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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Energy Justice Framework for Marine Energy: Considerations from PacWave

To accelerate the energy transition while addressing land use conflicts and community opposition, federal policies and incentives require that new energy infrastructure provide direct benefits to communities, and ensure that these benefits remain in the community. The objective of this study is to develop a framework for collecting demographic, socioeconomic, and environmental data and supporting communities in understanding how such data can support more just and equitable outcomes. Specifically, this study addresses two research questions: (1) How can energy justice be incorporated into ME project life cycle, and (2) what social and economic data are needed to assess how marine energy development supports energy justice? Based on our literature review and the data collection template, a case study of PacWave, a wave energy testing facility located on the central Oregon Coast, is employed to draw more specific implications for how energy justice can be integrated into marine energy. PacWave represents a flagship investment by Oregon State University, the State of Oregon, and the Department of Energy. The facility is situated in Lincoln County, which is home to the Confederated Tribes of Siletz Indians and family-scale fishing enterprises. PacWave has strong community support as a testing site and holds potential to provide valuable lessons for other communities.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

High-voltage and electrode system for a cryogenic experiment to search for the neutron electric dipole moment

The cryogenic approach to the search for the neutron electric dipole moment—performing the experiment in superfluid liquid helium—holds promise for a substantial increase in sensitivity, potentially enabling a sensitivity level of 10 −28 𝑒cm. A crucial component in realizing such an experiment is the high-voltage and electrode system capable of providing an electric field of 75 kV/cm. This, in turn, requires an electric potential of 635 kV to be applied to the high-voltage electrode, while simultaneously satisfying other experimental constraints, such as those on heat load and magnetic noise requirements. In this work, we describe the outcome of a comprehensive development program addressing these challenges. It outlines the system requirements, discusses new insights into relevant physical phenomena, and details selected technical solutions with their corresponding experimental demonstrations and expected performance. The results collectively demonstrate the successful development of the necessary technology for the high-voltage and electrode system for this approach.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

A Co-Registered In-Situ and Ex-Situ Dataset of Electrical, Acoustic, and CT Characteristics from Wire Arc Additive Manufacturing Process

Recent progress in sensing techniques and data analytics tools have significantly accelerated the development of Wire Arc Additive Manufacturing (WAAM) systems. This data centric approach emphasizes leveraging available data throughout the production process to optimize performance. Integration of extensive data analysis provides the opportunity to improve precision, reduce waste, and enhance the quality of produced parts. This method relies on AI/ML models and optimization techniques, which are developed using the data collected from various sources, including in-situ sensors, ex-situ imaging, and manufacturing process parameters. The quality and diversity of this data, along with the alignment between different data streams (achieved through spatiotemporal registration) are critical for the successful development of AI/ML and optimization models. In this work, we present a spatiotemporally registered dataset generated during the WAAM process of deposition of a rectangular block. The dataset includes the comprehensive description of deposition process, process parameters, in-situ collected welding characteristics, acoustic data, and X-Ray Computed Tomography analysis data for the build. Dataset A Co-Registered In-Situ and Ex-Situ Dataset of Electrical, Acoustic, and CT Characteristics from Wire Arc Additive Manufacturing Process has arisen under UT-Battelle, LLC’s Prime Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy (DOE) to manage and operate the Oak Ridge National Laboratory. UT-Battelle, LLC will not assert any rights under United States law or under the Prime Contract it has in the dataset against any user of the dataset, including any copyrights or patent rights. UT-Battelle, LLC requests that attribution to the dataset is provided as academically appropriate.

