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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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DOE OSTI2025

Hazard Detection Detector Cards

This report presents a comprehensive summary of five advanced anomaly detection tools developed and deployed by Oak Ridge National Laboratory in support of the VA’s Health Information Technology modernization. These detectors—Order Path Tracker, Trend Watcher, Pain Pointer, Performance Monitor, and Patient Record Flag Detector—leverage statistical and machine learning methods to monitor workflow disruptions, detect anomalies in care sequences and volumes, identify bottlenecks, and track system-level performance metrics across VistA and Millennium systems. All detectors have been integrated into the Health Data Analytics Platform (HDAP), with most having completed deployment and testing using live data from targeted stations in cardiology and oncology domains. This work enhances VA’s capacity for proactive system surveillance, promotes patient safety, and informs data-driven operational improvements across the EHR ecosystem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Gravity Well Commercial Economics Assessment: Potential Revenue and Cost: Cooperative Research and Development (Final Report)

In the Gravity Well Revenue Study, we evaluate the potential revenue from energy storage using historical energy-only electricity prices, forward-looking projections of hourly electricity prices, and actual reported revenue. This analysis examines the impact of storage duration and round-trip efficiency, as well as the location of the storage, on storage revenue within the current and projected U.S. power system. We also investigated the impact of round-trip efficiency on storage revenue. We found that the relationship between storage revenue and round-trip efficiency is nonlinear. The value of improved round-trip efficiency declines as round-trip efficiency increases. In the Gravity Well Future Cost Study, we applied learning curves to predict the future cost trajectory of Gravity Wells (GrWs). Two types of analysis were implemented. The first was a bottom-up analysis that used historical learning rates for cost components, such as motors and gearboxes, and cost categories (e.g., engineering and design, etc.) to determine the learning-by-doing based single-factor learning curve. The single factor learning curve expresses the relationship between the cost of GrW and the number of units deployed (or the cumulative capacity). In the second analysis, we predicted future GrW costs via a top-down approach. This approach accounts for historical cost trends in other renewable energy and storage technologies, which have similarities with GrWs. Using a multifactor learning curve that accounts for both intrinsic (cumulative capacity) and extrinsic (the elasticity in the price of steel) factors, we estimated the future cost of GrWs.

25 ENERGY STORAGE↗
DOE OSTI2022

Review of WEC-Sim development and applications

WEC-Sim (Wave Energy Converter Simulator) is an open-source code for simulating wave energy converters, which has been actively developed and applied to simulate a wide variety of device archetypes, and has become a popular tool since its release. This paper reviewed the development efforts and the usage of WEC-Sim. The publications considered in this study have been broken down into six topic areas, namely feature development, experimental validation, device modeling, control modeling, PTO and grid modeling, and novel applications, which even includes some non-wave energy applications. This review paper has also attempted to recognize the contributions of the broader WEC-Sim development effort, meaning not only the internal WEC-Sim development team but also the external efforts from the academia researchers and technology developers around the world. The growing trend of external applications of WEC-Sim has demonstrated the broader acceptance of the open-source code, and how WEC-Sim has been used in a certain topic area also highlights the potential future development needs.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Toolpath Optimization of Polymer Composite Additive Manufacturing AM Printed Mold to Improve Performance for Compression Molding Applications

This study explores the optimization of toolpaths for short fiber polymer composite additive manufacturing (AM) printed molds in compression molding applications, an emerging trend driven by advancements in 3D printing technology. AM printed molds offer reduced tooling costs and greater design flexibility, particularly for prototyping and low-volume production. The proposed optimization approach evaluates mold performance using two key metrics: deformation and shape accuracy. Multiple toolpaths are generated, and finite element (FE) simulations capture fiber orientation and material properties, which are then integrated into a mechanical performance model. Shape accuracy is assessed by comparing the deformed mold shape with the original using surface normals as a descriptor. The analysis identifies the toolpath that delivers the best overall performance, establishing it as the most suitable option for achieving optimal mold characteristics in compression molding applications.

