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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 901 records · Page 50
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Concrete Structural Health Monitoring in Nuclear Power Plants

Assessment and management of aging concrete structures in nuclear power plants require a more systematic approach than simple reliance on existing code margins of safety. Health monitoring of concrete structures is performed in order to understand the current condition of a structure based on heterogeneous measurements and then produce high confidence actionable information regarding structural integrity; this information can then be used to support operational and maintenance decisions. The paper describes a framework of research activities for the health monitoring of nuclear power plant concrete structures. The proposed framework consists of four elements: health monitoring, data analytics, uncertainty quantification, and prognosis. The goal of this framework is to enable plant operators to make risk-informed decisions on structural integrity, remaining useful life, and performance of concrete structures across the nuclear fleet. Among several concrete degradations, the research focuses on the degradation caused by the Alkali-Silica Reaction (ASR). ASR is a chemical reaction that in presence of sufficient moisture forms a gel that increases in volume and exerts an expansive pressure inside the material causing micro- to macro-cracks. The paper also presents development of controlled concrete samples with reactive aggregates to cause ASR degradation and application of different non-destructive evaluation techniques to detect the damage caused by ASR. Initial results are presented in the paper along with potential path forward.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Exploring Intentional Driving Volatility in Naturalistic Driving Environment Prior to Involvement in Unsafe Events

The sequence of instantaneous driving decisions and its variations prior to involvement in safety critical events, defined as event-based volatility, can be a leading indicator of safety. The research issue is characterizing volatility in instantaneous driving decisions in longitudinal and lateral directions, and how it varies across drivers involved in normal driving, crash, and near-crash events. A rigorous quasi-experimental study design is adopted to help compare driving behaviors in normal vs unsafe outcomes. Using a unique real-world naturalistic driving database from the SHRP 2, a test set of 9,593 driving events featuring 2.2 million temporal samples of real-world driving are analyzed. Twenty-four aggregate and segmented driving volatility measures based on acceleration and vehicular jerk are proposed, capturing variations in intentional and unintentional extreme instantaneous driving decisions. Given the possibility of unobserved heterogeneity/omitted variable bias, fixed- and random-parameter discrete choice models are developed that relate crash propensity to driving volatility and other factors. Statistically significant evidence is found that driver volatilities in near-crash and crash events are significantly greater than volatility in normal driving events. After controlling for traffic, roadway, and unobserved factors, the results suggest that greater intentional volatility increases the likelihood of both crash and near-crash events. Importantly, intentional volatility in longitudinal negative jerk has more negative consequences than intentional volatility in positive vehicular jerk. Compared to acceleration/deceleration, vehicular jerk can better characterize the volatility in microscopic driving decisions. The study demonstrates the value of big data analytics for understanding extreme driving behaviors in safe vs. unsafe driving outcomes.

Wali, Behram↗
DOE OSTI2019

Roundtable on Producing and Managing Large Scientific Data with Artificial Intelligence and Machine Learning (Brochure)

The US Department of Energy’s (DOE’s) twelve Basic Energy Sciences (BES) scientific user facilities provide access to the world’s most advanced research instruments, annually serving over 16,000 users with impact reported in nearly 7,000 publications and producing unprecedented quantities of scientific data. While impressive, reaching the full potential of these rapidly growing facilities will require new innovations to solve a variety of technical challenges in data acquisition, control, modeling, and analysis. Artificial intelligence and machine learning (AI/ML) have opened corresponding new avenues in optimization, efficient surrogate models, data analytics, and inverse problems. These intriguing capabilities suggest that AI/ML can greatly accelerate the quest to probe and understand fundamental phenomena across a vast range of lengths and timescales, potentially leading to transformative advances across scientific disciplines.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

ELINA

Explore the source record for details and available documents.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

NREL Transforms Energy for Innovative Smart and Connected Communities

Smart and connected communities use technology to better manage their urban energy systems and improve the quality and performance of government services by leveraging big data for data-driven decisions. NREL helps these communities reach their clean energy goals through cutting-edge expertise in planning, data, analytical tools, and technical support.

