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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Enabling Seamless Integration of EV Charging Infrastructure with Weak Electric Grids

Widespread adoption of Electric Vehicles (EVs) for light, medium, and heavy-duty applications has gained significant interest. Based on ownership and user preferences, light duty and local delivery medium-duty EVs are typically connected to grid distribution networks whereas larger medium duty and heavy-duty EVs are typically connected to distribution or even sub-transmission networks. Challenges with at-scale EVs and charging infrastructure supporting them include interoperability, distribution network upgrades, surge in demand charges, power quality issues, voltage stability, etc. Based on real-world data, steady-state and dynamic assessments of light-duty EV charging with a focus on DC Fast Charging (DCFC) for weak distribution grids is presented. Three main areas of contributions in this paper include - steady state and dynamic power quality measurements and assessments using real world data, transient assessments of light duty EV integration with weak distribution networks (IEEE 13 node feeder system) under grid-connected and microgrid modes, and formulation of an intelligent charging decision algorithm to enable an impact-minimal charging.

actionable intelligence↗
DOE OSTI2023

A comparison of machine learning methods to classify radioactive elements using prompt-gamma-ray neutron activation data

The detection of illicit radiological materials is critical to establishing a robust second line of defence in nuclear security. Neutron-capture prompt-gamma activation analysis (PGAA) can be used to detect multiple radioactive materials across the entire Periodic Table. However, long detection times and a high rate of false positives pose a significant hindrance in the deployment of PGAA-based systems to identify the presence of illicit substances in nuclear forensics. In the present work, six different machine-learning algorithms were developed to classify radioactive elements based on the PGAA energy spectra. The model performance was evaluated using standard classification metrics and trend curves with an emphasis on comparing the effectiveness of algorithms that are best suited for classifying imbalanced datasets. We analyse the classification performance based on Precision, Recall, F1-score, Specificity, Confusion matrix, ROC-AUC curves, and Geometric Mean Score (GMS) measures. The tree-based algorithms (Decision Trees, Random Forest and AdaBoost) have consistently outperformed Support Vector Machine and K-Nearest Neighbours. Based on the results presented, AdaBoost is the preferred classifier to analyse data containing PGAA spectral information due to the high recall and minimal false negatives reported in the minority class.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Hierarchical Control Architecture: Utilization of Behind-the-Meter Appliances with Increased Visibility and Controllability

Optimal operations of distribution systems to enhance reliability and resiliency can be achieved if information with higher fidelity can be accessed from grid premise devices. This paper discusses a hierarchical control architecture that can introduce higher visibility and controllability by utilizing the estimated injection and absorption of different behind-the-meter controllable loads, and DERs, including EVs, within a premise. The acquired information is used to make control decisions for scheduling the premise appliances to reduce energy consumptions. The premise controller can also optimize the consumptions on a day-to-day basis in addition to taking input signals from the utility operators to support grid-service events (e.g., peak shaving). The entire control architecture is designed in a modular and scalable fashion that provides benefits to the utility from the reuse of existing infrastructure while creating opportunities for various grid services to be enabled by the operation of premise controllers.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Explainable Artificial Intelligence Technology for Predictive Maintenance