Limaye, Madhura [ORNL]↗
DOE OSTI2024

Polymer Additive Manufacturing for Marine Renewable Energy Applications: Best Practices, Research Trends, and Current Challenges

Additive manufacturing (AM) is a rapidly growing technology space, not only for prototyping, but is also becoming more feasible at larger scales and increasing component quantities. There are a large variety of AM processes and materials available to users and effectively applying those processes and materials to a specific use case can be challenging. One specific area where AM could be particularly beneficial is marine renewable energy (MRE). Not only is MRE a relatively nascent industry with a near-term need for rapid deployments and prototype testing, but developers could also see long-term benefits from the broad variety of environmentally resistant materials available and the ability to manufacture complex geometries that AM technologies offer. Over the past 4 years, AM materials have played an increasing role in the Advanced Materials project; a multi-year, multi-laboratory research project funded by the U.S. Department of Energy's Water Power Technologies Office, with the main goal of reducing barriers to the adoption of complex materials in the MRE industry. The primary focus of this project is to develop test methods and generate datasets to understand the long-term performance of advanced materials in marine environmental and address specific material challenges as they arise. This report provides an extensive overview of the research that has been performed specific to AM polymers as part of the Advanced Materials project. The intention of this document is to provide recommendations of best practices with regards to material selection, mechanical test method development, and design practices, lessons learned along the way, current research trends, and ongoing challenges with regards to AM polymers in marine environments. In particular, this report focuses on several key aspects: Material and process selection, Environmental conditioning and subsequent degradation quantification through mechanical characterization, Composite reinforcements on AM polymer substrates, Adhesion of instrumentation for mechanical characterization and loads measurements, Protective coatings for preventing biofouling and water ingress, Other MRE case studies where AM has proved particularly useful. Ultimately, we hope that the test methods that have been developed, data generated, and lessons learned from this research will be valuable to the MRE community (researchers and developers alike), as well as other industries, and can be used as a reference point as the respective MRE and AM industries continue to grow and mature.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Average and Marginal Capacity Credit Values of Renewable Energy and Battery Storage in the United States Power System

As deployment of renewable resources and storage continue to significantly grow in the coming decades, these technologies will play increasingly important roles in maintaining power systems' resource adequacy. Few analyses so far offer comprehensive comparisons of forward-looking average and marginal capacity credits of variable renewable energy and storage in the U.S. interconnections across a wide range of possible futures. To fill this research gap, we quantify the average and marginal capacity credits of solar PV, onshore and offshore wind, and batteries between 2026 and 2050 across the U.S power systems to examine the temporal trends, spatial patterns, and trade-offs between these two capacity accreditation approaches. Across technologies, capacity credits of solar PV most clearly follow downward trends over time, reflecting the significant rise in solar PV generation share as the grid decarbonizes. While battery storages' generation shares also rise significantly over time, their capacity credits always remain stably high due to their capabilities to be dispatched strategically during critical periods to maintain reliability. On the other hand, capacity credits of wind technologies in general follow slight upward trends as their generation shares level off. There are strong spatial variabilities of both average and marginal capacity credits across technologies, but capacity credits of solar PV displaying the most obvious spatial patterns with high capacity credits concentrating in wind-rich, solar-poor regions in SPP, PJM, and MISO, suggesting potential reliability benefits of interconnection-wide planning for renewable energy deployments. Additionally, except for offshore wind, average capacity credits of all other renewable technologies tend to be higher than their marginal capacity credits, indicating that existing renewable resources tend to be accredited higher than new resources at almost any time.

25 ENERGY STORAGE↗
DOE OSTI2022

Dynamic land use implications of rapidly expanding and evolving wind power deployment

Abstract The expansion of wind power poses distinct and varied geographic challenges to a sustainable energy transition. However, current knowledge of its land use impacts and synergies is limited by reliance on static characterizations that overlook the role of turbine technology and plant design in mediating interactions with the environment. Here, we investigate how wind technology development and innovation have shaped landscape interactions with social and ecological systems within the United States and contribute to evolving land area requirements. This work assesses trends in key land use facets of wind power using a holistic set of metrics to establish an evidence base that researchers, technology designers, land use managers, and policymakers can use in envisioning how future wind-intensive energy systems may be jointly optimized for clean energy, social, and environmental objectives. Since 2000, we find dynamic land occupancy patterns and regional trends that are driven by advancing technology and geographic factors. Though most historical U.S. wind deployment has been confined to the temperate grassland biome in the nation’s interior, regional expansion has implicated diverse land use and cover types. A large percentage of the typical wind plant footprint (∼96% to > 99%) is not directly impacted by permanent physical infrastructure, allowing for multiple uses in the spaces between turbines. Surprisingly, turbines are commonly close to built structures. Moreover, rangeland and cropland have supported 93.4% of deployment, highlighting potential synergies with agricultural lands. Despite broadly decreasing capacity densities, offsetting technology improvements have stabilized power densities. Land use intensity, defined as the ratio of direct land usage to lifetime power generation of wind facilities, has also trended downwards. Although continued deployment on disturbed lands, and in close proximity to existing wind facilities and other infrastructure, could minimize the extent of impacts, ambitious decarbonization trajectories may predispose particular biomes to cumulative effects and risks from regional wind power saturation. Increased land-use and sustainability feedback in technology and plant design will be critical to sustainable management of wind power.