42 ENGINEERING↗
DOE OSTI2025

A co-registered in-situ and ex-situ dataset from wire arc additive manufacturing process

Recent progress in sensing techniques and data analytics tools have significantly accelerated the development of Wire Arc Additive Manufacturing (WAAM) systems. This data-centric approach emphasizes leveraging sensor data available throughout the production process to optimize performance. Integration of extensive data analysis provides opportunities for improving precision, reducing waste, and enhancing the quality of produced parts. This method relies on AI/ML models and optimization techniques, which are developed using the data collected from various sources, including in-situ sensors, ex-situ imaging, and manufacturing process parameters. The quality and diversity of this data, along with the alignment between different data streams (achieved through spatiotemporal registration) are critical for the successful development of AI/ML and optimization models. In this work, we present a spatiotemporally registered dataset generated during the WAAM process of deposition of a rectangular block. The dataset includes a comprehensive description of the deposition process, process parameters, welding characteristics and acoustic data collected in-situ, and X-Ray Computed Tomography data of the build.

42 ENGINEERING↗
DOE OSTI2022

Collection of Aerosol Particles in a Single-Stage, High Gradient Magnetic Collector

The collection of aerosol particles that have magnetic components such as actinides, Co/Ni/Fe, brass, and alloys are of interest in the nuclear industry and for regulatory applications. The collection of these particles is important for monitoring and a number of collection approaches have been developed based on separations mechanisms including size, mass, electrical or thermal properties. In this work, we present the performance of a single-stage, high gradient magnetic collector with a nickel foam matrix for removing airborne magnetic particles. The collection efficiencies for two different particle size regimes and compositions (NaCl and Fe 3 O 4 below 200 nm and SiO 2 and Fe 3 O 4 at 1-2 μm) were measured to assess the performance of the magnetic collector. The high gradient magnetic collector did not remove nanosized magnetic Fe 3 O 4 particles any more effectively than non-magnetic NaCl particles. However, in the larger size range, the collector more efficiently removed Fe 3 O 4 particles compared to SiO 2 with a collection efficiency of 99% compared to 84% for SiO 2 . The removal mechanism could be attributed to both filtration and the high gradient magnetic field.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

In Situ Transmission Electron Microscopy: Signal processing challenges and examples

Transmission electron microscopy (TEM) is a powerful tool for imaging material structure and characterizing material chemistry. Recent advances in data collection technology for TEM have enabled high-volume and high-resolution data collection at a microsecond frame rate. Here, taking advantage of these advances in data collection rates requires the development and application of data processing tools, including image analysis, feature extraction, and streaming data processing techniques. In this article, we highlight a few areas in materials science that have benefited from combining signal processing and statistical analysis with data collection capabilities in TEM and present a future outlook on opportunities of integrating signal processing with automated TEM data analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Framework for Closed-Loop Optimization of an Automated Mechanical Serial-Sectioning System via Run-to-Run Control as Applied to a Robo-Met.3D

Optimization of automated data collection is gaining increased interest for the purposes of enabling closed-loop self-correcting systems that inherently maximize operational efficiencies and reduce waste. Many data collection systems have several variables which influence data accuracy or consistency and which can require frequent user interaction to be monitored and maintained. Operating upon a Robo-MET.3D™ automated mechanical serial-sectioning system, a run-to-run control algorithm has been developed to accelerate data collection and reduce data inconsistency. Here, using historical data amassed over a decade of experiments, a linear regression model of the deterministic system dynamics is created and used to employ a run-to-run control algorithm that optimizes selected system inputs to reduce operator intervention and increase efficacy while reducing variance of system output.

42 ENGINEERING↗
DOE OSTI2021

Bio-project “derisking” through development of systematic methodologies and frameworks for risk assessment

