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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Immersive visualization for materials science data analysis using the Oculus Rift

In this paper, we propose strategies and objectives for immersive data visualization with applications in materials science using the Oculus Rift virtual reality headset. We provide background on currently available analysis tools for neutron scattering data and other large-scale materials science projects. In the context of the current challenges facing scientists, we discuss immersive virtual reality visualization as a potentially powerful solution. We introduce a prototype immersive visualization system, developed in conjunction with materials scientists at the Spallation Neutron Source, which we have used to explore large crystal structures and neutron scattering data. Finally, we offer our perspective on the greatest challenges that must be addressed to build effective and intuitive virtual reality analysis tools that will be useful for scientists in a wide range of fields.

Drouhard, Margaret↗
DOE OSTI2015

A Collaborative Extensible User Environment for Simulation and Knowledge Management

In scientific simulation, scientists use measured data to create numerical models, execute simulations and analyze results from advanced simulators executing on high performance computing platforms. This process usually requires a team of scientists collaborating on data collection, model creation and analysis, and on authorship of publications and data. This paper shows that scientific teams can benefit from a user environment called Akuna that permits subsurface scientists in disparate locations to collaborate on numerical modeling and analysis projects. The Akuna user environment is built on the Velo framework that provides both a rich client environment for conducting and analyzing simulations and a Web environment for data sharing and annotation. Akuna is an extensible toolset that integrates with Velo, and is designed to support any type of simulator. This is achieved through data-driven user interface generation, use of a customizable knowledge management platform, and an extensible framework for simulation execution, monitoring and analysis. This paper describes how the customized Velo content management system and the Akuna toolset are used to integrate and enhance an effective collaborative research and application environment. The extensible architecture of Akuna is also described and demonstrates its usage for creation and execution of a 3D subsurface simulation.

collaborative science tools↗
DOE OSTI2012

HERCULES: A Pattern Driven Code Transformation System

New parallel computers are emerging, but developing efficient scientific code for them remains difficult. A scientist must manage not only the science-domain complexity but also the performance-optimization complexity. HERCULES is a code transformation system designed to help the scientist to separate the two concerns, which improves code maintenance, and facilitates performance optimization. The system combines three technologies, code patterns, transformation scripts and compiler plugins, to provide the scientist with an environment to quickly implement code transformations that suit his needs. Unlike existing code optimization tools, HERCULES is unique in its focus on user-level accessibility. In this paper we discuss the design, implementation and an initial evaluation of HERCULES.

Computer Science↗
DOE OSTI2021

Antarctic Water Masses and Ice Shelves: Visualizing the Physics

High-resolution simulation of global climate physics enables us to model how the climate may change under a variety of future scenarios. Such simulations produce vast amounts of information and dense datasets. If interrogated in tandem, these datasets can provide holistic, vital information on Earth’s many integrated systems by revealing the manifold interrelated properties of the atmosphere, ocean, and polar ice, framed by real-world terrain in three-dimensional space as they vary over time. To accomplish this, climate scientists have joined with computer scientists and an artist to develop techniques enabling scientists to see these relationships. Finally, the impact of ocean water properties on Antarctic ice shelves illustrates the benefit of this analysis in understanding land ice melt rates and thus sea-level rise.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Understanding the Performance and Potential of Cloud Computing for Scientific Applications

In this paper, commercial clouds bring a great opportunity to the scientific computing area. Scientific applications usually require significant resources, however not all scientists have access to sufficient high-end computing systems, may of which can be found in the Top500 list. Cloud Computing has gained the attention of scientists as a competitive resource to run HPC applications at a potentially lower cost. But as a different infrastructure, it is unclear whether clouds are capable of running scientific applications with a reasonable performance per money spent. This work studies the performance of public clouds and places this performance in context to price. We evaluate the raw performance of different services of AWS cloud in terms of the basic resources, such as compute, memory, network and I/O. We also evaluate the performance of the scientific applications running in the cloud. This paper aims to assess the ability of the cloud to perform well, as well as to evaluate the cost of the cloud running scientific applications. We developed a full set of metrics and conducted a comprehensive performance evlauation over the Amazon cloud. We evaluated EC2, S3, EBS and DynamoDB among the many Amazon AWS services. We evaluated the memory sub-system performance with CacheBench, the network performance with iperf, processor and network performance with the HPL benchmark application, and shared storage with NFS and PVFS in addition to S3. We also evaluated a real scientific computing application through the Swift parallel scripting system at scale. Armed with both detailed benchmarks to gauge expected performance and a detailed monetary cost analysis, we expect this paper will be a recipe cookbook for scientists to help them decide where to deploy and run their scientific applications between public clouds, private clouds, or hybrid clouds.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Anticipating Technical Expertise and Capability Evolution in Research Communities Using Dynamic Graph Transformers

