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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Scalable Technologies Achieving Risk-Informed Condition-Based Predictive Maintenance Enhancing the Economic Performance of Operating Nuclear Power Plants

The primary objective of the research presented in this report is to develop scalable technologies that are deployable across plant assets and across the nuclear fleet to achieve risk-informed predictive maintenance (PdM) strategies at commercial nuclear power plants (NPPs). Over the years, the nuclear fleet has relied on labor-intensive and time-consuming preventive maintenance (PM) programs, driving up operation and maintenance (O&M) costs to achieve high capacity factors. A well-constructed risk-informed PdM approach for an identified plant asset has been developed in this research, taking advantage of advancements in data analytics, machine learning (ML), artificial intelligence (AI), physics-informed modeling, and visualization. These technologies would allow commercial NPPs to reliably transition from current labor-intensive PM programs to a technology driven PdM program, eliminating unnecessary O&M costs. The work presented in the report is being developed as part of a collaborative research effort between Idaho National Laboratory and Public Service Enterprise Group Nuclear, LLC. This report (1) reflects the results of work by LWRS Program researchers with PSEG, Nuclear LLC-owned Salem and Hope Creek Nuclear Power Plants; (2) presents utilization of circulating water system (CWS) heterogeneous data and fault modes from both the Salem and Hope Creek nuclear power plant sites to develop salient fault signatures associated with each fault mode; (3) describes the integration of component-level predictive models into a robust system-level model enabled by the federated-transfer learning; (4) describes the development of physics-informed model of circulating water pump and motor; (5) develops a scalable risk and economic model; and (6) outlines the development of a user-centric visualization application. The outcomes presented in this report lays the foundation and provides a much-needed technical basis to focus on explainability and trustworthiness of ML and AI-based technologies, as part of future research.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Resilience of Interdependent Water and Power Systems: A Literature Review and Conceptual Modeling Framework

As increasing pressures of population growth and climate change arise, water and power systems (WPS) are becoming increasingly interdependent. This interdependency has resulted in an increased potential for cascading failures, whereby the service interruption of one system can propagate to interdependent ones. This paper makes four contributions. First, we present an extensive literature review in the field of integrated water and power resilience, leveraging both institutional and technical literature research landscapes. We compare various modeling approaches used to model interdependent WPS and discuss the different metrics and definitions that are typically employed to quantify and define resilience. Relevant challenges and gaps related to modeling tools and metrics are also discussed, and appropriate recommendations are made. Second, the paper presents a visualization prototype for interdependent WPS to showcase water and power system interdependencies and reveal co-managed resilience strategies that can be used to improve resilience under different types of common threats. Third, we provide a conceptual decision support framework that simultaneously optimizes a portfolio of co-managed resilience strategies in the face of multiple, uncertain threats and addresses WPS interdependencies. Finally, we present future trends regarding digitalization, integrated planning, collaborative governance, and equity needs for building more resilient WPS.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

CRCNS21 Computational Models of Multisensory Integration by Upper Limb in Humanoids and Amputees

This international collaborative research project between Johns Hopkins University (JHU) and the Technical University of Munich (TUM) investigated how the human brain processes and integrates multiple types of sensory information, such as touch and force, with the goal of improving prosthetic limbs for amputees and advancing sensory capabilities in humanoid robots. The research advanced our understanding of how the brain responds to sensory feedback in upper-limb amputees. Through experiments in which amputees received electrical stimulation while performing phantom hand movements, we demonstrated that sensory feedback activates the cortical sensorimotor and multisensory regions, and that these regions communicate dynamically during stimulation. Experiments with intact-limb participants explored the integration of visual, haptic, and force feedback, as well as in virtual reality motor training, further showing how the brain processes multimodal sensory information. In addition, this research inspired work on examining the reliability of where amputees perceive sensations over time, which contributed to a successful doctoral fellowship for continued investigation. Our collaborators at TUM improved multimodal sensor technology combining tactile and thermal feedback for humanoid robots, demonstrating the feasibility of integrating multiple sensor types into a unified system for detecting and responding to environmental stimuli. The experimental methods and analysis techniques developed across both teams, including functional network analysis and multimodal sensor integration, provide a foundation for future research in prosthetics and robotics. This research benefits the public by generating knowledge about how amputees process restored sensory information. Advances in humanoid sensing contribute to safer human-robot interaction. The project also fostered international collaboration and cross-disciplinary training: one TUM doctoral student spent a summer at JHU working on multimodal sensor integration, while two JHU students traveled to TUM to host workshops on neuromorphic sensory encoding and sensory integration.