One of the primary hindrances to producing a viable, sustainable domestic biomass industry for renewable biofuels, bio-products and bio-power is the lack of understanding and quantification of the risks associated with both the biomass supply chain and preprocessing and conversion technologies. Currently a consistent method for assessing, comparing, and quantifying risks in biomass supply chains does not exist, creating a major investment barrier to bioenergy projects in the U.S. The lack of a standardized approach has resulted in bioenergy stakeholders independently using inconsistent approaches and evaluation criteria, leading to unreliable and incomparable assessments of risks and financing barriers to bio-project development. Along with the challenges of inconsistent risk assessment for supply chain risk, technology specific risks based on variability in biomass properties are not fully understood and can pose significant unforeseen challenges for bioenergy projects. In many cases these properties have not yet been identified and the impacts on the proposed technology and products unquantified. This is particularly challenging for emerging preprocessing and conversion technologies. Without a firm understanding of the preprocessing/conversion technology-specific critical properties, the risk of a proposed bio-project cannot be fully evaluated. To address inconsistent risk evaluation in the biomass supply chain supporting project financing, a Biomass Supply Chain Risk Standards (BSCRS) framework was developed. The BSCRS framework includes a comprehensive list of known and perceived risks (Risk Indicators) to the supply chain developed through 100’s of interviews with bioenergy industry experts spanning from feedstock growers and suppliers to representatives from the financial sector. These risks have been organized into a manageable hierarchy of Risk Categories and Risk Factors that can be practically assessed. This BSCRS framework also provides mitigation strategies for multiple Risk Indicators from best available industry practices and research findings. Additionally, a risk quantification methodology for each Risk Factor, Risk Category, and the bio-project as a whole was developed to enable capital markets to assess feedstock risk more efficiently and more accurately. Multiple case studies representing existing bio-projects have been used to evaluate and verify the BSCRS framework and scoring methodology. To address technological risk along with the supply chain risk captured in the developed BSCRS framework, this work also focuses on development of a systematic criticality assessment tool using well-accepted, quantitative risk analysis methods to evaluate bioenergy feedstock critical properties impacting system unit operations. The proposed Failure Mode and Effect Analysis (FMEA) approach uses a team of subject area experts (SAEs) for each targeted unit operation within a system. Collectively, the team will develop and use a quantitative scoring system to assess the material attributes, process parameters, and quality attributes for key unit operations that have already been identified. The FMEA process generates Risk Priority Numbers (RPNs) for the various failures and predominant causes for each material/process unit/product combination resulting in a semi-quantitative, standardized methodology for assessing technological risk and biomass properties contributing to that risk.

09 BIOMASS FUELS↗
DOE OSTI2021

Separate-Effects Tests for Studying Temperature-Gradient-Driven Cracking in UO 2 Pellets

We report a variety of normal operation and accident scenarios can generate thermal stresses large enough to cause cracking in light-water reactor (LWR) fuel pellets. Cracking of fuel pellets can lead to reduced heat removal, higher centerline temperatures, and localized stress in cladding, all of which impact fuel performance. It is important to experimentally characterize the thermal and mechanical behavior in the pellet before and after cracking to improve cracking models in fuel performance codes. However, in-reactor observation and measurement of cracking is very challenging due to the harsh environment and logistics. Recently, an experimental pellet cracking test stand was developed for separate effects testing of normal operations and accident temperature conditions, using thermal imaging to capture the pellet surface temperature for evaluation of thermal stresses and optical imaging to capture the evolution of cracking in real time. Experiments were performed using depleted uranium dioxide (UO 2 ) pellets, which are useful for collecting data valuable for development and validation of cracking models. A combination of induction and resistance heating was used to create an average temperature gradient of 236°C/cm and 193°C/cm before and after cracking respectively. Characterization of the pellets were done before as well as after cracking. The cracking patterns are somewhat different than those expected in a typical reactor because of the differences in thermal conditions and pellet microstructure. However, if the actual conditions of these experiments are reproduced in computational models, these out-of-pile tests on UO 2 pellets provide relevant data for modeling purposes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Robust surfactant-assisted one-pot sample preparation for label-free single-cell and nanoscale proteomics

