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At least 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Energy Resilience Through Grid Modernization and Renewables Integration

The National Renewable Energy Laboratory (NREL) is studying renewable energy system integration and security and resilience planning as part of the U.S. Department of Energy's Grid Modernization Initiative. Some recent work focused on incorporating the Value of Resilience into the decision making process for investing in distributed, behind-the-meter energy systems to meet critical loads during grid outages. Cost / benefit analyses show how accounting for the Value of Resilience as a benefit changes solar PV and battery energy storage system capacities for maximum owner benefit.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Modular space vehicle boards, control software, reprogramming, and failure recovery

A space vehicle may have a modular board configuration that commonly uses some or all components and a common operating system for at least some of the boards. Each modular board may have its own dedicated processing, and processing loads may be distributed. The space vehicle may be reprogrammable, and may be launched without code that enables all functionality and/or components. Code errors may be detected and the space vehicle may be reset to a working code version to prevent system failure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Real-time Intrusion Detection for High-bandwidth Research Networks using Unsupervised Deep Learning

Nowadays, data generated by large-scale scientific experiments are on the scale of petabytes per month. These data are transferred through dedicated high-bandwidth networks across distributed collaboration sites for processing, storage, and analysis. Like general purpose networks, research networks experience attacks. However, monitoring traffic in such high-bandwidth network environments is challenging with conventional deep packet inspection (DPI)- based network intrusion detection systems (NIDS). Recently, machine learning (ML) algorithms have shown potential in detecting anomalous patterns with better generalizability and faster speed. Compared to the general purpose networks, traffic movement in research network is less volatile and more likely to benefit from the ML-based anomaly analysis. We present ThunderSecure, an efficient AI-powered system for detecting anomalies in 100G research networks. ThunderSecure implements a high-throughput packet processing infrastructure using multi-cores and GPUs. It takes time-series statistics from a variety of network measurements and uses 2D Convolutional Neural Network (CNN) to explore both spatial and temporal dependencies in network data stream. Patterns of science data traffic are learned with a one-class neural network, which blends an Adversarial Autoencoder (AAE) with Gaussian mixture density estimation. Testing traffic flows exhibiting significant deviations from the learned baseline of normality are marked as anomalies. We trained ThunderSecure on hundreds of billions of science data packets recorded from a 100G research network at Fermi National Accelerator Laboratory. The detection performance was evaluated on traffic captured from the same research network days and weeks after the training with different types of attack flows injected. To the best of our knowledge, this is the first ML work in the area of network anomaly detection that has been validated on such extreme scale datasets. Results show that ThunderSecure can recognize science data traffic that are captured long after the training and make nearly certain detection on those with anomalous flows injected, even in the case when the anomaly-to-normal mixing ratio is 0.1%.

Gong, Qian↗
DOE OSTI2020

Performance and usability enhancements for continuous subgraph matching queries on graph-structured data

A query graph, which includes vertices and edges, represents a query on graph-structured data. The query graph is decomposed into query subgraphs. A network analysis tool performs continuous subgraph matching queries to facilitate analysis of computer network traffic, social media events, or other streams of data represented as a dynamic data graph (graph-structured data). This can help identify emerging trends in the data. Some features of the network analysis tool enhance performance by effectively utilizing distributed computing resources (including processing cores and memory at different nodes of a cluster) to speed up the process of updating the dynamic data graph and detecting matches of query subgraphs. Features of a query graph building tool enhance usability by providing intuitive ways to specify query graphs and their subgraphs. Features of a results visualization tool enhance usability by providing an intuitive way to present the results of continuous subgraph matching queries.

