Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “SCIENTISTS”

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.

At least 865 records · Page 48

Exascale Co-design for Modeling Materials in Extreme Environments

Computational materials science has provided great insight into the response of materials under extreme conditions that are difficult to probe experimentally. For example, shock-induced plasticity and phase transformation processes in single-crystal and nanocrystalline metals have been widely studied via large-scale molecular dynamics simulations, and many of these predictions are beginning to be tested at advanced 4th generation light sources such as the Advanced Photon Source (APS) and Linac Coherent Light Source (LCLS). I will describe our simulation predictions and their recent verification at LCLS, outstanding challenges in modeling the response of materials to extreme mechanical and radiation environments, and our efforts to tackle these as part of the multi-institutional, multi-disciplinary Exascale Co-design Center for Materials in Extreme Environments (ExMatEx). ExMatEx has initiated an early and deep collaboration between domain (computational materials) scientists, applied mathematicians, computer scientists, and hardware architects, in order to establish the relationships between algorithms, software stacks, and architectures needed to enable exascale-ready materials science application codes within the next decade. We anticipate that we will be able to exploit hierarchical, heterogeneous architectures to achieve more realistic large-scale simulations with adaptive physics refinement, and are using tractable application scale-bridging proxy application testbeds to assess new approaches and requirements. Such current scale-bridging strategies accumulate (or recompute) a distributed response database from fine-scale calculations, in a top-down rather than bottom-up multiscale approach.

36 MATERIALS SCIENCE↗

Performance Engineering Research Institute SciDAC-2 Enabling Technologies Institute Final Report

Enhancing the performance of SciDAC applications on petascale systems has high priority within DOE SC. As we look to the future, achieving expected levels of performance on high-end com-puting (HEC) systems is growing ever more challenging due to enormous scale, increasing archi-tectural complexity, and increasing application complexity. To address these challenges, PERI has implemented a unified, tripartite research plan encompassing: (1) performance modeling and prediction; (2) automatic performance tuning; and (3) performance engineering of high profile applications. The PERI performance modeling and prediction activity is developing and refining performance models, significantly reducing the cost of collecting the data upon which the models are based, and increasing model fidelity, speed and generality. Our primary research activity is automatic tuning (autotuning) of scientific software. This activity is spurred by the strong user preference for automatic tools and is based on previous successful activities such as ATLAS, which has automatically tuned components of the LAPACK linear algebra library, and other re-cent work on autotuning domain-specific libraries. Our third major component is application en-gagement, to which we are devoting approximately 30% of our effort to work directly with Sci-DAC-2 applications. This last activity not only helps DOE scientists meet their near-term per-formance goals, but also helps keep PERI research focused on the real challenges facing DOE computational scientists as they enter the Petascale Era.

97 MATHEMATICS AND COMPUTING↗

Center for Scalable Application Development Software (Final Report)

The Center for Scalable Application Development Software (CScADS) was established as a part- nership between Rice University, Argonne National Laboratory, University of California Berkeley, University of Tennessee – Knoxville, and University of Wisconsin – Madison. CScADS pursued an integrated set of activities with the aim of increasing the productivity of DOE computational scientists by catalyzing the development of systems software, libraries, compilers, and tools for leadership computing platforms. Principal Center activities were workshops to engage the research community in the challenges of leadership computing, research and development of open-source software, and work with computational scientists to help them develop codes for leadership computing platforms. This final report summarizes CScADS activities at Rice University in these areas.

97 MATHEMATICS AND COMPUTING↗

Runtime System for I/O Staging in Support of In-Situ Processing of Extreme Scale Data

Our research in this project focused on creating and evaluating an I/O infrastructure and tools for extreme-scale applications and machines so that scientists can reduce their time to discovery at small cost in machine resources and consequent power consumption. We wanted to provide tools that are highly scalable, portable, and easy-to-use, so that scientists can gain control of their science, and concentrate on producing important scientific discovery in their own domain. Accelerating the rate of insight and scientific productivity, therefore, demands new solutions to managing the avalanche of data expected at extreme scale.