42 ENGINEERING↗
DOE OSTI2023

Staffing Analyses Tool

The Staffing Analyses Tool is an interactive resource planning tool used for visualizing programmatic staffing financial information. This dashboard provides historical and current staff charging data under organizations’ and/or programs’ views. This tool is used to manage staffing cost and inform outyear planning. It was developed in collaboration with Associate Laboratory Directorate for Infrastructure and Capital Projects (ALDICP) Planning Office.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Grid Modernization of Cooperatives and Municipal Utilities via Breakthrough System Monitoring, Control and Optimization (CRADA Final Report)

This project aims at developing and demonstrating successful implementation of breakthrough approaches in real-time data visualization as well as real-time distributed DER control and optimization to provide ample benefits to both utilities and end users. The National Renewable Energy Laboratory (NREL), Holy Cross Energy (HCE), National Rural Electric Cooperative Association (NRECA) and Survalent are collaborating to enable Cooperative and Municipal utilities to fully leverage DERs as part of their strategies for providing safe, reliable, and affordable electric services to their customers and help meet DOE Grid modernization goal of achieving at least 10% active devices to provide grid flexibility by 2035. This project will use novel real-time control algorithms and approaches for distributed control recently developed under DOE-funded projects, using the date from the Survalent’s basic SCADA engine, GIS and AMI engines deployed at HCE combined with NRECA’s globally-used MultiSpeak(R) software interoperability specification for seamless and real-time communications between electric utility enterprise software to embrace DER as part of their strategies for providing safe, reliable and affordable electric service to their customers.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Performance Improvements of Poincaré Analysis for Exascale Fusion Simulations

Understanding the time-varying magnetic field in a fusion device is critical for the successful design and construction of clean-burning fusion power plants. Poincaré analysis provides a powerful method for the visualization of magnetic fields in fusion devices. However, Poincaré plots can be very computationally expensive making it impractical, for example, to generate these plots in situ during a simulation. In this short paper, we describe a collaboration among computer science and physics researchers to develop a new Poincaré tool that provides a significant reduction in the time to generate analysis results.

Pugmire, Dave↗
DOE OSTI2025

Transforming Energy Through Computational Excellence: A View From NREL

At the National Renewable Energy Laboratory (NREL)—a U.S. Department of Energy laboratory—computational science, high-performance computing, applied mathematics, advanced computer science, visualization, and data play a pivotal role in advancing energy abundance, affordability, security, and reliability. From fundamental scientifc discovery to systems engineering and analysis, NREL researchers tackle market-relevant challenges to develop solutions for an independent energy system that is reliable, resilient and secure. Collaborative partnerships with industry, government, and academia ensure that our research remains cutting edge, impactful, applicable, and aligned with real-world energy needs. This special issue of Computing in Science & Engineering highlights exemplary NREL projects where computational tools and methodologies drive discovery and accelerate innovation in scalable and integrated energy systems. The featured articles explore the role of computational modeling, high-performance computing, generative AI, and adaptive computing in advancing independent energy solutions, optimizing sustainability research, and enhancing decision-making for energy solutions using a broad mix of energy technologies. Here, these contributions demonstrate how NREL’s computational research bridges the gap between theoretical advancements and practical implementation, emphasizing interdisciplinary collaboration and a commitment to innovation, with a focus on translating computational excellence into real-world impact, thus accelerate progress toward national energy goals. By showcasing cutting-edge research at the intersection of computational science and energy systems, this issue aims to inspire and inform researchers, practitioners, and policymakers dedicated to shaping a more reliable energy future.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