17 WIND ENERGY↗
DOE OSTI2024

Statistical and Neural Network for Real Sensor-Data-Driven Anomaly Detection in Nuclear Applications

Anomaly detection (AD) in sensor data is critical to ensure uninterrupted functionality of nuclear power plants (NPPs). Consequently, AD model validation through real-world sensor data is important for applications in nuclear facilities. In this paper, we propose an Autoencoder (AE)—a multi-layered neural network, for AD in sensor data from an operational NPP testbed. Since the dataset lacks labels for irregularities, we introduce random noise and label them to effectively train our model. The proposed AE model assigns a higher reconstruction error to the abnormal samples that deviate from those encountered during the training phase and uses the reconstruction loss to detect anomalies in a representative imbalanced dataset. We also introduce an analytical solution—seasonal trend decomposition (STD)—as another AD scheme for identifying irregularities withinthe same time-series dataset. In contrast to the AE model which relies on reconstruction loss, the STD scheme decomposes the entire dataset into its trend, seasonality, and residual components to pinpoint irregularities. Our findings indicate that the proposed AE and STD models individually achieve recall scores of 97% and 92%, respectively. We validate the performance of the two models on both balanced and imbalanced data. We further solidify the results by picking the combined selected anomalies of the two solutions with an "AND" operator for more reliable predictions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

SMART Mobility. Multi-Modal Freight Capstone Report

The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The first three-year research phase of SMART Mobility occurred from 2017 through 2019, and included five research pillars: Connected and Automated Vehicles, Mobility Decision Science, Multi-Modal Freight, Urban Science, and Advanced Fueling Infrastructure. A sixth research thrust integrated aspects of all five pillars to develop a SMART Mobility Modeling Workflow to evaluate new transportation technologies and services at scale. This report summarizes the work of the Multi-Modal Freight Pillar. The Multi Modal Freight Pillar’s objective is to assess the effectiveness of emerging freight movement technologies and understand the impacts of the growing trends in consumer spending and e-commerce on parcel movement considering mobility, energy, and productivity. For information about the other Pillars and about the SMART Mobility Modeling Workflow, please refer to the relevant pillar’s Capstone Report.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Statistical and Neural Network for Real Sensor-Data-Driven Anomaly Detection in Nuclear Applications

Anomaly detection (AD) in sensor data is critical to ensure uninterrupted functionality of nuclear power plants (NPPs). Consequently, validation of AD models through real-world sensor data is important for their application in nuclear facilities. In this paper, we propose an Autoencoder (AE)— a multi-layered neural network, for AD in sensor data from an operational NPP testbed. Since the dataset lacks labels for irregularities, we introduce random noise and label them to effectively train our model. The proposed AE model assigns a higher reconstruction error to the abnormal samples that deviate from those encountered during the training phase and uses the reconstruction loss to detect anomalies in a representative imbalanced dataset. We also introduce an analytical solution—seasonal trend decomposition (STD) — as another AD scheme for identifying irregularities within the same time-series dataset. In contrast to the AE model which relies on reconstruction loss, the STD scheme decomposes the entire dataset into its trend, seasonality, and residual components to pinpoint irregularities. Our findings indicate that the proposed AE and STD models individually achieve recall scores of 97% and 92%, respectively. We also validate the performance of the two models on both balanced and imbalanced data. We further solidify the results by picking the combined selected anomalies of the two solutions with an "AND" operator for more reliable predictions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Emerging Pathways to Upgrade the US Housing Stock: A Review of the Home Energy Upgrade Literature