97 MATHEMATICS AND COMPUTING↗

Combinatorial Algorithms to Enable Computational Science and Engineering: Work from the CSCAPES Institute

This final progress report summarizes the work accomplished at the Combinatorial Scientific Computing and Petascale Simulations Institute. We developed Zoltan, a parallel mesh partitioning library that made use of accurate hypergraph models to provide load balancing in mesh-based computations. We developed several graph coloring algorithms for computing Jacobian and Hessian matrices and organized them into a software package called ColPack. We developed parallel algorithms for graph coloring and graph matching problems, and also designed multi-scale graph algorithms. Three PhD students graduated, six more are continuing their PhD studies, and four postdoctoral scholars were advised. Six of these students and Fellows have joined DOE Labs (Sandia, Berkeley), as staff scientists or as postdoctoral scientists. We also organized the SIAM Workshop on Combinatorial Scientific Computing (CSC) in 2007, 2009, and 2011 to continue to foster the CSC community.

97 MATHEMATICS AND COMPUTING↗

Theoretical and Computational Modeling of Magnetically Ordered Molecules & Electronic Nano-Transport of Spins: State of Art and Unanswered Questions : Workshop

As a culmination of a five-year Nordforsk Network project entitled “Nanospintronics: Theory and Simulations”, Professor Carlo Canali (Linneaus University, Sweden) and members of the network, and Vincenza Benza (Local Organizer Milan) organized a summer workshop in conjunction with the A. Volta Scientific Cultural Exchange program. This workshop took place 24-30 August 2013. Several Basic Energy Scientists from the US conducted lectures and also provided hands-on tutorials to developing materials and chemicals scientists. We have received a total of $10,000 of support to offset the travel expenses of US-based participants for this workshop.

36 MATERIALS SCIENCE↗

Sustaining and Extending the Open Science Grid: Science Innovation on a PetaScale Nationwide Facility (DE-FC02-06ER41436) SciDAC-2 Closeout Report

Under this SciDAC-2 grant the project’s goal w a s t o stimulate new discoveries by providing scientists with effective and dependable access to an unprecedented national distributed computational facility: the Open Science Grid (OSG). We proposed to achieve this through the work of the Open Science Grid Consortium: a unique hands-on multi-disciplinary collaboration of scientists, software developers and providers of computing resources. Together the stakeholders in this consortium sustain and use a shared distributed computing environment that transforms simulation and experimental science in the US. The OSG consortium is an open collaboration that actively engages new research communities. We operate an open facility that brings together a broad spectrum of compute, storage, and networking resources and interfaces to other cyberinfrastructures, including the US XSEDE (previously TeraGrid), the European Grids for ESciencE (EGEE), as well as campus and regional grids. We leverage middleware provided by computer science groups, facility IT support organizations, and computing programs of application communities for the benefit of consortium members and the US national CI.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Dynamic Non-Hierarchical File Systems for Exascale Storage

This constitutes the final report for “Dynamic Non-Hierarchical File Systems for Exascale Storage”. The ultimate goal of this project was to improve data management in scientific computing and high-end computing (HEC) applications, and to achieve this goal we proposed: to develop the first, HEC-targeted, file system featuring rich metadata and provenance collection, extreme scalability, and future storage hardware integration as core design goals, and to evaluate and develop a flexible non-hierarchical file system interface suitable for providing more powerful and intuitive data management interfaces to HEC and scientific computing users. Data management is swiftly becoming a serious problem in the scientific community – while copious amounts of data are good for obtaining results, finding the right data is often daunting and sometimes impossible. Scientists participating in a Department of Energy workshop noted that most of their time was spent “...finding, processing, organizing, and moving data and it’s going to get much worse”. Scientists should not be forced to become data mining experts in order to retrieve the data they want, nor should they be expected to remember the naming convention they used several years ago for a set of experiments they now wish to revisit. Ideally, locating the data you need would be as easy as browsing the web. Unfortunately, existing data management approaches are usually based on hierarchical naming, a 40 year-old technology designed to manage thousands of files, not exabytes of data. Today’s systems do not take advantage of the rich array of metadata that current high-end computing (HEC) file systems can gather, including content-based metadata and provenance1 information. As a result, current metadata search approaches are typically ad hoc and often work by providing a parallel management system to the “main” file system, as is done in Linux (the locate utility), personal computers, and enterprise search appliances. These search applications are often optimized for a single file system, making it difficult to move files and their metadata between file systems. Users have tried to solve this problem in several ways, including the use of separate databases to index file properties, the encoding of file properties into file names, and separately gathering and managing provenance data, but none of these approaches has worked well, either due to limited usefulness or scalability, or both. Our research addressed several key issues: High-performance, real-time metadata harvesting: extracting important attributes from files dynamically and immediately updating indexes used to improve search; Transparent, automatic, and secure provenance capture: recording the data inputs and processing steps used in the production of each file in the system; Scalable indexing: indexes that are optimized for integration with the file system; Dynamic file system structure: our approach provides dynamic directories similar to those in semantic file systems, but these are the native organization rather than a feature grafted onto a conventional system. In addition to these goals, our research effort will include evaluating the impact of new storage technologies on the file system design and performance. In particular, the indexing and metadata harvesting functions can potentially benefit from the performance improvements promised by new storage class memories.