partnering with cities↗
DOE OSTI2018

Advanced Sensors and Instrumentation Issue 10

The Advanced Sensors and Instrumentation (ASI) program, on its seventh fiscal year, continues to address key technology gaps for future nuclear energy systems. ASI’s goal is to enable the advanced instrumentation and control (I&C) technologies essential to the Office of Nuclear Energy’s research and development (R&D) efforts needed to realize its mission goals. Information on our projects can be found in our ASI annual awards summaries, annual review webinars, and newsletters posted in the Office of Nuclear Energy website (energy.gov/ne). This year, we have initiated three newly awarded projects in the area of data analytics. We also initiated a project to develop and demonstrate a prototype passive wireless sensor network. This sensor will use advanced manufacturing and printing, and be tested and demonstrated at a U.S. nuclear power plant facility. In addition, we are developing a sensor database to capture advanced sensor and instrumentation technology that is applicable to nuclear power plants. Under the In-Pile Instrumentation program, we continue to develop unique in-pile sensors and instrumentation to provide real-time, accurate, and spatially resolved information regarding the performance of fuels and materials. Due to the closure of the Halden reactor, we are focusing our near-term research in providing equivalent capability for the instrumentation of irradiation experiments in the U.S. that are needed by the nuclear industry. As the ASI program continues to award projects through competitive solicitations, we encourage the I&C community to visit our website for current funding opportunity announcements, especially for the industry-focused solicitation entitled, “U.S. Industry Opportunities for Advanced Nuclear Technology Development.”

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

xSDK: Building an ecosystem of highly efficient math libraries for exascale

Current efforts to build increasingly powerful computer architectures are opening up new avenues for more complex and higher fidelity simulations coupled with data analytics and learning, leading to new scientific insights and deeper understanding. At one extreme, exascale computers will be much faster than previous computer generations (performing 10 18 operations per second—that is, 1,000 times faster than petascale). To achieve these performance improvements, computer architectures are becoming increasingly complex, with deep memory hierarchies, very high node and core counts, and heterogeneous features such as graphics processing units (GPUs). Such architectural changes impact the full breadth of computing scales, as heterogeneity pervades even current-generation laptops, workstations, and moderate-sized clusters. While emerging advanced architectures provide unprecedented opportunities, they also present significant challenges for developers of scientific applications, such as multiphysics and multiscale codes, who must adapt their software to handle disruptive changes in architectures and new programming models that have not yet stabilized. Developers must consider increasing concurrency while reducing communication and synchronization, and other complexities such as the potential for using mixed precision to leverage the compute power available in low-precision tensor cores. On one hand, developers must implement new scientific capabilities, which in turn increase code complexity. On the other hand, the codes must be ported to new architectures, requiring the inclusion of new programming models and the restructuring of code to achieve good performance. Addressing these issues is beyond the capability of any single person or team—leading to the need for collaboration among many teams, who encapsulate their expertise in reusable software and work together to create sustainable software ecosystems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Quantifying High Temperature Corrosion

Alloys designed for high temperature service can form a variety of surface oxides or scales based on the alloy composition, service temperature, gas environment, surface deposits, etc. The most severe environments, including molten salts and liquid metals can result in significant metal loss, void formation and/or pitting. Standard and evolving practices are discussed for measuring these types of degradationincluding measuring oxide scale thickness, internal oxidation and/or metal loss, correlating them to mass change and reporting the results. Manual and automated methodologies to measure oxide thickness and metal loss are reviewed and compared in order to provide a summary of techniques and identify the best practices for quantifying a variety of materials and damage mechanisms and producing statistically meaningful results. Such techniques create datasets useful for improved understanding of corrosion degradation mechanisms, potentially better predictive models and enhanced data analytics.