With advanced mass spectrometry (MS)-based proteomics, genome-scale proteome coverage can be achieved from bulk cells. However, such bulk measurement obscures cell to cell heterogeneity, precluding proteome profiling of single cells and small numbers of cells of interest. To address this issue, in recent 5 years there are a surge of small sample preparation methods developed for robust effective collection and processing of single cells and small numbers of cells for in-depth MS-based proteome profiling. Based on their broad accessibility, they can be categorized into two types: specific device- and standard PCR tube- or multi-well plate-based methods. Herein we describe the detailed protocol of our recently developed, easily adoptable, Surfactant-assisted One-Pot (SOP) sample preparation coupled with MS method termed SOP-MS for label-free single-cell and nanoscale proteomics. SOP-MS capitalizes on the combination of a MS-compatible surfactant, DDM (n-Dodecyl-ß-D-maltoside), and standard low-bind PCR tube or multi-well plate for ‘all-in-one’ one-pot sample preparation without sample transfer. With its robust and convenient features, SOP-MS can be readily implemented in any MS laboratory for single-cell and nanoscale proteomics. With further improvements in MS detection sensitivity and sample throughput, we believe that SOP-MS could open an avenue for single-cell proteomics with broad applicability in the biological and biomedical research.

Single-cell proteomics, nanoscale proteomics, SOP-↗
DOE OSTI2022

Gen3 Gas Phase System Development and Demonstration (Final Technical Report)

Work undertaken in this project seeks to transform the current baseline technology – which is a collection of related but separately developed components and concepts – into a unified and operating test facility and an accompanying preliminary commercial design. This project is motivated by the primary goal of developing a system to absorb concentrated solar energy and deliver it into thermal energy storage at temperatures above 700°C, thereby enabling integration with a high-efficiency supercritical carbon dioxide (sCO 2 ) power cycle to achieve or exceed a levelized cost of electricity (LCOE) target of 6 ¢ per kilowatt-hour electric (kW e -hr). The proposed baseline solution utilizes a high-temperature gas phase (GP) system interfacing with a two-tank particle thermal energy storage (TES) and intermediate heat exchangers to supply sCO 2 at 20-25 megapascals (MPa) and 700°C at the turbine inlet.

14 SOLAR ENERGY↗
DOE OSTI2020

Microanalysis of Primary Biological Particles from Model Grass over Its Life Cycle

Airborne biological aerosols are an integral part of the atmosphere–biosphere interface and significantly impact the environment and Earth’s climate. Primary biological particles such as fungal spores, bacteria, and fragments of plant debris are emitted directly from the biosphere into the atmosphere. The phyllosphere (aboveground plant surfaces) is one of the major habitats for different microbes. Here, atmospheric emissions of primary biological particles from model grass Brachypodium distachyon were monitored by online particle number concentration counting at eight principal growth stages starting from leaf development to senescence. Particles collected at each of the growth stages were then characterized using microspectroscopy techniques. Our findings indicate that the morphological and compositional characteristics of emitted biological particles are highly variable at the different growth stages. We identified fungal spores as the most abundant biological particles in the heading stage, while bacteria were most abundant in the flowering and fruit development stages. Understanding differences in the composition and morphology of biological particles during the plant life cycle provides more accurate and reliable insights on structural, functional, and biochemical properties of plant systems, as well as their interactions with microbial communities. This study also provides insights into Earth geoscience system models that represent primary biological particle emissions from the biosphere.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Hydropower Biological Evaluation Toolset Best Practice Guide

Field studies using live fish are necessary for the evaluation of turbine biological performance, but they cannot determine the specific hydraulic conditions or physical stresses experienced by the fish, the locations where deleterious conditions occur, or the specific causes of the biological response. Using the Sensor Fish (SF) sensing technology, this deficiency can be overcome because the SF can be released independently or concurrently with live fish directly into operating infrastructure, and it takes high-frequency measurements of hydraulic conditions such as pressure, acceleration, and rotation acting on a body in situ during downstream passage. The Hydropower Biological Evaluation Tools (HBET; Hou et al. 2018) software package, developed by Pacific Northwest National Laboratory (PNNL), is designed to assemble, organize, and process data collected by the PNNL-developed SF and by live fish. HBET was developed specifically to design SF field studies, process the raw data, and analyze the processed data efficiently and scientifically. Its objectives are to facilitate SF studies focused on characterizing hydraulic conditions and to apply SF data for evaluating the impacts on fish from passage through hydro-structures. HBET allows users to design new studies, analyze data, perform statistical analyses, and evaluate predicted biological responses. It can be used by researchers, turbine designers, hydropower operators, and regulators to evaluate hydro-structures to enhance environmental sustainability in a cost-effective manner.