Choudhury, Sutanay↗
DOE OSTI2021

A Modularized Urban Scale Building Energy Modeling Framework Designed with An Open Mind

In recent years, physics-based building energy modeling (BEM) has started being used to evaluate the performance of buildings in the context of connected communities and on an urban scale to study their aggregated energy use, interactions, and impacts on the energy supply infrastructure and environment. The development of urban-scale BEM solutions needs extensive effort. Existing attempts tend to focus on different aspects of BEM on an urban scale, such as collecting as-built building data from different information sources, integrating geometry modeling with geographic information systems (GISs), representing operational and occupancy profiles, automating workflow, processing and visualizing the results, and conducting large-scale simulations. Urban-scale BEM development would benefit from multi-disciplinary research areas and from an open platform to adopt advancements on data sources and tools. For these purposes, this research proposes a modularized bottom-up model creation and simulation framework that is built on the state-of-the-art BEM tools and can accommodate different building stock data. This framework uses a standardized schema to describe building design and operational characteristics, and it can be instantiated from different building survey datasets with heterogeneous structures. The paper demonstrates how thousands of surveyed buildings from the 2012 U.S. Energy Information Administration’s Commercial Buildings Energy Consumption Survey (CBECS) were one-to-one converted to EnergyPlus models through the schema and the model generation process, then simulated with distributed computing, and their results are summarized.

Lei, Xuechen↗
DOE OSTI2021

Minimizing Cr-Evaporation from Balance of Plant Components by Utilizing Cost-Effective Alumina-Forming Austenitic Steels

A solid oxide fuel cell (SOFC) is a clean and efficient energy conversion device. The development of intermediate-temperature SOFCs has made it preferable to use metallic interconnects (MICs) to greatly reduce the cost and significantly increase the efficiency compared to ceramic interconnect materials. However, gaseous chromium species will evaporate from the chromium-containing layer formed on the surface of commonly used MICs and balance of plant (BoP) components. Volatile chromium species have been shown to form solid deposits which poison the cathodes of SOFCs, causing drastic cell performance degradation and thereby limiting commercialization. In order to alleviate the Cr poisoning and achieve long-term high performance of SOFC stacks, various Al2O3-forming austenitic (AFA) stainless steels applied at different temperatures are evaluated in this project. Based on our Phase I results (500 h operation), it is shown that on the AFAs, an alumina-based protective layer forms under high temperature that is invulnerable to water vapor effects and suppresses the diffusion of chromium and manganese which can prevent the generation of spinels on the alloy surface. Therefore, 310S, OC4 and OC5 at 800 °C and 625, OC11 and OC11LZ at 900 °C are selected to be tested in the Phase II long-term operation. From the results, we find that besides the lower Cr evaporation rates and better oxidation resistance of AFAs than benchmark alloys after short-term (500 h) operation, AFAs also possess the sturdy and compact alumina layer after a long-term operation (5000 h). A protective oxide layer is of great importance for the long-term high-temperature operation of structural materials. The high-temperature oxidation behavior of AFAs and commercial 310S and 625 alloys in Air + 10% H 2 O at 800 °C and 900 °C is systematically investigated. Severe breakaway oxidation and minor spallation are observed for the 310S and 625 after the long-term operation, while the AFAs show high oxidation resistance. It is found that the formation of a continuous alumina layer could greatly prevent the volatilization of chromium vapors. In addition, the corresponding evolving models are discussed. Chromium evaporation from BoP components in high-temperature environment could severely deteriorate the electrochemical performance of SOFC. Several methods were applied to evaluate the Cr evaporation rates of BoP after 500 h exposure at 800 °C to 900 °C in air with 10% H 2 O. An optimal method was designed to exclude the effect of silicon (Si) deposits from quartz tube and sodium (Na) deposits from the sodium carbonate on the oxidation process and the chemical interaction between Cr gaseous species and alumina tube which could provide further quantitative correlation of the evaporated Cr species quantities and degradation rates of SOFC. Based on the great performance of AFAs after long-term operation, AFAs are assembled with Anode-supported cells (ASC) to investigate the the Cr deposition of anode-supported cell under a constant current density of 0.5 A cm -2 at 800 °C with AFA alloys compared with commercial alloys. In addition, the anodic and cathodic processes are deconvoluted by distribution of relaxation times (DRT) method which are comprehensively discussed. It is found out that the voltage exhibited a slight decrease of 5.09 % and 1.54 % in the presence of OC11 and OC11LZA alloy, respectively. However, a considerable decrease of 22.14 % and 12.06 % was determined in the presence of 310S and 625 alloy, respectively. AFAs possessing the low-cost, low Cr evaporation rates and the high oxidation resistance will make it of great potential to replace the existing BoP components.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Alternate Endpoints for Deep Vadose Zone Environments: Challenges, Opportunities, and Progress - 13036

