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At least 1,711 records · Page 95

Particle-sampling statistics in laser anemometers Sample-and-hold systems and saturable systems

The effect of the data-processing system on the particle statistics obtained with laser anemometry of flows containing suspended particles is examined. Attention is given to the sample and hold processor, a pseudo-analog device which retains the last measurement until a new measurement is made, followed by time-averaging of the data. The second system considered features a dead time, i.e., a saturable system with a significant reset time with storage in a data buffer. It is noted that the saturable system operates independent of the particle arrival rate. The probabilities of a particle arrival in a given time period are calculated for both processing systems. It is shown that the system outputs are dependent on the mean particle flow rate, the flow correlation time, and the flow statistics, indicating that the particle density affects both systems. The results are significant for instances of good correlation between the particle density and velocity, such as occurs near the edge of a jet.

Edwards, R. V.↗

Impact of IPAD on CAD/CAM database university research

IPAD program has provided direction, focus and software products which impacted on CAD/CAM data base research and follow-on research. The relationship of IPAD to the research projects which involve the storage of geometric data in common data ase facilities such as data base machines, the exchange of data between heterogeneous data bases, the development of IGES processors, the migration of lrge CAD/CAM data base management systems to noncompatible hosts, and the value of RIM as a research tool is described.

Leach, L. M.↗

LDEXPT, an intelligent database system for the Composite Load Spectra project

The Composite Load Spectra project develops probabilistic models to simulate the probabilistic loads for selected components of a generic space propulsion system. Tremendous information such as engine load variables and their distributions is needed by the simulation program. An intelligent data base system was constructed and integrated with the probabilistic load simulation program to manage and maintain the knowledge base of the Composite Load Spectra project. The intelligent data base system takes care of the data retrieval and storage functions and has expert knowledge on engine load models and associated engine variables. The integration of the intelligent data base into the load simulation program achieves a smooth coupling between the numeric processing (load simulation calculation) and the symbolic processing (intelligent load information management).

Ho, H.↗

Preliminary Evaluation of MapReduce for High-Performance Climate Data Analysis

MapReduce is an approach to high-performance analytics that may be useful to data intensive problems in climate research. It offers an analysis paradigm that uses clusters of computers and combines distributed storage of large data sets with parallel computation. We are particularly interested in the potential of MapReduce to speed up basic operations common to a wide range of analyses. In order to evaluate this potential, we are prototyping a series of canonical MapReduce operations over a test suite of observational and climate simulation datasets. Our initial focus has been on averaging operations over arbitrary spatial and temporal extents within Modern Era Retrospective- Analysis for Research and Applications (MERRA) data. Preliminary results suggest this approach can improve efficiencies within data intensive analytic workflows.

Duffy, Daniel Q.↗

Hadoop for High-Performance Climate Analytics: Use Cases and Lessons Learned

Scientific data services are a critical aspect of the NASA Center for Climate Simulations mission (NCCS). Hadoop, via MapReduce, provides an approach to high-performance analytics that is proving to be useful to data intensive problems in climate research. It offers an analysis paradigm that uses clusters of computers and combines distributed storage of large data sets with parallel computation. The NCCS is particularly interested in the potential of Hadoop to speed up basic operations common to a wide range of analyses. In order to evaluate this potential, we prototyped a series of canonical MapReduce operations over a test suite of observational and climate simulation datasets. The initial focus was on averaging operations over arbitrary spatial and temporal extents within Modern Era Retrospective- Analysis for Research and Applications (MERRA) data. After preliminary results suggested that this approach improves efficiencies within data intensive analytic workflows, we invested in building a cyber infrastructure resource for developing a new generation of climate data analysis capabilities using Hadoop. This resource is focused on reducing the time spent in the preparation of reanalysis data used in data-model inter-comparison, a long sought goal of the climate community. This paper summarizes the related use cases and lessons learned.

analytics↗

Matrix error correction for digital data

A technique for digital data error detection and correction is disclosed which adds alignment and checksum bytes to three sides of a matrix (24) of digital data to be protected. This technique is particularly used for the recording and storage (16,18) of digital data on video tape medium (14). The digital data is treated as a matrix block (24). Checksum and alignment bytes are added (20) to the digital data before tape storage and stripped (22) therefrom after successful alignment checks and data validation. In particular, the first column may be used to provide alignment bytes of a predetermined value for each row. The last column provides row checksum bytes for the data in each row. The last row provides column check sum bytes for each column, excluding the column of alignment bytes. The data location at the intersection of the row of column checksum bytes and the column of row checksum bytes may be used as a checksum byte for either the row or column checksum bytes.

