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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Foil Bearing Supported Compressor-Expander

R&D Dynamics Corporation (RDD) is developing a fuel cell system compressor-expander designed for heavy-duty vehicle applications. The system emphasizes high reliability, versatility, long service life, and cost-effective mass production. Its design integrates a high-speed centrifugal compressor/expander, oil-free foil air/gas bearings, a direct current permanent magnet (DCPM) motor, and an inverter utilizing silicon carbide (SiC) switches. Key advantages include high efficiency, compact size and reduced weight, enhanced reliability, and elimination of oil contamination risks. The technology targets multiple sectors such as long-haul semi-trucks, mining and construction equipment, power generation, data centers, marine systems, and other heavy machinery. Evaluation units are ready for shipment, and manufacturing capabilities are currently being developed. Contract Number: DE-EE0009617 (2021).

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Energy Technology Commercialization and Entrepreneurship: Insights From the U.S. Department of Energy's Office of Technology Transitions 2024 Energy Technology University Prize Faculty Track

The Energy Technology University Prize (EnergyTech UP) was established in 2022 with the goal of challenging student teams to develop impactful business plans for energy technologies of their choosing. EnergyTech UP is part of the The American Made Challenges program portfolio, funded by the U.S. Department of Energy. EnergyTech UP is specifically funded by the Department of Energy's Office of Technology Transfer (OTT) and is administrated by staff at the National Renewable Energy Laboratory (NREL). Since its inception, the annual prize has attracted applications from approximately 1,948 students across nearly every U.S. state and territory, awarding over $1 million in funds to student teams. In 2024, the prize expanded to include a Faculty Track, which invited faculty members from degree-granting institutions across the U.S. to design entrepreneurship-based curricula or educational activities. This new track aims to foster innovation in energy entrepreneurship education by leveraging the expertise of faculty to create robust, practical, and inclusive learning experiences. This report summarizes the themes, strategies and impacts identified in proposals from EnergyTech UP's inaugural Faculty Track. Our primary data for organizing insights are the Faculty Track applications themselves. This evaluation of 2024 EnergyTech UP Faculty Track applications aims to support future EnergyTech UP Faculty Track applicants as well as others who are interested in promoting, developing, and/or implementing educational activities that focus on energy commercialization and entrepreneurship at their institutions. By presenting insights from 2024 entries to the Faculty Track, we hope to contribute to a growing inventory of open-source curriculum development resources and provide materials to facilitate the growth of similar programs at a variety of collegiate institutions.

adoption readiness level↗
DOE OSTI2023

Uncertainty Quantification of Metal Additive Manufacturing Processing Conditions Through the use of Exascale Computing

Metal additive manufacturing (AM) is a disruptive manufacturing technology that opens the design space for parts outside those possible from traditional manufacturing methods. In order to accelerate industry and R&D needs to certify AM parts, the Exascale Additive Manufacturing project (ExaAM) has developed a suite of exascale-ready computational tools to model the process-to-structure-to-properties (PSP) relationship for additively manufactured metal components. One such tool is an uncertainty quantification (UQ) pipeline to quantify the effect that uncertainty in processing conditions has on local mechanical responses. We present an overview of this pipeline and its required simulation and workflow codes. Using the Oak Ridge National Laboratory’s (ORNL) exascale computer, Frontier, we utilize this pipeline to cross multiple length and time scales to predict the local mechanical response of a location within a complex AM bridge part, AMB2018-01 produced by the National Institute of Standards and Technology (NIST) as part of their 2018 AM-Bench test series. Our results are then compared to experimental mechanical tests of parts from the NIST build to quantify the error in the ExaAM UQ workflow.

Carson, Robert↗
DOE OSTI2020

Volume IV. The DUNE far detector single-phase technology

The preponderance of matter over antimatter in the early universe, the dynamics of the supernovae that produced the heavy elements necessary for life, and whether protons eventually decay -- these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our universe, its current state, and its eventual fate. DUNE is an international world-class experiment dedicated to addressing these questions as it searches for leptonic charge-parity symmetry violation, stands ready to capture supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model. Central to achieving DUNE's physics program is a far detector that combines the many tens-of-kiloton fiducial mass necessary for rare event searches with sub-centimeter spatial resolution in its ability to image those events, allowing identification of the physics signatures among the numerous backgrounds. In the single-phase liquid argon time-projection chamber (LArTPC) technology, ionization charges drift horizontally in the liquid argon under the influence of an electric field towards a vertical anode, where they are read out with fine granularity. A photon detection system supplements the TPC, directly enhancing physics capabilities for all three DUNE physics drivers and opening up prospects for further physics explorations. The DUNE far detector technical design report (TDR) describes the DUNE physics program and the technical designs of the single- and dual-phase DUNE liquid argon TPC far detector modules. Volume IV presents an overview of the basic operating principles of a single-phase LArTPC, followed by a description of the DUNE implementation. Each of the subsystems is described in detail, connecting the high-level design requirements and decisions to the overriding physics goals of DUNE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Liquid metal walls