The ability to anticipate global technical expertise and capability evolution trends is essential for national and global security, especially in safety-critical domains such as nuclear nonproliferation (NN) and rapidly emerging fields like artificial intelligence (AI). Here, in this work, we extend traditional statistical relational learning approaches (e.g., link prediction in collaboration networks) and formulate a problem of anticipating technical expertise and capability evolution using dynamic heterogeneous graph representations. We develop novel capabilities to forecast collaboration patterns, authorship behavior, and technical capability evolution at different granularities (e.g., scientist and institution levels) in two distinct research fields. We implement a dynamic graph transformer (DGT) neural architecture, which pushes the state-of-the-art graph neural network models by: 1) forecasting heterogeneous (rather than homogeneous) nodes and edges; and 2) relying on both discrete- and continuous-time inputs. We demonstrate that our DGT models predict collaboration, partnership, and expertise patterns with 0.26, 0.73, and 0.53 mean reciprocal rank values for AI and 0.48, 0.93, and 0.22 for NN domains. DGT model performance exceeds the best-performing static graph baseline models by 30%–80% across AI and NN domains. Our findings demonstrate that DGT models boost inductive task performance when previously unseen nodes appear in the test data for the domains with emerging collaboration patterns (e.g., AI). Specifically, models accurately predict which established scientists will collaborate with early career scientists and vice versa in the AI domain.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Crystal Ball: A Naturalist Perspective of Microbiology: Examples from Methanogenic Archaea

Storytelling has been the primary means of knowledge transfer over human history. The effectiveness and reach of stories are improved when the message is appropriate for the target audience. Oftentimes, the stories that are most well received and recounted are those that have a clear purpose and that are told from a variety of perspectives that touch on the varied interests of the target audience. Whether scientists realize or not, they are accustomed to telling stories of their own scientific discoveries through the preparation of manuscripts, presentations, and lectures. Perhaps less frequently, scientists prepare review articles or book chapters that summarize a body of knowledge on a given subject matter, meant to be more holistic recounts of a body of literature. Yet, by necessity, such summaries are often still narrow in their scope and are told from the perspective of a particular discipline. In other words, interdisciplinary reviews or book chapters tend to be the rarity rather than the norm. Naturalists are scientists that tell stories of organisms and their natural environment within a historical context and from an interdisciplinary perspective. Such stories interweave observations of biological subjects from disparate disciplines into a cohesive story. To this end, the success of naturalists in recounting compelling natural history stories is enhanced when they allow themselves to become vulnerable by knowingly entering a literature base full of unfamiliar ways of observing and describing such subjects. When successful, this approach can broaden the scope of the story, making it of interest to a larger audience and attracting further recounting of that story (e.g., citations). To this end, when we look into our crystal ball, we see a need to tell more holistic stories of microorganisms and their functions from an interdisciplinary, naturalist perspective that combines what is known of their contemporary ecology, physiology, and biochemistry with their evolutionary history thereby placing them within a historical Earth context. With this conviction in mind, we take the opportunity to look back on the natural history of methanogens to identify some of their most profound biochemical innovations and their impact on the trajectory of life and the planet. Our approach was to integrate knowledge from the fields of biochemistry, physiology, ecology, evolution, geochemistry, and atmospheric sciences. We start with the origin of the first autotrophic cells and how this set Earth on a path towards biomass expansion and functional diversification. Next, we discuss the importance of iron and sulfur in methanogen metabolism, and how their innovative ways of accessing these elements allowed for expanded use of iron-sulfur clusters ([Fe-S]) as nanoscale catalysts that power(ed) Earth’s biochemical cycles. We next discuss the origin and evolution of [NiFe]-hydrogenase and how this versatile enzyme chassis was co-opted by a variety of anaerobes and aerobes as the foundation for respiratory complexes capable of sustaining higher forms of life, including humans. Lastly, we discuss the evolution of the enzyme molybdenum nitrogenase, and how this allowed the biosphere to overcome fixed nitrogen limitation by tapping the seemingly endless reserve of atmospheric dinitrogen (N 2 ). In telling these stories, we aimed to generate template(s) to motivate future interdisciplinary natural history studies focused on other organisms and processes. Further, we hope to stimulate scientific discourse and provide impetus to become vulnerable and peruse literature of relevance to understanding the causes and consequences of the evolution of your favorite microorganism or biochemical process.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Ecological information and approaches needed for risk communication dialogs for acute or chronic environmental crises