Dotson, Ronald S.↗

Next-generation geospatial-temporal information technologies for disaster management

Traditional geographic information systems (GIS) have been disrupted by the emergence of Big Data in the form of geo-coded raster, vector, and time-series Internet-of-Things data. This article discusses the application of new scalable technologies that go far beyond relational databases and file-based storage on spinning disk or tape to incorporate both storage and processing data in the same platform. The roles of the Apache Hadoop Distributed File Systems and NoSQL key-value stores such as the Apache Hbase are discussed, along with indexing schemes that optimally support geospatial-temporal use. Here, we highlight how this new approach can rapidly search multiple GIS data layers to obtain insights in the context of early warning, impact evaluation, response, and recovery to earthquake and wildfire disasters.

97 MATHEMATICS AND COMPUTING↗

System control module diagnostic Expert Assistant

The Orbiter EXperiments (OEX) Program was established by NASA's Office of Aeronautics and Space Technology (OAST) to accomplish the precise data collection necessary to support a complete and accurate assessment of Space Transportation System (STS) Orbiter performance during all phases of a mission. During a mission, data generated by the various experiments are conveyed to the OEX System Control Module (SCM) which arranges for and monitors storage of the data on the OEX tape recorder. The SCM Diagnostic Expert Assistant (DEA) is an expert system which provides on demand advice to technicians performing repairs of a malfunctioning SCM. The DEA is a self-contained, data-driven knowledge-based system written in the 'C' Language Production System (CLIPS) for a portable micro-computer of the IBM PC/XT class. The DEA reasons about SCM hardware faults at multiple levels; the most detailed layer of encoded knowledge of the SCM is a representation of individual components and layouts of the custom-designed component boards.

Flores, Luis M.↗

On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets

Advanced materials characterization techniques with ever-growing data acquisition speed and storage capabilities represent a challenge in modern materials science, and new procedures to quickly assess and analyze the data are needed. Machine learning approaches are effective in reducing the complexity of data and rapidly homing in on the underlying trend in multi-dimensional data. Here, we show that by employing an algorithm called the mean shift theory to a large amount of diffraction data in high-throughput experimentation, one can streamline the process of delineating the structural evolution across compositional variations mapped on combinatorial libraries with minimal computational cost. Data collected at a synchrotron beamline are analyzed on the fly, and by integrating experimental data with the inorganic crystal structure database (ICSD), we can substantially enhance the accuracy in classifying the structural phases across ternary phase spaces. We have used this approach to identify a novel magnetic phase with enhanced magnetic anisotropy which is a candidate for rare-earth free permanent magnet.

Kusne, AG↗

Speculative instruction wakeup to tolerate draining delay of memory ordering violation check buffers

A technique for speculatively executing load-dependent instructions includes detecting that a memory ordering consistency queue is full for a completed load instruction. The technique also includes storing data loaded by the completed load instruction into a storage location for storing data when the memory ordering consistency queue is full. The technique further includes speculatively executing instructions that are dependent on the completed load instruction. The technique also includes in response to a slot becoming available in the memory ordering consistency queue, replaying the load instruction. The technique further includes in response to receiving loaded data for the replayed load instruction, testing for a data mis-speculation by comparing the loaded data for the replayed load instruction with the data loaded by the completed load instruction that is stored in the storage location.

Kalamatianos, John↗

Consolidating WLCG topology and configuration in the Computing Resource Information Catalogue

Here, the Worldwide LHC Computing Grid infrastructure links about 200 participating computing centres affiliated with several partner projects. It is built by integrating heterogeneous computer and storage resources in diverse data centres all over the world and provides CPU and storage capacity to the LHC experiments to perform data processing and physics analysis. In order to be used by the experiments, these distributed resources should be well described, which implies easy service discovery and detailed description of service configuration. Currently this information is scattered over multiple generic information sources like GOCDB, OIM, BDII and experiment-specific information systems. Such a model does not allow to validate topology and configuration information easily. Moreover, information in various sources is not always consistent. Finally, the evolution of computing technologies introduces new challenges. Experiments are more and more relying on opportunistic resources, which by their nature are more dynamic and should also be well described in the WLCG information system. This contribution describes the new WLCG configuration service CRIC (Computing Resource Information Catalogue) which collects information from various information providers, performs validation and provides a consistent set of UIs and APIs to the LHC VOs for service discovery and usage configuration. The main requirements for CRIC are simplicity, agility and robustness. CRIC should be able to be quickly adapted to new types of computing resources, new information sources, and allow for new data structures to be implemented easily following the evolution of the computing models and operations of the experiments.

