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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Smart connected worker edge platform for smart manufacturing: Part 2—Implementation and on‐site deployment case study

Abstract In this paper, we describe specific deployments of the Smart Connected Worker (SCW) Edge Platform for Smart Manufacturing through implementation of four instructive real‐world use cases that illustrate the role of people in a Smart Manufacturing paradigm through which affordable, scalable, accessible, and portable (ASAP) information technology (IT) acquires and contextualizes data into information for transmission to operation technologies (OT). For case one, the platform captures the relationships between energy consumption and human workflows for improved energy productivity while workers interact with machines during semiconductor manufacturing. The platform utilizes human cognition to identify anomalous machine behavior for root cause analysis of system faults via neural network (NN) that recognize alarm postures of workers with cameras. For case two, a smart assembly line is demonstrated for state monitoring and fault detection. Machine learning (ML) models are used to recognize system states and identify fault scenarios with human intervention. For case three, the platform monitors human–machine interactions to classify manufacturing machine states for proper operations and energy productivity. Internal energy states of individual or collections of manufacturing equipment are determined via NN based algorithms that disaggregate signals associated with smart metering typically deployed at manufacturing facilities. These methods predict the real time energy profile of each machine from the total energy profile of a manufacturing site. For case four, a software defined sensor system built with scientific workflow engines is demonstrated for contextualizing data from laser surface refraction for characterization, and diagnostics in the processing of additively manufactured titanium alloy.

Donovan, Richard P.↗
DOE OSTI2020

Guidance on Including Social, Organizational and Technical Influences in Nuclear Utility and Plant Modernization Plans

The objective of this report is to provide the nuclear power industry with guidance on including socio-technical issues in plans developed for current and future modernization efforts. Socio-technical issues of concern include social, organizational and related technical aspects of modernized systems. These can be addressed using a set of systematic analysis and design methods developed to address socio-technical issues of the sort facing the nuclear power industry. The next phase of this work will involve developing and implementing a modernization plan that includes socio-technical tools and methods identified and discussed in a report by Dainoff and colleagues (2020). The plan is expected to be developed and implemented in cooperation with a nuclear utility that has expressed interest in participating. The Dainoff et al report provides the foundation for the present effort and presents a methodological tool set for analyzing socio-technical issues applicable to many modernization efforts. The tools described by Dainoff et al are based on principles of human factors engineering, safety engineering, and systems engineering and are discussed within the specific context of nuclear utility modernization and transformation efforts. It is expected that the plan and the concepts described in the current report will be developed and implemented with a nuclear utility partner. The plan developed with the nuclear utility partner will have a substantially higher level of detail than the guidance provided in this report, particularly with regard to details of plan logistics, etc.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

The Technical Resilience Navigator: Risk-Informed Decision Making to Support Resilience Planning

The Technical Resilience Navigator (TRN) helps organizations manage the risk to critical functions at a site from disruptions in energy and water services. Developed in partnership by the Pacific Northwest National Laboratory and the National Renewable Energy Laboratory, under direction and funding from the Department of Energy’s Federal Energy Management Program, the TRN helps organizations enhance their resilience to a variety of disruptive events, both natural and human-caused, that could interrupt normal operations for an unknown period of time. This report provides an overview of the TRN’s risk-informed approach to resilience planning and formally describes how it uses a streamlined risk model to identify effective strategies for improving resilience.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Energy-Efficient Maneuvering of Connected and Automated Vehicles (CAVs) with Situational Awareness at Intersections (Final Progress Report)

The increased development of Connected and Automated Vehicle (CAV) systems, currently used for safety and driver convenience, presents new opportunities to improve the energy efficiency of vehicles. Southwest Research Institute (SwRI) achieved a 20% energy consumption reduction in a 2017 Toyota Prius Prime plug-in hybrid by using connectivity (V2V, V2I, V2X) as part of the Next Generation Energy Technologies for Connected and Automated on-Road Vehicles (NEXTCAR) program. The energy consumption gains were achieved by a combination of vehicle dynamics and powertrain control algorithms with a focus on SAE L1 and L2 automated vehicles where a human is still responsible for safe operation. SwRI is now involved in NEXTCAR-II, focusing on energy-efficient control tech for SAE Level 4/5 automated vehicles, aiming for a 30% energy reduction compared to stock hybrids. The rise of Mobility as a Service (MaaS) is driving investments in L4 and L5 automated vehicles. A study by the University of Michigan shows these vehicles might increase energy use and emissions by 3-20%. Technology similar to NEXTCAR can enhance energy efficiency in highly automated vehicles, leveraging improved sensing and actuation capabilities. While the NEXTCAR programs targeted energy efficiency improvements for a single vehicle, this program adopts a more expansive approach. It places its focus on understanding and testing the cumulative effects within a region or corridor, aiming to assess how a subset of vehicles equipped with NEXTCAR-style technologies influence the overall energy consumption of all vehicles traveling within that area. Additionally, the program explores infrastructure-based mobility solutions to optimize efficiency, and seeks to understand and quantify public perception and likelihood of technology adoption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

