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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants (4th Annual Report)

Nuclear plant sites collect and store large volumes of data collected from various equipment and systems. These datasets typically include plant process parameters, maintenance records, technical logs, online monitoring data, and equipment failure data. The collection of such data affords an opportunity to leverage data-driven machine learning and artificial intelligence technologies to provide diagnostic and prognostic capabilities within the nuclear power industry to reduce operating and maintenance costs. In this way, nuclear energy can become more economically competitive with other energy sources, and premature closures can be avoided. From a maintenance standpoint, savings can be achieved by leveraging machine learning and artificial intelligence technologies to develop data-driven algorithms to better diagnose and predict potential faults within the system. Improved model accuracy can lead to reductions in unnecessary maintenance and more efficient planning of future maintenance, thus lowering the costs associated with parts, labor, and unnecessary planned, forced, or extended outages. From an operations perspective, cost savings can be generated by shifting from route-based monitoring to wireless technologies for online monitoring, and by transitioning from onsite- to cloud-based computing and storage services. Wireless monitoring would reduce the operator manhours required for taking routine measurements, while cloud computing services would generate cost savings by reducing the amount of hardware needing to be purchased and maintained—all while scaling to both computational and storage demands. This report summarizes this project’s effort to shift from costly, labor-intensive preventative maintenance to cheaper predictive maintenance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Laser Powder Bed Fusion of Steels for Nuclear Applications

This report provides an update on laser powder bed fusion of steels for nuclear applications. This report is a Milestone 3 deliverable in FY2024, under work package CT-24AN130401 to support research and qualification activities supported by the Advanced Materials and Manufacturing Technologies (AMMT) program here at Argonne National Laboratory (ANL). Continuing from FY23, the focus of FY24 for ANL includes further optimization of process parameters for three alloys: A709, G91, G92. The work package also includes fabricating test samples to conduct a thorough microstructural analysis and perform preliminary mechanical testing. For the same, we have used CALPHAD-based thermodynamic kinetic modeling to optimize the experimental activities.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Residual Stress Measurements of Cr-coated Cladding

Accident-tolerant fuel concepts for light-water reactor applications have been developed and tested in diverse research programs around the world. Industry teams have developed coated cladding concepts using different coating application methods, such as physical vapor deposition and coldspraying. Recently, variations in thickness and application method were investigated, and their effects on the microstructure and mechanical properties were reported. The results suggest an increase in performance in an unirradiated state, however, it remains unclear what phenomenon drove this performance. One explanation to this performance is associated with the coating process imparting a residual stress. Residual stress analysis was performed on coatings produced with various process parameters to understand how a thin layer of Cr impacts the mechanical properties after coating application and after simulated operating conditions. The gathered information is intended to address safety concerns or support margin identification related to accident-tolerant fuels and to support subsequent modeling and simulation efforts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Fractal Nanostructured Solar Selective Surfaces for Next Gen Concentrating Solar Power (Final Report)

This project reports a novel coating with enhanced solar absorptance and reduced thermal emittance with high efficiency at elevated temperature for next-generation concentrated solar power (CSP) plants, with targeted operating temperatures around 750°C. Highly textured single and multimetallic oxide coatings were electrodeposited onto Inconel substrate by systematically varying the composition and process parameters. The optimized coating exhibited micro-to-nano structures designed to match the wavelengths in the visible region of the solar spectrum. These structures facilitate resonant absorption of solar radiation, significantly boosting solar absorption and accommodating thermal stress during high temperature exposure. A high solar absorptance exceeding 0.985 and a low thermal emittance below 0.5, yielding a thermal efficiency near 95%, was achieved for the optimized coatings without any anti-reflective overcoat, that remained robust after 750 h of isothermal exposure to 750°C in air. The coatings are also robust to severe mechanical and environmental stressors. The innovative approach presented in this study demonstrates the potential for tailoring air-stable solar absorber coatings to achieve high absorption, low emittance, and excellent high-temperature endurance, meeting the rigorous demands of next-generation CSP systems. A technoeconomic analysis reveals the economic advantage of the coatings for Gen3 CSP installations in different geographical zones globally.

14 SOLAR ENERGY↗
DOE OSTI2024

Renewable Aromatics from Syngas

This study focuses on developing a catalytic process for the aromatization of mixed olefin (comprised of C 2 -C 6 ) over various metal doped zeolites towards the formation of large aromatics (preferably C 8 -C 16 ) compliance to the sustainable aviation fuel (SAF). Experiments were carried out using model olefin feedstocks containing either ethylene (C 2 ) or mixture of C 2 and C 3+ olefins. Both the catalyst composition and process parameters were evaluated in detail to identify their impact on the formation large aromatics. We specifically focus on the aromatization of ethylene as the model reaction and evaluated the impact of C 2+ olefins and paraffins that may present in the real feedstock. In all the reaction condition, aromatization of ethylene was used as the baseline to evaluate the effect of larger olefins or paraffins. Our results show that at this reaction condition paraffins remained either inert or show positive impact on the formation of large aromatics. While C 2 and C 3 olefins are highly reactive towards aromatization during reaction condition, large olefins are less reactive and thus, strategy was developed to oligomerize these large olefins (C 4+ ) alongside the aromatization of smaller olefins (C 2 /C 3 ).