Current requirements for site remediation and closure are standards-based and are often overly conservative, costly, and in some cases, technically impractical to achieve. Use of risk-informed alternate endpoints provide a means to achieve remediation goals that are permitted by regulations and are protective of human health and the environment. Alternate endpoints enable establishing a path for cleanup that may include intermediate remedial milestones and transition points and/or regulatory alternatives to standards-based remediation. A framework is presented that is centered around developing and refining conceptual models in conjunction with assessing risks and potential endpoints as part of a system-based assessment that integrates site data with scientific understanding of processes that control the distribution and transport of contaminants in the subsurface and pathways to receptors. This system-based assessment and subsequent implementation of the remediation strategy with appropriate monitoring are targeted at providing a holistic approach to addressing risks to human health and the environment. This holistic approach also enables effective predictive analysis of contaminant behavior to provide defensible criteria and data for making long-term decisions. Developing and implementing an alternate endpoint-based approach for remediation and waste site closure presents a number of challenges and opportunities. Categories of these challenges include scientific and technical, regulatory, institutional, and budget and resource allocation issues. Opportunities exist for developing and implementing systems-based approaches with respect to supportive characterization, monitoring, predictive modeling, and remediation approaches. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Quantum for Energy Systems and Technologies

Quantum information science (QIS) is creating a transformative opportunity to improve processes for energy production, distribution, and consumption. In this presentation, we will show the works that are currently pursued into two major directions, (i) quantum sensing and (ii) quantum computing is undergoing in order to find applications in the areas of carbon captures, critical minerals and gas sensing applications.

Paudel, Hari P.↗
DOE OSTI2015

Characterisation of metals in the electronic waste of complex mixtures of end-of-life ICT products for development of cleaner recovery technology

Highlights: • New characterisation methodology has been established to understand an industrially processed ICT waste. • Particle size distribution, composition, thermal–chemical behaviour and occurrence of metals were considered. • The characterisation provides direct guidelines for values recovery from the waste. - Abstract: Recycling of valuable metals from electronic waste, especially complex mixtures of end-of-life information and communication technology (ICT) products, is of great difficulty due to their complexity and heterogeneity. One of the important reasons is the lack of comprehensive characterisation on such materials, i.e. accurate compositions, physical/chemical properties. In the present research, we focus on developing methodologies for the characterisation of metals in an industrially processed ICT waste. The morphology, particle size distribution, compositional distribution, occurrence, liberation as well as the thermo-chemical properties of the ICT waste were investigated with various characterisation techniques, including X-ray Fluorescence Spectrometry (XRF), differential scanning calorimetry (DSC) and scanning electron microscopy (SEM) with energy dispersed spectroscopy (EDS). Due to the high heterogeneity of the material, special sample preparation procedures were introduced to minimise the discrepancies during compositional analyses. As a result, a clearer overview of the ICT waste has been reached. This research provides better understanding of the extractability of each metal and improves the awareness of potential obstacles for extraction. It will lead to smarter decisions during further development of a clean and effective recovery process.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Distributed Wind Resource Assessment: State of the Industry