42 ENGINEERING↗

Adding Data Management Services to Parallel File Systems

The objective of this project, called DAMASC for “Data Management in Scientific Computing”, is to coalesce data management with parallel file system management to present a declarative interface to scientists for managing, querying, and analyzing extremely large data sets efficiently and predictably. Managing extremely large data sets is a key challenge of exascale computing. The overhead, energy, and cost of moving massive volumes of data demand designs where computation is close to storage. In current architectures, compute/analysis clusters access data in a physically separate parallel file system and largely leave it scientist to reduce data movement. Over the past decades the high-end computing community has adopted middleware with multiple layers of abstractions and specialized file formats such as NetCDF-4 and HDF5. These abstractions provide a limited set of high-level data processing functions, but have inherent functionality and performance limitations: middleware that provides access to the highly structured contents of scientific data files stored in the (unstructured) file systems can only optimize to the extent that file system interfaces permit; the highly structured formats of these files often impedes native file system performance optimizations. We are developing Damasc, an enhanced high-performance file system with native rich data management services. Damasc will enable efficient queries and updates over files stored in their native byte-stream format while retaining the inherent performance of file system data storage via declarative queries and updates over views of underlying files. Damasc has four key benefits for the development of data-intensive scientific code: (1) applications can use important data-management services, such as declarative queries, views, and provenance tracking, that are currently available only within database systems; (2) the use of these services becomes easier, as they are provided within a familiar file-based ecosystem; (3) common optimizations, e.g., indexing and caching, are readily supported across several file formats, avoiding effort duplication; and (4) performance improves significantly, as data processing is integrated more tightly with data storage. Our key contributions are: SciHadoop which explores changes to MapReduce assumption by taking advantage of semantics of structured data while preserving MapReduce’s failure and resource management; DataMods which extends common abstractions of parallel file systems so they become programmable such that they can be extended to natively support a variety of data models and can be hooked into emerging distributed runtimes such as Stanford’s Legion; and Miso which combines Hadoop and relational data warehousing to minimize time to insight, taking into account the overhead of ingesting data into data warehousing.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Institute for Ultralscale Visualization (Final Report)

The SciDAC Institute for Ultrascale Visualization brought together leading experts from visualization, high-performance computing, and science application areas to make advanced visualization solutions for SciDAC scientists and the broader community. Over the five-year project, the Institute introduced many new enabling visualization techniques, which have significantly enhanced scientists’ ability to validate their simulations, interpret their data, and communicate with others about their work and findings. This Institute project involved a large number of junior and student researchers, who received the opportunities to work on some of the most challenging science applications and gain access to the most powerful high-performance computing facilities in the world. They were readily trained and prepared for facing the greater challenges presented by extreme-scale computing. The Institute’s outreach efforts, through publications, workshops and tutorials, successfully disseminated the new knowledge and technologies to the SciDAC and the broader scientific communities. The scientific findings and experience of the Institute team helped plan the SciDAC3 program.

