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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 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Open Source Modeling for Power Systems Dynamics

Time-domain simulations for power systems (both phasor and electromagnetic transients) are mostly dominated by commercial tools, such as PSS/E or PSCAD. However, with the increasing penetration of inverter-based resources and changes in model paradigm, new open-source tools are looking into become a possible alternative for a range of stakeholders. In this presentation we will focus on how to improve communication with industry and other stakeholders to properly showcase the value of open source simulators. Participants will identify current barriers facing the adoption of open-source time-domain tools, and will discuss how to improve the bench-marking processes across different tools, while maintaining software modularity to include new features and models as new challenges continue to happen in energy systems.

ENERGY PLANNING, POLICY, AND ECONOMY,MATHEMATICS A↗
DOE OSTI2022

Coupled Multiphysics Primary Loop Simulations of the Mk1-FHR in the Virtual Test Bed

To support advanced reactor demonstrations, the Virtual Test Bed (VTB) [1] repository hosts a wide range of challenge problems for showcasing modeling and simulation capabil- ities in support of advanced reactor demonstrations. This document presents a coupled multiphysics model of the Mark 1 pebble-bed fluoride-salt-cooled high-temperature reactor (PB-FHR). The analysis leverages NEAMS tools (Griffin [2], SAM [3], Pronghorn [4], and the MOOSE [5] heat con- duction module) for core neutronics, thermal hydraulics of the core and primary loop, and multiscale fuel performance simulations. The analysis was entirely created by coupling standalone simulations of the reactor that were previously available on the VTB. All input files and documentation de- veloped for this example are available on the VTB website: mooseframework.inl.gov/ virtual_test_bed/ . This model was featured in the National Reactor Innovation Cen- ter Tech Talk presented in December 2021.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Developing a Mapping Inquiry Tool and Geodatabase to Support Characterization of Candidate Geologic Carbon Storage Sites

Poster to showcase the Class VI Mapping Inquiry Tool (C6 MapIT) and support geodatabase available on EDX. C6 MapIT and the underpinning Class VI Data Support Tool Geodatabase serve as comprehensive resources to aid with understanding data availability and supporting the initial stages of Class VI siting and permit applications. In addition to enabling users to explore available spatial data, additional resources are provided to empower users to find more information, such as links to documentation to support project goals, a query tool for USGS publications, and links to relevant sections of the EPA Class VI documentation. Safe and appropriate selection of UIC Class VI well sites is key as the U.S. makes the energy transition. The Class VI Mapping Inquiry Tool and Class VI Data Tool Geodatabase are valuable, intuitive resources that empower commercial and industry stakeholders to explore, analyze, and acquire Class VI related data.

Pantaleone, Scott↗
DOE OSTI2024

INSEN 2023 Yearly Updates

The International Nuclear Security Education Network (INSEN) is the primary international network for nuclear security educational initiatives. INSEN plays a central role in fostering collaboration and knowledge-sharing among nuclear security education experts worldwide. In the wake of the global pandemic, INSEN remained committed to strengthening nuclear security education and resumed in-person activities. The International Atomic Energy Agency (IAEA) confirmed its continued dedication and support to the network’s mission and presented new activities aimed at elevating global nuclear security efforts. Throughout the year and during the annual and leadership meetings, working groups showcased their vibrant work and presented innovative ideas, invigorating the attendees’ dedication to their crucial work. Comprehensive presentations by the IAEA during the INSEN annual and leadership meetings highlighted the importance of the network and its continued commitment to promoting nuclear security on a global scale. One notable and encouraging trend was the increase in network membership, signifying a growing commitment to nuclear security worldwide and the continued interest in nuclear security from member states and international organizations. The value of initiatives such as the Women in Nuclear Security Initiative was emphasized at the annual meeting, accenting the need for diverse perspectives and expertise to ensure a secure nuclear landscape.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Development of creep resistant ferritic-martensitic steels for operation at 650°C