PDB‐101: Molecular Explorations through Biology and Medicine

PDB‐101 is an online portal for teachers, students, and the general public to promote exploration of the structural biology of proteins and nucleic acids ( pdb101.rcsb.org ). Learning about the diverse shapes and functions of these biological macromolecules helps to understand all aspects of biomedicine and agriculture, from protein synthesis to health and disease to biological energy. Why PDB‐101? Researchers around the world are studying these molecules at the atomic level. These 3D structures are freely available at the Protein Data Bank (PDB), the central storehouse of biomolecular structures. This website builds introductory materials to help beginners get started in the basics of biomolecular structure and function (“101”, as in an entry level course) as well as resources for extended learning. Since 2011, PDB‐101 has been developed by the RCSB PDB , a global resource for the advancement of research and education in biology and medicine. Along with our Worldwide PDB collaborators, RCSB PDB curates, annotates, and makes publicly available the PDB data deposited by scientists around the globe. The RCSB PDB then provides a window to these data through a rich online resource with powerful searching, reporting, and visualization tools for researchers. This information is then streamlined for students and teachers at PDB‐101. Features include the ongoing Molecule of the Month series, educational materials such as paper models, posters, molecular animations, educational curricula and more. The section “Guide to Understanding PDB Data” is a primer for detailed PDB‐specific information: PDB Data, Visualizing Structures, Reading Coordinate Files, scientific methods for structure determination, and more. PDB‐101 also runs annual Video Challenges for high school students. Participants create short videos that tell molecular stories that connect structural biology and medicine. Previous topics have included HIV/AIDS, diabetes, and antimicrobial resistance. The 2022 challenge will focus on Molecular Mechanisms of Cancer. PDB‐101 activities are evaluated using user surveys, feedback from in‐person activities, and website analytics. In 2020, PDB‐101 hosted >850,000 users and >2.6 million page views.

Zardecki, Christine↗
DOE OSTI2025

The Surface-Topography Challenge: A Multi-Laboratory Benchmark Study to Advance the Characterization of Topography

Surface performance is critically influenced by topography in virtually all real-world applications. The current standard practice is to describe topography using one of a few industry-standard parameters. The most commonly reported number is Ra, the average absolute deviation of the height from the mean line (at some, not necessarily known or specified, lateral length scale). However, other parameters, particularly those that are scale-dependent, influence surface and interfacial properties; for example the local surface slope is critical for visual appearance, friction, and wear. The present Surface-Topography Challenge was launched to raise awareness for the need of a multi-scale description, but also to assess the reliability of different metrology techniques. In the resulting international collaborative effort, 153 scientists and engineers from 64 research groups and companies across 20 countries characterized statistically equivalent samples from two different surfaces: a “rough” and a “smooth” surface. The results of the 2088 measurements constitute the most comprehensive surface description ever compiled. We find wide disagreement across measurements and techniques when the lateral scale of the measurement is ignored. Consensus is established through scale-dependent parameters while removing data that violates an established resolution criterion and deviates from the majority measurements at each length scale. Our findings suggest best practices for characterizing and specifying topography. The public release of the accumulated data and presented analyses enables global reuse for further scientific investigation and benchmarking.