Su, Yi Feng↗
DOE OSTI2021

Geothermal Operational Optimization with Machine Learning

The Geothermal Operational Optimization with Machine Learning (GOOML) project has developed a generic and extensible component-based system modeling framework to study complex geothermal fields using a data-driven approach. Through building a digital twin of a geothermal steam field with the GOOML modeling framework, operators can analyze historical and forecasted power production, explore possible steam field configurations, and optimize real world operations, all in a cost-effective digital environment. The GOOML modeling software is based on a historical data-assimilation framework that uses first-principal thermodynamics to model steam field components using historical data, and a forecast framework that uses machine-learning-driven models of steam field components to predict future operations. This modeling framework creates countless new opportunities for digital exploration of steam field design and operations. To date, digital twins have been developed for several steam fields in New Zealand and the United States. These digital twins have been validated by comparing hindcast predictions against historical production data. Field design and operations have been explored using genetic optimization and reinforcement learning. Initial results show compelling and often surprising opportunities for improved design and operation of fields with 2 to 5 percent improvements in annual energy production. GOOML is driving a step-change in geothermal operations by applying state-of-the-art machine learning algorithms, comprehensive data analytics, and a first-of-its-kind intelligent geothermal systems model.

40 EE - Geothermal Technologies Office (EE-4G)↗
DOE OSTI2021

Smart Semi-Supervised Accumulation of Large Repositories for Industrial Control Systems Device Information

Industrial Control Systems device manufacturers frequently add new features to improve their product performance. Oftentimes, these changes are mainly vendor-driven initiatives, and customers may not be aware of the full impact of these new capabilities on their cybersecurity posture. In the energy sector, this can lead to considerable dissonance between vendor-provided cybersecurity claims and a customer’s responsibility for Operation Technology cybersecurity compliance. Thus, the resulting dynamic verification burden is shifted towards the customer and may pose a significant cybersecurity risk to the energy sector landscape. We found that there is very limited research into cybersecurity auditing for Operational Technology. However, a solution is needed for vetting the vendor-supplied feature claims and their adherence to cybersecurity requirements and standards. We are presently engaged in an effort to develop such a system. This paper demonstrates one vital aspect of this effort in proposing an end-to-end framework to accumulate a large repository of ICS device information for this vetting system, curate the dataset, and conduct extensive processing. This framework is designed to use web scraping, data analytics and Natural Language Processing (NLP) techniques to identify vendor websites, automate the collection of website-accessible documents and automatically derive metadata from them for identification of product documents relevant to the repository. We have found that this automated approach to vendor identification, document extraction into a product repository, and NLP pre-processing is unique and has not been previously presented in the literature. The preliminary work shows that this is feasible and can produce reliable results with minimum supervision. Future work will be built upon this foundation in order to achieve semi-supervised vetting of device technical information – a vital capability for ensuring that vendor-claimed device cybersecurity capabilities match industry requirements.

Ameri, Kimia↗
DOE OSTI2021

Occupancy-Driven Stochastic Decision Framework for Ranking Commercial Building Loads

For effective integration of building operations into the evolving demand response programs of the power grid, real-time decisions concerning the use of building appliances for grid services must excel on multiple criteria, ranging from the added value to occupants' comfort to the quality of the grid services. In this paper, we present a data-driven stochastic decision-support framework to dynamically rank load control alternatives in a commercial building, addressing the needs of multiple decision criteria (e.g. occupant comfort, grid service quality) under uncertainties in occupancy patterns. We adopt a stochastic multi-criteria decision algorithm recently applied to prioritize residential on/off loads, and extend it to i) consider complex load control decisions (e.g. dimming of lights, changing zone temperature set-points) in a commercial building; and ii) systematically integrate zonal occupancy patterns to better identify short-term (and time-varying) opportunities for grid service participation. We evaluate the performance of the proposed framework for curtailment of air-conditioning, lighting, and plug-loads in a multi-zone commercial office building for a range of design choices. With the help of a prototype system that integrates an interactive \textit{Data Analytics and Visualization} frontend we demonstrate a way for the building operators to monitor and change in real-time the available flexibility in energy consumption and to develop trust in the decision recommendations by interpreting the rationale behind the ranking.

