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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Representing Small Commercial Building Faults in EnergyPlus, Part I: Model Development

Small commercial buildings (those with less than approximately 1000 m2 of total floor area) often do not have access to cost-effective automated fault detection and diagnosis (AFDD) tools for maintaining efficient building operations. AFDD tools based on machine-learning algorithms hold promise for lowering cost barriers for AFDD in small commercial buildings; however, such algorithms require access to high-quality training data that is often difficult to obtain. To fill the gap in this research area, this study covers the development (Part I) and validation (Part II) of fault models that can be used with the building energy modeling software EnergyPlus® and OpenStudio® to generate a cost-effective training data set for developing AFDD algorithms. Part I (this paper) presents a library of fault models, including detailed descriptions of each fault model structure and their implementation with EnergyPlus. This paper also discusses a case study of training data set generation, representing an actual building.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Prediction of DIII-D Pedestal Structure from Externally Controllable Parameters

The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. An experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (ne) and electron temperature (Te) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (Ip), toroidal magnetic field (Bφ), neutral beam heating power (PNBI) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Image fusion using sparse overcomplete feature dictionaries

Approaches for deciding what individuals in a population of visual system "neurons" are looking for using sparse overcomplete feature dictionaries are provided. A sparse overcomplete feature dictionary may be learned for an image dataset and a local sparse representation of the image dataset may be built using the learned feature dictionary. A local maximum pooling operation may be applied on the local sparse representation to produce a translation-tolerant representation of the image dataset. An object may then be classified and/or clustered within the translation-tolerant representation of the image dataset using a supervised classification algorithm and/or an unsupervised clustering algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Getting to Net Zero

The technology necessary to build net zero energy buildings (NZEBs) is ready and available today, however, building to net zero energy performance levels can be challenging. Energy efficiency measures, onsite energy generation resources, load matching and grid interaction, climatic factors, and local policies vary from location to location and require unique methods of constructing NZEBs. It is recommended that Components start looking into how to construct and operate NZEBs now as there is a learning curve to net zero construction and FY 2020 is just around the corner.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

The Joint Convention, Its Global Impact and U.S Continuing Involvement - 16645

The Joint Convention on the Safety of Spent Fuel Management and on the Safety of Radioactive Waste Management (hereafter called the Joint Convention) is an international convention, conducted under the auspices of the International Atomic Energy Agency (IAEA). It is one of a suite of international instruments dealing with nuclear safety and physical security, which serve to promote a global culture for the safe use of radioactive materials. Unlike other safety or security conventions, however, the Joint Convention deals exclusively with the end of the nuclear fuel cycle. The Joint Convention focuses on the lessons to be learned from past experience and existing facility operations, which can be implemented in the policies and designs for future operating facilities in the nuclear fuel cycle. The U.S. has participated fully in the review process, as well as setting forth initiatives to sponsor increased ratification. The U.S. holds the current Joint Convention presidency for the Fifth Review Cycle. Because of the important benefits associated with active participation, the U.S. has strongly supported a Regional Conference Initiative outreach program to increase membership. We also provided an expert for each of the conferences to assist in advancing the message to non-Member States, in particular developing nations considering ratification. The Joint Convention process also provides opportunities to identify future areas of bilateral and multilateral technical and regulatory cooperation with other Parties. Furthermore, the Joint Convention is consistent with U.S. foreign policy considerations to support, as a priority, the strengthening of the worldwide safety culture in the use of nuclear energy. (authors)

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

Full-Scale Development and Piloting of a Hybrid Digital Twin for Wastewater Operations Optimization

This full-scale pilot of a machine learning based nutrient controller/digital twin is being done as part of The Water Research Foundation (WRF) project 5121: Development of Innovative Predictive Control Strategies for Nutrient Removal. The overall goal of this project is to develop and full-scale test a hybrid (machine learning + mechanistic model) nutrient management controller at four different water resource recovery facilities (WRRFs). The controller demonstrates both short-term optimization functions and predictive capabilities. This project includes the design of the controller, its performance at the WRRFs, and a description of the future improvements.

Johnson, Bruce↗
DOE OSTI2024

Updates and Lessons Learned from NuMI Beamline at Fermilab

The Neutrinos at the Main Injector (NuMI) beamline at Fermilab generates an intense muon neutrino beam for the NOvA (NuMI Off-axis $\nu_e$ Appearance) long-baseline neutrino experiment. Over the years, the NuMI beamline has been pivotal in advancing neutrino physics, providing invaluable data and insights. This proceeding paper discusses updates and the lessons learned from recent experiences during the beam operations, maintenance, and monitoring of the NuMI beamline. Key topics include the optimization of beam performance and challenges in maintaining beamline stability. The paper aims to share best practices and provide a road-map for future beamline projects, including the Long-Baseline Neutrino Facility (LBNF).

