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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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

State and Local Planning for Energy (SLOPE) Platform

This fact sheet outlines the basic overview features, purpose, and additional information for the State and Local Planning for Energy (SLOPE) Platform. SLOPE is a collaboration between nine U.S. Department of Energy (DOE) technology offices and the National Renewable Energy Laboratory (NREL), integrates and delivers jurisdictionally resolved potential and projection data on energy efficiency, renewable energy, and sustainable transportation in an easy-to-access online platform to enable data-driven state and local energy planning and decision-making.

48 EE - Weatherization and Intergovernmental Progr↗
DOE OSTI2021

Two-DoF Model-Informed Controller Gain Tuning for Several Floating Wind Platforms

Much effort has recently been put into the development of collective blade pitch controllers for floating offshore wind turbines, with the aim of overcoming negative damping issues that arise with traditional control methods. One proposed approach to this challenge involves using a two-degree-of-freedom model to inform the gain schedule of a nacelle velocity feedback term in an otherwise conventional proportional-integral controller. The model uses tower-top fore-aft and rotor angular displacements, and is used to calculate a nacelle velocity feedback gain that results in a specified increase in platform pitch damping. Earlier performance evaluations of this tuning method were favorable, suggesting its potential as an easy way for researchers to obtain an adequate controller. This paper expands on those previous results by examining the performance of the tuning method relative to baseline controllers for several hull configurations, and for several prescribed increases in platform pitch damping. Simulations were run in OpenFAST for several load cases above rated wind speed and show results consistent with trends in the earlier study. The tuning method is thus shown to be adaptable to many different types of hulls, making it useful for the evaluation of prototype designs.

artificial intelligence↗
DOE OSTI2019

Toward Simulating Turbulent Wall-Bounded Flows at High Reynolds Numbers on Exascale Platforms

Given that wall resolved LES (WRLES) of high Reynolds number flows will continue to be intractable on even exascale computing platforms, there is considerable effort to make hybrid RANS/LES (HRLES) methods the tool of choice for predictive simulations of high-Re separated flows. The fidelity of HRLES depends on the ability of the subgrid model to account for the effects of the filtered scales on the resolved scales, and on the numerical schemes that are used to evolve the Navier-Stokes equations. While higher-order numerical schemes, with low dissipation/dispersion errors (and higher arithmetic intensity), are commonly used for canonical flow simulations (on simpler geometries), the bulk of the high-Re flow simulations on complex geometry still employ nominally second-order accurate schemes in structured/unstructured flow solvers. Hence, there is a need for better sub-grid models that can improve the predictive capability of both the higher-order flow solvers and existing second-order accurate flow solvers, for simulating turbulent separated flows. This talk is about ongoing efforts to develop HRLES subgrid models from DNS/WRLES simulations of canonical flows over curved and sharply discontinuous surfaces and their ability to predict high-Re flows on current petascale platforms.

Balakrishnan, Ramesh↗
DOE OSTI2022

Development of a Data Platform for High-Temperature Gas-cooled Reactor (HTGR) Nuclear Energy University Program (NEUP) Thermal-Fluid Experiments

Since the U.S. Department of Energy (DOE)’s Office of Nuclear Energy (NE) initiated the Nuclear Energy University Program (NEUP) in 2009, a total of 30 NEUP projects focused on High-Temperature Gas-cooled Reactor (HTGR) thermal-fluid experiments were funded up to fiscal year (FY) 2021. This represents a total DOE investment of approximately $23M over the 12-year period, covering thermal fluid phenomena important to both pebble bed and prismatic HTGR designs. The NEUP projects have produced a large amount of high-quality experimental and computational data that were published in final project reports, journal articles, dissertations, and conference proceedings, but in most cases the actual data sets and supporting information such as facility and instrumentation descriptions were not publicly disseminated to the HTGR community. To the authors’ best knowledge, a data platform that organizes and summarizes these NEUP-funded projects for HTGR research does not currently exist. To improve access to this HTGR validation data and optimize the return on the significant investment made by DOE, the Advanced Reactor Technologies (ART) Gas-Cooled Reactor (GCR) program started a survey of completed and ongoing HTGR NEUP projects with the aim of developing a public-access data platform that can be used to retrieve computational fluid dynamics (CFD) and system code validation data and guide future NEUP investments. This paper summarizes the status of the current ART-GCR database, provides an overview of the NEUP-funded HTGR-related research projects from FY2009 to FY2021 and identify validation knowledge gaps still existing in HTGR thermal-fluid research.