Here, the plasma performance of fusion devices depends strongly on the chosen wall materials. Solid plasma-facing components (PFCs) are predominantly used in present devices, and are the most investigated candidates for fusion designs. High-Z materials such as tungsten (W) are the leading solid PFC material candidates. To date, a material choice that scales to steady-state reactor conditions has not been identified. Moreover, if plasma transient events such as edge-localized modes and disruptions cannot be altogether avoided or sufficiently mitigated, the projected peak heat and particle loads far exceed the power exhaust capabilities of solids. Liquid metal (LM) PFCs represent an intrinsic self-healing boundary that are both resilient to surface damage from transients, and that could handle high steady-state heat and particle fluxes. Flowing LM PFCs can be designed to remove “slag,” the buildup of material erosion due to plasma-material interactions, including charge exchange sputtering in the main chamber. Further, LM offer the prospect to manage hydrogenic species otherwise retained in the PFCs, which is important from a safety and inventory standpoint. The two most promising LMs are lithium (Li) and tin (Sn), although Sn–Li eutectics may be considered. While Sn offers a higher temperature window with low vapor pressure and low hydrogen retention, Li offers the prospect of enhanced energy confinement and higher acceptable core contamination limits, and this section focuses on Li PFCs. An LM PFC development research program developed LM PFC concepts for a nuclear fusion device via engineering design calculations, single-effect experiments, and staged prototypical experiments. A self-consistent design window was identified with liquid Li flow speeds ~5–10 m/s; plasma contamination was negligible for predicted Li evolution rates. While these preconceptual designs hold promise, there is substantial R&D needed to advance the technical readiness levels of LM PFCs for application to fusion power plants.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Pseudomonas effector AvrB is a glycosyltransferase that rhamnosylates plant guardee protein RIN4

The plant pathogen Pseudomonas syringae encodes a type III secretion system avirulence effector protein, AvrB, that induces a form of programmed cell death called the hypersensitive response in plants as a defense mechanism against systemic infection. Despite the well-documented catalytic activities observed in other Fido (Fic, Doc, and AvrB) proteins, the enzymatic activity and target substrates of AvrB have remained elusive. Here, we show that AvrB is an unprecedented glycosyltransferase that transfers rhamnose from UDP-rhamnose to a threonine residue of the Arabidopsis guardee protein RIN4. We report structures of various enzymatic states of the AvrB-catalyzed rhamnosylation reaction of RIN4, which reveal the structural and mechanistic basis for rhamnosylation by a Fido protein. Collectively, our results uncover an unexpected reaction performed by a prototypical member of the Fido superfamily while providing important insights into the plant hypersensitive response pathway and foreshadowing more diverse chemistry used by Fido proteins and their substrates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Floating Offshore Wind US Manufacturing and Commercialization: Cooperative Research and Development (Final Report)

NREL assessed the supply chain and workforce considerations for the OCG-Wind floater technology, a floating semi-submersible offshore wind substructure, as well sharing vessel needs to inform their installation strategy. This technical assistance was in support of the FLoating Offshore Wind ReadINess (FLOWIN) Prize Phase 2 submission. NREL provided an assessment of domestic supplier capabilities for the main components of their floating offshore wind platform design and analyzed US regional and national supply chain constraints and gaps. Thirteen interviews with companies including steel distributors, forges, foundries, ports, large component fabricators, subcomponent fabricators, and secondary suppliers provided key insights such as 1) assembly ports are the key infrastructure barrier standing in the way of unlocking the domestic assembly and component fabrication for steel-based FOW platforms, 2) domestic steel producers can supply the types and quantities of steel necessary for FOW platforms, and 3) coordination between stakeholders will be a vital part of successfully developing the supply chain and infrastructure needed to domestically produce FOW platforms. In the workforce assessment, NREL documented a step-by-step approach to conduct a place-based assessment of the foundational workforce consideration for recruiting, upskilling, and retaining a workforce, such as supportive local and state policy, nearby education and training programs, and existing relevant industry. This approach was applied to Tacoma, Washington. Tacoma was indicated to have the potential be a successful location for fabrication and assembly of floating offshore wind energy in terms of workforce development. To share data on vessel requirements to install the OCG-Wind floater, NREL compiled resources that help answer the questions related to anchor handling tug vessels, shared a database of cable laying vessels, and answered questions on complying with the Jones Act.