97 MATHEMATICS AND COMPUTING↗

Organizing and Typing Persistent Objects Within an Object-Oriented Framework

Conventional operating systems provide little or no direct support for the services required for an efficient persistent object system implementation. We have built a persistent object scheme using a customization and extension of an object-oriented operating system called Choices. Choices includes a framework for the storage of persistent data that is suited to the construction of both conventional file system and persistent object system. In this paper we describe three areas in which persistent object support differs from file system support: storage organization, storage management, and typing. Persistent object systems must support various sizes of objects efficiently. Customizable containers, which are themselves persistent objects and can be nested, support a wide range of object sizes in Choices. Collections of persistent objects that are accessed as an aggregate and collections of light-weight persistent objects can be clustered in containers that are nested within containers for larger objects. Automated garbage collection schemes are added to storage management and have a major impact on persistent object applications. The Choices persistent object store provides extensible sets of persistent object types. The store contains not only the data for persistent objects but also the names of the classes to which they belong and the code for the operation of the classes. Besides presenting persistent object storage organization, storage management, and typing, this paper discusses how persistent objects are named and used within the Choices persistent data/file system framework.

Madany, Peter W.↗

Interface of the transport systems research vehicle monochrome display system to the digital autonomous terminal access communication data bus

An upgrade of the transport systems research vehicle (TSRV) experimental flight system retained the original monochrome display system. The original host computer was replaced with a Norden 11/70, a new digital autonomous terminal access communication (DATAC) data bus was installed for data transfer between display system and host, while a new data interface method was required. The new display data interface uses four split phase bipolar (SPBP) serial busses. The DATAC bus uses a shared interface ram (SIR) for intermediate storage of its data transfer. A display interface unit (DIU) was designed and configured to read from and write to the SIR to properly convert the data from parallel to SPBP serial and vice versa. It is found that separation of data for use by each SPBP bus and synchronization of data tranfer throughout the entire experimental flight system are major problems which require solution in DIU design. The techniques used to accomplish these new data interface requirements are described.

Easley, W. C.↗

District wide water resources investigation and management using LANDSAT data. Phase 1: Lake volume

A technique for estimating available water storage volume using LANDSAT data was developed and applied to Lake Washington and Lake Harris in central Florida. The technique can be applied two ways. First, where the historical stage records are available, the historical LANDSAT data can be used to establish the relationship between lake volume and lake stage. In the second case, where the historical stage records are not available, the historical LANDSAT data can be used to estimate the historical lake stage after the lake volume and stage information become available in the future.

Shih, S. F.↗

Fate of Listeria monocytogenes Serotypes on Frozen Mixed Vegetables During Consumer‐Simulated Thawing and Storage

ABSTRACT Recent outbreaks and recalls associated with frozen vegetables in the United States and Europe have been linked to Listeria monocytogenes . This study aims to understand the extent to which frozen vegetables support the growth of L. monocytogenes once thawed and held at different temperatures. Six L. monocytogenes strains, two of each from serotypes 1/2a, 1/2b, and 4b, were individually inoculated onto frozen vegetables and stored at −18°C for 7 days. After 7 days, the vegetables were thawed and stored at 5°C or 10°C for up to 14 days or at 25°C for up to 7 days. L. monocytogenes was enumerated from the thawed vegetables throughout the storage period. Population data were fitted to the primary Baranyi model to estimate growth rates and lag phase durations; the secondary Ratkowsky square root model was used to model the relationship of the growth rates with storage temperature. Five of the L. monocytogenes strains survived and grew on the thawed vegetables (population increases of > 1 log CFU/g) stored at 5°C, and all six of the strains proliferated at 10°C and 25°C (population increases of > 3 log CFU/g after 14 days and > 4 log CFU/g after 7 days, respectively). A secondary model was successfully generated based on the growth rates of the six L. monocytogenes strains on the thawed vegetables ( r 2 = 0.8888, RMSE = 0.2057). Results from this study fill a data gap associated with L. monocytogenes survival on thawed vegetables and can be used to determine safe handling and storage practices for these products to protect public health.