13 HYDRO ENERGY↗
DOE OSTI2021

Design Readiness And Maturity Assessment (DRAMA) Tool for Advanced Reactors

Abstract – This research is developing a formal, repeatable method to assess the readiness and maturity of an advanced nuclear reactor design for licensing and deployment. This design readiness and maturity assessment (DRAMA) tool will be capable of determining the readiness of the design and of all parties needed to bring a particular advanced reactor design to fruition. Beneficiaries and stakeholders include the design team, research organizations (required to collect needed data and develop design tools), standards organizations, research facilities to support gathering of needed data, supply chain and construction organizations, and everyone responsible for legal and regulatory infrastructure (including defining import-export requirements). Recent experience has shown that even the most experienced engineering and construction organizations, with decades of experience in the nuclear power business, have had significant challenges in bringing designs to completion, licensing the designs, and constructing new plants. For new entries into the field, simply understanding the unique environmental and regulatory requirements have been daunting. A significant part of the challenge is that new entries into the market do not know what they don't know. This tool will provide design teams a better understanding of their readiness to proceed to licensing (and other steps in the process), while at the same time providing a valuable metric for other interested organizations, such as funding agencies, national regulators, and international markets. The DRAMA tool will provide an assessment of the likelihood of successfully completing licensing and deployment and will also create the capability to assess the ability of regulatory infrastructure and the supply chain to support the deployment of any design or class of designs. It will also help prioritize research and policy efforts to improve the likelihood of deployment of the next generation of advanced reactors.

Arndt, Steven↗
DOE OSTI2022

A guideline to document occupant behavior models for advanced building controls

The availability of computational power, and a wealth of data from sensors have boosted the development of model-based predictive control for smart and effective control of advanced buildings in the last decade. More recently occupant-behavior models have been developed for including people in the building control loops. However, while important objectives of scientific research are reproducibility and replicability of results, not all information is available from published documents. Therefore, the aim of this paper is to propose a guideline for a thorough and standardized occupant-behavior model documentation. For that purpose, the literature screening for the existing occupant behavior models in building control was conducted, and the occupant behavior modeling processes were studied to extract practices and gaps for each of the following phases: problem statement, data collection, and preprocessing, model development, model evaluation, and model implementation. Here, the literature screening pointed out that the current state-of-the-art on model documentation shows little unification, which poses a particular burden for the model application and replication in field studies. In addition to the standardized model documentation, this work presented a model-evaluation schema that enabled benchmarking of different models in field settings as well as the recommendations on how OB models are integrated with the building system.

Building control↗
DOE OSTI2024

An Approach to Dynamic Human Reliability Analysis and Its Data Collection Framework

