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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

SANs and Large Scale Data Migration at the NASA Center for Computational Sciences

Evolution and migration are a way of life for provisioners of high-performance mass storage systems that serve high-end computers used by climate and Earth and space science researchers: the compute engines come and go, but the data remains. At the NASA Center for Computational Sciences (NCCS), disk and tape SANs are deployed to provide high-speed I/O for the compute engines and the hierarchical storage management systems. Along with gigabit Ethernet, they also enable the NCCS's latest significant migration: the transparent transfer of 300 Til3 of legacy HSM data into the new Sun SAM-QFS cluster.

Salmon, Ellen M.↗
NASA NTRS2009

Serial Network Flow Monitor

Using a commercial software CD and minimal up-mass, SNFM monitors the Payload local area network (LAN) to analyze and troubleshoot LAN data traffic. Validating LAN traffic models may allow for faster and more reliable computer networks to sustain systems and science on future space missions. Research Summary: This experiment studies the function of the computer network onboard the ISS. On-orbit packet statistics are captured and used to validate ground based medium rate data link models and enhance the way that the local area network (LAN) is monitored. This information will allow monitoring and improvement in the data transfer capabilities of on-orbit computer networks. The Serial Network Flow Monitor (SNFM) experiment attempts to characterize the network equivalent of traffic jams on board ISS. The SNFM team is able to specifically target historical problem areas including the SAMS (Space Acceleration Measurement System) communication issues, data transmissions from the ISS to the ground teams, and multiple users on the network at the same time. By looking at how various users interact with each other on the network, conflicts can be identified and work can begin on solutions. SNFM is comprised of a commercial off the shelf software package that monitors packet traffic through the payload Ethernet LANs (local area networks) on board ISS.

Robinson, Julie A.↗
NASA NTRS2013

RUSHMAPS: Real-Time Uploadable Spherical Harmonic Moment Analysis for Particle Spectrometers

RUSHMAPS is a new onboard data reduction scheme that gives real-time access to key science parameters (e.g. moments) of a class of heliophysics science and/or solar system exploration investigation that includes plasma particle spectrometers (PPS), but requires moments reporting (density, bulk-velocity, temperature, pressure, etc.) of higher-level quality, and tolerates a lowpass (variable quality) spectral representation of the corresponding particle velocity distributions, such that telemetry use is minimized. The proposed methodology trades access to the full-resolution velocity distribution data, saving on telemetry, for real-time access to both the moments and an adjustable-quality (increasing quality increases volume) spectral representation of distribution functions. Traditional onboard data storage and downlink bandwidth constraints severely limit PPS system functionality and drive cost, which, as a consequence, drives a limited data collection and lower angular energy and time resolution. This prototypical system exploit, using high-performance processing technology at GSFC (Goddard Space Flight Center), uses a SpaceCube and/or Maestro-type platform for processing. These processing platforms are currently being used on the International Space Station as a technology demonstration, and work is currently ongoing in a new onboard computation system for the Earth Science missions, but they have never been implemented in heliospheric science or solar system exploration missions. Preliminary analysis confirms that the targeted processor platforms possess the processing resources required for realtime application of these algorithms to the spectrometer data. SpaceCube platforms demonstrate that the target architecture possesses the sort of compact, low-mass/power, radiation-tolerant characteristics needed for flight. These high-performing hybrid systems embed unprecedented amounts of onboard processing power in the CPU (central processing unit), FPGAs (field programmable gate arrays), and DSP (digital signal processing) elements. The fundamental computational algorithm de constructs 3D velocity distributions in terms of spherical harmonic spectral coefficients (which are analogous to a Fourier sine-cosine decomposition), but uses instead spherical harmonics Legendre polynomial orthogonal functions as a basis for the expansion, portraying each 2D angular distribution at every energy or, geometrically, spherical speed-shell swept by the particle spectrometer. Optionally, these spherical harmonic spectral coefficients may be telemetered to the ground. These will provide a smoothed description of the velocity distribution function whose quality will depend on the number of coefficients determined. Successfully implemented on the GSFC-developed processor, the capability to integrate the proposed methodology with both heritage and anticipated future plasma particle spectrometer designs is demonstrated (with sufficiently detailed design analysis to advance TRL) to show specific science relevancy with future HSD (Heliophysics Science Division) solar-interplanetary, planetary missions, sounding rockets and/or CubeSat missions.