42 ENGINEERING↗
DOE OSTI2021

Resilient Information Architecture Platform for Smart Grid (RIAPS)

A number of emerging trends will substantially alter the operation and control of the electric grid over the next several decades. These trends include ensuring resiliency under severe weather events, increasing integration of renewable electricity generation, supporting changing electricity demand patterns, and the improving cost effectiveness of distributed energy resources. To address these challenges, the future “Smart Grid” management will need to transition from centralized to coordinated distributed control paradigm. Reliable operation of the Smart Grid depends on distributed intelligence realized through software applications that run on distributed computing devices attached to the power system to collect data and collaboratively manage resources. However, much of the existing software for Smart Grid-enabled devices is either proprietary or developed with custom solutions, which limits interoperability among the heterogeneous devices and hinders the ability to manage system-level reliability, security, and resiliency requirements. Additionally, this approach makes Smart Grid applications hard to maintain, evolve, verify, and replace; resulting in high development and deployment costs. Further development of the Smart Grid requires a reusable software base-layer to move from hard-coded functionality to a plug-and-play architecture capable of managing system-level objectives and constraints in addition to providing consistent common services across heterogeneous devices and applications. Vanderbilt University, in collaboration with North Carolina State University and Washington State University has developed a foundation ‘software platform’ for developing and deploying robust, reliable, effective and secure software applications for the Smart Grid. The Resilient Information Architecture Platform for the Smart Grid (RIAPS) provides core services for building effective and powerful smart grid applications. It offers unique services for real-time data dissemination, fault tolerance, and coordination across apps distributed over the network.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Integrated Large-Scale Data Management Platform for Photovoltaic Power Conversion Equipment (PCE) Reliability Data: Preprint

To meet the demand for accuracy and real-time capability of PV system degradation evaluation, massive volume data is needed to run high-fidelity and high-efficiency simulations and perform advanced data analysis. However, PV farm operators have a series of difficulties with PV inverter data, such as data collection from multiple channels, massive data storage, data management and massive data analysis. To address these challenges, we developed an integrated data management platform capable of data acquisition, processing, storage, query, and performing big data analysis utilizing AI algorithms. The platform can also achieve data correctness verification and provide an effective distributed data management solution to retrieve massive data and establish a connection to distributed computational frameworks.

data management↗
DOE OSTI2017

Performance Assessment of a New Gamma Spectroscopy Toolkit: Peak Doctor and the Spectral Nondestructive Assay Platform (SNAP) - 17397