The domestic nuclear power plant fleet has relied on labor-intensive and time-consuming preventive maintenance programs, thus driving up operation and maintenance costs to achieve high-capacity factors. Artificial intelligence and machine learning can help simplify complex problems, such as diagnosing equipment degradation, to enable more effective decision-making. Benefits will be felt not only within existing analog and digital instrumentation and control, but also work processes, the integration of people with technology, and most importantly, the business case. Together, these hold promise to make nuclear power more efficient and reduce costs associated with operation and maintenance. While the artificial intelligence and machine learning technologies hold significant promise in the nuclear industry, there are challenges or barriers to their adoption. This report outlines the those different machine learning adoption barriers (categorized as historical, technical, economic, regulatory, and user) that the industry must overcome to realize the full benefits of artificial intelligence and machine learning capabilities for long-term economic sustainability. This report also provides solutions for some of these barriers by focusing on improving the explainability of machine learning to encourage trust from the end-user. Trust and explainability are essential to machine learning adoption. This report focuses on research-developed solutions to some of these barriers while analyzing a non-safety-related system, namely the circulating water system. This system frequently experiences waterbox fouling which our models preemptively diagnoses then explains to the operator how those conclusions were reached. This report presents and discusses the inherent trade-off between machine learning performance (in terms of accuracy) and explainability, where highly accurate machine learning methods (such as deep-learning) are the least explainable, and the most explainable methods (such as decision trees) are the least accurate. In addition, explainability of artificial intelligence techniques in terms of transparency and post-hoc metrics are discussed. This report outlines the importance of data novelty and value of new information in evaluating both the explainability and trustworthiness. Novelty detection helps to establish consistency or inconsistency of the new data with respect to the training data. On the other hand, value of information could be a part of the user-centric visualization recommendation system that request additional information to be collected, thereby strengthening the machine learning outcomes. During this project, a copyrighted user-centric visualization that aligns with a human-in-the-loop approach was developed. The user-centric visualization presents different levels of information and can be tailored as per user credentials to gain user confidence. One of the salient features of the user-centric visualization is it presents machine learning methods with explainability metrics. A simplified version of the user-centric visualization was presented to 32 users with varying levels of machine learning expertise. Feedback was solicited to test the hypothesis that the app contained sufficient explainability and that the users would trust the algorithm. Overall, the app was positively received, and the hypothesis was supported. This report discusses the trust-but-verify framework – a potential approach to build user trust artificial intelligence. The framework discusses trust from the human level to artificial intelligence level. The fundamental premise of the trust but verify framework is derived from an observation of nuclear safety culture (i.e., nuclear power plant personnel do not rely on a singular source of data to make a decision). This also ties back to the user-centric visualization that presents different levels of information to achieve both explainability and trustworthiness of artificial intelligence. Even so, the adoption of artificial intelligence and machine learning in the nuclear industry faces additional barriers, namely regulatory and stakeholder readiness. To overcome these challenges, new solutions must gain regulatory approval and cater to stakeholder needs. The Nuclear Regulatory Committee has a 5-year strategic plan which prepares them for reviewing artificial intelligence technologies in licensee submissions. Early and frequent engagement with the regulator is encouraged. Additionally, artificial intelligence solutions should incorporate human-in-the-loop considerations and offer explainability. Stakeholders must prepare by hiring or training staff to adapt to advancing technology in everyday plant tasks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Multi-Game Modeling for Counter-Smuggling

International trade provides an avenue for terrorists to smuggle illicit materials into a target country. Policymakers need models and decision support tools that are both reliable and germane to inform their responses to this. Game theoretic approaches have been used in the analysis of counterterrorism strategies, but the models involved have often been small and abstracted. To address this need, we have developed a multi-game model to account for both security-related and economic aspects of counter-smuggling interdiction efforts. Additionally, we use different games to represent different types of interactions, and these games are then coupled to each other to form an integrated model. In this paper, we demonstrate the model's capabilities on a representative system of ports (both foreign and domestic), trade routes, and commodities. We specifically investigate the impacts of changes in screening policies at domestic ports, detection device capabilities, shipping subsidies, and worldwide corruption levels. In these studies, we find that economic factors play a large role in the interdiction task and provide correspondingly important tools for deterrence.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

Post-2026 Environmental Impact Statement Rate Analysis for the Colorado River Storage Project

The Glen Canyon Dam (GCD) is a principal power-generating asset within the Colorado River Storage Project (CRSP), accounting for approximately 70–80% of CRSP power production over the past two decades. In June 2023, the U.S. Bureau of Reclamation (Reclamation) issued a Notice of Intent to prepare an Environmental Impact Statement (EIS) outlining operational guidelines and strategies for Colorado River Basin reservoirs after 2026 (Reclamation, 2023). Power generation is among CRSP’s statutory purposes under the Colorado River Storage Project Act of 1956 (U.S. Congress, 1956). Assessing how alternative policy frameworks affect CRSP power production and the resulting electricity rates for U.S. customers is therefore essential to inform decision-making. This report evaluates projected electricity rates and the market value of electricity from the Western Area Power Administration (WAPA) CRSP GCD under multiple post-2026 policy scenarios to support Reclamation’s EIS development. Results from advanced econometric and machine learning models indicate that the Enhanced Coordination alternative (EnhanCoor), Maximum Operational Flexibility alternative (CCA), and Supply Driven - 55 alternative (SD55) alternatives yield more favorable hydropower generation and capacity outcomes, which are objectives outlined in Reclamation’s documentations (Reclamation, 2007; Reclamation, 2016). Specifically, these scenarios are associated with higher electricity production, lower projected rate trajectories, and greater economic value to the U.S. power system from CRSP generation. The remaining five scenarios generally produce lower generation, higher rate trajectories, and reduced long-term market values.

13 HYDRO ENERGY↗
DOE OSTI2021

City Decision Analysis Resources and Tools

The City Decision Analysis at Any Scale Workshop was held February 17-18, 2021. This brochure was created to provide participants with a collection of tools and resources that support planning and implementation of clean energy goals for communities and businesses.