Jain, Milan↗
DOE OSTI2021

TEA Modeling to Quantify Economic Implications for Biorefinery Processing of Isolated Anatomical Fractions of Corn Stover

The Feedstock-Conversion Interface Consortium (FCIC; https://www.energy.gov/sites/prod/files/2020/01/f70/beto-fcic-overview-web.pdf), a collaboration of nine national laboratory partners, seeks to understand impacts of feedstock attributes on biorefinery performance. It is hypothesized that different individual anatomical fractions of corn stover vary in composition and recalcitrance, such that processing each fraction on its own through dedicated campaigns may enable better biorefinery economics overall relative to processing the whole stover material. This presentation focuses on techno-economic analysis (TEA) modeling to quantify the yield and cost ramifications for processing isolated anatomical fractions of corn stover through a low-temperature conversion biorefinery, reflecting a biochemical processing pathway consisting of biomass deconstruction through pretreatment and enzymatic hydrolysis, sugar fermentation and upgrading to hydrocarbon fuels, and lignin upgrading to value-added coproducts. Commercial-scale process simulation and economic evaluation leveraged experimental and analytical data from FCIC researchers for conversion of whole corn stover plus three individual anatomical fractions (cobs, husks, and stalks) across key steps of the conversion process. Our assessment found encouraging potential for biorefinery economic gains that may be achieved through this approach. TEA results indicated fuel yields varying from 29-44 gallons gasoline equivalent (GGE)/dry ton for the individual anatomical fractions compared to whole stover at 34 GGE/ton, equating to minimum fuel selling prices (MFSPs) between $6.37-$10.18/GGE for the fractions versus $8.76/GGE for whole stover (when lignin is burned), or $9.15-$15.19/GGE for the fractions versus $13.11/GGE for whole stover (when lignin is upgraded to coproducts, based on current experimental performance levels). Cobs and husks demonstrated the ability to achieve the highest fuel yields and lowest MFSPs, outperforming whole stover, while stalks led to the opposite result, as a composite reflection of compositional differences and process convertibility. Notably, even when taking the weighted average of the results reflecting each anatomical fraction weighted by its corresponding makeup of corn stover, this feasibility TEA screening supports feedstock cost allowances on the order of roughly $22-$29/ton as may reflect accommodating additional biomass fractionation equipment during feedstock pre-processing upstream of the conversion biorefinery gate to separate corn stover into such constituent fractions. Or viewed differently, the weighted average MFSP for the fractions was found to be $0.31-$0.32/GGE lower than the MFSP for the whole stover basis across either lignin scenario, when maintaining a fixed biomass feedstock cost. These findings highlight favorable implications for biorefinery economics as may be achieved by moving to a staged campaign approach for processing different corn stover fractions sequentially. Further opportunities exist for future work to fill in data gaps for remaining anatomical constituents (e.g. leaves) that were not included in the initial experimental studies, though are expected to maintain similar trends.

biochemical processing pathway↗
DOE OSTI2022

GOOML - Real World Applications of Machine Learning in Geothermal Operations

GOOML (Geothermal Operational Optimization with Machine Learning) is a machine-learning based framework that enables geothermal power plant operators to explore optimization opportunities for their assets in an efficient and robust digital environment. Backed by real-world data sources, thermodynamic constraints and steamfield intelligence, the GOOML environment provides new tools to explore how to best operate steamfields as well as test new scenarios and configurations prior to implementation in the field. To prove the effectiveness of GOOML, we have undertaken optimization experiments using reinforcement learning (RL) to generate operational suggestions using a balance of mass-take targets, sustainability considerations and net generation. Our experiments use the GOOML construct to explore different field parameters and perform multiple reinforcement learning experiments. Like a comprehensive laboratory workbench, we can change out components of a steamfield to perform testing under a variety of conditions (restrict mass, increase pressure, reroute steam, etc.). This flexibility allows us to explore conditions that would require significant infrastructure changes in a real-world setting at a fraction of the cost and time in a digital environment. The results highlight the benefits of using digital twins and advanced data analytics for the geothermal industry.