High resolution gamma spectroscopy (HRGS) is a widely used technique for non-destructive characterization of nuclear materials, and assay of radioactive waste. The ability to accurately evaluate raw spectral data is an important step in producing high-quality results. A spectral analysis application known as Peak Doctor has been developed under a Cooperative Research and Development Agreement (CRADA) between LANL and Pajarito Scientific Corporation (PSC). The software has undergone several years of testing and validation by the Radioactive Waste Characterization Team of the LANL Waste Management Services Group (WM-SVS). PeakDoctor is designed to work with the Spectral Nondestructive Assay Platform (SNAP{sup TM}) to provide a complete HRGS analysis engine. SNAP{sup TM} performs model specific corrections for attenuation and geometry. These applications are the major components of a new toolkit designed primarily for portable far-field non-destructive assay. Peak Doctor performs a very accurate peak fit analysis with the goal of improving upon the capabilities of existing commercially available routines. It is designed to be user-configurable and can be customized to any given type of detector and any given type of spectrum. Peak Doctor was developed to resolve many of the inherent complexities in gamma ray spectra including overlapping peaks, complex multiplet regions, large step peaks, backscatter regions and Compton edges. Unlike most spectral analysis platforms, Peak Doctor does not rely on the energy calibration and detector resolution parameters that are saved by the operator in the spectrum file. Instead, key parameters are pre-defined for individual detectors. The software fits a continuum using a series of splines between a series of junctions or 'knots'. Fitting techniques are used to join the splines together to create a near-continuous multi-element curve. Optimizing the location of the knots is one of Peak Doctor's most powerful features. The knots are initially configured using a pre-defined list and are removed or added automatically during the fitting process. Knots are also created when new features are identified such as Step Peaks and Compton Edges. The user can also manually specify knots in order to improve the continuum fit. The initial fit is improved on by removing any outlier regions (which are converted to peaks) and the continuum fitting process is repeated. At this stage, an experienced user can work interactively with the fit by manually converting peak regions to continuum or vice versa. The final step is to fit a series of peaks above the continuum based on detector parameters. Single and multiplet peaks are then processed by either the tailing (piecewise) or non-tailing (Gaussian) Levenberg Marquardt non-linear peak fitting routine depending on the user defined selection. Only a rough knowledge of the detector characteristics (energy calibration and resolution) is required for the fitting process to proceed with good accuracy. Any peaks that are missed can be manually added by the user and will also be fitted. Discontinuities in the peak / continuum fit can be rapidly identified by reviewing the 'residuals scale' plot. For waste streams that are well characterized, it is often the case that routinely encountered spectra will have similar peaks and continuum features. For these cases a handy 'One Step' method is available that proceeds in an automated fashion without user interaction. Peak Doctor has been configured with a set of default spectrum types including plutonium / transuranic waste (including Weapons Grade Pu, Heat Source Pu, Am-241, Am-243 and Np-237), depleted or enriched uranium, nuclear power plant / intermediate level waste (with fission and activation products such as Cs-137 and Co-60) as well as Naturally Occurring Radioactive Materials (NORM). The user can easily add new spectrum types for specific waste streams or other types of radiological / special nuclear materials. The output from PeakDoctor (including net peak area, centroid energy and a list of minimum detectable activity levels at pre-defined regions) is used by SNAP{sup TM} to complete the data analysis process. Isotope activities are calculated using configurable template-driven geometry and attenuation correction methods. The software accounts for matrix and container wall effects, collimator / angular corrections, detector efficiency (based on calibration curves) and stand-off distance. SNAP{sup TM} uses published algorithms incorporating a series of decision points during its operation to allow the analyst to provide logical input to guide the software during the analysis of each unique spectrum. The Peak Doctor / SNAP{sup TM} toolkit has been extensively tested and validated under a variety of conditions using 'benchmark' reference spectra. Test scenarios reported include HPGe-based measurements of well characterized uranium and plutonium standards and radioactive sources with a variety of different geometries and matrix materials including surrogate drums and boxes. (authors)

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

Robotic Platforms for Remote Access and Sensing Applications for Nuclear Decommissioning - 18325

This paper provides an updated summary of the research being conducted by the University of Manchester Robotics group (UoM Robotics) [1] at the University of Manchester, UK, in the area of remote characterization through the use of robotic systems. The research is in support of the decommissioning plan for the Sellafield nuclear site in west Cumbria, UK and Fukushima Diiachi, Japan. UoM Robotics works in collaboration with a number of companies and research institutes including Sellafield Ltd, the National Nuclear Laboratory, Universities of Lancaster, Birmingham, Bristol Robotics Laboratory, Italian Institute of Technology and the National Maritime Research Institute in Japan. The progress of a number of novel mobile robotic platforms being developed at the University of Manchester is presented in this poster presentation. Also presented is the on-going work on the remote sensing technologies and techniques which will be deployed on the robotic platforms. (authors)

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

Automated Work Package: Initial Wireless Communication Platform Design, Development, and Evaluation

The Department of Energy's Light Water Reactor Sustainability Program is developing the scientific basis to ensure long-term reliability, productivity, safety, and security of the nuclear power industry in the United States. The Instrumentation, Information, and Control (II and C) pathway of the program aims to increase the role of advanced II and C technologies to achieve this objective. One of the pathway efforts at Idaho National Laboratory (INL) is to improve the work packages execution process by replacing the expensive, inefficient, bulky, complex, and error-prone paper-based work orders with automated work packages (AWPs). An AWP is an automated and dynamic presentation of the work package designed to guide the user through the work process. It is loaded on a mobile device, such as a tablet, and is capable of communicating with plant equipment and systems to acquire plant and procedure states. The AWP replaces those functions where a computer is more efficient and reliable than a human. To enable the automatic acquisition of plant data, it is necessary to design and develop a prototype platform for data exchange between the field instruments and the AWP mobile devices. The development of the platform aims to reveal issues and solutions generalizable to large-scale implementation of a similar system. Topics such as bandwidth, robustness, response time, interference, and security are usually associated with wireless communication. These concerns, along with other requirements, are listed in an earlier INL report. Specifically, the targeted issues and performance aspects in this work are relevant to the communication infrastructure from the perspective of promptness, robustness, expandability, and interoperability with different technologies. The development of a prompt system entailed significant performance optimization, including the use of parallel processing, parallel communication, modularity, noise filtering, and frequency tuning. A robust system was achieved by enforcing a persistent continuous data exchange that automatically detects and conveys a failure of any link or component in any part of the system, and that automatically restores the communication once the link or component is restored. An expandable topology was enabled by modularity and load sharing. The number of servers, mobile devices, and field instruments is not limited from a system structure point of view. The system interoperability was achieved by enforcing the independence of the field communication application from the mobile device application. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Plastic Parallel Pathways Platform- 4P Model

