SEARCH · Engineering Papers
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.
dCache is A distributed petabyte-scale storage system for scientific data. It is a joint effort between DESY(2000), FNAL(2001) and NDGF(2006) and supports standard and HEP specific access protocols and authentication mechanisms. dCache was developed for HERA and Tevatron, used for LHC and others such as Belle II, LOFAR, CTA, IceCUBE, EUXFEL, Petra3, DUNE and many more.
The rotational motion of Mars and its geophysical ramifications were investigated. Solar system dynamics and the laws of gravitation were also studied. The planetary ephemeris program, which was the central element in data analysis for this project, is described in brief. Viking Lander data were used in the investigation.
NASA-Goddard is responsible for the development of a ground system for the Upper Atmosphere Research Satellite (UARS) observatory, whose launch is scheduled for 1991. This ground system encompasses a dedicated Central Data Handling Facility (CDHF); attention is presently given to the management of software systems design and implementation phases for CDHF by the UARS organization. Also noted are integration and testing activities performed following software deliveries to the CDHF. The UARS project has an obvious requirement for a powerful and flexible data base management system; an off-the-shelf commercial system has been incorporated.
Digital image analysis system processes Landsat multispectral data, aircraft-acquired scanner data, digitized topographical data, and such ancillary data as soil types and rainfall information. Over 230 modules aid user in performing wide range of land cover analyses and manipulations.
Photosynthetic organisms have recently gained considerable attention for a role in development of renewable energy sources. Genome-enabled systems biology methods, coupled with functional and synthetic genomics, present opportunities to develop sustainable and economical applications such as fuel production within the next 10 to 15 years. However, optimization of light-driven metabolism for biomass or biofuel production will require a detailed systems biology understanding of photosynthetic processes and cellular metabolism. Genome-scale metabolic models (GEMs) are at the core of systems analysis of cellular processes and form a common organizational framework for analyses of data resulting from functional genomics experimental work and computational studies. Therefore, there is a clear demand for high quality photosynthetic model organisms and the appropriate computational tools that enable systems analysis of light-driven metabolism. Through research conducted we expanded the currently available repertoire of photosynthetic GEMs to include the commercially valuable model diatom Phaeoctylum tricornutum. Diatoms have a peculiar and distinct evolutionary footprint and represent a major eukaryotic lineage that is taxonomically and functionally distinct from green and red algae and vascular plants. Therefore, the true potential for light-driven metabolism aimed at biofuel production remains poorly understood at a systems level for a large subset of the global diversity of photosynthetic organisms. The metabolic capabilities of P. tricornutum were comparatively modeled with those from other photosynthetic groups in order to elucidate the occurrence of metabolic traits within and between phototrophs. Additionally, this research resulted in significant extension of the COnstraints Based Reconstruction and Analysis (COBRA) Toolbox to accommodate the crucial need for infrastructure required for ‘omics data integration and analysis in the context of genome-scale models. Therefore, the proposed research achieved two important goals. First, within the broad scope of photosynthetic organisms, we functionally compared and, as a result, identified cellular processes that require optimization in order to enable deployment as biofuel feedstock. Second, the proposed research resulted in development of key computational infrastructure, which can be further extended to other biological systems, that is currently lacking but necessary for multiple ‘omics data integration.
Modeling complex physical dynamics is a fundamental task in science and engineering. Traditional physics-based models are first-principled, explainable, and sample-efficient. However, they often rely on strong modeling assumptions and expensive numerical integration, requiring significant computational resources and domain expertise. While deep learning (DL) provides efficient alternatives for modeling complex dynamics, they require a large amount of labeled training data. Furthermore, its predictions may disobey the governing physical laws and are difficult to interpret. Physics-guided DL aims to integrate first-principled physical knowledge into data-driven methods. It has the best of both worlds and is well equipped to better solve scientific problems. Recently, this field has gained great progress and has drawn considerable interest across discipline Here, we introduce the framework of physics-guided DL with a special emphasis on learning dynamical systems. We describe the learning pipeline and categorize state-of-the-art methods under this framework. We also offer our perspectives on the open challenges and emerging opportunities.
This paper will discuss a proposed CubeSat size (3 Units / 6 Units) telemetry system concept being developed at Marshall Space Flight Center (MSFC) in cooperation with Auburn University. The telemetry system incorporates efficient, high-bandwidth communications by developing flight-ready, low-cost, PROTOFLIGHT software defined radio (SDR) payload for use on CubeSats. The current telemetry system is slightly larger in dimension of footprint than required to fit within a 0.75 Unit CubeSat volume. Extensible and modular communications for CubeSat technologies will provide high data rates for science experiments performed by two CubeSats flying in formation in Low Earth Orbit. The project is a collaboration between the University of Alabama in Huntsville and Auburn University to study high energy phenomena in the upper atmosphere. Higher bandwidth capacity will enable high-volume, low error-rate data transfer to and from the CubeSats, while also providing additional bandwidth and error correction margin to accommodate more complex encryption algorithms and higher user volume.
