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.
The New York State land use and natural resource inventory is described with emphasis on its design, implementation, and user requirements. Other topics discussed include: classification, data acquisition, geographic referencing, data storage, data retrieval, and documentation.
Significant considerations are described for performing a Severe Storms Measurement program in real time. Particular emphasis is placed on the sizing and timing requirements for a minicomputer-based system. Analyses of several factors which could impact the effectiveness of the system are presented. The analyses encompass the problems of data acquisition, data storage, data registration, correlation, and flow field computation, and error induced by aircraft motion, moment estimation, and pulse integration.
A computer-driven traversing mechanism combined with mass data storage, data reduction programs, and general-purpose graphics programs permits a visualization of complex flows. A unique seven-hole probe is used which permits reasonably accurate measurements of all average flow properties if the local flow angle does not exceed 80 degrees. A description is given of the wake of a lifting canard surface as this wake passes over a wing. The flow includes concentrated and dissipating vortices, large regions of reduced total pressure, and local flow angles up to 60 deg. All these features can be clearly seen and accurately located in the graphical output.
A method for processing complex radar data with a computer using correlation functions is reviewed. Parameters including data storage, data reduction, and real time operation are addressed. Since complex auto- and cross-correlation functions are calculated and stored, almost no information is lost. These also can be analyzed in terms of the full correlation analysis of the spaced-antenna-drifts technique. The proposed approach therefore appears to be very feasible to suit most Mesosphere-Stratosphere-Troposphere (MST) radar applications.
An optical imager, called the Scanning Imaging Spectroradiometer (SIS), was built for NASA in the early 1970's. Later, the Solidstate Array Spectroradiometer (SAS) was developed on the basis on a substitution of a Charge Injection Device (CID) array for the vidicon of the SIS. The Advanced Solidstate Array Spectroradiometer (ASAS) represents an improvement of the SAS. ASAS is a solidstate multispectral optical imaging radiometer which is currently the most versatile instrument of its type in the visible part of the spectrum. The instrument is capable of absolute radiometric measurements in all of its 32 bands. ASAS can be easily mounted in small aircraft as well as high altitude craft such as the U-2, taking into account also the possibility of a use in the Space Shuttle. The instrument consists of three separate subsystems. Attention is given to the optics, the detector array, data storage, data retrieval, and system performance.
Rapid efficient access to Earth sciences data is fundamental to the Nation's efforts to understand the effects of global environmental changes and their implications for public policy. It becomes a bigger challenge in the future when data volumes increase further and missions with constellations of satellites start to appear. Demands on data storage, data access, network throughput, processing power, and database and information management are increased by orders of magnitude, while budgets remain constant and even shrink. The Global Change Data Center's (GCDC) mission is to provide systems, data products, and information management services to maximize the availability and utility of NASA's Earth science data. The specific objectives are (1) support Earth science missions be developing and operating systems to generate, archive, and distribute data products and information; (2) develop innovative information systems for processing, archiving, accessing, visualizing, and communicating Earth science data; and (3) develop value-added products and services to promote broader utilization of NASA Earth Sciences Enterprise (ESE) data and information. The ultimate product of GCDC activities is access to data and information to support research, education, and public policy.
The Telescience Resource Kit (TReK) is a PC based ground control system. It can be used by a single individual or in a group environment to monitor and control spacecraft systems and payloads. Capabilities include data receipt, data processing, data storage, data management, and data transmission. Commercial-Off-The-Shelf (COTS) hardware and software have been employed to reduce development costs, operations and maintenance costs, and to effectively take advantage of new commercial products as they become available. The TReK system is currently being used to monitor and control payloads aboard the International Space Station. It is located at sites around the world.