The residential buildings sector is responsible for about 20% of total US energy use. In order to achieve climate goals, we need ways to reduce carbon emissions and energy use in this sector. In addition, resiliency, electric grid stability, emergency survivability and other energy and building-related issues are becoming increasingly important challenges. New homes in most of the US meet various energy codes and are reasonably energy efficient. However, the vast majority of energy use is from existing homes that were not required to conform to energy performance requirements. It is becoming imperative to reach as many of these existing homes as possible and find ways to improve their energy-related performance. This must be done in such a way that it meets the needs and desires of homeowners and building occupants, as well as those of the contractors and design professionals engaged in doing the upgrades themselves. Energy retrofits of homes started in the 1970’s in response to the energy crisis, however, these retrofits were very limited in scope and relatively few homes were upgraded. Those homes that have been upgraded generally still have much scope for improvement. A huge effort is needed to get to scale to address the energy use in housing. The target population is effectively every home in the country, whether a large suburban single-family home, or a small downtown apartment. In order to provide a framework for analysis and the basis for plans to get to large-scale retrofits of homes, this literature review summarizes the state-of-the art in the US buildings industry. It identifies where more research, engineering, or technology is needed, as well as relevant industry trends, such as electrification, one-stop shop program design and others. It also examines other key topics, such as availability of financing, minimizing household disruption, and engaging home owners and occupants. This literature review builds on a similar review from several years ago (Less and Walker, 2014). The current review focuses on efforts in the intervening years. This literature review is part of a larger DOE study of deep energy upgrades that includes industry surveys and development of cost-stack analyses. For this review, we gathered data not just from the published literature, but also from practitioners in conjunction with other aspects of the larger DOE study. In some cases, we refer to comments from specific individuals or companies, or refer to specific products by name. This is not intended as an endorsement, but rather to provide clarity on sources of information and examples of relevant technologies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

What can surface wind observations tell us about interannual variation in wind energy output?

The past decade of wind power growth was supported by capacity factor improvements and associated cost reductions. But are higher capacity factors a technology success story or, as suggested by recent research, has the influence of technology been overstated by ignoring positive surface wind speed trends? The answer could influence estimates of wind energy's cost and even future deployment rates. We find that US surface wind speed observations imply a 2.6% improvement in capacity factors from 2010 to 2019. Yet newer vintages of wind plants have recorded capacity factors that are ~25% larger than plants built close to 2010. It follows that technological factors and improved site quality, not higher wind speeds, drove most of the improvement in capacity factors. Additionally, we match hundreds of meteorological stations to nearby (< 25 km) wind plants and compare annual estimated generation, based on a function of surface wind speed observations, to annual recorded generation. Researchers rely on this publicly available surface data because measurements co-located with wind plants are generally considered proprietary. Our analysis addresses a research gap: interannual variation in observed surface wind speeds is rarely compared to observed data at wind plant locations and turbine heights. We find that despite its common use for this purpose, generation estimates based on publicly available surface observational data provide a poor proxy for interannual variability in recorded wind generation. These findings suggest that caution is generally needed when researchers use surface wind speed measurements to investigate long-term wind energy trends.

17 WIND ENERGY↗
DOE OSTI2021

Corrosion Protection and Dissimilar Material Joining for Next Generation Lightweight Vehicles

The Arconic Technology Center working with Honda R&D Americas, LLC and the Ohio State University evaluated the corrosion performance of several multi-material conditions and demonstrated the production worthiness of the Resistance Spot Riveting (RSR™) process. RSR is a new technology being developed by Howmet Fastening Systems (formerly Arconic, Inc.) that employs a fastener that is installed using conventional resistance spot welding equipment to produce multi-material joints. The goal of the 3-year project was to demonstrate the use of RSR to join aluminum to steel and aluminum to carbon fiber composites on a prototype scale. Deployment of this technology would help the automotive industry achieve an additional 10 to 20% weight reduction over high strength steels. These weight-savings to the body in white generally translates to 2.5-5.0% of overall vehicle curb weight. The resulting total weight-savings could provide a 1.5% to 3.0% total improvement in fuel efficiency for vehicles that incorporate RSR for multi-material joining. The RSR technology addresses several production barriers to achieving DOE’s fuel efficiency targets including eliminating the need of additional capital for new joining technologies and the flexibility to process conventional steel and multi-material structures with the same equipment. Additionally, the trend towards ultra-high strength steels limits the availability of conventional joining technologies that can effectively process these multi-material combinations. In order to accomplish these goals, the following program milestones were completed by the team: 1) Developed RSR process parameters, producing multi-material joints for mechanical testing and corrosion assessments. 2) Conducted corrosion evaluation of RSR and baseline joints assembled between automotive type aluminum alloys, steels, and carbon fiber using several corrosion mitigation strategies. 3) Developed a production ready feed system and integrate into a robotic resistance spot welding station to simulate automotive production conditions. 4) Produced demonstration assemblies for testing and evaluation.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Review of Ultrafast Switching Power Modules: Trends, Challenges, and Technical Solutions