Figueroa-Vinas, Adolfo↗
NASA NTRS1992

Applied Information Systems Research Program (AISRP). Workshop 2: Meeting Proceedings

The Earth and space science participants were able to see where the current research can be applied in their disciplines and computer science participants could see potential areas for future application of computer and information systems research. The Earth and Space Science research proposals for the High Performance Computing and Communications (HPCC) program were under evaluation. Therefore, this effort was not discussed at the AISRP Workshop. OSSA's other high priority area in computer science is scientific visualization, with the entire second day of the workshop devoted to it.

Source record↗
NASA NTRS1994

Performance evaluation of the Engineering Analysis and Data Systems (EADS) 2

The Engineering Analysis and Data System (EADS)II (1) was installed in March 1993 to provide high performance computing for science and engineering at Marshall Space Flight Center (MSFC). EADS II increased the computing capabilities over the existing EADS facility in the areas of throughput and mass storage. EADS II includes a Vector Processor Compute System (VPCS), a Virtual Memory Compute System (CFS), a Common Output System (COS), as well as Image Processing Station, Mini Super Computers, and Intelligent Workstations. These facilities are interconnected by a sophisticated network system. This work considers only the performance of the VPCS and the CFS. The VPCS is a Cray YMP. The CFS is implemented on an RS 6000 using the UniTree Mass Storage System. To better meet the science and engineering computing requirements, EADS II must be monitored, its performance analyzed, and appropriate modifications for performance improvement made. Implementing this approach requires tool(s) to assist in performance monitoring and analysis. In Spring 1994, PerfStat 2.0 was purchased to meet these needs for the VPCS and the CFS. PerfStat(2) is a set of tools that can be used to analyze both historical and real-time performance data. Its flexible design allows significant user customization. The user identifies what data is collected, how it is classified, and how it is displayed for evaluation. Both graphical and tabular displays are supported. The capability of the PerfStat tool was evaluated, appropriate modifications to EADS II to optimize throughput and enhance productivity were suggested and implemented, and the effects of these modifications on the systems performance were observed. In this paper, the PerfStat tool is described, then its use with EADS II is outlined briefly. Next, the evaluation of the VPCS, as well as the modifications made to the system are described. Finally, conclusions are drawn and recommendations for future worked are outlined.

Debrunner, Linda S.↗
DOE OSTI2025

Advanced Computing Annual Report 2024

In fiscal year (FY) 2024, the National Renewable Energy Laboratory (NREL) took a major leap forward with the completed full buildout of Kestrel, the Office of Energy Efficiency and Renewable Energy's newest high-performance computing (HPC) system. Kestrel is already supporting science across the portfolio, bringing roughly 44 petaflops of computing power, which is more than five times the capacity of our previous supercomputer, Eagle. By delivering greater GPU capacity, Kestrel enables faster progress in artificial intelligence (AI) and opens new avenues in energy research - from defining long-term planning scenarios to accommodate a growing power system to material discovery to improving energy efficiency in photovoltaics (PV). Across the portfolio, research is being accelerated by Kestrel's impressive power. During FY24, 427 projects and more than 700 researchers used NREL's HPC, supporting the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy across 13 funding areas. Through these collaborations, researchers produced more than 450 technical outputs, including 195 articles in peer-reviewed publications, pushing the boundaries of science and engineering. This year's report features new sections spotlighting the expanding roles of Artificial Intelligence and Accelerated Computing. We also introduce an early career section to celebrate the accomplishments of our up-and-coming researchers, whose pioneering work is shaping the future of energy. We hope you enjoy the new insights and discoveries highlighted in these pages.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2016