97 MATHEMATICS AND COMPUTING↗

Mississippi State University Cooling, Heating, and Power (Micro-CHP) and Bio-Fuel Center

Between 2008 and 2014, the U.S. Department of Energy funded the MSU Micro-CHP and Bio-Fuel Center located at Mississippi State University. The overall objective of this project was to enable micro-CHP (micro-combined heat and power) utilization, to facilitate and promote the use of CHP systems and to educate architects, engineers, and agricultural producers and scientists on the benefits of CHP systems. Therefore, the work of the Center focused on the three areas: CHP system modeling and optimization, outreach, and research. In general, the results obtained from this project demonstrated that CHP systems are attractive because they can provide energy, environmental, and economic benefits. Some of these benefits include the potential to reduce operational cost, carbon dioxide emissions, primary energy consumption, and power reliability during electric grid disruptions. The knowledge disseminated in numerous journal and conference papers from the outcomes of this project is beneficial to engineers, architects, agricultural producers, scientists and the public in general who are interested in CHP technology and applications. In addition, more than 48 graduate students and 23 undergraduate students, benefited from the training and research performed in the MSU Micro-CHP and Bio-Fuel Center. The project scope consisted of six separate, but related areas: 1. Micro-CHP modeling - Computational simulations and verification/validation studies of selected models for technical and economic viability. 2. Fuel flexibility in micro-CHP applications – Demonstrating and quantifying the operation of micro-CHP systems on alternative fuels. 3. Outreach activities – Providing provide educational materials for engineers and architects and conducting outreach seminars, workshops, and demonstrations for engineers and architects as well as for more general audiences. 4. Biofuel and opportunity fuel processing and utilization – Determining the processing requirements and feasibility to utilize biofuels and disaster debris for micro-CHP systems. 5. CHP Systems Optimization – Optimization of internal combustion and Stirling engines integrated with heat recovery systems for CHP operation. 6. Distributed Generation and Grid Interconnection – Developing requirements and control systems for interconnection of micro-CHP systems with the electrical grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Roadmap to MaRIE May 2015

Los Alamos National Laboratory (LANL) hosted the Stewardship Science Academic Programs Symposium, which is designed to foster relationships among young scientists, sponsors and the National Nuclear Security Administration national laboratories. The event highlights much of the work done by prominent scientists and allows attendees to view the multiple on site facilities at LANL.

36 MATERIALS SCIENCE↗

Collaborative project. Ocean-atmosphere interaction from meso-to planetary-scale. Mechanisms, parameterization, and variability

This project aims to improve long term global climate simulations by resolving ocean mesoscale activity and the corresponding response in the atmosphere. The main computational objectives are; i) to perform and assess Community Earth System Model (CESM) simulations with the new Community Atmospheric Model (CAM) spectral element dynamical core; ii) use static mesh refinement to focus on oceanic fronts; iii) develop a new Earth System Modeling tool to investigate the atmospheric response to fronts by selectively filtering surface flux fields in the CESM coupler. The climate research objectives are 1) to improve the coupling of ocean fronts and the atmospheric boundary layer via investigations of dependency on model resolution and stability functions: 2) to understand and simulate the ensuing tropospheric response that has recently been documented in observations: and 3) to investigate the relationship of ocean frontal variability to low frequency climate variability and the accompanying storm tracks and extremes in high resolution simulations. This is a collaborative multi-institution project consisting of computational scientists, climate scientists and climate model developers. It specifically aims at DOE objectives of advancing simulation and predictive capability of climate models through improvements in resolution and physical process representation.

54 ENVIRONMENTAL SCIENCES↗

Flexible HPC Environments [Poster]

New computational paradigms require new tools and environments. A new class of data-analysis tools are available for scientists. These tools do not integrate well with traditional HPC resource management systems. Differing and often conflicting requirements exist. Some fields require validated environments including high energy physics and cosmology. By adopting cloud technologies in an HPC environment we are merging the compute-intensive and data-intensive worlds in one converged system. HPC Systems will support a Larger Set of Technologies. Scientists will be able to leverage the latest tools on HPC resources at previously unavailable scales. User-defined environments will enable rapid experimentation and adoption of emerging tools. HPC systems will support a consolidated workflow from simulation to analysis and discovery. HPC systems will have increased utility to a broader set of users.