Salazar, Joelle K. [Division of Food Processing Sc↗

Argonne National Laboratory Annual Report of Laboratory Directed Research and Development Program Activities FY 2012

Our plans to upgrade the Advanced Photon Source are proceeding on schedule, and we hope to receive CD-2 approval sometime in spring 2013. When complete, the APS Upgrade will equip this vital facility for continued scientific leadership in the 21st century, providing the 5,000-plus members of our user community with greatly increased time, energy and spatial resolution. We also have successfully installed “Mira,” the new IBM Blue Gene/Q system at the Argonne Leadership Computing Facility. The world’s fourth-fastest supercomputer, Mira is now providing leading-edge computational power across all scientific disciplines. I am also pleased to report that Argonne’s new Energy Sciences Building is proceeding on schedule; we expect it to be finished and ready for occupancy by June 30, 2013. When complete, the ESB will be one of the most modern and most flexible energy labs in the world, creating a new home for researchers focused on a wide range of energy science challenges. Research is under way at the new multidisciplinary Institute for Molecular Engineering (IME), a joint program of the University of Chicago and Argonne. Over the past year, four new faculty members, including IME’s director, – all pre-eminent researchers who hold joint appointments at Argonne – have joined the IME. We look forward to working in collaboration with the IME to find new ways to address the interfaces between molecular-level science and powerful new technologies. In FY 2012, we continued support for the final year of the “Beyond Lithium-Ion” Director’s Grand Challenge LDRD project. That project enabled significant work on the performance of lithium-based energy storage systems, pointing the way to new high-energy-density lithium-air batteries. I believe this Grand Challenge contributed significantly to our successful bid to win designation of the Argonne-based Joint Center for Energy Storage Research (JCESR) as the DOE’s new Batteries and Energy Storage Hub. JCESR, which brings together a world-class “dream team” of national laboratories, universities and private sector partners, strongly benefited from LDRD program investments, both in FY2012 and in previous years. As we move forward in FY2013, the Director’s Grand Challenge is now focused on new ways to address the challenges of “big data” – the acquisition, storage, transfer, and analysis of both experimental and simulated data at scales approaching the limits of our current computational capacities. In managing Argonne’s LDRD program, we have adhered to the requirements of DOE Order 413.2b and associated guidelines. Our program management continually strives for increased efficiency. In addition to meeting all reporting requirements during FY 2012, our LDRD Program Office continues to implement intranet-based process enhancements to better serve all LDRD stakeholders, including our DOE Site Office colleagues. As the following individual project reports demonstrate, Argonne’s researchers are pursuing projects at the leading edge of science and engineering, and making significant contributions to the advancement of our laboratory’s major strategic initiatives. Evidence of the continuing strength of this program is found in the follow-on sponsorship won by many of these research projects, as well as in the numbers of publications, invited presentations, inventions, and new staff hires that are direct results of LDRD. LDRD enables exploration of new scientific directions and makes it possible to adapt quickly to a changing landscape with unexpected opportunities, while enhancing the capabilities of our researchers and our scientific facilities. For these reasons, LDRD remains an invaluable resource to address the future science and technology needs of DOE, and of our nation.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Post-Irradiation Examination of Zircaloy-4 Samples in Target Capsules and Initiation of Bending Fatigue Testing for Used Nuclear Fuel Vibration Integrity Investigations (FY 2013 Summary Report)

This report documents ongoing work performed at Oak Ridge National Laboratory (ORNL) for the Department of Energy, Office of Fuel Cycle Technology Used Fuel Disposition Campaign (UFDC), and satisfies the deliverable for milestone M2FT-13OR0805041, “Data Report on Hydrogen Doping and Irradiation in HFIR.” This work is conducted under WBS 1.02.08.05, Work Package FT-13OR080504 ST “Storage and Transportation-Experiments – ORNL.” The objectives of work packages that make up the S&T Experiments Control Account are to conduct the separate effects tests (SET) and small-scale tests that have been identified in the Used Nuclear Fuel Storage and Transportation Data Gap Prioritization (FCRD-USED-2012-000109). In FY 2013, the R&D focused on cladding and container issues and small-scale tests as identified in Sections A-2.9 and A-2.12 of the prioritization report.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Creation of lumped parameter thermal model by the use of finite elements

In the finite difference technique, the thermal network is represented by an analogous electrical network. The development of this network model, which is used to describe a physical system, often requires tedious and mental data preparation and checkout by the analyst which can be greatly reduced through the use of the computer programs to develop automatically the mathematical model and associated input data and graphically display the analytical model to facilitate model verification. Three separate programs are involved which are linked through common mass storage files and data card formats. These programs are SPAR, CINGEN and GEOMPLT, and are used to (1) develop thermal models for the MITAS II thermal analyzer program; (2) produce geometry plots of the thermal network; and (3) produce temperature distribution and time history plots.

Source record↗