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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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Viewgraphs on data collection with the ACTS propagation terminal are included. Topics covered include: DACS system overview; DACS board; APT data collection computer; APT software downloading; data storage for APT; and ACTS propagation experiment data flow.
In the future, NASA expects to gather over a tera-byte per day of data requiring space for levels of archival storage. Data compression will be a key component in systems that store this data (e.g., optical disk and tape) as well as in communications systems (both between space and Earth and between scientific locations on Earth). We propose to develop algorithms that can be a basis for software and hardware systems that compress a wide variety of scientific data with different criteria for fidelity/bandwidth tradeoffs. The algorithmic approaches we consider are specially targeted for parallel computation where data rates of over 1 billion bits per second are achievable with current technology.
VBL technology potentially offers a variety of desirable data storage technology attributes for a variety of applications.
Optoelectronic sensors of a proposed type would perform the functions of both electronic cameras and triangulation- type laser range finders. That is to say, these sensors would both (1) generate ordinary video or snapshot digital images and (2) measure the distances to selected spots in the images. These sensors would be well suited to use on robots that are required to measure distances to targets in their work spaces. In addition, these sensors could be used for all the purposes for which electronic cameras have been used heretofore. The simplest sensor of this type, illustrated schematically in the upper part of the figure, would include a laser, an electronic camera (either video or snapshot), a frame-grabber/image-capturing circuit, an image-data-storage memory circuit, and an image-data processor. There would be no moving parts. The laser would be positioned at a lateral distance d to one side of the camera and would be aimed parallel to the optical axis of the camera. When the range of a target in the field of view of the camera was required, the laser would be turned on and an image of the target would be stored and preprocessed to locate the angle (a) between the optical axis and the line of sight to the centroid of the laser spot.
Each day, the global air transportation industry generates a vast amount of heterogeneous data from air carriers, air traffic control providers, and secondary aviation entities handling baggage, ticketing, catering, fuel delivery, and other services. Generally, these data are stored in isolated data systems, separated from each other by significant political, regulatory, economic, and technological divides. These realities aside, integrating aviation data into a single, queryable, big data store could enable insights leading to major efficiency, safety, and cost advantages. In this paper, we describe an implemented system for combining heterogeneous air traffic management data using semantic integration techniques. The system transforms data from its original disparate source formats into a unified semantic representation within an ontology-based triple store. Our initial prototype stores only a small sliver of air traffic data covering one day of operations at a major airport. The paper also describes our analysis of difficulties ahead as we prepare to scale up data storage to accommodate successively larger quantities of data -- eventually covering all US commercial domestic flights over an extended multi-year timeframe. We review several approaches to mitigating scale-up related query performance concerns.
Each day, the global air transportation industry generates a vast amount of heterogeneous data from air carriers, air traffic control providers, and secondary aviation entities handling baggage, ticketing, catering, fuel delivery, and other services. Generally, these data are stored in isolated data systems, separated from each other by significant political, regulatory, economic, and technological divides. These realities aside, integrating aviation data into a single, queryable, big data store could enable insights leading to major efficiency, safety, and cost advantages. In this paper, we describe an implemented system for combining heterogeneous air traffic management data using semantic integration techniques. The system transforms data from its original disparate source formats into a unified semantic representation within an ontology-based triple store. Our initial prototype stores only a small sliver of air traffic data covering one day of operations at a major airport. The paper also describes our analysis of difficulties ahead as we prepare to scale up data storage to accommodate successively larger quantities of data -- eventually covering all US commercial domestic flights over an extended multi-year timeframe. We review several approaches to mitigating scale-up related query performance concerns.
Schema-on-read is an agile approach to data storage and retrieval that defers investments in data organization until production queries need to be run by working with data directly in native form. Schema-on-read functions have been implemented in a wide range of analytical systems, most notably Hadoop. SchemaOnRead is a CRAN package that uses R’s flexible data representations to provide transparent and convenient support for the schema-on-read paradigm in R. The schema-on- read tools within the package include a single function call that recursively reads folders with text, comma separated value, raster image, R data, HDF5, NetCDF, spreadsheet, Weka, Epi Info, Pajek network, R network, HTML, SPSS, Systat, and Stata files. The provided tools can be used as-is or easily adapted to implement customized schema-on-read tool chains in R. This paper’s contribution is that it introduces and describes SchemaOnRead, the first R package specifically focused on providing explicit schema-on-read support in R.
Schema-on-read is an agile approach to data storage and retrieval that defers investments in data organization until production queries need to be run by working with data directly in native form. Schema-on-read functions have been implemented in a wide range of analytical systems, most notably Hadoop. SchemaOnRead is a CRAN package that uses R’s flexible data representations to provide transparent and convenient support for the schema-on-read paradigm in R. The schema-on- read tools within the package include a single function call that recursively reads folders with text, comma separated value, raster image, R data, HDF5, NetCDF, spreadsheet, Weka, Epi Info, Pajek network, R network, HTML, SPSS, Systat, and Stata files. The provided tools can be used as-is or easily adapted to implement customized schema-on-read tool chains in R. This paper’s contribution is that it introduces and describes SchemaOnRead, the first R package specifically focused on providing explicit schema-on-read support in R.
