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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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At least 937 records · Page 52

Training package 1 for slitting data analysis

This document and accompanying files are intended as a first training package on how to analyze slitting data. The end goal is to have Idaho National Laboratory (INL) personnel trained to analyze future slitting data taken in the INL Hot Cell on clad, Low-Enriched Uranium (LEU) fuel plates. This first data package will cover data analysis for a monolithic material (as compared to a layered material like the clad fuel plates). The additional issues for layered specimens will be covered in a future training package.

42 ENGINEERING↗

HEP-FCE Working Group on Libraries and Tools

The High-Energy Physics Forum for Computational Excellence (HEP-FCE) was formed by the Department of Energy as a follow-up to a recent report from the Topical Panel on Computing and the associated P5 recommendation. It is a pilot project distributed across the DOE Labs. During this initial incubation period the Forum is to develop a plan for a robust, long-term organization structure and a functioning web presence for forum activities and outreach, and a study of hardware and software needs across the HEP program. In the following sections we give this working group’s “vision” for aspects and qualities we wish to see in a future HEP-FCE. We then give a prioritized list of technical activities with suggested scoping and deliverables that can be expected to provide cross-experiment benefits. The remaining bulk of the report gives a technical survey of some specific “areas of opportunity” for cross-experiment benefit in the realm of software libs/tools. This survey serves as support for the vision and prioritized list. For each area we describe the ways that cross-experiment benefit is achieved today, as well as describe known failings or pitfalls where such benefit has failed to be achieved and which should be avoided in the future. For both cases, we try to give concrete examples. Each area then ends with an examination of what opportunities exist for improvements in that particular area.

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Coupling of Thermochemistry Solver THERMOCHIMICA with MOOSE/BISON

THERMOCHIMICA is a computational library for chemical thermodynamics. It determines a unique combination of phases and their compositions for a prescribed chemical composition, temperature and pressure. The solver and the underlying thermodynamic models can be used to estimate the chemical state and various constitutive and transport properties necessary for nuclear fuel performance modeling. Our NEAMS research project focuses on the development of nuclear fuel thermodynamics models and computational thermodynamics equilibrium solvers, and their implementation into Idaho National Laboratory (INL) nuclear fuel performance simulation system MOOSE/BISON. This report documents the software integration of THERMOCHIMICA with MOOSE/BISON using the current INL development framework GitLab. The THERMOCHIMICA implementation is demonstrated on modeling oxygen related phenomena in irradiated fuel, such as calculation of oxygen to metal ratio in the fluorite phase, oxygen partial pressure, oxygen chemical potential and oxygen transport. Experimental measurements from the open literature were used to validate the implemented models and illustrate functionality of the developed thermodynamics module. The calculations are based on chemical element inventory provided by neutronics, isotopic depletion, transmutation and decay calculations in the SCALE system. The burnup calculation in BISON can also be used for elemental composition although it provides only a limited set. Coupling of THERMOCHIMICA with other codes that can provide chemical composition of irradiated fuel for BISON simulations is currently being explored.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Query Optimization for Graph Analytics on Linked Data Using SPARQL

Triplestores that support query languages such as SPARQL are emerging as the preferred and scalable solution to represent data and meta-data as massive heterogeneous graphs using Semantic Web standards. With increasing adoption, the desire to conduct graph-theoretic mining and exploratory analysis has also increased. Addressing that desire, this paper presents a solution that is the marriage of Graph Theory and the Semantic Web. We present software that can analyze Linked Data using graph operations such as counting triangles, finding eccentricity, testing connectedness, and computing PageRank directly on triple stores via the SPARQL interface. We describe the process of optimizing performance of the SPARQL-based implementation of such popular graph algorithms by reducing the space-overhead, simplifying iterative complexity and removing redundant computations by understanding query plans. Our optimized approach shows significant performance gains on triplestores hosted on stand-alone workstations as well as hardware-optimized scalable supercomputers such as the Cray XMT.