This paper describes the packet switched Instrumenters Communication System (ICS) that was developed for the Command Management Facility at GSFC to support the Gamma Ray Observatory (GRO) spacecraft. The GRO ICS serves as a vital science data acquisition link to the GRO scientists to initiate commands for their spacecraft instruments. The system is ready to send and receive messages at any time, 24 hours a day and seven days a week. The system is based on X.25 and the International Standard Organization's (ISO) 7-layer Open Systems Interconnection (OSI) protocol model and has client and server components. The components of the GRO ICS are discussed along with how the Communications Subsystem for Interconnection (CSFI) and Network Control Program Packet Switching Interface (NPSI) software are used in the system.
The Solar Maximum Repair Mission is scheduled for launch in April, 1984. The mission was made possible through a spacecraft designed for on-orbit repair, the availability of spacecraft repair hardware and the availability of the Shuttle System. Repair is expected to result in the availability of significant new science data and a demonstration of the capability for on-orbit repair of spacecraft. Considerations of the capability to maintain and repair spacecraft on-orbit for the future are also discussed.
The Modern Era Retrospective-analysis for Research and Applications, Version 2 (MERRA-2) is the global atmospheric data reanalysis for the satellite era produced by NASA’s Global Modeling and Assimilation Office (GMAO), using the Goddard Earth Observing System Model (GEOS)version 5.12.4. The data are officially distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). MERRA-2 data have been widely used by the Earth sciences and application community. Since MERRA-2 data were released in early 2016, the number of registered data users has grown steadily from 1,252 in 2016 to 6477 in 2020. By the end of October 2021, ~16 petabytes (over 360 million files) of data have been distributed to more than 18,900 users. Searching in Google Scholar (https://scholar.google.com/), we have found over 7,000 articles, published between January 2017 and May 2021, involving the use ofMERRA-2 data. The figure shows the numbers for various application areas in which theMERRA-2 data have been used, covering almost all of the application areas defined in NASA Applied Sciences (http://appliedsciences.nasa.gov). The largest number of articles are found in disaster research, with the subcategories ordered in flood, wildfires, hurricanes and cyclones, and other forms of severe weather. In this presentation, we will discuss the preliminary findings from a review of the selected literature that uses MERRA-2 data in applied sciences. The current analytic and interoperable data services at GES DISC are listed, such as the on-the-fly subset and analysis service, NASA Giovanni; THREDDS Data Server(TDS); and Python Jupyter notebooks. In addition, we will introduce two new services for supporting the open sciences: My Dashboard and Related Publications.
The R&D funding from this award has been a significant tool to move the Muon Scattering Experiment (MUSE) at the Paul Scherrer Institute in Switzerland forward to the stage of realization. Specifically, this award has enabled Dr. Michael Kohl and his working group at Hampton University to achieve substantial progress toward the goal of providing beam particle tracking with Gas Electron Multiplier (GEM) detectors for MUSE experiment. Establishing a particle detection system that is capable of operating in a high-intensity environment, with a data acquisition system capable of running at several kHz, combined with robust tracking software providing high efficiency for track reconstruction in the presence of noise and backgrounds will have immediate application in many other experiments.
For the high-resolution imaging radar systems, real-time data compression of raw imaging data is required to accomplish the science requirements and satisfy the given communication and storage constraints. The Block Adaptive Quantizer (BAQ) algorithm and its associated VLSI processor design have been developed to provide a real-time data compressor for high-resolution imaging radar systems.
The exploration of Mars will be a multi-decadal activity. Currently, a scientific program is underway, sponsored by NASA's Office of Space Science in the United States, in collaboration with international partners France, Italy, and the European Space Agency. Plans exist for the continuation of this robotic program through the first automated return of Martian samples in 2014. Mars is also a prime long-term objective for human exploration, and within NASA, efforts are being made to provide the best integration of the robotic program and future human exploration missions. From the perspective of human exploration missions, it is important to understand the scientific objectives of human missions, in order to design the appropriate systems, tools, and operational capabilities to maximize science on those missions. In addition, data from the robotic missions can provide critical environmental data - surface morphology, materials composition, evaluations of potential toxicity of surface materials, radiation, electrical and other physical properties of the Martian environment, and assessments of the probability that humans would encounter Martian life forms. Understanding of the data needs can lead to the definition of experiments that can be done in the near-term that will make the design of human missions more effective. This workshop was convened to begin a dialog between the scientific community that is central to the robotic exploration mission program and a set of experts in systems and technologies that are critical to human exploration missions. The charge to the workshop was to develop an understanding of the types of scientific exploration that would be best suited to the human exploration missions and the capabilities and limitations of human explorers in undertaking science on those missions.