The Marshall Space Flight Center (MSFC) Ground Systems Department (GSD) recently undertook an architecture change in the product line that serves the ISS program. As a result, the architecture tradeoffs between data system product lines that serve remote users versus those that serve control center flight control teams were explored extensively. This paper describes the resulting architecture that will be used in the ISS payloads program, and the resulting functional breakdown of the products that support that architecture. It also describes the lessons learned from the path that was followed, as a migration of products cause the need to reevaluate the allocation of functions across the architecture. The result is a set of innovative ground system solutions that is scalable so it can support facilities of wide-ranging sizes, from a small site up to large control centers. Effective use of system automation, custom components, design optimization for data management, data storage, data transmissions, and advanced local and wide area networking architectures, plus the effective use of Commercial-Off-The-Shelf (COTS) products, provides flexible Remote Ground System options that can be tailored to the needs of each user. This paper offers a description of the efficiency and effectiveness of the Ground Systems architectural options that have been implemented, and includes successful implementation examples and lessons learned.
The Marshall Space Flight Center (MSFC) Ground Systems Department (GSD) recently undertook an architecture change in the product line that serves the ISS program. As a result, the architecture tradeoffs between data system product lines that serve remote users versus those that serve control center flight control teams were explored extensively. This paper describes the resulting architecture that will be used in the International Space Station (ISS) payloads program, and the resulting functional breakdown of the products that support this architecture. It also describes the lessons learned from the path that was followed, as a migration of products cause the need to reevaluate the allocation of functions across the architecture. The result is a set of innovative ground system solutions that is scalable so it can support facilities of wide-ranging sizes, from a small site up to large control centers. Effective use of system automation, custom components, design optimization for data management, data storage, data transmissions, and advanced local and wide area networking architectures, plus the effective use of Commercial-Off-The-Shelf (COTS) products, provides flexible Remote Ground System options that can be tailored to the needs of each user. This paper offers a description of the efficiency and effectiveness of the Ground Systems architectural options that have been implemented, and includes successful implementation examples and lessons learned.
Current mission operations systems are built as a collection of monolithic software applications. Each application serves the needs of a specific user base associated with a discipline or functional role. Built to accomplish specific tasks, each application embodies specialized functional knowledge and has its own data storage, data models, programmatic interfaces, user interfaces, and customized business logic. In effect, each application creates its own walled-off environment. While individual applications are sometimes reused across multiple missions, it is expensive and time consuming to maintain these systems, and both costly and risky to upgrade them in the light of new requirements or modify them for new purposes. It is even more expensive to achieve new integrated activities across a set of monolithic applications. These problems impact the lifecycle cost (especially design, development, testing, training, maintenance, and integration) of each new mission operations system. They also inhibit system innovation and evolution. This in turn hinders NASA's ability to adopt new operations paradigms, including increasingly automated space systems, such as autonomous rovers, autonomous onboard crew systems, and integrated control of human and robotic missions. Hence, in order to achieve NASA's vision affordably and reliably, we need to consider and mature new ways to build mission control systems that overcome the problems inherent in systems of monolithic applications. The keys to the solution are modularity and interoperability. Modularity will increase extensibility (evolution), reusability, and maintainability. Interoperability will enable composition of larger systems out of smaller parts, and enable the construction of new integrated activities that tie together, at a deep level, the capabilities of many of the components. Modularity and interoperability together contribute to flexibility. The Mission Control Technologies (MCT) Project, a collaboration of multiple NASA Centers, led by NASA Ames Research Center, is building a framework to enable software to be assembled from flexible collections of components and services.
Rapid, efficient access to Earth sciences data from satellites and ground validation stations is fundamental to the nation's efforts to understand the effects of global environmental changes and their implications for public policy. It becomes a bigger challenge in the future when data volumes increase from current levels to terabytes per day. Demands on data storage, data access, network throughput, processing power, and database and information management are increased by orders of magnitude, while budgets remain constant and even shrink.The Global Change Data Center's (GCDC) mission is to develop and operate data systems, generate science products, and provide archival and distribution services for Earth science data in support of the U.S. Global Change Program and NASA's Earth Sciences Enterprise. The ultimate product of the GCDC activities is access to data to support research, education, and public policy.
Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components: (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine.
Over the next 10 years, the Department of Energy will be transitioning from Petascale to Exascale Computing resulting in data storage, networking, and infrastructure requirements to increase by three orders of magnitude. The technologies and best practices used today are the result of a relatively slow evolution of ancestral technologies developed in the 1950s and 1960s. These include magnetic tape, magnetic disk, networking, databases, file systems, and operating systems. These technologies will continue to evolve over the next 10 to 15 years on a reasonably predictable path. Experience with the challenges involved in transitioning these fundamental technologies from Terascale to Petascale computing systems has raised questions about how these will scale another 3 or 4 orders of magnitude to meet the requirements imposed by Exascale computing systems. This report is focused on the most concerning scaling issues with data storage systems as they relate to High Performance Computing- and presents options for a path forward. Given the ability to store exponentially increasing amounts of data, far more advanced concepts and use of metadata will be critical to managing data in Exascale computing systems.
Currently ferroelectric materials with designed domain structures are considered as a perspective material for new generation of photonic, data storage and data processing devices. Application of external electric field is the most convenient way of the domain structure formation. Lots of papers are devoted to investigation of the domain kinetics on polar surface of crystals while the forward growth remains one of the most mysterious stages due to lack of experimental methods allowing to study it. Here we performed tip-induced polarization reversal on X- and Y-non-polar cuts in single-crystal of congruent lithium niobate allows us to study the forward growth with high spatial resolution. The revealed difference in the shape and length of domains induced on X- and Y-cuts is beyond previously developed theoretical approaches used for the theoretical consideration of the domains growth at non-polar ferroelectric surfaces. Lastly, to explain experimental results we used kinetic approach with anisotropy of screening efficiency along different crystallographic directions.
The goal of this project is to enable exascale computational science applications to interact conveniently and efficiently with storage through abstractions that match their data models. We will accomplish this through three major activities: (1) identifying major data model motifs in computational science applications and developing representative benchmarks; (2) developing a data model storage library, called Damsel, that supports these motifs, provides efficient storage data layouts, incorporates optimizations to enable exascale operation, and is tolerant to failures; and (3) productizing Damsel and working with computational scientists to encourage adoption of this library by the scientific community.
The goal of this project is to enable exascale computational science applications to interact conveniently and efficiently with storage through abstractions that match their data models. We will accomplish this through three major activities: (1) identifying major data model motifs in computational science applications and developing representative benchmarks; (2) developing a data model storage library, called Damsel, that supports these motifs, provides efficient storage data layouts, incorporates optimizations to enable exascale operation, and is tolerant to failures; and (3) productizing Damsel and working with computational scientists to encourage adoption of this library by the scientific community.
The Offshore Geologic Carbon Storage Data Collection Web Application is an interactive data collection which aggregates and disseminates publicly available data to support offshore geologic carbon storage (GCS) in the United States. This data collection can be leveraged by stakeholders to understand where GCS may be viable offshore, create GCS project analogs, and address challenges to GCS in offshore environments. Use of this tool is solely at the discretion of the user. See full Federal Disclaimer for further information (https://netl.doe.gov/home/disclaimer).
Long-duration, on-orbit microgravity experiments require a combination of high resolution and high frame rate video data acquisition. The digitized high-rate video stream presents a difficult data storage problem. Data produced at rates of several hundred million bytes per second may require a total mission video data storage requirement exceeding one terabyte. A NASA-designed, VLSI-based, highly parallel digital state machine generates a digital trigger signal at the onset of a video event. High capacity random access memory storage coupled with newly available fuzzy logic devices permits the monitoring of a video image stream for long term (DC-like) or short term (AC-like) changes caused by spatial translation, dilation, appearance, disappearance, or color change in a video object. Pre-trigger and post-trigger storage techniques are then adaptable to archiving only the significant video images.