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DOE Advanced Scientific Advisory Committee (ASCAC): Workforce Subcommittee Letter

Simulation and computing are essential to much of the research conducted at the DOE national laboratories. Experts in the ASCR ¬relevant Computing Sciences, which encompass a range of disciplines including Computer Science, Applied Mathematics, Statistics and domain Computational Sciences, are an essential element of the workforce in nearly all of the DOE national laboratories. This report seeks to identify the gaps and challenges facing DOE with respect to this workforce. This letter is ASCAC’s response to the charge of February 19, 2014 to identify disciplines in which significantly greater emphasis in workforce training at the graduate or postdoctoral levels is necessary to address workforce gaps in current and future Office of Science mission needs.

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DOE Advanced Scientific Computing Advisory Subcommittee (ASCAC) Report: Top Ten Exascale Research Challenges

Exascale computing systems are essential for the scientific fields that will transform the 21st century global economy, including energy, biotechnology, nanotechnology, and materials science. Progress in these fields is predicated on the ability to perform advanced scientific and engineering simulations, and analyze the deluge of data. On July 29, 2013, ASCAC was charged by Patricia Dehmer, the Acting Director of the Office of Science, to assemble a subcommittee to provide advice on exascale computing. This subcommittee was directed to return a list of no more than ten technical approaches (hardware and software) that will enable the development of a system that achieves the Department's goals for exascale computing. Numerous reports over the past few years have documented the technical challenges and the non¬-viability of simply scaling existing computer designs to reach exascale. The technical challenges revolve around energy consumption, memory performance, resilience, extreme concurrency, and big data. Drawing from these reports and more recent experience, this ASCAC subcommittee has identified the top ten computing technology advancements that are critical to making a capable, economically viable, exascale system.

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DOE Advanced Scientific Computing Advisory Committee (ASCAC) Subcommittee Report on Scientific and Technical Information

The Advanced Scientific Computing Advisory Committee (ASCAC) was charged to form a standing subcommittee to review the Department of Energy’s Office of Scientific and Technical Information (OSTI) and to begin by assessing the quality and effectiveness of OSTI’s recent and current products and services and to comment on its mission and future directions in the rapidly changing environment for scientific publication and data. The Committee met with OSTI staff and reviewed available products, services and other materials. This report summaries their initial findings and recommendations.

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What is the current state of the science of Cyber defense?

My overall sense of the cyber defense field is one of an adolescent discipline currently bogged down in a cloud of issues, the most iconic of which is the great diversity of approaches that are being aggregated to form a coherent field. Because my own expertise is complex systems and materials physics research, I have limited direct experience in cyber security sciences except as a user of secure networks and computing resources. However, in producing this report, I have found with certainty that there exists no calculus for cyber risk assessment, mitigation, and response, although some hopeful precepts toward this end are emerging.

Computer Science↗

Next Generation Safeguards Initiative Efforts at Los Alamos National Laboratory: Developing Our Human Capital FY2015

This report documents the accomplishments of the Safeguards HCD Fiscal Year 2015 (FY15) Project Work Plan, highlighting LANL’s work as well as the accomplishments of our NGSI-sponsored students, graduate and postdoctoral fellows, and mid-career professionals during this past year. While fiscal year 2015 has been a year of transition in the Human Capital Development area for LANL, we are working to revitalize our efforts to promote and develop Human Capital in Safeguards and Non-proliferation and are looking forward to implementing new initiatives in the coming fiscal year and continuing to transition the knowledge of staff who have been on assignment at IAEA and Headquarters to improve our support to HCD.

Human Capital Development University Engagement↗

WMC Database Evaluation. Case Study Report

The WMC Database is ultimately envisioned to hold a collection of experimental data, design information, and information from computational models. This project was a first attempt at using the Database to access experimental data and extract information from it. This evaluation shows that the Database concept is sound and robust, and that the Database, once fully populated, should remain eminently usable for future researchers.