10 SYNTHETIC FUELS↗
DOE OSTI2021

Out of the Lab: 3D Printing on Non-Ideal Surfaces

In this work, coaxial direct ink writing is utilized to print sensitive or encapsulated materials onto heterogeneous and rough substrates. By enclosing the core fluid within a rigid ceramic shell, continuity is maintained even when printing on substrates traditionally considered challenging. This work presents the development of a coaxial ceramic direct ink writing system and investigates co-flow dynamics through microfluidic principles. A specially designed coaxial nozzle enables the co-extrusion of an alumina shell, with indium-tin-oxide inks serving as the core material. This approach allows the fabrication of core-shell ceramic structures suitable for future high-temperature applications on rough surfaces. The colloidal inks are tailored to achieve the necessary rheological properties and sintering performance. Additionally, flow simulations, guided by microfluidic co-flow principles, are employed to define the processing parameters for coaxial printing, ensuring precise control over the core-shell architectures. Physical modeling is further applied to study core deformation and eccentricity. These simulations are experimentally validated, supporting the successful deposition of coaxial ceramic features on heterogeneous, high-temperature ceramic substrates.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Co-gasification of Plastic, Coal Waste and Biomass for Hydrogen Rich Syngas Production

Plastic waste has increasingly become one of the most pressing environmental issues. To mitigate plastic emissions, extraordinary efforts are needed for plastic waste recycling and management. This study investigates the co-gasification of plastic, coal refuse, and biomass into hydrogen-rich syngas through steam gasification. The study focuses on the correlations between process parameters and feedstock composition. Coal refuse and biomass are used as co-feedstocks for gasification, aiming to improve the handling of plastic waste and investigating their synergistic effects on product distribution.

Bashir, Muhammad↗
DOE OSTI2024

Benchmark Study Matrix for Microreactor Geometries Relevant to Multiple Developers

A benchmark study was developed that include design, development, manufacturing, and performance measurement of agnostic reactor relevant geometries to support industry’s adoption of advanced manufacturing in a variety of structures. A matrix of five microreactor component geometries, specifically based on the recent feasibility study on a Marvel microreactor liner, was developed and the Pacific Northwest National Laboratory team initiated one material/process combination, namely 316H using laser powder directed energy deposition (DED). Although the benchmark starts initially with simplistic cubical and cylindrical forms, it builds up to a mock-up of a non-proprietary design that can demonstrate a variety of features potentially useful for presenting knowledge to specific designers of microreactors and for the matter also for other reactor type designers. The initial cubical and cylindrical forms are initial steps to obtain surface features and dimensional responses to the identified process parameters to be used in the non-proprietary design mockup.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Materials Scorecards, Phase 2: Advanced Materials and Manufacturing Technology

This report presents updated material score cards with detailed justification of the proposed rating provided and quantifies the readiness of the advanced manufacturing of materials for nuclear applications. Based on an expanded literature review of available data, the scores in this report (Phase 2) have either been modified or remained constant as compared with Phase 1 scoring. The rating justification are provided for each scoring criteria whereas the maturity quantification of additively manufactured alloys and the knowledge gaps are discussed with reference traceability. This report focuses on the detailed justification and traceability for the adoption of additively manufactured 316SS, SS304, Alloy 800H, Graphite C/C, Alloy N, Silicon Carbide, HT9, Alloy 617, and Alloy 718. Significant variability in data and knowledge gaps exist among the identified materials. The scorecards are therefore based on published literature data, industry response to a survey, stakeholder input collected at workshops, and expert opinion. The interpretation and assessment were somewhat challenged as experimental process parameters and methodology varied and best judgment was exercised for score quantification. This assessment reflects the overall level of technical maturity for AM materials and their deployment in Gen-VI nuclear systems. These Phase 2 scorecards are likely to be revised based on additional input from stakeholders and as new research results become available.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Direct Metallization with Reactive Inks – Assessment of Reliability and Process Sensitivities

This project will reduce silver consumption in photovoltaic cells by a factor of almost ten – from 95 mg/cell (the median across technologies) to 10 mg/cell. To achieve this goal, we will replace screen-printed silver pastes with contact dispensed reactive inks that produce lower resistivity metallizations at lower temperatures and with thinner films. This project will generate the understanding necessary to scale this reactive ink technology from the bench-scale to commercial throughputs. Specifically, it will combine fundamental understanding on physics and chemistries involved in contact printing of reactive inks with detailed performance and reliability studies to quantify how tightly processing parameters need to be controlled in order to reliably metallize high efficiency solar cells at commercial throughputs of 36,000 cells/hour.