Creep resistant ferritic-martensitic steels capable of operating at 650°C have long been under development for electricity generation. This presentation will showcase the design and development of alloys CPJ-7 and JMP with approximately 420,000 h of in-house creep testing, the equivalent of ~50 years of cumulative creep tests. Approximately fifty different alloy compositions were manufactured as part of an ICME approach. CPJ-7 present superior creep properties compared to COST alloys for steam turbine and P91/92 for boiler applications. The prolonged creep life was attributed to slowing down the destabilization of fine precipitates at 650°C. The JMP steels were designed with higher Co for solid solution strengthening and increased W for matrix strength and stability. The creep life compared to CPJ-7 increased between 118 and 150% at 650°C depending on the stress. On a Larson-Miller plot, the performance of the JMP steels surpasses that of MARBN steel. Composition-microstructure-properties relationships will be discussed.

Detrois, Martin↗
DOE OSTI2022

COUPLED MULTIPHYSICS PRIMARY LOOP SIMULATIONS OF THE Mk1-FHR IN THE VIRTUAL TEST BED

The Virtual Test Bed (VTB) repository hosts a wide range of challenge problems to showcase modeling and simulation capabilities to support advanced reactor demonstration. A coupled multiphysics model of the Mk1 pebble-bed fluoride-salt-cooled high-temperature reactor (PB-FHR) is presented here. The analysis leverages NEAMS tools (Griffin), SAM, Pronghorn, and the MOOSE heat conduction module) for core neutronics, thermal hydraulics of the core and the primary loop, and multiscale fuel performance simulations. The analysis was entirely created by coupling standalone simulations of the reactor previously available on the VTB. All input files and documentation developed for this example are available on the VTB website: \url{mooseframework.inl.gov/virtual_test_bed/}}. This model was featured in the NRIC Tech Talk presented in December 2021.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

"Reinforcement Learning Based Oscillation Dampening: Scaling up Single-Agent RL algorithms to a 100 AV highway field operational test"

In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smooth traffic flow in history as of 2023, uncovering the challenges and breakthroughs that come with developing RL controllers for automated vehicles. We delve into the fundamental concepts behind RL algorithms and their application in the context of self-driving cars, discussing the developmental process from simulation to deployment in detail, from designing simulators to reward function shaping. We present the results in both simulation and deployment, discussing the flow-smoothing benefits of the RL controller. From understanding the basics of Markov decision processes to exploring advanced techniques such as deep RL, our article offers a comprehensive overview and deep dive of the theoretical foundations and practical implementations driving this rapidly evolving field. We also showcase real-world case studies and alternative research projects that highlight the impact of RL controllers in revolutionizing autonomous driving. From tackling complex urban environments to dealing with unpredictable traffic scenarios, these intelligent controllers are pushing the boundaries of what automated vehicles can achieve. Furthermore, we examine the safety considerations and hardware-focused technical details surrounding deployment of RL controllers into automated vehicles. As these algorithms learn and evolve through interactions with the environment, ensuring their behavior aligns with safety standards becomes crucial. We explore the methodologies and frameworks being developed to address these challenges, emphasizing the importance of building reliable control systems for automated vehicles.

Jang, Kathy↗
DOE OSTI2024

"Hierarchical Speed Planner for Automated Vehicles: A Framework for Lagrangian Variable Speed Limit in Mixed Autonomy Traffic"

This paper introduces a novel control framework for Lagrangian variable speed limits in hybrid traffic flow environments utilizing automated vehicles (AVs). The framework was validated using a fleet of 100 connected automated vehicles as part of the largest coordinated open-road test designed to smooth traffic flow. The framework includes two main components: a high-level controller deployed on the server side, named Speed Planner, and low-level controllers called vehicle controllers deployed on the vehicle side. The Speed Planner designs and updates target speeds for the vehicle controllers based on real-time Traffic State Estimation (TSE) [1]. The Speed Planner comprises two modules: a TSE enhancement module and a target speed design module. The TSE enhancement module is designed to minimize the effects of inherent latency in the received traffic information and to improve the spatial and temporal resolution of the input traffic data. The target speed design module generates target speed profiles with the goal of improving traffic flow. The vehicle controllers are designed to track the target speed meanwhile responding to the surrounding situation. The numerical simulation indicates the performance of the proposed method: the bottleneck throughput has increased by 5.01%, and the speed standard deviation has been reduced by a significant 34.36%. We further showcase an operational study with a description of how the controller was implemented on a field-test with 100 AVs and its comprehensive effects on the traffic flow.