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Independent malware detection architecture

A system and method (referred to as the system) detect malware by training a rule-based model, a functional based model, and a deep learning-based model from a memory snapshot of a malware free operating state of a monitored device. The system extracts a feature set from a second memory snapshot captured from an operating state of the monitored device and processes the feature set by the rule-based model, the functional-based model, and the deep learning-based model. The system identifies identifying instances of malware on the monitored device without processing data identifying an operating system of the monitored device, data associated with a prior identification of the malware, data identifying a source of the malware, data identifying a location of the malware on the monitored device, or any operating system specific data contained within the monitored device.

Smith, Jared M.↗
DOE OSTI2023

Two-Stage Deep Reinforcement Learning for Distribution System Voltage Regulation and Peak Load Management: Preprint

The growing integration of distributed solar photovoltaic (PV) in distribution systems could result in adverse effects during grid operation. This paper develops a soft actor critic-based deep reinforcement learning (SAC-DRL) solution to simultaneously control PV inverters and battery energy storage systems for voltage regulation and peak load demand shaving. The novel two-stage framework, featured with two different control agents, is applied for daytime and nighttime operation to enhance the control performance. Comparison results with other control methods on a real feeder in Western Colorado demonstrate that the proposed method can provide advanced voltage regulation with modest active power curtailment for peak demand reduction.

deep reinforcement learning↗
DOE OSTI2021

A review of machine learning in building load prediction

The surge of machine learning in recent years has been empowering engineer modeling in various fields. The decreasing hardware cost, increasing data accessibility, and advances of building automation system (BAS) allow the collection and storage of a significant amount of building operation data. The two facts provide great opportunities of applying machine learning to building energy systems modeling and analysis. There are a great number of research papers on this topic but there lacks a comprehensive and general review to summarize the current development, limitations, gaps and future trend. In this review paper series, machine learning techniques in building energy system modeling and analysis are reviewed under the organization and logic of the machine learning definition by Tom M. Mitchell: a computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. This paper is the first part of the review paper series, which focuses on building load prediction. First, the applications of building load prediction model (task T) are reviewed. Then, the modeling algorithms improving machine learning performance and accuracy (performance P) are reviewed. At the same time, the literature on the data perspective for modeling (experience E), including data engineering from sensors level to data level, pre-processing, feature extraction and selection, is reviewed. Finally, what is well-studied and what is lacking but with great potential are concluded; the gaps between present and future utilization of machine learning techniques are identified; the future trend and development are also predicted. The target readers of this paper are not only researchers from the building side who can get exposed to cutting edge machine learning tools, but also those from machine learning side who can understand the potential and challenge to apply machine learning in buildings.

Liang, Zhang↗
DOE OSTI2021

Deep Learning-Based Adaptive Remedial Action Scheme with Security Margin for Renewable-Dominated Power Grids

The Remedial Action Scheme (RAS) is designed to take corrective actions after detecting predetermined conditions to maintain system transient stability in large interconnected power grids. However, since RAS is usually designed based on a few selected typical operating conditions, it is not optimal in operating conditions that are not considered in the offline design, especially under frequently and dramatically varying operating conditions due to the increasing integration of intermittent renewables. The deep learning-based RAS is proposed to enhance the adaptivity of RAS to varying operating conditions. During the training, a customized loss function is developed to penalize the negative loss and suggest corrective actions with a security margin to avoid triggering under-frequency and over-frequency relays. Simulation results of the reduced United States Western Interconnection system model demonstrate that the proposed deep learning–based RAS can provide optimal corrective actions for unseen operating conditions while maintaining a sufficient security margin.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Laser Line Scan Characterization of Cavitation-Induced Erosion to SNS Mercury Target Vessels

The mercury-facing surfaces of the Spallation Neutron Source target vessel are damaged during operation by a phenomenon called cavitation-induced erosion. To date, erosion damage has caused leaks to develop in two target modules, which necessitated interruptions in operation to replace the target module. Samples are routinely removed from the beam entrance region of SNS targets after removal from service and analyzed to learn about the damage processes occurring in the targets during operation. A laser line scan system was used to produce point-cloud models of the target samples, which were used to calculate various erosion metrics and characterize the erosion damage to each sample. The scans revealed the maximum depth of erosion and the location where the maximum erosion occurred for each sample. The results quantified and illustrated the difference in erosion patterns between different target designs. Data from the scans also showed the maximum depth of erosion was greatly reduced at all locations examined in the SNS targets operated with gas injection. The data produced by these scans will be used to compare erosion damage to future targets in order to quantify the erosion in targets with different design features and operational histories.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