ACES↗
DOE OSTI2021

Analysis and mitigation of parasitic resistance effects for analog in-memory neural network acceleration

To support the increasing demands for efficient deep neural network processing, accelerators based on analog in-memory computation of matrix multiplication have recently gained significant attention for reducing the energy of neural network inference. However, analog processing within memory arrays must contend with the issue of parasitic voltage drops across the metal interconnects, which distort the results of the computation and limit the array size. This work analyzes how parasitic resistance affects the end-to-end inference accuracy of state-of-the-art convolutional neural networks, and comprehensively studies how various design decisions at the device, circuit, architecture, and algorithm levels affect the system's sensitivity to parasitic resistance effects. Here, a set of guidelines are provided for how to design analog accelerator hardware that is intrinsically robust to parasitic resistance, without any explicit compensation or re-training of the network parameters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

National Reactor Innovation Center NRC Early Site Permit Roadmap

The National Reactor Innovation Center (NRIC) at Idaho National Laboratory (INL) in Idaho Falls, Idaho, was authorized under the Nuclear Energy Innovation Capabilities Act (Public Law 115–248) to provide private sector technology developers with resources and infrastructure for testing, demonstration, and performance assessment to accelerate deployment of new advanced reactor technology concepts. NRIC’s mission is to enable clean, affordable, reliable energy by supporting U.S. government investments in nuclear energy research, development, demonstration, and commercialization of new nuclear energy systems. It intends to provide existing facilities and other undeveloped and previously developed sites at INL to advanced reactor vendors for prototype technology testing and deployment. Some of these demonstration projects may be subject to DOE authorization, while other projects may require approval by the U.S. Nuclear Regulatory Commission (NRC) prior to construction and operation. NRIC and INL are considering whether to pursue one or more ESPs to help facilitate new reactor deployment at the INL Site. An ESP would allow early investment in licensing infrastructure in advance of a reactor design being ready to deploy. Through the ESPs, NRIC would help reduce the cost and time required to deploy demonstration reactors at the INL Site. This also will allow INL to bring its nuclear and siting experience to the ESP development process allowing for better and more efficient improvements and utilization of the Site. A preapproved ESP at INL will also remove the uncertainty of NRC siting approvals from the demonstration project proponents, who may not be well positioned to complete the analyses in a timely manner. The purpose of this NRIC ESP roadmap is to provide rigor and understanding needed for INL and DOE decision-makers to make data-informed decisions on pursuing NRC ESPs in support of future advanced reactor demonstrations at INL. The roadmap provides a structured method for addressing potential challenges and identifying options for resolution.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

National Reactor Innovation Center NRC Early Site Permit Roadmap

The National Reactor Innovation Center (NRIC) at Idaho National Laboratory (INL) in Idaho Falls, Idaho, was authorized under the Nuclear Energy Innovation Capabilities Act (Public Law 115–248) to provide private sector technology developers with resources and infrastructure for testing, demonstration, and performance assessment to accelerate deployment of new advanced reactor technology concepts. NRIC’s mission is to enable clean, affordable, reliable energy by supporting U.S. government investments in nuclear energy research, development, demonstration, and commercialization of new nuclear energy systems. It intends to provide existing facilities and other undeveloped and previously developed sites at INL to advanced reactor vendors for prototype technology testing and deployment. Some of these demonstration projects may be subject to DOE authorization, while other projects may require approval by the U.S. Nuclear Regulatory Commission (NRC) prior to construction and operation. NRIC and INL are considering whether to pursue one or more ESPs to help facilitate new reactor deployment at the INL Site. An ESP would allow early investment in licensing infrastructure in advance of a reactor design being ready to deploy. Through the ESPs, NRIC would help reduce the cost and time required to deploy demonstration reactors at the INL Site. This also will allow INL to bring its nuclear and siting experience to the ESP development process allowing for better and more efficient improvements and utilization of the Site. A preapproved ESP at INL will also remove the uncertainty of NRC siting approvals from the demonstration project proponents, who may not be well positioned to complete the analyses in a timely manner. The purpose of this NRIC ESP roadmap is to provide rigor and understanding needed for INL and DOE decision-makers to make data-informed decisions on pursuing NRC ESPs in support of future advanced reactor demonstrations at INL. The roadmap provides a structured method for addressing potential challenges and identifying options for resolution.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Skillful Multi‐Month Predictions of Ecosystem Stressors in the Surface and Subsurface Ocean