Developing the Next Generation of Science Data System Engineers

At Goddard, engineers and scientists with a range of experience in science data systems are needed to employ new technologies and develop advances in capabilities for supporting new Earth and Space science research. Engineers with extensive experience in science data, software engineering and computer-information architectures are needed to lead and perform these activities. The increasing types and complexity of instrument data and emerging computer technologies coupled with the current shortage of computer engineers with backgrounds in science has led the need to develop a career path for science data systems engineers and architects.The current career path, in which undergraduate students studying various disciplines such as Computer Engineering or Physical Scientist, generally begins with serving on a development team in any of the disciplines where they can work in depth on existing Goddard data systems or serve with a specific NASA science team. There they begin to understand the data, infuse technologies, and begin to know the architectures of science data systems. From here the typical career involves peermentoring, on-the-job training or graduate level studies in analytics, computational science and applied science and mathematics. At the most senior level, engineers become subject matter experts and system architect experts, leading discipline-specific data centers and large software development projects. They are recognized as a subject matter expert in a science domain, they have project management expertise, lead standards efforts and lead international projects. A long career development remains necessary not only because of the breadth of knowledge required across physical sciences and engineering disciplines, but also because of the diversity of instrument data being developed today both by NASA and international partner agencies and because multidiscipline science and practitioner communities expect to have access to all types of observational data.This paper describes an approach to defining career-path guidance for college-bound high school and undergraduate engineering students, junior and senior engineers from various disciplines.

Next Generation of Science↗
DOE OSTI2021

Multiscale Modeling Meets Machine Learning: What Can We Learn?

Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learn- ing to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.

machine learning, multiscale modeling, physics-bas↗
DOE OSTI2024

R-Adaptivity to Enable Compression of Elementary Computations in Extreme-Scale Finite Element Simulators

Modern computing systems are capable of exascale calculations, which are revolutionizing the development and application of high-fidelity numerical models in computational science and engineering. While these systems continue to grow in processing power, the available system memory has not increased commensurately, and electrical power consumption continues to grow. A predominant approach to limit the memory usage in large-scale applications is to exploit the abundant processing power and continually recompute many low-level simulation quantities, rather than storing them. However, this approach can adversely impact the throughput of the simulation and diminish the benefits of modern computing architectures. We present three novel contributions to reduce the memory burden while maintaining, and sometimes improving, performance in simulations based on finite element discretizations. The first contribution develops dictionary-based data compression schemes that detect and exploit the structure of the discretization, due to redundancies across the finite element mesh. While these schemes are shown to reduce memory requirements by more than 99% on meshes with large numbers of identical mesh cells, there are applications where this structure does not exist. The second contribution leverages a recently developed augmented Lagrangian optimization algorithm to enable r-adaptivity for meshes with the goal of enhancing the redundancies in the mesh. The third contribution extends these methods to patch-based linear solvers and preconditioners by compressing local matrices. Numerical results demonstrate the effectiveness of the proposed methods to detect, enhance and exploit mesh structure on a suite of examples inspired by large-scale applications.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2004

Data Management as a Cluster Middleware Centerpiece

Through earth and space modeling and the ongoing launches of satellites to gather data, NASA has become one of the largest producers of data in the world. These large data sets necessitated the creation of a Data Management System (DMS) to assist both the users and the administrators of the data. Halcyon Systems Inc. was contracted by the NASA Center for Computational Sciences (NCCS) to produce a Data Management System. The prototype of the DMS was produced by Halcyon Systems Inc. (Halcyon) for the Global Modeling and Assimilation Office (GMAO). The system, which was implemented and deployed within a relatively short period of time, has proven to be highly reliable and deployable. Following the prototype deployment, Halcyon was contacted by the NCCS to produce a production DMS version for their user community. The system is composed of several existing open source or government-sponsored components such as the San Diego Supercomputer Center s (SDSC) Storage Resource Broker (SRB), the Distributed Oceanographic Data System (DODS), and other components. Since Data Management is one of the foremost problems in cluster computing, the final package not only extends its capabilities as a Data Management System, but also to a cluster management system. This Cluster/Data Management System (CDMS) can be envisioned as the integration of existing packages.