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Development of a SPARK Training Dataset

In its first five years, the National Nuclear Security Administration’s (NNSA) Next Generation Safeguards Initiative (NGSI) sponsored more than 400 undergraduate, graduate, and post-doctoral students in internships and research positions (Wyse 2012). In the past seven years, the NGSI program has, and continues to produce a large body of scientific, technical, and policy work in targeted core safeguards capabilities and human capital development activities. Not only does the NGSI program carry out activities across multiple disciplines, but also across all U.S. Department of Energy (DOE)/NNSA locations in the United States. However, products are not readily shared among disciplines and across locations, nor are they archived in a comprehensive library. Rather, knowledge of NGSI-produced literature is localized to the researchers, clients, and internal laboratory/facility publication systems such as the Electronic Records and Information Capture Architecture (ERICA) at the Pacific Northwest National Laboratory (PNNL). There is also no incorporated way of analyzing existing NGSI literature to determine whether the larger NGSI program is achieving its core safeguards capabilities and activities. A complete library of NGSI literature could prove beneficial to a cohesive, sustainable, and more economical NGSI program. The Safeguards Platform for Automated Retrieval of Knowledge (SPARK) has been developed to be a knowledge storage, retrieval, and analysis capability to capture safeguards knowledge to exist beyond the lifespan of NGSI. During the development process, it was necessary to build a SPARK training dataset (a corpus of documents) for initial entry into the system and for demonstration purposes. We manipulated these data to gain new information about the breadth of NGSI publications, and they evaluated the science-policy interface at PNNL as a practical demonstration of SPARK’s intended analysis capability. The analysis demonstration sought to answer the question, “Who leads research and development at PNNL, scientists or policy researchers?” The analysis was inconclusive as to whether policy researchers or scientists are primary drivers for research at PNNL. However, the dataset development and analysis activity did demonstrate the utility and usability of the SPARK dataset. After the initiation of the NGSI program there is a clear increase in the number of publications of safeguards products. Employing the natural language analysis tool IN SPIRE™ showed the presence of vocation- and topic-specific vernacular within NGSI sub-topics. The methodology developed to define the scope of the dataset was useful in describing safeguards applications, but may be applicable for research on other topics beyond safeguards. The analysis emphasized the need for an expanded dataset to fully understand the scope of safeguards publications and research both nationally and internationally. As the SPARK dataset grows to include publications outside PNNL, topics crosscutting disciplines and DOE/NNSA locations should become more apparent. NGSI was established in 2008 to cultivate the next generation of safeguards professionals and support the development of core safeguards capabilities (NNSA 2012). Now a robust system to preserve and share institutional memory such as SPARK is needed to inspire and equip the next generation of safeguards experts, technologies, and policies.

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Lessons Learned from the Development of an Example Precision Information Environment for International Safeguards

The International Atomic Energy Agency (IAEA) is interested in increasing capabilities of IAEA safeguards inspectors to access information that would improve their situational awareness on the job. A mobile information platform could potentially provide access to information, analytics, and technical and logistical support to inspectors in the field, as well as providing regular updates to analysts at IAEA Headquarters in Vienna or at satellite offices. To demonstrate the potential capability of such a system, Pacific Northwest National Laboratory (PNNL) implemented a number of example capabilities within a PNNL-developed precision information environment (PIE), and using a tablet as a mobile information platform. PNNL’s safeguards proof-of-concept PIE intends to; demonstrate novel applications of mobile information platforms to international safeguards use cases; demonstrate proof-of-principle capability implementation; and provide “vision” for capabilities that could be implemented. This report documents the lessons learned from this two-year development activity for the Precision Information Environment for International Safeguards (PIE-IS), describing the developed capabilities, technical challenges, and considerations for future development, so that developers working to develop a similar system for the IAEA or other safeguards agencies might benefit from our work.

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Final Technical Report for Terabit-scale hybrid networking project.

This report describes our accomplishments and activities for the project titled Terabit-Scale Hybrid Networking. The key accomplishment is that we developed, tested and deployed an Alpha Flow Characterization System (AFCS) in ESnet. It is being run in production mode since Sept. 2015. Also, a new QoS class was added to ESnet5 to support alpha flows.