The Screening Tool for Industrial Resilience: Risk-Informed Decision Making to Support Resilience Planning

The Screening Tool for Industrial Resilience (STIR) helps small and medium-sized manufacturing plants manage risk to their production. Developed by the Pacific Northwest National Laboratory under direction and funding from the Department of Energy’s Office of Manufacturing and Energy Supply Chains, the STIR helps manufacturers enhance their resilience to a variety of disruptive events, both natural and human-caused, that could interrupt normal operations. This report provides an overview of the STIR’s risk-informed, high-level approach to resilience planning, which identifies potential solutions that could enhance site resilience based on calculated major risk drivers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Commercial integration of advanced nuclear energy with Artificial Intelligence (AI): Possible implications

The integration of advanced nuclear technologies (both fission and fusion) with artificial intelligence (AI) presents unprecedented national security challenges and opportunities. As fusion energy approaches commercial viability alongside advanced Small Modular Reactors (SMRs), their integration with AI and Artificial General Intelligence (AGI) systems could fundamentally transform the global energy and AI landscapes — two pillars of national security. This document briefly examines how AI could accelerate nuclear energy development and deployment while altering existing power structures, a lot could be done to deepen the discussions. Simultaneously, it observes how nuclear-powered AI may expedite advances toward AGI and beyond. These issues are deeply interconnected and thus need to be examined as a whole and more comprehensively than what’s being summarized here. For instance, AI-powered autonomous operation of nuclear facilities could reduce human error but introduce new cybersecurity vulnerabilities and uncertainties. Further investigation would also address how AI-enhanced nuclear technologies might complicate proliferation concerns through advanced fuel cycle management, nuclear materials production and safeguard. The strategic advantage gained by first entities achieving successful AI-nuclear integration could reshape global and national security framework. Timely analysis of these implications may be crucial for policymakers seeking to harness these technologies' benefits while effectively mitigating their potential risks.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Advanced Transmission Technologies – GETs and HPCs Session 1: ATT Foundations and Dynamic Line Ratings (DLRs)

The INL TADA GETs Cohort Session 1, held on November 4, 2025, convened experts to address the integration of advanced transmission technologies, including Grid-Enhancing Technologies (GETs) and High Performance Conductors (HPCs), with a focus on digital assurance challenges. The session highlighted the growing importance of cybersecurity, supply chain transparency, reliability, and business risk management in deploying GETs, especially Dynamic Line Ratings (DLRs). Participants examined how expanded attack surfaces, limited vendor pools, and new regulatory requirements—such as FERC Orders 881, 2023, and 1920—are influencing utilities and technology providers. The workshop underscored the need for cyber-informed engineering, secure-by-design principles, and practical risk management strategies, while fostering collaboration and knowledge sharing among industry peers. Technical discussions covered the evolution from static to dynamic line ratings, complexities of cloud-based architectures, and NERC CIP compliance challenges. The session concluded with a collaborative risk exercise and a preview of future workshops on advanced power flow control and transmission topology optimization, reinforcing the cohort’s commitment to advancing digital assurance in the energy sector.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Distributed Maneuver Planning With Connected and Automated Vehicles for Boosting Traffic Efficiency

Connected and automated vehicles (CAVs) have the potential to improve traffic throughput and achieve a more efficient utilization of the available roadway infrastructure. They also have the potential to reduce energy consumption through traffic motion harmonization, even when operating in mixed traffic with other human-driven vehicles. The key to realizing these potentials are coordinated control schemes that can be implemented in a distributed manner with the CAVs. In this paper, we propose a distributed predictive control framework that features a two-dimensional maneuver planner incorporating explicit coordination constraints between connected vehicles operating in mixed traffic at various penetration levels. Here, the framework includes a distributed implementation of a reference speed assigner that estimates local traffic speed from on-board measurements and communicated information. We present an extensive evaluation of the proposed framework in traffic micro-simulations at various CAV penetrations from traffic flow, energy use, and lane utilization points of view. Results are compared to a baseline scenario with no CAVs, as well as, a benchmark one-dimensional planner.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Assessment of Electric Grid Transmission System Simulator for Human Factors Research