Poster on Carbon Storage Data Task 4: International Offshore Geologic Carbon Storage Inventory and Meta-analysis to be presented at the AAPG CCUS conference March 11-13, 2024.
We have endeavored to classify the commonly seen data movement needs, as observed in data-intensive institutions (both commercial and non-profit), into four categories. Knowing how to map a data movement task into one of the four categories helps select proper data mover tools. For each category, how the data storage is involved, high-level examples and the nature of typical solutions are described. Finally, some general remarks are provided to help further orient readers new to this field - the 4th IT pillar.
Managing massive volumes of data and effectively making it accessible to researchers poses significant challenges and is a barrier to scientific discovery. In many cases, critical data is locked up in unwieldy file formats or one-off databases and is too large to effectively process on a single machine. This talk explores the role of Kubernetes, an open-source container orchestration platform, in addressing research data management challenges. I will discuss how we are using a set of publicly available open-source and home-grown tools in the National Renewable Energy Lab (NREL) Data, Analysis, and Visualization (DAV) group to help researchers overcome data-related bottlenecks. The talk will begin by providing an overview of the data challenges faced in research data management, including data storage, processing, and analysis. I will highlight Kubernetes' ability to handle large-scale data by leveraging containerization and distributed computing, including distributed storage. Kubernetes allows researchers to encapsulate data processing infrastructure and workflows into portable containers, enabling reproducibility and ease of deployment. Kubernetes can then schedule and manage the resource allocation of these containers to enable efficient utilization of limited computing resources, leading to more efficient data processing and analysis. I will discuss some limitations of traditional, siloed approaches to dealing with data and emphasize the need for solutions which foster collaboration. I will highlight how we are using Kubernetes at NREL to facilitate data sharing and cooperation among research teams. Kubernetes' flexible architecture enables the deployment of shared computing environments, such as Apache Superset, where researchers can seamlessly access and analyze shared datasets. Providing the ability to have one research team easily consume data generated by another, utilizing Kubernetes' as a central data platform, is one of the major wins we've encountered by adopting the platform. Finally, I will showcase real-world use cases from NREL where we have used Kubernetes to solve some persistent data challenges involving large volumes of sensor and monitoring data. I will discuss the challenges we encountered when creating our cluster and making it available as a production-ready resource. I will also discuss the specific suite of tools, including Postgres and Apache Druid for columnar and timeseries data, and Redpanda Kafka for streaming data we have deployed in our infrastructure, and the process that went into the selection of these tools.
A compact memory uses magnetic bubble technology for providing data storage. A three-dimensional arrangement, in the form of stacks of magnetic bubble layers, is used to achieve high volumetric storage density. Output tracks are used within each layer to allow data to be accessed uniquely and unambiguously. Storage can be achieved using either current access or field access magnetic bubble technology. Optical sensing via the Faraday effect is used to detect data. Optical sensing facilitates the accessing of data from within the three-dimensional package and lends itself to parallel operation for supporting high data rates and vector and parallel processing.
Non-destructive evaluation (NDE) methods for quantifying and locating damage are essential for inspecting structures to ensure safety and reliability. Transmittance function monitoring is a potentially new NDE technique being tested as a tool to detect, quantify, and locate damage on flexible structures. The technique has a large spatial range that is practical for detecting damage on large composite material structures such as a reusable launch vehicle. The Transmittance Function (TF) theory is based on structural dynamics principles that define how vibration at one point in a structure is related to a force at another point. This relationship is called the Frequency Response Function (FRF). A Transmittance Function (TF) is derived as the ratio of FRFs, and can detect damage because the FRFs change due to damage. If one excitation is used for the testing, the force does not need to be measured to compute the TF. In the damage detection procedure, the structure is subjected to wide-band vibration and TFs are computed between different accelerometers to detect changes in the structure, presumably due to damage. In the first year of the project the TF method was tested on a bolted panel, a curved panel, and beams, all made of fiberglass. It was shown that damage could be detected using low frequency vibration, 250 to 1,250 Hz. The technique is sensitive to damage, but it requires storage of historical or pre-damage TFs for the healthy structure. This would become a large data storage requirement for large structures. Thus one objective for the second year of the project was to eliminate the need to store historical data. The second year report gives details of how storage of historical data was eliminated. Further results of testing panel structures are also given.