The U.S. generates 42 million metric tons of plastic waste each year - the most of any country - of which less than 9% is recycled. This represents an estimated loss of $2.3 billion and 3.4 EJ embodied energy per year. Plastic use reduction, reuse and recycling are thus increasingly important, but making informed policy and research decisions within this space can be challenging given the diverse range of available solutions. Various recycling and waste management options are available for plastics, such as conversion into energy (incineration, pyrolysis, gasification), conversion to lower- or higher-grade plastics (mechanical recycling, chemical or enzymatic depolymerization processes, dissolution) or up-cycling to feedstock chemicals (photo/electro/catalytic methods, pyrolysis, gasification). While many previous publications have investigated a subset of these options, there is currently no modelling platform that can quantitatively assess and compare the economic and environmental impacts of all these different plastic management pathways and their respective products simultaneously. To directly compare the benefits and disadvantages of plastic-to-x technologies, we propose a novel analysis framework: the Plastic Parallel Pathways Platform (4P). 4P will model a systems analysis framework for capturing plastic material flows processed via different waste management scenarios. The total economic cost and greenhouse gas emissions (as well as additional economic, energetic, and environmental metrics indicators) of a given scenario will be calculated enabling comparison to other scenarios, thereby informing decisions on plastic recycling pathways.

circular economy↗
DOE OSTI2024

Gains in operational flexibility, safety margins, and cost efficiencies via integrated Plant Reload Optimization platform

The U.S. Department of Energy Light Water Reactor Sustainability Program Risk-Informed Systems Analysis Pathway Plant Reload Optimization Project aims to develop an integrated, comprehensive framework offering an all-in-one solution for reload evaluations with a special focus on optimizing core design. Optimizing the fuel loading pattern is one of the most important considerations in reducing the amount of new fuel used in the core. Due to thousands of possible core configuration options, finding optimal solutions is an unachievable task for a human. The Plant ReLoad Optimization platform, which supports artificial-intelligence-based reactor core designing, is now fully capable of handling realistic problems. The Plant ReLoad Optimization platform development project aims to build a reactor core design tool that includes reactor safety and fuel performance analyses and uses artificial intelligence to support the optimization of core design solutions. The NSGA-II (Non-dominated Sorting Genetic Algorithm II) optimizer was developed and tested within RAVEN (Risk Analysis and Virtual ENvironment) to handle many constraints by using an augmented objectives methodology. The demonstration was performed with constrained multiobjective optimization of a 17 × 17 pressurized-water reactor core loading patterns to minimize fuel cost and maximize fuel cycle length.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Distributed Multi-GPU Community Detection on Exascale Computing Platforms

Community detection is a fundamental operation in graph mining, and by uncovering hidden structures and patterns within complex systems it helps solve fundamental problems pertaining to social networks, such as information diffusion, epidemics, and recommender systems. Scaling graph algorithms for massive networks becomes challenging on modern distributed-memory multi-GPU (Graphics Processing Unit) systems due to limitations such as irregular memory access patterns, load imbalances, higher communication-computation ratios, and cross-platform support. We present a novel algorithm HiPDPL-GPU (Distributed Parallel Louvain) to address these challenges. We conduct experiments involving different partitioning techniques to achieve an optimized performance of HiPDPL-GPU on the two largest supercomputers: Frontier and Summit. Remarkably, HiPDPL-GPU processes a graph with 4.2 billion edges in less than 3 minutes using 1024 GPUs. Qualitatively, the performance of HiPDPL-GPU is similar or better compared to other state-of-the-art CPU- and GPU-based implementations. While prior GPU implementations have predominantly employed CUDA, our first-of-its-kind implementation for community detection is cross-platform, accommodating both AMD and NVIDIA GPUs.