Zero, Jose↗
NASA NTRS2024

From Blood Platelets Classification to Earth System Digital Twins: A Career in Computer Vision

IEEE SIgnal Processing Society (SPS) and Women in Signal Processing (WiSP) Webinar (https://signalprocessingsociety.org/blog/webinar-wisp-blood-platelets-classification-earth-system-digital-twins-career-computer-vision): Join us for an interactive session with Dr. Jacqueline Le Moigne, Manager of the NASA Science Mission Directorate Earth Science Technology Office’s Advanced Information Systems Technology Program. In this webinar, Dr. Le Moigne will share her journey through academia, the private sector, and her pivotal roles at NASA, emphasizing her work in signal processing, computer vision, and related technologies. The webinar will conclude with an interactive Q&A session, providing attendees the opportunity to engage directly with Dr. Le Moigne about her experiences and insights. Dr. Jacqueline Le Moigne manages NASA's Advanced Information Systems Technology Program, focusing on novel technologies that support Earth Science missions. She previously held roles at NASA Goddard, including Assistant Chief for Technology, and has worked on NASA's Space Technology Research Grants Program and Technology Roadmaps. Before NASA, she was a Research Scientist at the University of Maryland's Computer Vision Laboratory and also gained experience in the private sector. Jacqueline earned her Ph.D. in Computer Science from the University Pierre and Marie Curie in Paris. Her research interests include Image Registration, Computer Vision, Artificial Intelligence, Autonomous Systems, Distributed Spacecraft Missions (DSM), and Earth System Digital Twins (ESDT), as well as high-performance and onboard computing. She has authored over 180 publications, including books and patents, and has received multiple awards, including the NASA Exceptional Service Medal and the Goddard Information Science and Technology Award in 2012.

Computer Vision; Image Processing; Earth Science R↗
NASA NTRS2016

Goddard Earth Science Data and Information Center (GES DISC)

The GES DIS is one of 12 NASA Earth science data centers. The GES DISC vision is to enable researchers and educators maximize knowledge of the Earth by engaging in understanding their goals, and by leading the advancement of remote sensing information services in response to satisfying their goals. This presentation will describe the GES DISC approach, successes, challenges, and best practices.

data management↗
DOE OSTI2017

SymPy: symbolic computing in Python

SymPy is an open source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provide additional examples and further outline details of the architecture and features of SymPy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

High Resolution Multimodal Chemical Imaging Platform for Organics and Inorganics

Chemical analysis at the nanoscale is critical to advance our understanding of materials and systems from medicine and biology to material science and computing. Macroscale-observed phenomena in these systems are in the large part driven by processes that take place at the nanoscale and are highly heterogeneous. Therefore, there is a clear need to develop a new technology that enables correlative imaging of material functionalities with nanoscale spatial and chemical resolutions that will enable us to untangle the structure–function relationship of functional materials. As such, here, we report on the analytical figures of merit of the newly developed correlative chemical imaging technique of helium ion microscopy coupled with secondary ion mass spectrometry (HIM-SIMS) that enables multimodal topographical/chemical imaging of organic and inorganic materials at the nanoscale. In HIM-SIMS, a focused ion beam acts as a sputtering and ionization source for chemical analysis along with simultaneous high-resolution surface imaging, providing an unprecedented level of spatial resolution for gathering chemical information on organic and inorganic materials. In this work, we demonstrate HIM-SIMS as a platform for a next-generation tool for an in situ material design and analysis capable of down to 8 nm spatial resolution chemical imaging, layered metal structure imaging in depth profiling, single graphene layer detection, and spectral analysis of metals, metal oxides, and polymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