Abstract Anthropogenic carbon emissions and associated climate change are driving rapid warming, acidification, and deoxygenation in the ocean, which increasingly stress marine ecosystems. On top of long‐term trends, short term variability of marine stressors can have major implications for marine ecosystems and their management. As such, there is a growing need for predictions of marine ecosystem stressors on monthly, seasonal, and multi‐month timescales. Previous studies have demonstrated the ability to make reliable predictions of the surface ocean physical and biogeochemical state months to years in advance, but few studies have investigated forecast skill of multiple stressors simultaneously or assessed the forecast skill below the surface. Here, we use the Community Earth System Model (CESM) Seasonal to Multiyear Large Ensemble (SMYLE) along with novel observation‐based biogeochemical and physical products to quantify the predictive skill of dissolved inorganic carbon (DIC), dissolved oxygen, and temperature in the surface and subsurface ocean. CESM SMYLE demonstrates high physical and biogeochemical predictive skill multiple months in advance in key oceanic regions and frequently outperforms persistence forecasts. We find up to 10 months of skillful forecasts, with particularly high skill in the Northeast Pacific (Gulf of Alaska and California Current Large Marine Ecosystems) for temperature, surface DIC, and subsurface oxygen. Our findings suggest that dynamical marine ecosystem prediction could support actionable advice for decision making.

Environmental Sciences & Ecology↗
DOE OSTI2023

Los Alamos Enterprise Analysis and Data System (LEADS)

The Los Alamos Enterprise Analysis and Data System (LEADS) is an integrated database system and analysis application used to examine the Laboratory’s assets and equities. The data mapping framework is structured to follow laboratory organizations, programs, geographical technical areas, and other functional groupings, such as facilities and equipment. LEADS application goal is to quantify the size and information related to the current condition of assets and equities. The data and inter-dependent relationships allow for insights and further analyses to be conducted of current program-of-records, the laboratory agenda, and “what-if” scenario studies. The understanding and the outputs of this application will support understanding of the laboratory’s current state, planning, and decision-making processes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

The ParFlow Sandtank: An interactive educational tool making invisible groundwater visible

Physical aquifer models are a highly effective teaching tool for hydrology education, however they come with inherent limitations that include the high cost to purchase, the static configuration of the model materials, the time required to visualize hydrogeological phenomena, and the effort to reset and clean them over time. To address these and other limitations, we have developed an interactive computer simulation of a physical aquifer model called the ParFlow Sandtank. In this gamified interface, users run the simulation using a familiar web-app like interface with sliders and buttons while learning real hydrologic concepts. Our user interface allows participants to dive into the world of hydrology, understanding assumptions about model parameters such as hydraulic conductivity, making decisions about inputs to groundwater aquifer systems such as pumping rates, visualizing outputs such as stream flow, transport, and saturation, and exploring various factors that impact real environmental systems such as climate change. The ParFlow Sandtank has already been used in a variety of educational settings with more than 9,000 users per year, and we feel this emerging educational tool can be used broadly in educational environments and can be scaled-up to provide greater accessibility for students and educators. Here we present the capabilities and workflow of the ParFlow Sandtank, two use cases, and additional tools and custom templates that have been developed to support and enhance the reach of the ParFlow Sandtank.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Enterprise Risks for Scientific Software in the Post Exascale Era

The Department of Energy’s Office of Science’s Advanced Scientific Computing Research (ASCR) Office hosts three high performance computing Facilities and one high performance networking Facility. ASCR’s Facilities Division and these four Facilities established a joint working group to address the most severe threats to the software capabilities that can prevent the Facilities from supporting a diverse array of mission critical scientific research carried out by thousands of researchers from national labs, academia, and industry. This report outlines the risk categories, threat matrix calculations, and number of risks in each of the categories along with the threat levels. Two categories—programming environment/tools and system software—account for 13 out of the 20 risks encountered in the risk register. A significant risk to ASCR’s software ecosystem also arises from the possible loss of personnel and expertise past the end of the exascale computing project. The working group recommends that the software enterprise risks are reevaluated regularly, and are considered in future decision making regarding system acquisition, knowledge sharing, partnership development, and strategic planning

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Techno-Economic Case Study: Low-Temperature Conversion Performance Based on Isolated Anatomical Fractions of Corn Stover