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Machine Learning for Big Data: A Study to Understand Limits at Scale

This report aims to empirically understand the limits of machine learning when applied to Big Data. We observe that recent innovations in being able to collect, access, organize, integrate, and query massive amounts of data from a wide variety of data sources have brought statistical data mining and machine learning under more scrutiny, evaluation and application for gleaning insights from the data than ever before. Much is expected from algorithms without understanding their limitations at scale while dealing with massive datasets. In that context, we pose and address the following questions How does a machine learning algorithm perform on measures such as accuracy and execution time with increasing sample size and feature dimensionality? Does training with more samples guarantee better accuracy? How many features to compute for a given problem? Do more features guarantee better accuracy? Do efforts to derive and calculate more features and train on larger samples worth the effort? As problems become more complex and traditional binary classification algorithms are replaced with multi-task, multi-class categorization algorithms do parallel learners perform better? What happens to the accuracy of the learning algorithm when trained to categorize multiple classes within the same feature space? Towards finding answers to these questions, we describe the design of an empirical study and present the results. We conclude with the following observations (i) accuracy of the learning algorithm increases with increasing sample size but saturates at a point, beyond which more samples do not contribute to better accuracy/learning, (ii) the richness of the feature space dictates performance - both accuracy and training time, (iii) increased dimensionality often reflected in better performance (higher accuracy in spite of longer training times) but the improvements are not commensurate the efforts for feature computation and training and (iv) accuracy of the learning algorithms drop significantly with multi-class learners training on the same feature matrix and (v) learning algorithms perform well when categories in labeled data are independent (i.e., no relationship or hierarchy exists among categories).

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Nuclear Data Needs and Capabilities for Applications

In July 2014, DOE NP carried out a review of the US Nuclear Data Program. This led to several recommendations, including that the USNDP should “devise effective and transparent mechanisms to solicit input and feedback from all stakeholders on nuclear data needs and priorities.” The review also recommended that USNDP pursue experimental activities of relevance to nuclear data; the revised 2014 Mission Statement accordingly states that the USNDP uses “targeted experimental studies” to address gaps in nuclear data. In support of these recommendations, DOE NP requested that USNDP personnel organize a Workshop on Nuclear Data Needs and Capabilities for Applications (NDNCA). This Workshop was held at Lawrence Berkeley National Laboratory (LBNL) on 27-29 May 2015. The goal of the NDNCA Workshop was to compile nuclear data needs across a wide spectrum of applied nuclear science, and to provide a summary of associated capabilities (accelerators, reactors, spectrometers, etc.) available for the required measurements. The first two days of the workshop consisted of 25 plenary talks by speakers from 16 different institutions, on nuclear energy (NE), national security (NS), isotope production (IP), and industrial applications (IA). There were also shorter “capabilities” talks that described the experimental facilities and instrumentation available for the measurement of nuclear data. This was followed by a third day of topic-specific “breakout” sessions and a final closeout session. The agenda and copies of these talks are available online at http://bang.berkeley.edu/events/NDNCA/agenda. The importance of nuclear data to both basic and applied nuclear science was reflected in the fact that while the impetus for the workshop arose from the 2014 USNDP review, joint sponsorship for the workshop was provided by the Nuclear Science and Security Consortium, a UC-Berkeley based organization funded by the National Nuclear Security Administration (NNSA).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Co-ordination of the International Network of Nuclear Structure and Decay Data Evaluators

The 21st meeting of the International Network of Nuclear Structure and Decay Data Evaluators was convened at the IAEA Headquarters, Vienna, from 20 to 24 April 2015 under the auspices of the IAEA Nuclear Data Section. This meeting was attended by 36 scientists from 15 Member States, plus IAEA staff, concerned with the compilation, evaluation and dissemination of nuclear structure and decay data. A summary of the meeting, data centre reports, various proposals considered, and actions agreed by the participants, as well as recommendations/conclusions are presented within this document.

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