Industrial data analytics methods play a central role in improving energy storage performance and efficiency, impacting the future of electrified transportation and renewable electricity generation. However, significant challenges hinder the large-scale deployment of batteries. Conventional methods rely on centralized collection and processing of fleet-level data, leading to database size issues and privacy concerns due to potential data breaches. To enable scalable deployment of battery management systems, this article proposes a federated battery diagnosis and prognosis model, which distributes the processing of battery standard current-voltage-time-usage data in a privacy-preserving manner. Instead of transferring the raw data, this approach communicates only the locally processed parameters, thus reducing communication load and preserving data confidentiality. The federated model offers a paradigm shift in battery health management through privacy-preserving distributed methods for battery data processing and lifetime prediction, ensuring the reliable and sustainable deployment of lithium-ion batteries in a rapidly evolving world.
The demand that began a couple of years ago for increased data storage capacity continues. Peripheral Strategies (a Santa Barbara, California, Storage Market Research Firm) projects the amount of data stored on the average enterprise network will grow by 50 percent to 100 percent per year. Furthermore, Peripheral Strategies says that a typical mid-range workstation system containing 30GB to 50GB of storage today will grow at the rate of 50 percent per year. Dan Friedlander, a Boulder, Colorado-based consultant specializing in PC-LAN backup, says, 'The average NetWare LAN is about 8GB, but there are many that have 30GB to 300GB.....' The substantial growth of storage requirements has created various tape technologies that seek to satisfy the needs of today's and, especially, the next generations's systems and applications. There are five leading tape technologies in the market today: QIC (Quarter Inch Cartridge), IBM 3480/90, 8mm, DAT (Digital Audio Tape) and DLT (Digital Linear Tape). Product performance specifications and user needs have combined to classify these technologies into low-end, mid-range, and high-end systems applications. Although the manufacturers may try to position their products differently, product specifications and market requirements have determined that QIC and DAT are primarily low-end systems products while 8mm and DLT are competing for mid-range systems applications and the high-end systems space, where IBM compatibility is not required. The 3480/90 products seem to be used primarily in the IBM market, for interchangeability purposes. There are advantages and disadvantages for each of the tape technologies in the market today. We believe that DLT technology offers a significant number of very important features and specifications that make it extremely attractive for most current as well as emerging new applications, such as Hierarchical Storage Management (HSM). This paper will demonstrate why we think that the DLT technology and family of DLT products will become the technology of choice for most new applications in the mid-range and high-end (non-IBM) markets.
Stabilizing and manipulating topological magnetic quasiparticles in thin films is of great interest for potential applications in data storage and information processing. Here, we present a strategy for stabilizing magnetic vortices and Bloch lines with controlled position, vorticity, and chirality in a continuous exchange bias system. By tailoring vectorially the unidirectional anisotropy of the system at the nanoscale, via thermally assisted magnetic scanning probe lithography, we show experimentally and via micromagnetic simulations the non-volatile creation of vortex-antivortex pairs. In addition, we demonstrate the deterministic stabilization of cross and circular Bloch lines within patterned Néel magnetic domain walls. This work enables the implementation of complex functionalities based on the control of tailored topological spin-textures in spintronic and magnonic nanodevices.
Magnetic skyrmions are topologically protected spin textures with promising prospects for applications in data storage. They can form a lattice state due to competing magnetic interactions and are commonly found in a small region of the temperature—magnetic field phase diagram. Recent work has demonstrated that these magnetic quasi-particles fluctuate at the μeV energy scale. Here, we use a coherent x-ray correlation method at an x-ray free-electron laser to investigate these fluctuations in a magnetic phase coexistence region near a first-order transition boundary where fluctuations are not expected to play a major role. Surprisingly, we find that the relaxation of the intermediate scattering function at this transition differs significantly compared to that deep in the skyrmion lattice phase. The observation of a compressed exponential behavior suggests solid-like dynamics, often associated with jamming. We assign this behavior to disorder and the phase coexistence observed in a narrow field-window near the transition, which can cause fluctuations that lead to glassy behavior.
Ge-Sb-Te (GST) alloys are leading phase-change materials for data storage due to the fast phase transition between amorphous and crystalline states. Ongoing research aims at improving the stability of the amorphous phase to improve retention. This can be accomplished by the introduction of carbon as a dopant to Ge 2 Sb 2 Te 5 , which is known to alter the short- and mid-range structure of the amorphous phase and form covalently bonded C clusters, both of which hinder crystallization. The relative importance of these processes as a function of C concentration is not known. Here we used molecular dynamics simulation based on density functional theory to study how carbon doping affects the atomic structure of GST-C. Carbon doping results in an increase in tetrahedral coordination, especially of Ge atoms, and this is known to stabilize the amorphous phase. We observe an unexpected, non-monotonous trend in the number of tetrahedral bonded Ge with the amount of carbon doping. Our simulations show an increase in the number of tetrahedral bonded Ge up to 5 at.% C, after which the number saturates and begins to decrease above 14 at.% C. The carbon atoms aggregate into clusters, mostly in the form of chains and graphene flakes, leaving less carbon to disrupt the GST matrix at higher carbon concentrations. Different degrees of carbon clustering can explain divergent experimental results for recrystallization temperature for carbon doped GST.