An Energy Efficient Ethernet Strategy Based on Traffic Prediction and Shaping

Recently, different communities in computer science, telecommunication, and control systems have devoted a huge effort towards the design of energy efficient solutions for data transmission and network management. This paper collocates along this research line and presents a novel energy efficient strategy conceived for Ethernet networks. The proposed strategy, which exploits the opportunities offered by the IEEE 802.3az amendment to the Ethernet standard (known as energy efficient Ethernet) is based on the possibility of predicting the future traffic from the analysis of the current data flow. In agreement with the results of such a dynamic prediction, Ethernet links can be forced into a low power consumption state for variable intervals. Theoretical bounds are derived to detail how the performance figures depend on the parameters of the designed strategy and scale with respect to traffic load. Furthermore, simulation results carried out with both real and synthetic traffic traces are presented to prove the effectiveness of the strategy, which leads to considerable energy savings at the cost of only a limited bounded delay in data delivery.

Cenedese, Angelo (ORCID:0000000322495094)↗
DOE OSTI2025

Design-to-Deployment Continuum Platform for Microscopes and Computing Ecosystems

Science ecosystems with networked computing systems and physical instruments are increasingly being deployed with a goal to achieve the productivity promised by AI-supported remote automation. In support of these efforts, the virtual infrastructure twins (VITs) have been successfully utilized to develop the orchestration codes for these ecosystems without requiring physical access to expensive instruments, such as electron microscopes. Currently, the utility of such a VIT is severely limited by the computing capacity and capability of the computing system used as its host. Furthermore, codes developed on the VIT typically need to be transferred and refactored for production use, particularly, on high-performance systems with accelerators. In response, we develop a design-to-deployment continuum platform wherein a VIT runs natively on the ecosystem's own computing system, and thereby facilitates the continual in-situ testing and transition of codes for production use. Here, we describe the development and testing of software for remote microscope steering and GPU-based image reconstruction using this platform on a multi-GPU computing system networked to Nion microscopes. We demonstrate a continual transition of steering and reconstruction codes developed under VIT platform to production ecosystem deployment.

Al-Najjar, Anees [Oak Ridge National Laboratory (O↗
DOE OSTI2019

Combustion Research I.1 Sprays, Flow, Heat Transfer and Turbulent Mixing

LANL is working with the Partnership for Advance Combustion Engines (PACE) a DOE sponsored consortium of national laboratories for the research and development for creating smarter engine design tools for sustainable transportation. Much of that effort is focused on heat, mass and momentum transfer within and engine. LANL’s focus in 2019, in particular under PACE has been spray modeling and engine mixture formulation, development of conjugate heat transfer analysis (heat transfer to the environment surrounding the combustion chamber). Our efforts relay on the Fast, Easy, Accurate and Robust Continuum Engineering (FEARCE) software developed at LANL as the next generation of engine modeling software for turbulent reactive and multiphase flow, a 2019 R&D 100 winner. This work is relevant to the DOE Vehicle Technologies Office efforts at addressing national energy security as well. Less dependence on petroleum products leads to greater energy security. By Environmental Protection Agency standards, some vehicles are now reaching 42–50 mpg mark. These are conventional gasoline engines. With continued investment and research into new technical innovations, the potential exists to save more than 4 million barrels of oil per day or approximately $200 to $400 million per day. This would be a significant decrease in emissions and use of petroleum and a very large stimulus to the U.S. economy. Better understanding of fuel injection and fuel–air mixing, thermodynamic combustion losses, and combustion/emission formation processes enhances our ability to minimize fuel use and unwanted emissions. Helping to accomplish this understanding, the FEARCE modeling program is working to provide state-of-theart capability for accurately simulating combustion processes: to have a predictive methodology in a software helping industry and researchers not only meet national goals on fuel usage and emissions, but global goals. In addition, a predictive, robust, and accurate capability for simulating the engine combustion process helps to minimize time and labor for development of new engine technology.

33 ADVANCED PROPULSION SYSTEMS↗