Scientists, social scientists, risk communicators, and many others are often thrust into a crisis situation where they need to interact with a range of stakeholders, including governmental personnel (tribal, U.S. federal, state, local), local residents, and other publics, as well as other scientists and other risk communicators in situations where information is incomplete and evolving. This paper provides: (1) an overall framework for thinking about communication during crises, from acute to chronic, and local to widespread, (2) a template for the types of ecological information needed to address public and environmental concerns, and (3) examples to illustrate how this information will aid risk communicators. The main goal is providing an approach to the knowledge needed by communicators to address the challenges of protecting ecological resources during an environmental crisis, or for an on-going, chronic environmental issue. To understand the risk to these ecological resources, it is important to identify the type of event, whether it is acute or chronic (or some combination of these), what receptors are at risk, and what stressors are involved (natural, biological, chemical, radiological). For ecological resources, the key information a communicator needs for a crisis is whether any of the following are present: threatened or endangered species, species of special concern, species groups of concern (e.g., neotropical bird migrants, breeding frogs in vernal ponds, rare plant assemblages), unique or rare habitats, species of commercial and recreational interest, and species/habitats of especial interest for medicinal, cultural, or religious activities. Communication among stakeholders is complicated with respect to risk to ecological receptors because of differences in trust, credibility, empathy, perceptions, world view valuation of the resources, and in many cases, a history of misinformation, disinformation, or no information. Exposure of salmon spawning in the Columbia River to hexavalent chromium from the Hanford Site is used as an example of communication challenges with different stakeholders, including Native Americans with Tribal Treaty rights to the land.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Xenotransplantation in China: Present status

Abstract The main obstacle to organ transplantation is the shortage of organs from deceased individuals. Especially in China, the ratio of patients on the waiting list versus the transplant recipients is 30:1. Therefore, there is an urgent need for organ donors. Genetically modified pig organs have proved to be a new source for xenotransplantation, and Chinese scientists have made considerable progress in this area during recent years. In this paper, we review four important aspects of the xenotransplantation field in China. First, a large variety of genetically modified pigs have been generated by Chinese scientists: all these genetically modified pigs and the purpose of these modifications will be summarized. Second, the preclinical research in pig‐to‐nonhuman primate xenotransplantation is outlined. The survival time and major biochemical parameters for the xenografts are summarized. Third, regarding the bench‐to‐bed approach, more suitable organs have been developed for xenotransplantation in humans, and in particular, pig islet transplantation into diabetic patients as well as pig‐to‐human cornea and skin transplantation. Fourth, we briefly address the regulations and prospects for recruiting xenotransplantation experts in China. Based on recent progress, we anticipate that genetically modified pigs will offer suitable organs for the treatment of end‐stage organ diseases in humans in the near future. Given the recent influx of world‐renowned scientists in xenotransplantation to China, our country will definitely become one of the major centers of xenotransplantation research and development in the world.

Wang, Yi↗
DOE OSTI2013

The DOE fellows program-a workforce development initiative for the US department of energy