forecasting↗
DOE OSTI2013

An Initial Evaluation of Characterization and Closure Options for Underground Pipelines within a Hanford Site Single-Shell Tank Farm - 13210

The Hanford Site includes 149 single-shell tanks, organized in 12 'tank farms,' with contents managed as high-level mixed waste. The Hanford Federal Facility Agreement and Consent Order requires that one tank farm, the Waste Management Area C, be closed by June 30, 2019. A challenge to this project is the disposition and closure of Waste Management Area C underground pipelines. Waste Management Area C contains nearly seven miles of pipelines and 200 separate pipe segments. The pipelines were taken out of service decades ago and contain unknown volumes and concentrations of tank waste residuals from past operations. To understand the scope of activities that may be required for these pipelines, an evaluation was performed. The purpose of the evaluation was to identify what, if any, characterization methods and/or closure actions may be implemented at Waste Management Area C for closure of Waste Management Area C by 2019. Physical and analytical data do not exist for Waste Management Area C pipeline waste residuals. To develop estimates of residual volumes and inventories of contamination, an extensive search of available information on pipelines was conducted. The search included evaluating historical operation and occurrence records, physical attributes, schematics and drawings, and contaminant inventories associated with the process history of plutonium separations facilities and waste separations and stabilization operations. Scoping analyses of impacts to human health and the environment using three separate methodologies were then developed based on the waste residual estimates. All analyses resulted in preliminary assessments, indicating that pipeline waste residuals presented a comparably low long-term impact to groundwater with respect to soil, tank and other ancillary equipment residuals, but exceeded Washington State cleanup requirement values. In addition to performing the impact analyses, the assessment evaluated available sampling technologies and pipeline removal or treatment technologies. The evaluation accounted for the potential high worker risk, high cost, and schedule impacts associated with characterization, removal, or treatment of pipelines within Waste Management Area C for closure. This assessment was compared to the unknown, but estimated low, long-term impacts to groundwater associated with remaining waste residuals should the pipelines be left 'as is' and an engineered surface barrier or landfill cap be placed. This study also recommended that no characterization or closure actions be assumed or started for the pipelines within Waste Management Area C, likewise with the premise that a surface barrier or landfill cap be placed over the pipelines. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Radioactive Waste Disposal Strategies within the US Army Corps of Engineers FUSRAP - 18328