Benefiting from the superior properties of wide-bandgap semiconductor materials, wide-bandgap power devices demonstrate exceptional switching performance, enabling more efficient and compact power electronics systems. However, ultrafast switching poses challenges to the reliability of the system in terms of severe oscillations and voltage overshoot, electromagnetic interference, and increased risk of partial discharge. By developing advanced power module packaging for fast-switching power devices, the above-mentioned challenges can be mitigated at the packaging level, enabling the full utilization of the fast-switching capability of wide-bandgap devices. Meanwhile, such technology also lays the groundwork for packaging next-generation power devices with even higher blocking voltage and faster switching speed. In this paper, a comprehensive review of ultrafast switching power modules has been made, including the benefits and status of ultrafast switching power modules, challenges brought by ultrafast switching, and promising technologies to address these challenges. In addition, future development trends and research gaps are also discussed in this paper. Furthermore, this review can serve as a reference for future wide-bandgap power module packaging design.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

tell: a Python package to model future total electricity loads in the United States

The purpose of the Total ELectricity Load (tell) model is to generate 21st century profiles of hourly electricity load (demand) across the Conterminous United States (CONUS). tell loads reflect the impact of climate and socioeconomic change at a spatial and temporal resolution adequate for input to an electricity grid operations model. tell uses machine learning to develop profiles that are driven by projections of climate/meteorology and population. tell also harmonizes its results with United States (U.S.) state-level, annual projections from a national- to global-scale energy-economy model. This model accounts for a wide range of other factors affecting electricity demand, including technology change in the building sector, energy prices, and demand elasticities, which stems from model coupling with the U.S. version of the Global Change Analysis Model (GCAM-USA). tell was developed as part of the Integrated Multisector Multiscale Modeling (IM3) project. IM3 explores the vulnerability and resilience of interacting energy, water, land, and urban systems in response to compound stressors, such as climate trends, extreme events, population, urbanization, energy system transitions, and technology change

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Heat pumps in the United States: Market potentials, challenges and opportunities, technology advances

The US heat pump market has been affected by the socioeconomic impacts of the COVID-19 pandemic. However, the Biden administration’s goal of net-zero greenhouse gas emissions by 2050 through electrification and clean energy technologies is accelerating the research, development, and deployment of heat pumps in the United States for improved energy performance, reduced greenhouse gas emissions, and wider adoption. The US heat pump market has experienced steady growth since 2010. In 2020, heat pumps surpassed gas furnace shipments for the first time, and the trend maintains through 2022. The current priority is to improve the affordability of energy and equitable access to heat pump technologies through cost reductions and further accelerate this trend. In addition, current R&D includes emphases on alternative refrigeration technology and lower–global warming potential refrigerants to reduce direct emissions. Heat pump market share is expected to grow as regulatory policies and financial incentives steer the building sector toward decarbonization. This paper reviews policies and market trends, discusses the challenges and opportunities in the current policy landscape, and reviews current research in the United States.

Malhotra, Mini↗
DOE OSTI2024

Frontiers in Scientific Workflows: Pervasive Integration With High-Performance Computing

Herein we address the increasing complexity of scientific workflows in the context of high-performance computing (HPC) and their associated need for robust, adaptable, and flexible computational support systems. We explore five key trends as well as future challenges and opportunities for scientific workflows and HPC technologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Solar Energy from a Big Picture Perspective to Nanoscale Insights via TOF-SIMS

The world is undergoing a rapid transformation in the ways that we generate and store energy. This has been driven not only by concerns about the climate but by simple economic factors due to the dramatic cost decreases in wind in solar power. In most places of the world where one would now want to build a new power plant, the cheapest option is to use wind of solar for power generation. Abundant clean energy when the sun shines most is driving new research for daily and seasonal energy storage in many different technologies. Here, we will briefly these discuss energy trends as a whole, before diving into our recent contributions to the field using time-of-flight secondary-ion mass spectrometry (TOF-SIMS) to improve the performance and reliability of solar cells.

14 SOLAR ENERGY↗