17 WIND ENERGY↗
DOE OSTI2020

Delivering Contextual Knowledge and Critical Skills of Disruptive Technologies through Problem-Based Learning in Research Experiences for Undergraduates Setting

The recent development in transportation, such as energy-efficient and autonomous vehicles, defines a condition for the students in transportation engineering. Students in the field of transportation engineering should be ready upon their graduation with new knowledge and skills that are compatible with the need of the industry and sustainable engineering practices.During summers of 2018 and 2019, we developed and implemented an eight-week program to increase the knowledge and skills of students coming from multidisciplinary fields related to autonomous vehicles. Problem of “How much will platooning reduce fuel consumption and emissions per vehicle mile traveled?” was instrumentalized in subsequent activities to introduce the comprehensive knowledge structure of autonomous vehicles.The engineering concept of reducing the cost and sustainability was embedded in the leading research question that helped us to develop and implement activities on an overall knowledge structure in autonomous vehicles. The goal of using problem-based learning activities was not to encourage the students to focus on reaching the solution merely. We aimed to introduce the multidisciplinary knowledge and critical skills aspects of learning about disruptive technologies.In this paper, we will discuss how a multidisciplinary research approach was incorporated into a problem-based learning activity. The students were introduced the subjects related to math, physics, computer science, and biology as the integration of the knowledge structure of autonomous vehicles. We will also present the results on students’ use of critical skills such as machine learning and computer programming.

Comert, Gurcan↗
DOE OSTI2025

Real-Twin

Real-Twin is a unified, model-agnostic scenario generation tool designed to streamline and standardize the evaluation of emerging mobility technologies. It provides an end-to-end framework that includes robust workflows, integrated tools, and comprehensive metrics to generate, calibrate, and benchmark microscopic traffic simulation scenarios across multiple platforms. Key Features of Real-Twin include: - Unified Scenario Generation: generate transferable, simulation-ready scenarios from heterogeneous data sources using a consistent workflow. - Automated Calibration Workflow: bridges simulation and real-world data, minimizing manual effort and making traffic simulation more accessible to researchers and engineers. - Model-Agnostic Compatibility: supports SUMO, VISSIM, and AIMSUN for cross-platform scenario generation and benchmarking. Enables reliable comparisons and reproducibility across different simulation tools. - Consistent Scenarios across Different Simulators: generate comparable simulation scenarios across different microscopic traffic simulators, providing users the ability to conduct benchmarking and cross-validation that are crucial for ensuring the reliability and reproducibility of simulation results. - Emerging Technology Support: includes a scenario database and pipeline for studying autonomous vehicles (AVs), with planned extensions to CAVs, EVs, and other advanced technologies.

Wang, Chieh (Ross) [Oak Ridge National Laboratory ↗
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 OSTI2025

Scale-Up of Friction Self-piercing Riveting Process for Multi-material Joints

A single-class joining process known as “friction self-piercing riveting (F-SPR)” has been developed for joining various low-ductility lightweight materials on a laboratory scale. The frictional heat generated during the F-SPR process improved local ductility, resulting in crack-free joints and robust mechanical performance. This innovative joining technology was further advanced through the scale-up of the process using a new system with several key features (e.g., automatic rivet feeding and clamping system, vacuum system) toward industry readiness. The new integrated F-SPR systems were effectively demonstrated for joining different material combinations (e.g., carbon fiber composite to 7075 Al alloy, 7075 Al alloy to 7075 Al alloy, and 7075 Al alloy to casting Al Aural 5) with a unified technique. Crack-free joint with adequate mechanical interlocking resulted in good mechanical joint strength for each material combination. Then, the process was successfully scaled up by producing multiple joints without any cracks on larger CFC-Al and Al-Al components by the new integrated system, bringing it closer to industrial application.