The US Department of Energy Office of Environmental Management (DOE-EM) oversees one of the largest and most technically challenging cleanup programs in the world. The mission of DOE-EM is to complete the safe cleanup of the environmental legacy from five decades of nuclear weapons development and government-sponsored nuclear energy research. Since 1995, Florida International University's Applied Research Center (FIU-ARC) has supported the DOE-EM mission and provided unique research capabilities to address some of these highly technical and difficult challenges. This partnership has allowed FIU-ARC to create a unique infrastructure that is critical for the training and mentoring of science, technology, engineering, and math (STEM) students and has exposed many STEM students to 'hands-on' DOE-EM applied research, supervised by the scientists and engineers at ARC. As a result of this successful partnership between DOE and FIU, DOE requested FIU-ARC to create the DOE-FIU Science and Technology Workforce Development Initiative in 2007. This innovative program was established to create a 'pipeline' of minority STEM students trained and mentored to enter DOE's environmental cleanup workforce. The program was designed to help address DOE's future workforce needs by partnering with academic, government and private companies (DOE contractors) to mentor future minority scientists and engineers in the research, development, and deployment of new technologies and processes addressing DOE's environmental cleanup challenges. Since its inception in 2007, the program has trained and mentored 78 FIU STEM minority students. Although, the program has been in existence for only six years, a total of 75 internships have been conducted at DOE National Laboratories, DOE sites, DOE Headquarters and field offices, and DOE contractors. Over 100 DOE Fellows have participated in the Waste Management (WM) Symposia since 2008 with a total of 84 student posters and 7 oral presentations given at WM. The DOE Fellows participation at WM has resulted in three Best Student Poster Awards (WM09, WM10, and WM11) and one Best Professional Poster Award (WM09). DOE Fellows have also presented their research at ANS DD and R and ANS Robotics Topical meetings and this year two Fellows will present at the International Conference on Environmental Remediation and Radioactive Waste Management (ICEM13) in Brussels, Belgium. Moreover, several of our DOE Fellows have already obtained employment with DOE-EM, other federal agencies, DOE contractors, commercial nuclear power companies, and other STEM industry (GE, Boeing, Lockheed Martin, Johnson and Johnson, Beckman-Coulter, and other top companies). This paper will discuss how DOE Fellows program is training and mentoring FIU STEM students in Department of Energy's Office of Environmental Management technical challenges and research. This training and mentoring has resulted in the development of well-trained and polished young scientists and engineers that will become the future workforce in charge of carrying on DOE-EM's environmental cleanup mission. The paper will showcase FIU's DOE Fellows model and highlight some of the applied research the DOE Fellows have conducted at FIU's Applied Research Center and across the DOE Complex by participating in summer internship assignments. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Toward unrestricted use of public genomic data

Despite some notable progress in data sharing policies and practices, restrictions are still often placed on the open and unconditional use of various genomic data after they have received official approval for release to the public domain or to public databases. These restrictions, which often conflict with the terms and conditions of the funding bodies who supported the release of those data for the benefit of the scientific community and society, are perpetuated by the lack of clear guiding rules for data usage. Existing guidelines for data released to the public domain recognize but fail to resolve tensions between the importance of free and unconditional use of these data and the “right” of the data producers to the first publication. This self-contradiction has resulted in a loophole that allows different interpretations and a continuous debate between data producers and data users on the use of public data. We argue that the publicly available data should be treated as open data, a shared resource with unrestricted use for analysis, interpretation, and publication.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Evaluation of Graph Pattern Matching Workloads in Graph Analysis Systems

Graph analysis has emerged as a powerful method for data scientists to represent, integrate, query, and explore heterogeneous data sources. As a result, graph data management and mining became a popular area of research, and led to the development of plethora of systems in recent years. Unfortunately, the number of emerging graph analysis systems and the wide range of applications, coupled with a lack of apples-to-apples comparisons, make it difficult to understand the trade-offs between different systems and the graph operations for which they are designed. A fair comparison of these systems is a challenging task for the following reasons: multiple data models, non-standardized serialization formats, various query interfaces to users, and diverse environments they operate in. To address these key challenges, in this paper we present a new benchmark suite by extending the Lehigh University Benchmark (LUBM) to cover the most common capabilities of various graph analysis systems. We provide the design process of the benchmark, which generalizes the workflow for data scientists to conduct the desired graph analysis on different graph analysis systems. Equipped with this extended benchmark suite, we present performance comparison for nine subgraph pattern retrieval operations over six graph analysis systems, namely NetworkX, Neo4j, Jena, Titan, GraphX, and uRiKA. Through the proposed benchmark suite, this study reveals both quantitative and qualitative findings in (1) implications in loading data into each system; (2) challenges in describing graph patterns for each query interface; and (3) different sensitivity of each system to query selectivity. We envision that this study will pave the road for: (i) data scientists to select the suitable graph analysis systems, and (ii) data management system designers to advance graph analysis systems.