The U.S. Army Corps of Engineers' (USACE) highest priority is the protection of human health and the environment and public and worker safety during the cleanup, transportation, and disposal of Formerly Utilized Sites Remedial Action Program (FUSRAP) material. In addition to following all appropriate federal and state rules and regulations, the USACE has its own safety program that ensures that we are being protective. One aspect of the USACE FUSRAP is the radioactive waste disposal strategies that we must consider. The USACE will only dispose of FUSRAP material in a facility that is licenses/permitted to accept it. The estimated exposures to facility workers and members of the public from the permitted disposal of FUSRAP wastes is evaluated as part of this process. The USACE uses the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) process to study and remediate FUSRAP sites. An evaluation is performed to decide whether it is best to manage the waste on-site or off-site and the requirements for disposal. The action selected must be protective of human health and the environment and must comply with regulations. When making the decision, there are several other factors taken into consideration including cost of the disposal and whether the public and state agencies support the action. Prior to determining disposal options, wastes must first be characterized. This involves an evaluation of both site history and analytical data. Determining the proper disposal requirements and subsequent disposal option involves: Characterizing the waste as stated above. This is important because there are several regulatory agencies which may oversee management of the waste. The US Nuclear Regulatory Commission, the US Environmental Protection Agency (US EPA), the US Department of Transportation, and state hazardous waste and/or radiation protection agencies may all have varying degrees of involvement. Identifying disposal requirements. Certain wastes must be disposed at licensed radioactive waste disposal facilities. Wastes containing US EPA regulated hazardous waste must be disposed at permitted hazardous waste disposal facilities. Certain wastes must be disposed at facilities licensed/permitted for both radioactive and hazardous waste. Some lower activity wastes may be accepted for disposal at other types of landfills. Considering acceptance criteria of the disposal facility. Radionuclides and/or hazardous constituents in the waste, as well as physical and chemical form of the waste, are evaluated to ensure they meet the acceptance criteria of the receiving facility's license and/or permit. If they don't, another disposal location must be used. Considering the accessibility and location of the disposal facility. Some facilities are able to accept shipments only by truck and others can accept material via railcar as well. The distance to a disposal facility impacts the risk of transportation accidents and efficient use of project resources such as the number of trucks required to perform the transportation. These factors are also important in evaluating whether material can be cost effectively shipped to a disposal facility. Evaluating the compliance status of the receiving facility. It is not assumed that just because a facility has a license or permit that it is acceptable for the USACE' waste. Prior to selecting a facility for disposal of FUSRAP waste, a site visit, audit and consultation with the US EPA is required to determine whether there are any known relevant violations at the facility. Facilities that have violations that negatively impact the USACE or US EPA approval of the facility are not used for disposal of FUSRAP waste. Once a facility is selected. The USACE procures contracts, or utilizes existing contracts, to facilitate the transportation and disposal action. Additionally, the USACE continues to consult with US EPA routinely to confirm the compliance status of the facility. Considering state acceptance of wastes to a facility. The USACE seeks to obtain written authorization from the facility state regulator indicating the action is compliant with existing licenses, permits, laws, and regulations. Each of these factors is considered by the USACE in selecting a FUSRAP disposal location. The goal is to provide safe, cost effective disposal which is protective of human health and the environment and which complies with all regulatory requirements. These steps all feed in to the disposal strategy consideration that USACE must go through for our FUSRAP waste streams. There are a number of challenges in the process to include In order to achieve the Congressional mandate of completing cleanup work under FUSRAP in a more cost effective, timely manner, while assuring protection of public health and safety. USACE must determine all disposal options that are available for the types of waste at each FUSRAP site. As a solution, completion of a disposal strategy document can help ensure this goal is met and will foster consistency across the FUSRAP. (authors)

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

Tracking Restoration And Closure (TRAC): Status Update on Story Maps for Communicating Site Cleanup and Closure Status - 19033

The U.S. Department of Energy (DOE) Office of Environmental Management (EM) initiated the development of a customized, web-based mapping tool for communicating technical information on groundwater plumes at sites within the EM complex. Although the online tool was initially conceived to be a simple conversion of a paper report on contaminant plume sizes, remedial approaches, and regulatory status, it has advanced to providing specific information on cleanup status and site closure. The story maps provide robust geospatial visualization of plumes at DOE sites, as well as explanatory text, photographs, and video for an entire site, or for a management unit within the site. This collection of story maps is called TRAC (Tracking Restoration And Closure), and not only communicates information for each individual site, but also summarizes pertinent metrics on cleanup and remaining contaminants across all EM sites. TRAC is focused on communicating cleanup status, technical challenges, and needs for site closure, with a key challenge of transforming data into relevant, easily understood information. Although DOE managers are the target audience, the story maps can also be used to contribute to public understanding of groundwater cleanup progress at EM sites. Unlike the data access tools maintained at some EM sites (e.g., Hanford, Portsmouth, and Paducah), TRAC is neither a portal for data downloads, nor a tool for performing data analytics. Rather, the goal of TRAC is to provide relevant information on cleanup progress and closure that is consistent across all sites, allowing the user to view summary information across all sites, or examine information relevant to a particular site. This information sharing is needed to maintain open communications with interested parties and communicate efforts that ensure long-term protection of human health and the environment. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