In support of the U.S. Department of Energy (DOE) Wind and Water Power Technologies Office (WWPTO) goals, researchers from DOE's National Renewable Energy Laboratory (NREL), National Wind Technology Center (NWTC) are investigating the Distributed Wind Resource Assessment (DWRA) process, which includes pre-construction energy estimation as well as turbine site suitability assessment. DWRA can have a direct impact on the Wind Program goals of maximizing stakeholder confidence in turbine performance and safety as well as reducing the levelized cost of energy (LCOE). One of the major components of the LCOE equation is annual energy production. DWRA improvements can maximize the annual energy production, thereby lowering the overall LCOE and improving stakeholder confidence in the distributed wind technology sector by providing more accurate predictions of power production. Over the long term, one of the most significant benefits of a more defined DWRA process could be new turbine designs, tuned to site-specific characteristics that will help the distributed wind industry follow a similar trajectory to the low-wind-speed designs in the utility-scale industry sector. By understanding the wind resource better, the industry could install larger rotors, capture more energy, and as a result, increase deployment while lowering the LCOE. a direct impact on the Wind Program goals of maximizing stakeholder confidence in turbine performance and safety as well as reducing the levelized cost of energy (LCOE). One of the major components of the LCOE equation is annual energy production. DWRA improvements can maximize the annual energy production, thereby lowering the overall LCOE and improving stakeholder confidence in the distributed wind technology sector by providing more accurate predictions of power production. Over the long term, one of the most significant benefits of a more defined DWRA process could be new turbine designs, tuned to site-specific characteristics that will help the distributed wind industry follow a similar trajectory to the low-wind-speed designs in the utility-scale industry sector. By understanding the wind resource better, the industry could install larger rotors, capture more energy, and as a result, increase deployment while lowering the LCOE.

17 WIND ENERGY↗
DOE OSTI2014

Angular distributions of surface produced H{sup −} ions for reflection and desorption processes

A numerical simulation code, Atomic Collision in Amorphous Target, has been run to clarify the effects due to the incident angle of hydrogen flux onto surface collision cascade in the subsurface region of a Cs covered Mo plasma grid. The code has taken into account the threshold energy for negative hydrogen (H{sup −}) ions to leave the surface. This modification has caused the shift of energy distribution functions of H{sup −} from that of hydrogen atoms leaving the surface. The results have shown that large incident angle of hydrogen particle tilt the angular distribution of reflection component, while it caused a small effect onto the angular distribution of desorption component. The reflection coefficient has increased, while the desorption yield has decreased for increased angle of incidence measured from the surface normal.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Out-of-distribution detection with non-parametric density estimation for models predicting processing history of uranium ore concentrates

The rapid advancement in machine learning (ML) and computer vision (CV) coincides with the growth of interest in deploying these ML/CV models in numerous fields from medicine to social science. Similar to those areas, we have witnessed a great number of works in materials science employing ML/CV models – neural networks in particular – in their studies in recent years. These models have proven to obtain accurate performance in various tasks. However, these models struggle to attain a similar performance when encountering test samples coming from a distribution that is different from the training set. More importantly, they fail without providing any warning to the users. Therefore, we propose a framework for detecting out-of-distribution (OOD) samples to alert users when a human intervention might be necessary in this work. Specifically, we explore the use of a non-parametric density estimation method to detect OOD samples. Here, we assess OOD detection capability of the proposed framework on ML models developed for categorizing precipitation routes of U 3 O 8 when encountering OOD datasets that contain samples (1) undergone different imaging acquisition process, (2) undergone different material synthesis process, and (3) different materials than ID set. Through those experiments, we achieve an average area under the receiver operating characteristic (AUROC) of at least 91% on average in detecting OOD samples. With minimal overhead cost and superior performance, the proposed framework enables a reliable and safe system when deploying in real-world scenarios.