42 ENGINEERING↗
DOE OSTI2025

Microreactor Automated Control System Test Bed Digital Architecture for Real-Time, Hardware-in-the-Loop Simulation

This work describes progress made towards the development of a real-time hardware-in-the-loop (HIL) test bed for non-nuclear testing of microreactor control schemes and failure modes. Non-nuclear testing is a crucial step in developing robust control algorithms for managing microreactor dynamics. The creation of an HIL simulation harnesses the realistic dynamics of physical analogue systems while additionally considering the challenges of variable communication delay. This collaborative effort between Oak Ridge National Laboratory and Idaho National Laboratory has resulted in a LabVIEW-based gRPC communication protocol which couples a TRANSFORM Modelica simulation of nuclear components to the ViBRANT physical hardware for realistic feedback and visual representation of control action in real time. A modular python client structure is developed to manage FMU-based Modelica simulation and real-time gRPC communication. HIL testing suggests that the modeled reactor with natural convection molten salt loop coolant configuration responds well to PID control of drum positioning for modulation of reactor core power, however, future efforts will be made to explore the added thermal inertial delay of system level control and downstream demand changes. Development of this platform with a generalized methodology provides a foundation for exploring a variety of reactor configurations and failure modes in rapid order to provide insight into the most effective avenues of study for further research and development.

McConnell, Jono [ORNL] (ORCID:0000000238984741)↗
DOE OSTI2022

First Plant Cell Atlas symposium report

The Plant Cell Atlas (PCA) community hosted a virtual symposium on December 9 and 10, 2021 on single cell and spatial omics technologies. The conference gathered almost 500 academic, industry, and government leaders to identify the needs and directions of the PCA community and to explore how establishing a data synthesis center would address these needs and accelerate progress. This report details the presentations and discussions focused on the possibility of a data synthesis center for a PCA and the expected impacts of such a center on advancing science and technology globally. Community discussions focused on topics such as data analysis tools and annotation standards; computational expertise and cyber-infrastructure; modes of community organization and engagement; methods for ensuring a broad reach in the PCA community; recruitment, training, and nurturing of new talent; and the overall impact of the PCA initiative. These targeted discussions facilitated dialogue among the participants to gauge whether PCA might be a vehicle for formulating a data synthesis center. The conversations also explored how online tools can be leveraged to help broaden the reach of the PCA (i.e., online contests, virtual networking, and social media stakeholder engagement) and decrease costs of conducting research (e.g., virtual REU opportunities). Major recommendations for the future of the PCA included establishing standards, creating dashboards for easy and intuitive access to data, and engaging with a broad community of stakeholders. The discussions also identified the following as being essential to the PCA's success: identifying homologous cell-type markers and their biocuration, publishing datasets and computational pipelines, utilizing online tools for communication (such as Slack), and user-friendly data visualization and data sharing. In conclusion, the development of a data synthesis center will help the PCA community achieve these goals by providing a centralized repository for existing and new data, a platform for sharing tools, and new analytical approaches through collaborative, multidisciplinary efforts. A data synthesis center will help the PCA reach milestones, such as community-supported data evaluation metrics, accelerating plant research necessary for human and environmental health.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

2022 Quantum Materials Young Investigators Workshop Report

The fifth edition of the workshop “Quantum Materials Young Investigators” was held August 18-19, 2022 at the Oak Ridge National Laboratory (ORNL). This workshop followed up on previous meetings that took place at ORNL starting in 2016. This important series of annual workshops, organized by Adam Aczel and Stuart Calder, ensures continuous engagement with the early career principal investigators (PIs) of the quantum materials neutron scattering community. Through a series of short scientific talks presented by the external PIs, members of the Neutron Scattering Division (NSD) learn about their scientific interests and needs. Presentations from ORNL staff members also help to inform the external PIs about the current state of ORNL’s quantum materials neutron scattering program. Topics covered include neutron scattering instruments, sample environment, neutron polarization, data reduction, visualization, and analysis, and future neutron scattering and complementary capabilities. Feedback is solicited from the external PIs in some or all of these key areas and communicated to NSD management in the form of a workshop report. This workshop series also helps to foster collaborations between NSD scientists and the research groups of the external PIs and plays an important role in attracting new neutron scattering users.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Large-Scale Visualization of 3D Unstructured Groundwater Model Using Cave Automated Virtual Environment