Sattar, Naw Safrin↗
DOE OSTI2024

Pressurized-Water Reactor Core Design using Multi-Objective Plant Fuel Reload Optimization Platform

The United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program Risk-Informed Systems Analysis (RISA) Pathway Plant Reload Optimization Project aims to develop an integrated, comprehensive framework offering an all-in-one solution for reload evaluations with a special focus on optimization of core design. The optimization of the fuel loading pattern is one of the most important considerations in reducing the amount of new fuel used in the core. Due to thousands of possible options of core configuration, finding optimal solutions is an unachievable task for a human. The Plant ReLoad Optimization (PRLO) platform which supports artificial-intelligence-based reactor core designing is now fully capable of handling realistic problems. The PRLO Platform development project aims to build a reactor core design tool that includes reactor safety and fuel performance analyses and uses artificial intelligence to support the optimization of core design solutions. The NSGA-II (Non-dominated Sorting Genetic Algorithm-II) optimizer was developed and tested within RAVEN (Risk Analysis and Virtual Environment) to handle many constraints by using an augmented objectives methodology. The demonstration was performed with constrained multi-objective optimization of a 17 × 17 pressurized-water reactor core loading patterns to minimize fuel cost and maximize fuel cycle length.

42 ENGINEERING↗
DOE OSTI2021

DESIGN & DEVELOPMENT OF A SECURE SELF-LEVELING WIRELESS RECHARGING PLATFORM FOR AN AERIAL DRONE ON AN UNMANNED SURFACE VESSEL

The Design and Development of an automated recharging station for an aerial drone, onboard a small, unmanned surface vessel, is described. Drones require a landing surface that is level within five degrees of the surrounding terrain for repeated reliable landing and takeoff. System constraints and at-sea application necessitate a compact, lightweight, and secure solution. A passive self-leveling platform and an accompanying automated parallel-pusher drone restraint mechanism have been designed and fabricated to aid in achieving a level landing surface and holding the drone in place while it charges. The self-leveling mechanism has been analyzed and subjected to initial laboratory tests. The testing of the drone restraint mechanism to verify its weight capacity and closing time, and the integration of the platform with a custom conductive contact wireless charging pad are identified as future work. The resulting cohesive unit will be tested for performance optimization and implementation onboard the unmanned surface vehicle.

McKinney, Adriana↗
DOE OSTI2024

SEED Platform for Building Performance Standards Implementation Guide (Spanish Translation)

This guide provides an overview of the Standard Energy Efficiency Data (SEED) Platform. The SEED Platform developed by the U.S. Department of Energy (DOE) to provide a low-cost, user-friendly tool for jurisdictions to launch and manage energy benchmarking and Building Performance Standard (BPS) programs. It has been translated into Spanish. This is the Spanish translation of NREL/FS-5500-90691.

benchmarking↗
DOE OSTI2024

SEED Platform for Building Performance Standards Implementation Guide (French Translation)

This guide provides an overview of the Standard Energy Efficiency Data (SEED) Platform. The SEED Platform developed by the U.S. Department of Energy (DOE) to provide a low-cost, user-friendly tool for jurisdictions to launch and manage energy benchmarking and Building Performance Standard (BPS) programs. It has been translated into Spanish. This is the French translation of NREL/FS-5500-90691.

benchmarking↗
DOE OSTI2024

SEED Platform for Building Performance Standards Implementation Guide (Arabic Translation)

This guide provides an overview of the Standard Energy Efficiency Data (SEED) Platform. The SEED Platform developed by the U.S. Department of Energy (DOE) to provide a low-cost, user-friendly tool for jurisdictions to launch and manage energy benchmarking and Building Performance Standard (BPS) programs. It has been translated into Spanish. This is the Arabic translation of NREL/FS-5500-90691.

benchmarking↗
DOE OSTI2024

SEED Platform for Building Performance Standards Implementation Guide (Mandarin Translation)

This guide provides an overview of the Standard Energy Efficiency Data (SEED) Platform. The SEED Platform developed by the U.S. Department of Energy (DOE) to provide a low-cost, user-friendly tool for jurisdictions to launch and manage energy benchmarking and Building Performance Standard (BPS) programs. It has been translated into Spanish. This is the Mandarin translation of NREL/FS-5500-90691.

benchmarking↗