Lim, Yong Chae [ORNL] (ORCID:0000000321773988)↗
DOE OSTI2026

AI-Based Analytics and Energy Modeling Framework for Characterizing Urban Energy Systems

Developing location-specific district energy models is essential for understanding energy patterns and supporting efficient management and planning decisions. However, accurately characterizing these models remains challenging due to gaps in building characteristics and labor-intensive traditional modeling workflows. To address these challenges, we develop an AI-based framework that integrates top-down and bottom-up building energy data to automate urban energy model characterization. The framework trains multimodal deep learning models using heterogeneous ResStockTM datasets to infer missing building characteristics from varying levels of known information and generate simulation-ready inputs for district-scale energy modeling. It also employs a conditioning-based injection approach to generate ”what-if” scenarios, enabling users to explore retrofit, efficiency, and technology-upgrade pathways. Integrated within URBANoptTM, a bottom-up district energy modeling platform for simulating co-located buildings, the framework infers detailed building-level inputs required for bottom-up simulations. Both localized and generalized AI models are developed to learn relationships across categorical, numerical, and time-series data, enabling reconstruction of missing attributes and generation of targeted upgrade scenarios. We demonstrate this methodology on a residential neighborhood in Baltimore, MD, assessing internal consistency against ResStock reference data and URBANopt simulation, and comparing selected attributes against real-world building characteristics. Results show strong overall predictive accuracy in data completion and scenario generation, with localized and generalized models offering complementary trade-offs between precision and scalability. Overall, our automated framework streamlines energy modeling and provides a reliable framework for urban building energy characterization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Supplementing Gamma Sterilization with X-Ray and E-Beam Technologies: An International Industry and Academia Collaboration

Ionizing-technology–based industries are growing rapidly around the world. The expansion is driven mostly by the technology’s myriad applications, including polymer crosslinking, medical device sterilization, food pasteurization, and phytosanitary treatment. Ionization also is used in the manufacture of some healthcare products such as medical devices and biopharmaceuticals. Industrial sterilization methods render single-use products and manufacturing components safe and ready for their intended use. ISO11137-1 describes the validation and routine control of a sterilization process for medical devices and mentions the three types of radiation considered under this scope: gamma, X-ray, and electron beam (e-beam)

gamma sterilization, electron beam sterilization, ↗
DOE OSTI2022

The BREKTRIA 500 – A Breakthrough in Technology and Power Density for an Advanced 500kW Utility-Scale String Inverter (Final Technical Report)

The primary goal of this development project, planned to be 36 months in duration, was to create a 500kW utility-scale string inverter, demonstrating an advanced hybrid architecture that would achieve unprecedented high power density, and bring the inverter to the stage of production readiness. Specific key objectives for the 500kW utility-scale string inverter were: Create a 500kW 3-phase 600Vac inverter that was similar in overall size to the existing 250kW string inverters in the market, thereby demonstrating dramatically increased power density; Achieve a cost of goods, including all manufacturing-related costs and overheads, at or below 2.5¢/Wac ($12,500); Demonstrate full-power operation at a 45-50C ambient temperature with no power de-rating; Accommodate a PV array DC input of up to 1MWdc, aka a DC/AC Ratio of 2.0; Demonstrate peak efficiency greater than 99% at any operating dc voltage, and a CEC weighted average efficiency greater than or equal to 98.5%. The motivation for this project was to leapfrog the competition by creating the world’s most powerful string inverter, utilizing an innovative topology and achieving a step change increase in power density. Achieving the goals of the project would have enabled Yaskawa Solectria Solar to demonstrate its technology leadership, manufacture the utility-scale string inverter in the company’s facilities in Illinois, and bring to the market a highly-differentiated and compelling product to help the company grow its share in the large and growing utility market segment. During the course of this project, BREK Electronics’ hybrid architecture inverter was taken from an early-stage 125kW prototype, to a more mature and successfully demonstrated power stage at twice the power. Two 250kW power stages were planned to build the 500kW inverter. Significant progress was made in the inverter controls, achieving a clean AC sinewave and closed-loop operation into the grid, and improved efficiency by control of the high-speed switching. Further, the updated 250kW power stage was on track to achieve full power operation at an ambient temperature of 50C. At the time of the project’s closure in September 2022, global semiconductor supply remained limited, with shortages creating dramatic swings in both availability and price. Given this situation, our ability to predict cost-of-goods two years out with any accuracy or confidence was limited. As a result, the probability of attaining the target cost of 2.5¢/Wac for the 500kW inverter remained uncertain at the closure of the project. This project successfully demonstrated the potential for the advanced hybrid architecture inverter that emerged from a decade of university research on Si IGBTs and SiC mosfets, leading to this unique inverter topology. The team has taken the first steps toward an evolutionary advancement in inverter technology that remains to be fully realized.

14 SOLAR ENERGY↗