Climate Science models are flagship codes for the largest of high performance computing (HPC) resources, both in visibility, with the newly launched Department of Energy (DOE) Accelerated Climate Model for Energy (ACME) effort, and in terms of significant fractions of system usage. The performance of the DOE ACME model is captured with application level timers and examined through a sizeable run archive. Performance and variability of compute, queue time and ancillary services are examined. As Climate Science advances in the use of HPC resources there has been an increase in the required human and data systems to achieve programs goals. A description of current workflow processes (hardware, software, human) and planned automation of the workflow, along with historical and projected data in motion and at rest data usage, are detailed. The combination of these two topics motivates a description of future systems requirements for DOE Climate Modeling efforts, focusing on the growth of data storage and network and disk bandwidth required to handle data at an acceptable rate.
The precision power monitor (PPM) is a precision radiometric instrument used to improve the efficiency of signal reception by the Deep Space Network. Real time estimates of the system operating temperature, the present (signal + noise)-to-noise ratio, and the signal power are utilized to increase the accuracy and resolution of the received spacecraft signal. Due to the critical nature of radio science data returning from Voyager 2 at Saturn Encounter, PPM support was required. The task was undertaken to validate the performance of equipment technically under research and development in time to meet the encounter deadline. Initial studies revealed PPM performance to be out of tolerance. Action was immediately taken to identify the system problems. Using data analysis as feedback, the system failures were identified and corrected in time to contribute to the encounter support efforts. As a result, the radio science data were collected successfully.
The Hubble Space Telescope (HST) will be an orbiting observatory which will extend the capability of large earth-based observatories to view the universe by approximately 350 times in volume and approximately seven times in distance. The HST program, which includes the Orbiting Observatory, the Space Telescope Science Institute, and the Space Telescope Operations Control Center, is supported by the Space Shuttle, the Tracking and Data Relay Satellite System, and the NASA Communications Network. This paper describes the Command and Data Management System for the HST spacecraft, including flexibility, operation modes, and end-to-end data flow paths used to meet stringent synchronization and data quality requirements. The results of system tests indicate that the use of concatenated coding techiques consisting of both block and convolutional codes as a protection against random and burst errors has been successful. Diagrams of the HST data system are included.
NASA Ames' Exploration Ground Data Systems (xGDS) supports rapid scientific decision making by synchronizing information in time and space, including video and still images, scientific instrument data, and science and operations notes in geographic and temporal context. We have deployed xGDS at multiple NASA field analog missions over the past decade.In the last two years, we have participated in SUBSEA, a multi-institution collaborative project. SUBSEA used the research ship E/V Nautilus along with its two remotely operated vehicles (ROVs), Hercules and Argus, to explore deep ocean volcanic vents as an analog for ocean worlds (e.g. Enceladus). This work allowed us to compare the existing oceanographic operations methods and technologies used for ocean exploration with corresponding tools and approaches developed and used at NASA. In the first year of SUBSEA we observed existing remote science operations from the Inner Space Center (ISC). In the second year, we deployed xGDS at ISC to complement existing capabilities with xGDS tools designed to support remote Nautilus science operations from the ISC. During operations, video, ROV position and instrument telemetry were streamed from the ship to the ISC. As the science team watched dive operations, they could annotate the data with observations that were relevant to their work domain. Later, the team members could review the data at their own pace to collaboratively develop a dive plan for the next day, which had to be delivered on a fixed daily schedule.The opportunity to compare operations under different conditions enabled us to make several key observations about conducting remote science and planning operations efficiently: (i) Reviewing data collaboratively and interactively with temporal and spatial context was critical for the remote science team's ability to plan dive operations on the Nautilus. (ii) Science team members were actively engaged with the remote dive operations because they could interact with the collected data and visualize it as they desired. (iii) Being able to replay past events at accelerated speeds, and jump to points in time and spaced based on search results, provided efficient access to critical points of interest in a massive volume of data, so the remote science team could deliver plans on time. SUBSEA (Systematic Underwater Biogeochemical Science and Exploration Analog) is a multi-institution collaboration supported by NASA, NOAA's Office of Exploration Research (OER), the Ocean Exploration Trust (OET) and the University of Rhode Island's Graduate School of Oceanography (GSO). ISC is GSO's telepresence operations facility.
Abstract Artificial intelligence and machine learning (ML) continue to see increasing interest in science and engineering every year. Polymer science is no different, though implementation of data-driven algorithms in this subfield has unique challenges barring widespread application of these techniques to the study of polymer systems. In this Prospective, we discuss several critical challenges to implementation of ML in polymer science, including polymer structure and representation, high-throughput techniques and limitations, and limited data availability. Promising studies targeting resolution of these issues are explored, and contemporary research demonstrating the potential of ML in polymer science despite existing obstacles are discussed. Finally, we present an outlook for ML in polymer science moving forward. Graphical Abstract