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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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The following subject areas are covered: a short history; uses of network STSCII (general communication, science collaboration, functional activities, internal data management, and external data access); proposal/observation handling; DMF access; and future uses and requirements.
This paper presents in viewgraph form several applications of artificial intelligence (AI) to the conceptual design of aircraft, including: an access manager for automated data management, AI techniques applied to optimization, and virtual reality for scientific visualization of the design prototype.
Giovanni, the Goddard Earth Sciences Data and Information Services Center (GES DISC) Interactive Online Visualization and Analysis Infrastructure, has provided researchers with advanced capabilities to perform data exploration and analysis with observational data from NASA Earth observation satellites. In the past 5-10 years, examining geophysical events and processes with remote-sensing data required a multistep process of data discovery, data acquisition, data management, and ultimately data analysis. Giovanni accelerates this process by enabling basic visualization and analysis directly on the World Wide Web. In the last two years, Giovanni has added new data acquisition functions and expanded analysis options to increase its usefulness to the Earth science research community.
Biology has developed next-generation data science and alternative analytical approaches with methodologies which require principal investigator (PI) experimental assay data be re-used. This new approach involves mining multiple datasets at once from various hierarchical organizations of biological complexity, while concurrently evaluating how experimental factors affect endpoints of standard assays. The purpose of the NASA Ames Life Sciences Data Archive (ALSDA) is to collect, curate, and make findable, accessible, interoperable, and reusable (FAIR) all non-human space-relevant biological data. These data include mission metadata, subject metadata, assay metadata (parameters), raw and processed assay data, assay imagery, and subject-experienced telemetry (radiation, temperature, humidity, acoustics, vibrations). ALSDA has transformed to bring current biological repository data and all future collected data into this new scientific data mining reality. It has integrated into the ‘NASA Open Science’ group of projects to facilitate a suite of new tools and workflows to improve data accessibility and reusability by implementing data management plans, automating data submission agreements, and adopting the single-point-of-entry data submission portal, originally developed by NASA GeneLab. These systems required ALSDA to develop science assay configurations for the submission portal, capturing essential assay parameters according to established norms in each sub-field within biology. The submission portal expedites data collection by enhancing ease of PI data submission, providing a user interface and specificity for which data is to be submitted. ALSDA datasets are curated to maintain rich metadata, accuracy of datasets, data transparency, provenance, and additionally ensure data are machine-readable (e.g., R and Python languages). ALSDA integration with GeneLab and its analysis portals enable higher-order physiological-level datasets be mined in conjunction with -omics datasets. As ALSDA physiological-level datasets are published (micro-computed tomography, histology, intraocular pressure, hormonal assays, immunostaining, ultrasonography), the merging of hierarchical organizations of biological complexity from spaceflight will enable new knowledge discovery approaches.
In this paper, we present HEPnOS, a distributed data service for managing data produced by high-energy physics (HEP) experiments. Using HEPnOS, HEP applications can use HPC resources more effciently than traditional fle-based applications. The fle-based model leads to a rigid, chunk-based allocation of computational resources and limits the number of cores that can be used concurrently by an HEP application. The fundamental problem is that organizing domain-specifc data into fles inadvertently introduces a single, artifcial, confated tuning parameter that puts key optimization goals into confict: larger fle sizes reduce metadata overhead and thus improve I/O effciency, but smaller fle sizes provide more opportunity for workfow parallelism and load balancing. In this work, we introduce a domain-specifc data service that decouples that constraint so that data can be accessed and processed in its natural granularity while still maintaining I/O effciency. By removing the constraints introduced by fle handling we are able to obtain better scaling and make effcient use of more cores for processing a fxed-sized data sample. We demonstrate the improved scalability by using an application developed in the fle-based paradigm and comparing it to a version modifed to use HEPnOS.