European Union's Action Plan for Power Sector Decarbonisation

A collaborative report from the Clean Energy Ministerial (CEM) on Lessons Learned for Rapid Decarbonization of Power Sectors was delivered to energy ministers and presented at CEM13 in the United States in September 2022. In light of these lessons learned and discussed at CEM13, several countries signaled intent to develop Action Plans for power sector decarbonization that will be presented at CEM14 in India. These action plans complement - but are differentiated from - other international power sector initiatives such as the Breakthrough Agenda - whose broad purpose is to raise collective ambition - and the Global Power System Transformation (G-PST) Consortium - whose goals are to convene power system operators to accelerate research innovations and foster peer learning. These action plans, supported by the 21st Century Power Partnership (21CPP) and other CEM workstreams via direct technical assistance and capacity building, are instead intended to focus on implementation actions given each country's existing power sector goals and activities and are an opportunity for countries to display leadership in power sector decarbonization. These action plans are voluntary, developed by each country individually, and can be viewed as a living document that is subject to change.

decarbonization↗
DOE OSTI2023

Chile's Action Plan for Power Sector Decarbonization

A collaborative report from the Clean Energy Ministerial (CEM) on Lessons Learned for Rapid Decarbonization of Power Sectors was delivered to energy ministers and presented at CEM13 in the United States in September 2022. In light of these lessons learned and discussed at CEM13, several countries signaled intent to develop Action Plans for power sector decarbonization that will be presented at CEM14 in India. These action plans complement but are differentiated from - other international power sector initiatives such as the Breakthrough Agenda whose broad purpose is to raise collective ambition and the Global Power System Transformation (G-PST) Consortium whose goals are to convene power system operators to accelerate research innovations and foster peer learning. These action plans, supported by the 21st Century Power Partnership (21CPP) and other CEM workstreams via direct technical assistance and capacity building, are instead intended to focus on implementation actions given each country's existing power sector goals and activities and are an opportunity for countries to display leadership in power sector decarbonization. These action plans are voluntary, developed by each country individually, and can be viewed as a living document that is subject to change.

Chile↗
DOE OSTI2023

Australia's Action Plan for Power Sector Decarbonisation

A collaborative report from the Clean Energy Ministerial (CEM) on Lessons Learned for Rapid Decarbonization of Power Sectors was delivered to energy ministers and presented at CEM13 in the United States in September 2022. In light of these lessons learned and discussed at CEM13, several countries signaled intent to develop Action Plans for power sector decarbonization that will be presented at CEM14 in India. These action plans complement but are differentiated from other international power sector initiatives such as the Breakthrough Agenda whose broad purpose is to raise collective ambition and the Global Power System Transformation (G-PST) Consortium whose goals are to convene power system operators to accelerate research innovations and foster peer learning. These action plans, supported by the 21st Century Power Partnership (21CPP) and other CEM workstreams via direct technical assistance and capacity building, are instead intended to focus on implementation actions given each country's existing power sector goals and activities and are an opportunity for countries to display leadership in power sector decarbonization. These action plans are voluntary, developed by each country individually, and can be viewed as a living document that is subject to change.

Australia↗
DOE OSTI2023

India's Action Plan for Power Sector Decarbonisation

A collaborative report from the Clean Energy Ministerial (CEM) on Lessons Learned for Rapid Decarbonization of Power Sectors was delivered to energy ministers and presented at CEM13 in the United States in September 2022. In light of these lessons learned and discussed at CEM13, several countries signaled intent to develop Action Plans for power sector decarbonization that will be presented at CEM14 in India. These action plans complement but are differentiated from other international power sector initiatives such as the Breakthrough Agenda whose broad purpose is to raise collective ambition and the Global Power System Transformation (G-PST) Consortium whose goals are to convene power system operators to accelerate research innovations and foster peer learning. These action plans, supported by the 21st Century Power Partnership (21CPP) and other CEM workstreams via direct technical assistance and capacity building, are instead intended to focus on implementation actions given each country's existing power sector goals and activities and are an opportunity for countries to display leadership in power sector decarbonization. These action plans are voluntary, developed by each country individually, and can be viewed as a living document that is subject to change.

decarbonization↗
DOE OSTI2023

United Kingdom's Action Plan for Power Sector Decarbonisation

A collaborative report from the Clean Energy Ministerial (CEM) on Lessons Learned for Rapid Decarbonization of Power Sectors was delivered to energy ministers and presented at CEM13 in the United States in September 2022. In light of these lessons learned and discussed at CEM13, several countries signaled intent to develop Action Plans for power sector decarbonization that will be presented at CEM14 in India. These action plans complement but are differentiated from other international power sector initiatives such as the Breakthrough Agenda whose broad purpose is to raise collective ambition and the Global Power System Transformation (G-PST) Consortium whose goals are to convene power system operators to accelerate research innovations and foster peer learning. These action plans, supported by the 21st Century Power Partnership (21CPP) and other CEM workstreams via direct technical assistance and capacity building, are instead intended to focus on implementation actions given each country's existing power sector goals and activities and are an opportunity for countries to display leadership in power sector decarbonization. These action plans are voluntary, developed by each country individually, and can be viewed as a living document that is subject to change.

decarbonization↗