The immersive three-dimensional (3D) virtual reality (VR) visualization of groundwater models allows us to deepen our understanding of aquifer systems and provide better solutions to present groundwater-related problems, such as groundwater recharge, water quality, and sustainability. Visualization assists in accurately developing groundwater models and revealing important subsurface features, including faulting, folding, and unconformity. However, assessing model accuracy poses challenges due to the complexity of geology and groundwater systems. This research demonstrates a workflow to visualize and analyze raw 3D unstructured groundwater model data using an immersive Cave Automated Virtual Environment (CAVE). To visualize the unstructured groundwater model data, the raw dataset is converted into interactive CAVE-compatible formats utilizing a set of tools: ParaView, Blender, and Unity. This enables researchers to immerse themselves in the data, identifying influential patterns and relationships. e resulting insights can inform the development of sophisticated machine-learning models for groundwater level prediction. The CAVE’s immersive capabilities allow intuitive exploration from various perspectives, providing a more holistic understanding of the factors affecting groundwater levels. These insights are crucial to improve predictive models. The CAVE results also facilitate collaborative analysis and have potential applications in training and education. is research demonstrates the value of immersive VR tools such as the CAVE for unraveling intricacies within high-dimensional scientific data to drive real-world forecasting and modeling applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Standardized nomenclature for photovoltaic connectors

Photovoltaic (PV) systems rely on discrete connectors for the efficient and safe flow of power from module to module and from strings to combiner boxes and inverters. Despite their functional importance, no common nomenclature for PV connectors currently exists, resulting in confusion and miscommunication. Misunderstood terms like "MC4 compatible", "cross-mating", "intermating", "female", and "male" can lead to installation and maintenance errors and compromise system reliability. We believe a standardized terminology will reduce confusion, help support installation best practices, aid in maintenance and repair, inform next-generation designs, and provide a technical basis for improved codes and standards. To that end, we are proposing a standardized glossary for 4 mm PV connectors (the most common type of connector used in PV applications) based on, and validated by, a Sandia National Laboratories' investigation that included the following sources: 1) a comprehensive review of official documents from 20 connector manufacturers, including schematics, datasheets, installation manuals, and catalogs, as well as relevant patents; 2) two rounds of surveys distributed to stakeholders, including connector manufacturers, engineers, asset owners, test labs, and researchers; and 3) visual examination of 25 different models of 4 mm single-pole DC PV connectors to document variations in design and functionality. This work provides a foundation for establishing a clear and consistent terminology for PV connectors that will in turn enable progress toward greater reliability and collaboration across the industry.

14 SOLAR ENERGY↗
DOE OSTI2026

Machine Learning-Based Anomaly Detection for PMT Data Quality Monitoring in the SBN and DUNE

Maintaining high-quality detector data is essential for achieving the scientific objectives of the Short-Baseline Neutrino (SBN) Program at Fermilab. Current data quality monitoring (DQM) procedures rely primarily on threshold-based metrics and manual inspection of detector monitoring plots, making the detection of subtle or gradually developing anomalies both time-consuming and dependent on expert interpretation. This project developed and evaluated a machine-learning workflow for automatically identifying anomalous photomultiplier tube (PMT) channels in the Short-Baseline Near Detector (SBND) using optical-hit amplitude data. A Python-based analysis program was developed to process ROOT files, extract statistical features describing individual PMT amplitude distributions, and generate feature vectors for anomaly detection. These features were used to train an Isolation Forest model using data representing normal detector operation. The trained model was subsequently applied to independent detector runs to identify channels exhibiting statistically unusual behavior relative to the learned reference response. To support expert interpretation, the workflow generated complementary diagnostic products, including anomaly score distributions, normalized amplitude comparisons, decision-tree visualizations, and principal component analysis (PCA) projections. This project demonstrated the feasibility of integrating unsupervised machine learning into detector data-quality monitoring and developed a complete workflow for automated PMT performance assessment to aid expert-driven review. Beyond its technical contributions, the VFP appointment fostered a research collaboration between Aurora University and Fermilab and provided direct workforce development benefits by training the visiting faculty member in detector-scale machine-learning methods that are now being incorporated into undergraduate coursework and research. The methodology developed here provides a foundation for future applications to ProtoDUNE and other liquid argon time projection chamber (LArTPC) detectors, contributing to ongoing efforts to improve detector reliability, reduce manual monitoring requirements, and enable scalable data quality monitoring for future large-scale neutrino experiments, including the Deep Underground Neutrino Experiment (DUNE).