Over the past decades, various technologies have been developed for electric grid operations to support clean energy, meet rising electricity demands, and address infrastructure concerns. However, the human factors aspect is often overlooked during rapid integration. Questions persist about how these technologies impact human performance. Simulators play a critical role in supporting investigation of human factors design concepts and conducting comprehensive usability testing to evaluate human performance and assess human reliability. This paper aims to address human factors research simulator requirements and conduct a comparative study of six different simulators. A detailed evaluation reveals that the evaluated simulators lack the ability to customize user interfaces. Additionally, their user interface designs do not fulfill the basic human factors design principles, potentially leading to increased response variability and reduced statistical power when conducting controlled experimental research. In the future, it is essential to develop scripting tools to integrate customizable user interfaces and simulation models, ensuring meeting research requirements.

Li, Ruixuan↗
DOE OSTI2024

Machine Learning Classification Strategy to Improve Streamflow Estimates in Diverse River Basins in the Colorado River Basin

Streamflow in the Colorado River Basin (CRB) is significantly altered by human activities including land use/cover alterations, reservoir operation, irrigation, and water exports. Climate is also highly varied across the CRB which contains snowpack-dominated watersheds and arid, precipitation-dominated basins. Recently, machine learning methods have improved the generalizability and accuracy of streamflow models. Previous successes with LSTM modeling have primarily focused on unimpacted basins, and few studies have included human impacted systems in either regional or single-basin modeling. We demonstrate that the diverse hydrological behavior of river basins in the CRB are too difficult to model with a single, regional model. We propose a method to delineate catchments into categories based on the level of predictability, hydrological characteristics, and the level of human influence. Lastly, we model streamflow in each category with climate and anthropogenic proxy data sets and use feature importance methods to assess whether model performance improves with additional relevant data. Overall, land use cover data at a low temporal resolution was not sufficient to capture the irregular patterns of reservoir releases, demonstrating the importance of having high-resolution reservoir release data sets at a global scale. On the other hand, the classification approach reduced the complexity of the data and has the potential to improve streamflow forecasts in human-altered regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A multi-scale cognitive interaction model of instrument operations at the Linac Coherent Light Source

The Linac Coherent Light Source (LCLS) is the world’s first x-ray free electron laser. It is a scientific user facility operated by the SLAC National Accelerator Laboratory, at Stanford, for the U.S. Department of Energy. As beam time at LCLS is extremely valuable and limited, experimental efficiency—getting the most high quality data in the least time—is critical. Our overall project employs cognitive engineering methodologies with the goal of improving experimental efficiency and increasing scientific productivity at LCLS by refining experimental interfaces and workflows, simplifying tasks, reducing errors, and improving operator safety and stress. Here, in this study, we describe a multi-agent, multi-scale computational cognitive interaction model of instrument operations at LCLS. Our model simulates the aspects of human cognition at multiple cognitive and temporal scales, ranging from seconds to hours, and among agents playing multiple roles, including instrument operator, real time data analyst, and experiment manager. The model can roughly predict impacts stemming from proposed changes to operational interfaces and workflows. Example results demonstrate the model’s potential in guiding modifications to improve operational efficiency. We discuss the implications of our effort for cognitive engineering in complex experimental settings and outline future directions for research. The model is open source, and the videos of the supplementary material provide extensive detail.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Laser-Induced Plasma as a Method for the Metallic Materials Hardness Estimation: An Alternative Approach

The application of an alternative laser-induced breakdown spectroscopy (LIBS) method based on transversely excited atmospheric (TEA) CO2 laser was investigated for the first time for estimating the hardness of metallic materials. The human eye-safe (TEA) CO2 laser, operating at 10.6 µm, was used for plasma generation. The LIBS spectra were recorded by employing a cost-effective CCD camera for the time-integrated and spatial resolved measurements. The cast iron and aluminum alloys samples with different hardness have been tested. The ratio between the magnesium ionic and neutral lines in LIBS spectra was applied for estimating the material hardness. In addition, the hardness of all samples included in this study was determined using the conventional method for material hardness determination, i.e. Vickers hardness test. The linear dependence of magnesium lines intensity ratio on the sample’s hardness was obtained for both kinds of materials. Profilometric measurements were used to verify that the newly-introduced method is almost nondestructive for the investigated metals. The results obtained in the present work confirmed the potential of using this original LIBS system not only for the fast elemental analysis but also for the direct estimation of the hardness of metals and alloys. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Heavy metal imprints in Antarctic snow from research and tourism