A quench detection system was developed for protecting and monitoring the superconducting solenoids for the Muon-to-Electron Conversion Experiment (Mu2e) at Fermilab. The quench system was designed for a high level of dependability and long-term continuous operation. It is based on three tiers: Tier-I, FPGA-based Digital Quench Detection (DQD); Tier-II, Analog Quench Detection (AQD); and Tier-3, the quench controls and data management system. The Tier-I and Tier-II are completely independent and fully redundant systems. The Tier-3 system is based on National Instruments (NI) C-RIO and provides the user interface for quench controls and data management. It is independent from Tiers I & II. The DQD provides both quench detection and quench characterization (monitoring) capability. Both DQD and AQD have built-in high voltage isolation and user programmable gains and attenuations. The DQD and AQD also includes user configured current dependent thresholding and validation times. A 1 st article of the three-tier system was fully implemented on the new Fermilab magnet test stand for the HL-LHC Accelerator Up-grade Project (AUP). It successfully provided quench protection and monitoring (QPM) for a cold superconducting bus test in November 2020. The Mu2e quench detection design has since been implemented for production testing of the AUP magnets. A detailed description of the system along with results from the AUP superconducting bus test will be presented.
The North Atlantic Bloom Experiment (NABE) of JGOFS presents a unique opportunity and challenge to the data management community because of the diversity and large size of biogeochemical data sets collected. NABE was a pilot study for JGOFS and has also served as a pilot study within the U.S. NODC for management and archiving of the data sets. Here I present an overview to some of the scientific results of NABE, which will be published as an Introduction to a special volume of NABE results in Deep-Sea Research later this year. An overview of NABE data management is given elsewhere in the present report. This is the first collection of papers from the Joint Global Ocean Flux Study (JGOFS). Formed as an international program in 1987, JGOFS has four principal elements: modelling and data management, multidisciplinary regional process studies, a global survey of biogeochemical properties and long-term time series observatories. In 1989-1990 JGOFS conducted a pilot process study of the spring phytoplankton bloom, the North Atlantic Bloom Experiment (NABE). JGOFS decided to conduct a large scale, internationally-coordinated pilot study in the North Atlantic because of its proximity to the founding nations of the project, the size and predictability of the bloom and its fundamental impact on ocean bio-geochemistry (Billett et al., 1983; Watson and Whitfield, 1985; Pfannkuche, 1992). In 1989, six research vessels from Canada, Germany, The Netherlands, the United Kingdom and the USA and over 200 scientists and students from more than a dozen nations participated in NABE. Some of their initial results are reported in this volume.
Engineering data base development which has become increasingly widespread to industry with the availability of data management systems is examined. A large data base was developed for wind tunnel data and related model test information, using RIM as the data base manager. The arrangement of the wind tunnel data into the proper schema for the most efficient database utilization is discussed. The FORTRAN interface program of RIM is used extensively in the loading phases of the data base and by the users. Several examples to illustrate how the Wind Tunnel Data base might be searched for specific data items and test information using RIM are presented.
New computer technology and resources must be successfully integrated into CDSLR station operations to manage new complex operational tracking requirements, support the on site production of new data products, support ongoing station performance improvements, and to support new station communication requirements. The NASA CDSLR Network is in the process of upgrading station computer resources with HP UNIX workstations, designed to automate a wide range of operational station requirements. The primary HP upgrade objective was to relocate computer intensive data system tasks from the controller computer to a new advanced computer environment designed to meet the new data system requirements. The HP UNIX environment supports fully automated real time data communications, data management, data processing, and data quality control. Automated data compression procedures are used to improve the efficiency of station data communications. In addition, the UNIX environment supports a number of semi-automated technical and administrative operational station tasks. The x window user interface generates multiple simultaneous color graphics displays, providing direct operator visibility and control over a wide range of operational station functions.
This paper presents an abbreviated history of NASA science data management system development over the past ten years by selecting two case studies, each representative of a distinct era of science data management systems.
Slides representing Nuclear Data Management and Analysis System (NDMAS) Data for properly preserving our publicly funded data, including DOE Public Access Plan and the users of NDMAS. From Capture, Storage, Delivery & Analysis, Archival, and Qualification of Fuel fabrication, PIE, ART Experiment monitoring and operations, and processes for recording work and for looking ahead.
This paper explores the challenges and solutions for managing and processing the vast amount of data generated by the Advanced Photon Source (APS), a synchrotron light source facility producing ultra-bright x-rays for diverse scientific domains. With 68 experimental beamlines covering materials research, biology, and more, the APS serves a wide user base across academia, government, and industry. The ongoing upgrade of the APS storage ring and installation of new instruments will amplify data generation and processing demands. This paper discusses the approach to address these demands through automated data processing using standardized workflows that produce faster scientific insights. The APS Data Management System coordinates various data related tasks to manage storage, data transfer, metadata cataloging, data processing, and interfaces with tools provided by Globus. Through integration with the Argonne Leadership Computing Facility (ALCF), APS users can efficiently access high-performance computing resources. Standardized workflows have led to reduced computational burdens on scientists and greater accessibility of high performance computing resources. We demonstrate how standardization and collaboration enable scientists to rapidly convert raw data into meaningful scientific results, establishing a streamlined path from data collection to analysis and ultimately to publication.