Wang, Han↗
DOE OSTI2024

Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) for Prospective Impact Analysis of Novel Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 Degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics. The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Additionally we compare our results by linking two other prospective models with LiAISON - GCAM (Global Change Assessment Model) and ReEDS (Regional Energy Deployment System) to analyze the effect of changing background scenarios using varying predictions in life cycle analysis.

decarbonizing↗
DOE OSTI2023

Annular Metallic Nuclear Fuel Informatics at 50 nm Resolution

U-10wt.% Zr (U-10Zr) based metallic fuel is the leading candidate for next-generation sodium cooled fast reactor in United States. Advanced post-irradiation characterization (from sub-nanometer to micrometer) helps to understand fuel microstructure and property change during irradiation, benefiting fuel qualification for commercial application. With high velocity image data generating method, an automatic way to extract the microstructural information quantitively can better serve the needs from post irradiation characterization. A trained machine learning model, named Decision Tree, is employed to categorize pores caused by fission gas release and to aid phase identification. This work presents a showcase of this approach on different irradiated U-10Zr metallic fuels. This quantitative data offers insights into the fission product migration and potentially thermal conductivity degradation. This information from machine learning will be fed into fuel design code for better prediction of fuel performance.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

MOOSE: A Modular Platform for Fission and Fusion Multiphysics

The Multiphysics Object-Oriented Simulation Environment (MOOSE) Framework, as well as MOOSE-based simulation tools, have accelerated the development of fission energy and advanced reactor technologies through the United States Department of Energy, Office of Nuclear Science, Nuclear Energy Advanced Modeling & Simulation (NEAMS) Program. MOOSE contains a complete platform of multiphysics simulation capabilities, capable of running on massively parallel systems, and is developed in an open-source manner with great attention paid to high-quality software quality assurance practices. This overall approach could greatly benefit the fusion energy community, which requires rapid design iteration and improvement in order to facilitate the successful development of fusion as an alternative energy source to fossil fuels. In the first half of this talk, applications of MOOSE and MOOSE-based tools for advanced reactor designs will be showcased, as well as MOOSE ecosystem infrastructure (such as the NEAMS Virtual Test Bed) that enables and accelerates fission reactor design. In the second half, a discussion of how the MOOSE approach to modeling and simulation is currently being applied internationally in fusion energy research and development at the United Kingdom Atomic Energy Authority will be discussed, and ongoing/future domestic research efforts will be highlighted.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) for Analyzing Emerging Low-Carbon Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics. The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Under baseline projections (i.e., no decarbonization goals), neither process reaches parity with the incumbent technology across several environmental metrics. Under the decarbonization scenarios, the underlying sectoral shifts result in declining impacts over time, compared to 2020 levels, except for metal depletion levels, which increase. The background shifts postulate a heavily decarbonized economy and energy system, which help technologies reach parity with SMR between 2040-2050 (RCP2.6) and 2030-2040 (RCP1.9) for global warming. Despite declines across several other metrics over time, neither PtH2 technology break even with SMR by 2100 besides for global warming.

decarbonizing↗
DOE OSTI2024

An Open-Source Decarbonization Analytics Framework: Designing for Low-Carbon Emission Districts and Communities: Preprint

This paper introduces an open-source analytics framework designed to assist in creating low or net-zero carbon buildings and urban districts. Integrated within URBANopt, an open-source platform for energy analysis in districts and communities, this framework equips researchers, architects, engineers, and other stakeholders with tools to evaluate the carbon footprint implications of their design choices. The framework enables the analysis of various scenarios, incorporating both historical and future emission factors, and can span across different climate zones, each with distinct grid and emissions characteristics. The results showcase the framework's capability to evaluate the impact of design upgrades and control strategies on carbon emissions in districts and communities. An illustrative analysis using a hypothetical district in Denver, Colorado, shows reduced emissions from energy efficiency upgrades and control strategies, highlighting the sensitivity in their effects on emissions and energy use.