Human reliability analysis (HRA) is a method for evaluating human errors in a variety of complex systems such as nuclear power plants, military systems, aircraft, and chemical plants. Most HRA methods currently used by regulatory institutes or utilities are called static HRA and are carried out by simple worksheets or simple calculators. To date, there are many unsolved or intrinsic challenges in static HRA. For example, existing static HRA does not realistically model and evaluate human actions as they would be performed at actual systems. There is no method with HRA to objectively estimate the time required for human actions despite being essential to HRA processes. In addition, many HRA methods still rely on a dataset generated prior to the 1980s, from unrelated industry experience or simply from expert judgment. Accordingly, this study attempted to research how to overcome the challenges of existing HRA via dynamic risk assessment (a.k.a., simulation-based or computation-based risk assessment) techniques. First, this study developed a dynamic HRA method, named as PRocedure-based Investigation Method of EMRALD Risk Assessment – HRA (PRIMERA-HRA). The PRIMERA-HRA mainly concentrates on providing HRA analysts with specific guidelines on how to reasonably model human actions, assign human reliability data and evaluate output of simulation within a dynamic probabilistic risk assessment tool, called as Event Modeling Risk Assessment using Linked Diagrams (EMRALD). Second, this study also developed a module for performance shaping factors (i.e., the key concept in HRA quantification) applicable to dynamic HRA, then implemented it based on PRIMERA-HRA within the EMRALD tool. Third, this study developed an HRA data collection framework to support dynamic HRA, called as Simplified Human Error Experimental Program (SHEEP). Originally, the SHEEP study aimed to support static HRA and its data collection, but recently extended the scope to the new technologies such as dynamic HRA or HRA for advanced reactors. SHEEP focuses on the use of data collected from simplified simulators to complement—but not replace—data collection studies using full-scope simulators and actual operators. To date, many experiments were conducted under the SHEEP framework. Multiple analyses, such as human performance analysis, human error analysis, task complexity analysis, learning effect analysis and time distribution analysis, were also carried out using the collected data. Then, based on the major insights, an approach to inferring full-scope data based on simplified simulator data was proposed. The PRIMERA-HRA and SHEEP research are expected to evaluate human actions more realistically than existing static HRA, provide an opportunity to collect more HRA data with reasonable cost and labor, then contribute to enhance the quality of HRA.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

An Approach to Dynamic Human Reliability Analysis and Its Data Collection Framework

Human reliability analysis (HRA) is a method for evaluating human errors in a variety of complex systems such as nuclear power plants, military systems, aircraft, and chemical plants. Most HRA methods currently used by regulatory institutes or utilities are called static HRA and are carried out by simple worksheets or simple calculators. To date, there are many unsolved or intrinsic challenges in static HRA. For example, existing static HRA does not realistically model and evaluate human actions as they would be performed at actual systems. There is no method with HRA to objectively estimate the time required for human actions despite being essential to HRA processes. In addition, many HRA methods still rely on a dataset generated prior to the 1980s, from unrelated industry experience or simply from expert judgment. Accordingly, this study attempted to research how to overcome the challenges of existing HRA via dynamic risk assessment (a.k.a., simulation-based or computation-based risk assessment) techniques. First, this study developed a dynamic HRA method, named as PRocedure-based Investigation Method of EMRALD Risk Assessment – HRA (PRIMERA-HRA). The PRIMERA-HRA mainly concentrates on providing HRA analysts with specific guidelines on how to reasonably model human actions, assign human reliability data and evaluate output of simulation within a dynamic probabilistic risk assessment tool, called as Event Modeling Risk Assessment using Linked Diagrams (EMRALD). Second, this study also developed a module for performance shaping factors (i.e., the key concept in HRA quantification) applicable to dynamic HRA, then implemented it based on PRIMERA-HRA within the EMRALD tool. Third, this study developed an HRA data collection framework to support dynamic HRA, called as Simplified Human Error Experimental Program (SHEEP). Originally, the SHEEP study aimed to support static HRA and its data collection, but recently extended the scope to the new technologies such as dynamic HRA or HRA for advanced reactors. SHEEP focuses on the use of data collected from simplified simulators to complement—but not replace—data collection studies using full-scope simulators and actual operators. To date, many experiments were conducted under the SHEEP framework. Multiple analyses, such as human performance analysis, human error analysis, task complexity analysis, learning effect analysis and time distribution analysis, were also carried out using the collected data. Then, based on the major insights, an approach to inferring full-scope data based on simplified simulator data was proposed. The PRIMERA-HRA and SHEEP research are expected to evaluate human actions more realistically than existing static HRA, provide an opportunity to collect more HRA data with reasonable cost and labor, then contribute to enhance the quality of HRA.

99 - GENERAL AND MISCELLANEOUS↗