Versatile command-driven data management system offers users, through simplified command language, a means of storing and searching data files, sorting data files into specified orders, performing simple or complex computations, effecting file updates, and printing or displaying output data. Commands are simple to use and flexible enough to meet most data management requirements.
Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of ML models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based modeling of material behavior at various length scales and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using ML techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus, effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train ML models and the defining model parameters and architectures within the Granta MI Platform. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in the prediction of material behavior, while following outlined best practices for effective data management. An effective schema for ML data and models can help prevent the recreation of virtual/real training data and surrogate models, help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.
In 2010/2011 the Global Data Assembly Center (GDAC) at NASA's Physical Oceanography Distributed Active Archive Center (PO.DAAC) continued its role as the primary clearinghouse and access node for operational Group for High Resolution Sea Surface Temperature (GHRSST) datastreams, as well as its collaborative role with the NOAA Long Term Stewardship and Reanalysis Facility (LTSRF) for archiving. Here we report on our data management activities and infrastructure improvements since the last science team meeting in June 2010.These include the implementation of all GHRSST datastreams in the new PO.DAAC Data Management and Archive System (DMAS) for more reliable and timely data access. GHRSST dataset metadata are now stored in a new database that has made the maintenance and quality improvement of metadata fields more straightforward. A content management system for a revised suite of PO.DAAC web pages allows dynamic access to a subset of these metadata fields for enhanced dataset description as well as discovery through a faceted search mechanism from the perspective of the user. From the discovery and metadata standpoint the GDAC has also implemented the NASA version of the OpenSearch protocol for searching for GHRSST granules and developed a web service to generate ISO 19115-2 compliant metadata records. Furthermore, the GDAC has continued to implement a new suite of tools and services for GHRSST datastreams including a Level 2 subsetter known as Dataminer, a revised POET Level 3/4 subsetter and visualization tool, a Google Earth interface to selected daily global Level 2 and Level 4 data, and experimented with a THREDDS catalog of GHRSST data collections. Finally we will summarize the expanding user and data statistics, and other metrics that we have collected over the last year demonstrating the broad user community and applications that the GHRSST project continues to serve via the GDAC distribution mechanisms. This report also serves by extension to summarize the activities of the GHRSST Data Assembly and Systems Technical Advisory Group (DAS-TAG).
The engineering-specified requirements for integrated information processing by means of the Integrated Programs for Aerospace-Vehicle Design (IPAD) system are presented. A data model is described and is based on the design process of a typical aerospace vehicle. General data management requirements are specified for data storage, retrieval, generation, communication, and maintenance. Information management requirements are specified for a two-component data model. In the general portion, data sets are managed as entities, and in the specific portion, data elements and the relationships between elements are managed by the system, allowing user access to individual elements for the purpose of query. Computer program management requirements are specified for support of a computer program library, control of computer programs, and installation of computer programs into IPAD.
In a successful data acquisition program, the information needs must be evaluated, the design and cost factors of the program must be determined, and a data management loop must be organized and operated in order to collect, process, and disseminate the needed information in useable formats. This paper describes each of these program elements in detail as an aid for the solar heating and cooling data manager and user to implement effective data acquisition and monitoring systems. Consideration is given to the development of evaluation techniques which will aid in the determination of solar energy systems performances.
The Activated Materials Laboratory (AML) is a new radiological facility located at the Advanced Photon Source (APS) in Argonne National Laboratory (ANL), adjacent to the high-energy x-ray microscopy (HEXM) beamline in the long beamline building (LBB) constructed under the APS-upgrade (APS-U) project. The AML is a centralized facility to facilitate the safe conduct of experiments on activated materials at the APS. This report provides an overview of the status of the AML as a Nuclear Science User Facilities (NSUF) partner user facility in preparation for the general user access in 2024 upon the commissioning of the APS-U beamlines. Specifically, details are provided regarding the laboratory's scope, functionality, components, operational blueprint, and data management approach. The plans on augmenting the instrumentation, refining operational procedures and developing a robust data management strategy for FY24 and beyond are also discussed.