Colón Santana, Juan A. [Unlisted, US, IL]↗
DOE OSTI2022

Advanced Collision Detection and Site Monitoring for Avian and Bat Species for Offshore Wind Energy (Final Technical Report)

This final technical report summarizes the outcomes from a project that aimed to design, build, and test a persistent and autonomous monitoring system for avian and bat collisions with offshore wind turbines blades and structures. The system comprises four primary sensor modules: 1) on-blade sensor modules for collision detection and dual-vision image capture on each blade with both visible light and near-infrared imagers; 2) additional on-blade collision sensors mounted further from the root; 3) a nacelle-mounted unit including a 360º camera and ultrasonic microphone array; and, 4) an on-blade, high-performance infrared camera module. Primary targeted outcomes were high sensitivity for the detection of blade strikes from bats and small birds, and automatically captured visual confirmation of the striking object; these features are critical for monitoring offshore wind turbine installations, where ground-based methods are not viable. In addition, local recording will provide a long-term sensor recording database. Following laboratory validation, field testing was conducted on an operational wind turbine in collaboration with the National Wind Technology Center at the NREL Flatirons campus over two planned field tests.

17 WIND ENERGY↗
DOE OSTI2021

Engage the Public in Science and Embrace Future Change with Human-Centric Stories, Art, and Imaginings

Human-centric stories that weave in real scientific data may be able to engage the public in environmental issues that do not yet directly affect them. Science fiction and artistically rendered futuristic scenarios can unleash the imagination and act as a lens to envision technological, social, and cultural aspects of transitioning to clean energy. Cultures with strong oral traditions use stories to record history, develop a shared identity, pass on environmental lessons, and prepare for future change. Through the lenses of our non-physical science disciplines (cultural anthropology and environmental sociology), we discuss challenges to communicating science, the use of narratives to illustrate ecosystem processes, and we report on a collaborative, interdisciplinary workshop and book project that is creating narratives of hope and visions for the future through inspiring art, short stories, and essays. We explore the surprising potential of 'cli-fi,' humor, games, and the research behind emotion and imagination-driven engagement. We describe methods that help people visualize their future well-being and we explore opportunities to spread those methods.

clean energy↗
DOE OSTI2024

Human Factors Design for Particle Accelerator Control Room Interfaces

Fermilab, the birthplace of many scientific discoveries in physics and particle accelerator sciences, is in the midst of a widescale modernization effort. The Accelerator Control Operations Research Network (ACORN project’s goal is to modernize the accelerator control system by replacing end-of-life power supplies and enhance future operations of the Fermilab accelerator complex with megawatt particle beams. Within ACORN, opportunities for process improvement concerning software development, human-system interface design, and task performance are also being considered. Human factors researchers from Idaho National Laboratory in collaboration with usability experts from Fermilab, are currently investigating human-centered design improvements for the accelerator control system. For example, substantial tribal knowledge and memory recall are required to effectively operate the accelerator system. This contributes to high cognitive workload and potential burnout of accelerator operators. Developing guidance for consistent visual and functional design enables a more intuitive interaction and relieves operators of cognitive burden. Additionally, developing more intuitive and integrated interfaces can also lead to improved accelerator efficacy by empowering operators with greater understanding and control of the systems. The challenge in developing such interfaces is in designing for a wide variety of user goals, system specifications, and level of experience in users. The challenges need to be met while e also considering the maintainability of the control system. The purpose of this paper is to detail the human factors process and design within the ACORN project, describe results gathered thus far, and discuss the larger implications for this work.

43 PARTICLE ACCELERATORS↗