This report summarizes analysis conducted to support a case study under the Feedstock Conversion Interface Consortium (FCIC) focused on techno-economic analysis (TEA) modeling to quantify the process yield and resulting process cost impacts for processing isolated anatomical fractions of corn stover through a low-temperature conversion (biochemical) pathway. It is hypothesized that different individual anatomical fractions of corn stover vary in both composition and recalcitrance, giving biorefineries options in whether and how to deal with fractionated or whole biomass feedstock. By quantifying the techno-economic impacts of this variability, we provide actionable information for end users to understand tradeoffs in conversion system yields and economics in considering feedstock processing decisions at the biorefinery gate. For this study, we worked with FCIC researchers to obtain data on the compositional analysis and conversion performance of whole corn stover alongside three individual anatomical fractions (cobs, husks, and stalks) across key steps of the biorefinery conversion process within FCIC’s research scope—pretreatment and enzymatic hydrolysis. This TEA screening assessment highlighted biorefinery economic trade-offs observed through this approach. Namely, relative to processing whole stover biomass, two of the three anatomical fractions for which composition/conversion data were available (cobs and husks) demonstrated the ability to achieve higher fuel yields and lower minimum fuel selling prices (MFSPs), while the third fraction (stalks) led to the opposite result, as a composite reflection of compositional differences and process convertibility.

cost impacts↗
DOE OSTI2024

NEWTS Integrated Dataset (version 1.0)

The National Energy Water Treatment and Speciation (NEWTS) Integrated Dataset v1.0 provides water researchers, community leaders, and regulators with a unified and standardized energy-related wastewater stream database. This resource is derived from 27 state and federal entities, and scientific publications, and contains more than 400,000 sample records, many of which also provide geospatial information. The dataset includes data for several different energy-related wastewater types including produced water, other oil and gas wastewaters, mine drainage, coal ash leachate, and power plant wastewater. The NEWTS Integrated Dataset was built to support environmentally prudent decision-making, explore treatment opportunities, and identify potential critical mineral sources. A subset of this novel resource is also featured on NETL NEWTS State-Level Database Dashboard. Additional data can be found in the NEWTS EDX Group and the NEWTS Federal Database Dashboard.

abandoned mine drainage↗
DOE OSTI2024

Review of Feasibility and Cost Drivers for Floating Offshore Wind Energy in Washington State

The state of Washington must double its clean electricity supply by 2050 to meet its clean energy goals and comply with the Clean Energy Transformation Act. With more than 6.6 GW of technical resource potential in federal waters where Bureau of Ocean Energy Management has leasing authority, offshore wind energy could play an important role in diversifying Washington State's clean energy mix, reducing dependence on out-of-state energy sources, and helping meet state decarbonization goals. Decision makers need technology-specific information to assist with long-term energy system planning, so the Bureau of Ocean Energy Management requested that the National Renewable Energy Laboratory provide an overview of several drivers of offshore wind energy feasibility and cost in Washington. This study summarizes some of the existing engagement efforts and perspectives on offshore wind energy in the region and quantifies the offshore wind resources in Washington as well as technology costs and performance of potential projects. Furthermore, this report reviews existing grid and port infrastructure and discusses infrastructure needs along with information gaps. This study also explores opportunities and barriers to Washington entities supporting the broader floating offshore wind energy supply chain along the U.S. West Coast. Note that this study is not part of a formal project planning process or official engagement effort, nor does it assess environmental or economic impacts from potential offshore wind energy development.

17 WIND ENERGY↗
DOE OSTI2026

ARM FY2026 Radar Plan

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility maintains a suite of advanced atmospheric radar systems that serve as critical tools in ARM’s mission to provide continuous, high-quality observations for advancing the understanding and modeling of atmospheric processes. These radar systems enable detailed characterization of clouds, precipitation, and dynamic structures in the atmosphere, supporting a broad range of scientific applications. The number of deployed systems exceeds what current staffing levels can fully support for continuous 24/7/365 operation. As such, it is essential to have a clearly defined and community-informed plan that prioritizes radar operations and communicates ARM’s strategy for sustaining and evolving these observational assets. This FY2026 Radar Plan outlines ARM’s approach to managing its radar portfolio—balancing scientific impact, operational feasibility, and long-term sustainability. It reflects ARM’s continued commitment to delivering calibrated, well-documented radar data products that enable process-level studies and support the development and evaluation of weather and climate models. Through this plan, ARM aims to ensure transparency in decision-making, alignment with user needs, and support for innovative science across the facility’s fixed and mobile observatories. Given uncertainties around the Fiscal Year (FY) 2026 budget, this plan was developed to assume business as usual and will be updated as budgets and plans may change. It should be noted that, given the limited timeframe involved, this plan will be more succinct than previous plans.

47 OTHER INSTRUMENTATION↗