Hong, Seokyong↗
DOE OSTI2014

Multiphysics Object Oriented Simulation Environment

The Multiphysics Object Oriented Simulation Environment (MOOSE) software library developed at Idaho National Laboratory is a tool. MOOSE, like other tools, doesn't actually complete a task. Instead, MOOSE seeks to reduce the effort required to create engineering simulation applications. MOOSE itself is a software library: a blank canvas upon which you write equations and then MOOSE can help you solve them. MOOSE is comparable to a spreadsheet application. A spreadsheet, by itself, doesn't do anything. Only once equations are entered into it will a spreadsheet application compute anything. Such is the same for MOOSE. An engineer or scientist can utilize the equation solvers within MOOSE to solve equations related to their area of study. For instance, a geomechanical scientist can input equations related to water flow in underground reservoirs and MOOSE can solve those equations to give the scientist an idea of how water could move over time. An engineer might input equations related to the forces in steel beams in order to understand the load bearing capacity of a bridge. Because MOOSE is a blank canvas it can be useful in many scientific and engineering pursuits.

Source record↗
DOE OSTI2014

ARM Data Integrator

The Atmospheric Radiation Measurement (ARM) Data Integrator (ADI) streamlines the development of scientific algorithms and analysis of time-series NetCDF data, and improves the content and consistency of the output data products produced by these algorithms. The framework automates the process of retrieving and preparing data for analysis, and allows users to design output data products through a graphical interface. It also provides a modular, flexible software development architecture that scientists can use to generate C, Python, and IDL source code templates that embed the pre and post processing logic allowing the scientist to focus on only their science. The input data, preprocessing, and output data specifications of algorithms are defined through a graphical interface and stored in a database. ADI implements workflow for data integration and supports user access to data through a library of software modules. Data preprocess capabilities supported include automated retrieval of data from input files, merging the retrieved data into appropriately sized chunks, and transformation of the data onto a common coordinate system grid. Through the graphical interface, users can view the details of both their data products and those in the ARM catalog and allows developers to use existing data product to build new data products. Views of the output data products include an overlay of how the design meets ARM archive's data standards providing the user with a visual cue indicating where their output violates an archive standard. The ADI libraries access the information provided through the GUI via a Postgres database. The ADI framework and its supporting components can significantly decrease the time and cost of implementing scientific algorithms while improving the ability of scientists to disseminate their results.

Source record↗
DOE OSTI2021

MLExchange v1

A Machine Learning Platform that connects beamline scientists and machine learning (ML) researchers so that the ML researchers can quickly distribute their ML models to beamline scientists, and beamline scientists can give feedback to help ML researchers improve their models.

Hexemer, Alexander↗
DOE OSTI2014

Science is innate!

I am a professional scientist. What does that mean? I think it is safe to say it means that, to the best of my ability, I apply the scientist method to test hypotheses and gain a clearer understanding of the world around me. Furthermore, I am also a human being, and so when I am asked, as I often am, about how my research findings have influenced my day-to-day activities, I like to take a step back and think about what it means to be a scientist.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Phase Mapper: Accelerating Materials Discovery with AI

From the stone age, to the bronze, iron age, and modern silicon age, the discovery and characterization of new materials has always been instrumental to humanity's progress and development. With the current pressing need to address sustainability challenges and find alternatives to fossil fuels, we look for solutions in the development of new materials that will allow for renewable energy. To discover materials with the required properties, materials scientists can perform high-throughput materials discovery, which includes rapid synthesis and characterization via X-ray diffraction (XRD) of thousands of materials. A central problem in materials discovery, the phase map identification problem, involves the determination of the crystal structure of materials from materials composition and structural characterization data. This analysis is traditionally performed mainly by hand, which can take days for a single material system. In this work we present Phase-Mapper, a solution platform that tightly integrates XRD experimentation, AI problem solving, and human intelligence for interpreting XRD patterns and inferring the crystal structures of the underlying materials. Phase-Mapper is compatible with any spectral demixing algorithm, including our novel solver, AgileFD, which is based on convolutive non-negative matrix factorization. AgileFD allows materials scientists to rapidly interpret XRD patterns, and incorporates constraints to capture prior knowledge about the physics of the materials as well as human feedback. With our system, materials scientists have been able to interpret previously unsolvable systems of XRD data at the Department of Energy’s Joint Center for Artificial Photosynthesis, including the Nb-Mn-V oxide system, which led to the discovery of new solar light absorbers and is provided as an illustrative example of AI-enabled high throughput materials discovery

Computer Science↗