97 MATHEMATICS AND COMPUTING↗

Nuclear forensics: Soil content

Nuclear Forensics is a growing field that is concerned with all stages of the process of creating and detonating a nuclear weapon. The main goal is to prevent nuclear attack by locating and securing nuclear material before it can be used in an aggressive manner. This stage of the process is mostly paperwork; laws, regulations, treaties, and declarations made by individual countries or by the UN Security Council. There is some preliminary leg work done in the form of field testing detection equipment and tracking down orphan materials; however, none of these have yielded any spectacular or useful results. In the event of a nuclear attack, the first step is to analyze the post detonation debris to aid in the identification of the responsible party. This aspect of the nuclear forensics process, while reactive in nature, is more scientific. A rock sample taken from the detonation site can be dissolved into liquid form and analyzed to determine its chemical composition. The chemical analysis of spent nuclear material can provide valuable information if properly processed and analyzed. In order to accurately evaluate the results, scientists require information on the natural occurring elements in the detonation zone. From this information, scientists can determine what percentage of the element originated in the bomb itself rather than the environment. To this end, element concentrations in soils from sixty-nine different cities are given, along with activity concentrations for uranium, thorium, potassium, and radium in various building materials. These data are used in the analysis program Python.

54 ENVIRONMENTAL SCIENCES↗

8th Annual Glycoscience Symposium: Integrating Models of Plant Cell Wall Structure, Biosynthesis and Assembly

The Complex Carbohydrate Research Center (CCRC) of the University of Georgia holds a symposium yearly that highlights a broad range of carbohydrate research topics. The 8th Annual Georgia Glycoscience Symposium entitled “Integrating Models of Plant Cell Wall Structure, Biosynthesis and Assembly” was held on April 7, 2014 at the CCRC. The focus of symposium was on the role of glycans in plant cell wall structure and synthesis. The goal was to have world leaders in conjunction with graduate students, postdoctoral fellows and research scientists to propose the newest plant cell wall models. The symposium program closely followed the DOE’s mission and was specifically designed to highlight chemical and biochemical structures and processes important for the formation and modification of renewable plant cell walls which serve as the basis for biomaterial and biofuels. The symposium was attended by both senior investigators in the field as well as students including a total attendance of 103, which included 80 faculty/research scientists, 11 graduate students and 12 Postdoctoral students.

09 BIOMASS FUELS↗

High Performance Data Analytics on a Commodity Private Cloud

Sandia is researching novel methods of analysis for large datasets. The term “Big Data” has been used frequently in recent years to describe a discipline of computer science that attempts to distil an increasing quantity of data into a result or meaningful subset that can be easily visualized and acted upon by human consumers. These data are frequently characterized by one or more of the following: high volume, velocity, variety, and veracity. Sandia scientists and researchers are no strangers to large datasets, which often typify the result of high-performance computing workloads. The challenge for Sandia data scientists, however, comes at the arrival of exascale computing, which has become mandated by executive order for national computing centers. Because of power and networking limitations, exascale computing precludes the movement of datasets at exascale from data production to data analysis. Such analysis must be done in-situ, to mitigate the time and power costs of network transport. Currently, many of Sandia’s data analytics environments exist as independent entities, often specialized for individual use cases, interconnected to other data analytics, high-performance computing, and storage systems. Sandia is procuring, installing, and researching future platforms for such data analytics. A memory footprint of 256 GB per node, fourteen data rate (FDR) InfiniBand networking, PCIe connected non-volatile memory (NVMe) data storage, and an object data store will characterize the coming institutional data analytics platforms. The combined systems will contain 300 compute nodes. A cloud-inspired software stack resulting from collaboration between the Sandia centers will allow the systems to support a flexible mix of user and system-facing workloads and use cases including code development and testing in R and Python, Apache Hadoop and Spark, distributed in-memory databases, graph analytics, emulytics, system log and predictive analytics, and data visualization for institutional and mission customers. These systems will arrive at the end of FY 2015 and production is planned by the spring of FY 2016.

97 MATHEMATICS AND COMPUTING↗

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.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