include various central processing units and randomaccess memories. Domain-specific reasoning modules (e.g., modules for determining orbits for spacecraft) can easily be plugged into ASPEN 3.0. Improvements over other, similar software that have been incorporated into ASPEN 3.0 include a provision for more expressive time-line values, new parsing capabilities afforded by an ASPEN language based on Extensible Markup Language, improved search capabilities, and improved interfaces to other, utility-type software (notably including MATLAB).
There is an increased interest in instrument-computing ecosystems (ICEs) that support science workflows empowered by AI-automated experiments and computations in diverse areas. In particular, electrochemistry ICEs are promising for accelerating the design and discovery of electrochemical systems for energy storage and conversion, by automating significant parts of workflows that combine synthesis and characterization experiments with computations. They require the integration of flow controllers, solvent containers, pumps, fraction collectors, and potentiostats, all connected to an electrochemical cell, as illustrated in Fig. 1. These are specialized instruments with custom software that is not originally designed for network integration. We developed network and software solutions for electrochemical workflows that adapt system and instrument settings in real-time for multiple rounds of experiments. In particular, we developed Python wrappers for Application Programming Interfaces (APIs) of instrument commands and Pyro client-server modules that enable them to be executed from remote computers. The entire workflow is orchestrated by a Jupyter notebook running on a remote computer.
Two categories of challenges confront the developer of computational spray models: those related to the computation and those related to the physics. Regarding the computation, the trend towards heterogeneous, multi- and many-core platforms will require considerable re-engineering of codes written for the current supercomputing platforms. Regarding the physics, accurate methods for transferring mass, momentum and energy from the dispersed phase onto the carrier fluid grid have so far eluded modelers. Significant challenges also lie at the intersection between these two categories. To be competitive, any physics model must be expressible in a parallel algorithm that performs well on evolving computer platforms. This work created an application based on a software architecture where the physics and software concerns are separated in a way that adds flexibility to both. The develop spray-tracking package includes an application programming interface (API) that abstracts away the platform-dependent parallelization concerns, enabling the scientific programmer to write serial code that the API resolves into parallel processes and threads of execution. The project also developed the infrastructure required to provide similar API s to other application. The API allow object-oriented Fortran applications direct interaction with Trilinos to support memory management of distributed objects in central processing units (CPU) and graphic processing units (GPU) nodes for applications using C++.
A set of software programs which simulates a (255,223) Reed-Solomon encoder/decoder pair is described. The transform decoder algorithm uses a modified Euclid algorithm, and closely follows the pipeline architecture proposed for the hardware decoder. Uncorrectable error patterns are detected by a simple test, and the inverse transform is computed by a finite field FFT. Numerical examples of the decoder operation are given for some test codewords, with and without errors. The use of the software package is briefly described.
The current tendency of flight control system designs is towards increased integration of applications and increased distribution of computational elements. The reliability analysis of such systems is difficult because subsystem interactions are increasingly interdependent. Researchers at NASA Langley Research Center have been working for several years to extend the capability of Markov modeling techniques to address these problems. This effort has been focused in the areas of increased model abstraction and increased computational capability. The reliability model generator (RMG) is a software tool that uses as input a graphical object-oriented block diagram of the system. RMG uses a failure-effects algorithm to produce the reliability model from the graphical description. The ASSURE software tool is a parallel processing program that uses the semi-Markov unreliability range evaluator (SURE) solution technique and the abstract semi-Markov specification interface to the SURE tool (ASSIST) modeling language. A failure modes-effects simulation is used by ASSURE. These tools were used to analyze a significant portion of a complex flight control system. The successful combination of the power of graphical representation, automated model generation, and parallel computation leads to the conclusion that distributed fault-tolerant system architectures can now be analyzed.
Computer simulation results are presented for intercepting final approach paths using various Microwave Landing System (MLS) path capture concepts. This study, conducted under the Advanced Transport Operating System (ATOPS) program, simulated these captures using the MD-80 aircraft as the study model. Several different capture concepts were investigated. Systems that could be retrofitted into existing aircraft with minimum hardware and software changes were considered. An enhanced ILS look-alike capture provided improved tracking performance over conventional ILS without using a full-up path computer. The other concepts used waypoint databases and path computers to provide smart captures. These captures included lateral path intercepts as well as vertical path control. Winds, turbulence, and MLS noise were included in the simulation. In all cases, acceptable tracking errors were obtained during transition to the final approach path.
In this paper, we report reimplementation of the core algorithms of relativistic coupled cluster theory aimed at modern heterogeneous high-performance computational infrastructures. The code is designed for parallel execution on many compute nodes with optional GPU coprocessing, accomplished via the new ExaTENSOR back end. The resulting ExaCorr module is primarily intended for calculations of molecules with one or more heavy elements, as relativistic effects on the electronic structure are included from the outset. In the current work, we thereby focus on exact two-component methods and demonstrate the accuracy and performance of the software. The module can be used as a stand-alone program requiring a set of molecular orbital coefficients as the starting point, but it is also interfaced to the DIRAC program that can be used to generate these. We therefore also briefly discuss an improvement of the parallel computing aspects of the relativistic self-consistent field algorithm of the DIRAC program.