Antarctica, long regarded as one of the last pristine environments on Earth, is increasingly affected by human activity. As tourism surges and scientific operations expand, air pollution from local emissions is raising new environmental concerns. Here, in this study, we analyze surface snow samples collected along a ~2,000 km transect, from the South Shetland Islands (62°S) to the Ellsworth Mountains (79°S), to map the geochemical fingerprints of aerosol deposition. We identify distinct spatial patterns shaped by crustal, marine, biogenic, and anthropogenic sources. Notably, we detect heavy metal imprints in the snow chemistry of the northern Antarctic Peninsula, where major research stations are concentrated and marine tourism traffic is most intense. Our findings shed light on the extent of the impacts from energy-intensive local activities in Antarctica, underscoring the need for enhanced environmental monitoring and sustainable management strategies in this fragile region.

Cordero, Raúl R. [Univ. of Groningen, Leeuwarden (↗
DOE OSTI2020

Viable Materials with a Giant Magnetocaloric Effect

This review of the current state of magnetocalorics is focused on materials exhibiting a giant magnetocaloric response near room temperature. To be economically viable for industrial applications and mass production, materials should have desired useful properties at a reasonable cost and should be safe for humans and the environment during manufacturing, handling, operational use, and after disposal. The discovery of novel materials is followed by a gradual improvement of properties by compositional adjustment and thermal or mechanical treatment. Consequently, with time, good materials become inferior to the best. There are several known classes of inexpensive materials with a giant magnetocaloric effect, and the search continues. View Full-Text

36 MATERIALS SCIENCE↗
DOE OSTI2024

Towards Agentic AI on Particle Accelerators

As particle accelerators grow in complexity, traditional control methods face increasing challenges in achieving optimal performance. This paper envisions a paradigm shift: a decentralized multi-agent framework for accelerator control, powered by Large Language Models (LLMs) and distributed among autonomous agents. We present a proposition of a self-improving decentralized system where intelligent agents handle high-level tasks and communication and each agent is specialized control individual accelerator components. This approach raises some questions: What are the future applications of AI in particle accelerators? How can we implement an autonomous complex system such as a particle accelerator where agents gradually improve through experience and human feedback? What are the implications of integrating a human-in-the-loop component for labeling operational data and providing expert guidance? We show two examples, where we demonstrate viability of such architecture.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Opdefender Network Monitoring And Control System

OpDefender is a new system designed to enhance the cyber security of control system networks. It accomplishes this by inspecting each network packet using a novel, patent-pending method that extends software defined networking into the application/ICS protocol layer. OpDefender consists of two key components: 1. Smart, “ICS-aware” network switches that analyze and filter network traffic in real time. 2. A network management human machine interface (HMI) that allows an operator to monitor and control network traffic in real time. The “ICS-aware” switches can serve as a drop-in replacement for typical network switches, or they can be used in conjunction with existing network switches. OpDefender is configured and controlled in real time via a custom-built, web-based HMI designed with the operator in mind.

Johnson, BriamE.↗
DOE OSTI2025

Plant Roots and the Fate of Mineral-Associated Soil Carbon (Final Report)

Atmospheric CO 2 concentrations continue to rise due to human activities, but there are also natural mechanisms operating in ecosystems that can store carbon in soil long-term. The organic carbon derived from organisms in soil becomes associated with minerals, where it can be protected for thousands of years. Plants are particularly important contributors of carbon belowground, via their roots. The long-term protection of carbon contributed to soil has great value to humans as climate change looms. But, to keep growing, living plants and soil microbes also need to be able to access and recycle soil nutrients that are stored along with the carbon. In this DOE-funded work, Cardon (MBL), Keiluweit (UMass Amherst/University of Lausanne), Malmstrom (MSU), and Riley (LBNL) used lab and greenhouse experiments coupled with mathematical modeling to determine mechanisms by which plant roots and their associated microbes can dislodge organic matter from soil minerals, making its components available for recycling, but also making its carbon vulnerable to re-release to the atmosphere as CO 2 .

54 ENVIRONMENTAL SCIENCES↗