Convolutional neural networks↗
DOE OSTI2014

University of Utah, Energy Commercialization Center

During the Energy Commercialization Center’s (ECC) three years in operation, the only thing constant was change. The world of commercialization and cleantech evolved significantly during the time the ECC was formed and operating, including: the availability of cleantech funding lessoned, the growth of incubators and accelerators skyrocketed, the State of Utah created an office dedicated to energy development, the University of Utah was both praised and criticized for its success in commercialization, and the Federal government temporarily shut down. During the three-year grant there were three principle investigators on the grant, as well as three directors for the University’s Commercialization Office. Change can be hard for an organization,but as we instruct the companies we support, “Fail fast and fail often, because it is the fastest path to success.” Although there were some unanticipated challenges along the way, the local ecosystem is stronger because of the ECC’s efforts. Perhaps the greatest lesson learned was the importance of aligned incentives between key stakeholders in the commercialization process and the need for resources at the company and individual entrepreneur levels. The universities have systems and incentives to commercialize technologies, but creating value and companies generally rest with the individuals and entrepreneurs. Unfortunately the ECC was unable to create a viable mechanism to transfer the commercialization process that successfully aligned incentives and achieve a more effective ecosystem within the Rocky Mountain West. However, the ECC was successful in adding value to the individual ecosystems, and connecting national resources to regional and local needs. Regarding the ECC’s effectiveness in developing a cleantech commercialization ecosystem, initial inroads and relationships were established with key stakeholders. However, incentives, perceived or real competition, differences in commercialization processes, and culture all played a role in inhibiting the development and distribution of a regional ecosystem and commercialization process. Had the University and the ECC been able to develop a software platform, some of these challenges may have been overcome, but without the final development and release of the Western Innovation Network, the ECC realistically could not scale and distribute a commercialization platform. Further, cleantech startups need to engage in a more intensive customer validation process, and establish strong community connections if they are to succeed in commercializing their products. The university system incentivizes research and access to research funding and risk capital is competitive, so by nature collaboration on commercialization was difficult. Each of the local ecosystems within the Rocky Mountain West was unique. Utah did not, and does not, have a system outside of the universities to support entrepreneurs and cleantech commercialization. Through the ECC’s efforts developing a regional ecosystem, it became clear that successful ecosystems had a community and associated mechanisms that supported local entrepreneurs and startups. Most importantly the ECC aided in the creation of Utah’s cleantech ecosystem, one that supports entrepreneurs and startup companies that need help and support in their efforts to commercialize clean technologies. The absence of support for clean tech from state government and local organizations was a significant impediment to cleantech commercialization. To overcome this challenge, the ECC has formed Sustainable Startups. Sustainable Startups is a new non-profit organization designed to build a culture and community in Utah that supports and understands the importance of cleantech and sustainable development. While the ECC generated mixed success in building a regional commercialization ecosystem for cleantech, the organization did provide tremendous benefit to startups and the broader public. Over 60 companies were given direct business development support by the ECC, many of whom then generated direct economic development impacts. In addition, the ECC served an important role as community convener, educator and relationship builder through hosting numerous public and private events including: Energize 2013; Millennial Train whistle stop; business plan competition supporter; Clean Tech Open Accelerator organizer; Sustainable Startups Series developer, and much more. While the ECC did not fully apply, develop, and transmit the University of Utah’s TCO commercialization model to cleantech, it nevertheless assisted numerous inventors, entrepreneurs and institutions in furthering the growth of clean energy and energy efficiency technologies. The TCO’s commercialization model was not applied to regional clean tech initiatives for several main reasons. First, flaws with the commercialization model were realized after the ECC’s formation. Second, leadership changes within the TCO and ECC hampered early organizational development and implementation initiatives. Third, misaligned incentives between the ECC, regional universities, institutions, and the State of Utah resulted in a lack of collaboration and knowledge transfer regarding commercialization. In principle, everyone was aligned and willing to collaborate, but reality was much different and challenging.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Chemical and structural characterization of particulate fallout isolated from air-filters

We report particulate nuclear fallout is the radioactive byproduct of a nuclear event formed by the mixture of proximate environmental materials with vaporized bomb debris. The fallout debris can be transported into the atmosphere during cloud rise, raining out locally and dispersing globally constituting a radiation hazard and contributing to the distribution of anthropogenic radionuclides in the environment. Questions remain on how entrainment of environmental material in the fireball affects fallout formation processes and radionuclide incorporation and distribution during cooling. To inform the characterization of fallout including the development of fallout size distributions and how radionuclides are incorporated into fallout debris where entrainment plays a role, we analyzed an archived historic US air-filter collected by aircraft in the aftermath of ground interacting nuclear tests. Particulate fallout collected on the filter was isolated and analyzed using scanning electron microscopy (SEM), transmission electron microscopy (TEM) and NanoSIMS (Secondary Ion Mass Spectrometry) to determine chemistry, structure, morphology, and size of the particles. Results demonstrate that the particles isolated from the filter have spherical shape, display complex internal structures, and are mainly composed of Fe and Si oxides. In these spherical particles, Pu is preferentially associated with Fe-rich composition. The characterization of fallout particulate samples can provide information on nucleation and particle growth from the vapor phase to improve modeling and simulation of fallout hazards.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2012