buildings energy efficiency↗
DOE OSTI2024

Unsupervised Learning for Equitable DER Control: Preprint

In the context of managing distributed energy resources (DERs) within distribution networks (DNs), this work focuses on the task of developing local controllers. We propose an unsupervised learning framework to train functions that can closely approximate optimal power flow (OPF) solutions. The primary aim is to establish specific conditions under which these learned functions can collectively guide the network towards desired configurations asymptotically, leveraging an incremental control approach. The flexibility of the proposed methodology allows to integrate fairness-driven components into the cost function associated with the OPF problem. This addition seeks to mitigate power curtailment disparities among DERs, thereby promoting equitable power injections across the network. To demonstrate the effectiveness of the proposed approach, power flow simulations are conducted using the IEEE 37-bus feeder. The findings not only showcase the guaranteed system stability but also underscore its improved overall performance.

asymptotic stability↗
DOE OSTI2024

Sustainable Public Transport: Providing Responsive, On-Demand Service with Clean Energy

The National Renewable Energy Laboratory (NREL) uses the Mobility Energy Productivity (MEP) as a metric and a lens to guide applied research into high performance public mobility. In the current initiative to abate global warming, the US needs not only zero-emission vehicles in the transit fleet (such as buses and shuttles) but also time- and cost-effective services to connect people with goods, services and employment toward a high-quality of life. Our current transportation system is overly dependent on personally-owned automobiles for high quality mobility, with public modes being less viable in many areas. Simply electrifying the drivetrains of existing public transit modes will fail to improve the quality of mobility for those that do not have access to private automobiles. The slow rebound by transit from the pandemic reveals the need to reinvent public transit service. Using the MEP lens, NREL researchers have tracked various novel developments in the public mobility space, with the confluence of shared, on-demand transit (ODT) services using light duty vehicles emerging as a key enabler of high-efficiency public mobility. Deployments such as those in Arlington, TX, Dallas, TX, Fort Erie, ON, and Innisfil, ON showcase the use of fleets of light-duty vehicles as the basis for community circulation and first/last mile to intra-regional transit. ODT services have demonstrated improvements in being more time efficient for riders, more energy efficient in operation (even before the introduction of fully electric vehicles), as well as being cost effective. It appears that aspects of the long-awaited promise of Personal Rapid Transit from the 1970s are beginning to be realized through ODT deployments, leveraging transportation network company (TNC) logistics, popularized by Uber and Lyft, but applied to public mobility. Currently, manually driven ODT operations are already cost competitive with traditional transit systems on a cost per ride basis, and full automation promises to reduce costs by 50% while providing additional safety and verified customer service. Connecting these ODT systems with efficient and effective intra-regional backbone transit service is the next step, with transit agencies like DART providing early results. This discussion will walk through the evidence for this postulated outcome and show results from a series of case-studies.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2024

Identifying Neutrino Final States and Energies in MicroBooNE with New Deep-Learning Based LArTPC Reconstruction Frameworks

MicroBooNE, a Liquid Argon Time Projection Chamber (LArTPC) located in the $\nu_{\mu}$-dominated Booster Neutrino Beam at Fermilab, has been studying $\nu_{e}$ charged-current (CC) interaction rates to shed light on the MiniBooNE low energy excess. The LArTPC technology employed by MicroBooNE provides the capability to image neutrino interactions with mm-scale precision. Computer vision and other machine learning techniques are promising tools for image processing that could boost efficiencies for selecting $\nu_{e}$-CC and other rare signals, reduce cosmic and beam-induced backgrounds, and improve the reconstruction of neutrino energies. The MicroBooNE experiment has been at the forefront of developing and testing such techniques for use in physics analyses. In this poster we overview deep-learning based reconstruction methods. We will showcase the use of a recurrent neural network to estimate neutrino energies and present a new reconstruction framework that uses convolutional neural networks to locate neutrino interaction vertices, tag pixels with track and shower labels, and perform particle identification on reconstructed clusters. We will present studies characterizing the performance of these new tools and demonstrate their effectiveness through their use in an inclusive $\nu_{e}$-CC event selection.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