NanoComposite Stainless Steel Powder Technologies

Oak Ridge National Laboratory has been investigating a new class of Fe-based amorphous material stemming from a DARPA, Defense Advanced Research Projects Agency initiative in structural amorphous metals. Further engineering of the original SAM materials such as chemistry modifications and manufacturing processes, has led to the development of a class of Fe based amorphous materials that upon processing, devitrify into a nearly homogeneous distribution of nano sized complex metal carbides and borides. The powder material is produced through the gas atomization process and subsequently utilized by several methods; laser fusing as a coating to existing components or bulk consolidated into new components through various powder metallurgy techniques (vacuum hot pressing, Dynaforge, and hot isostatic pressing). The unique fine scale distribution of microstructural features yields a material with high hardness and wear resistance compared to material produced through conventional processing techniques such as casting while maintaining adequate fracture toughness. Several compositions have been examined including those specifically designed for high hardness and wear resistance and a composition specifically tailored to devitrify into an austenitic matrix (similar to a stainless steel) which poses improved corrosion behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Phosphorus in agricultural soils: drivers of its distribution at the global scale

Phosphorus (P) availability in soils limits crop yields in many regions of the world, while excess of soil P triggers aquatic eutrophication in other regions. Numerous processes drive the global spatial distribution of P in agricultural soils, but their relative roles remain unclear. Here, we combined several global datasets describing these drivers with a soil P dynamics model to simulate the distribution of P in agricultural soils and to assess the contributions of the different drivers at the global scale. We analyzed both the labile inorganic P (P ILAB ), a proxy of the pool involved in plant nutrition and the total soil P (P TOT ). We found that the soil biogeochemical background (BIOG) and farming practices (FARM) were the main drivers of the spatial variability in cropland soil P content but that their contribution varied between P TOT vs P ILAB . Indeed, 97% of the P TOT spatial variability could be explained by BIOG, while BIOG and FARM explained 41% and 58% of P ILAB spatial variability, respectively. Other drivers such as climate, soil erosion, atmospheric P deposition and soil buffering capacity made only very small contribution. Lastly, our study is a promising approach to investigate the potential effect of P as a limiting factor for agricultural ecosystems and for global food production. Additionally, we quantified the anthropogenic perturbation of P cycle and demonstrated how the different drivers are combined to explain the global distribution of agricultural soil P.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

CMS Data transfer operations after the first years of LHC collisions

CMS experiment utilizes distributed computing infrastructure and its performance heavily depends on the fast and smooth distribution of data between different CMS sites. Data must be transferred from the Tier-0 (CERN) to the Tier-1s for processing, storing and archiving, and time and good quality are vital to avoid overflowing CERN storage buffers. At the same time, processed data has to be distributed from Tier-1 sites to all Tier-2 sites for physics analysis while Monte Carlo simulations sent back to Tier-1 sites for further archival. At the core of all transferring machinery is PhEDEx (Physics Experiment Data Export) data transfer system. It is very important to ensure reliable operation of the system, and the operational tasks comprise monitoring and debugging all transfer issues. Based on transfer quality information Site Readiness tool is used to create plans for resources utilization in the future. We review the operational procedures created to enforce reliable data delivery to CMS distributed sites all over the world. Additionally, we need to keep data and meta-data consistent at all sites and both on disk and on tape. In this presentation, we describe the principles and actions taken to keep data consistent on sites storage systems and central CMS Data Replication Database (TMDB/DBS) while ensuring fast and reliable data samples delivery of hundreds of terabytes to the entire CMS physics community.

Kaselis, Rapolas↗