14 SOLAR ENERGY↗
DOE OSTI2025

High-Quality Factor Microwave Resonators using Rhenium

Coplanar waveguide resonators are a perfect tool to evaluate the losses induced by defects and interfaces in superconducting devices. Even if niobium is the most used superconductor for resonators and qubits, its native oxide at the metal-air (MA) interface limits the device results. Tantalum has recently significantly improved qubit performances due to a thinner and less disordered oxide layer compared to Nb. To further improve the MA interface, we used rhenium, a superconducting material with 1.7 K critical temperature resistant to oxidation: it forms an oxide layer thinner than 1 nm. In this study, we will present a thorough investigation of rhenium CPW resonator measurements with internal quality factors at the single photon level exceeding 2 million. The devices have been fabricated on a sapphire substrate while the processing parameters have been varying and optimized. The measurements have been performed as a function of power and temperature to disentangle different sources of losses, such as two-level systems (TLS) and quasi-particles. A peculiar TLS temperature dependence has been measured and analyzed. In this work, we also vary the participation ratio of the devices to extract the losses introduced by the involved interfaces with higher fidelity and precision. We carried on a deep material characterization effort to link the results to the differences in the fabrication steps, and we will present material characterization measurements performed via AFM, ToF-SIMS, XPS, and TEM.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

In-Situ Powder-Directed Energy Process Control for Additively Manufactured Multi-Layer, Functionally Graded Components

Coatings and cladding can experience damage in harsh environments: Erosion Oxidation Delamination External mechanical damage Radiation induced damage Functionally Graded Materials (FGMs) were primarily designed to reduce thermal stress in thermal barrier coatings (TBCs), but can produce coatings which resist a variety of material damage (corrosion, surface damage, diffusion) Powder-fed DED with multiple independent feeders allows for AM FGMs, and provides robust control over process parameters to optimize microstructure and performance. FGMs have a great opportunity for applications in energy applications as a strategy to increase plant efficiency and lifetime.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Predicting Thermoset Resin Cure Kinetics for Composite Processing

Predicting a material’s thermal properties is not only useful for designing parts but also for defining processing parameters. This is especially applicable to wet-filament winding operations and the production of composite parts using thermoset res ins. For these, the formation of cross-linking bonds is affected by time and temperature, but the degree of cross-linking can be modeled using phenomenological methods. In the current study, the Kissinger and Flynn/Wall/Ozawa methods were both used to calculate kinetic parameters and produced values that were in agreement with each other. The kinetic parameters activation energy ( E a ) and pre-exponential factor (𝐴) calculated from exothermic peak data increased between an epoxy and resole phenol resin system, with a cyanate ester having the highest. The successful application of both methods suggests applicability to future studies for developing cure recipes.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Rapid Design and Manufacturing of High-Performance Materials for Turbine Blades

This research demonstrated the concept of carbide precipitation-strengthened refractory high entropy alloys (RHEA). The advantage of a precipitation strengthened alloy is all phases are in thermodynamic equilibrium promoting microstructural stability, and consequently retention of properties at elevated temperatures. Additionally, as with any precipitation strengthened – or age hardened - alloy, components can be heat-treated after manufacturing to manipulate the microstructure and optimize properties for performance. This is an advantage of the precipitation strengthened alloys over composites and ceramics materials, where the microstructure and properties are essentially fixed upon the initial materials manufacturing stage. High throughput (HT), multi-scale computer modeling was used to identifying novel RHEA compositions with desired characteristics needed for precipitation strengthening. Designs were validated by producing small ingots via are melting. The results showed that carbides precipitated and consequently, the strength of the alloys (measured in compression) increased after heat-treatment, which is the desired effect. The project also demonstrated the feasibility of producing articles from RHEA by additive manufacturing (AM). Electron beam melting (EBM) and laser direct energy deposition (L-DED) additive manufacturing methods, were explored with various processing parameters were interrogated for both methods. Sound (crack-free and dense) precipitation strengthened RHEA samples were produced via EDM AM, demonstrating the feasibility of the concept.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

42 ENGINEERING↗
DOE OSTI2025

Feature Based Qualification of 17-4PH Stainless Steel to Evaluate Location-Specific Variability in Wire Arc Additive Manufacturing

Qualifying large-scale metal additive manufacturing (M-AM) technologies such as wire arc additive manufacturing (WAAM) can be challenging. This is especially significant in precipitation hardened martensitic stainless steels like SS 17-4PH, where thermal histories induce location-specific microstructural variability and property anisotropy. The Department of Defense (DOD) and the United States Army Combat Capabilities Development Command Ground Vehicle Systems Center (GVSC) Ground Vehicle Materials Engineering (GVME) aim to build robust and qualified large-scale M-AM workflows that could reduce the time and cost through quick and informed evaluation, testing, and development of feedstock, processes, and parts. The report presents the findings from the collaborative efforts between Oak Ridge National Laboratory (ORNL) and the U.S. Army GVSC GVME. The aim of this project was to develop a geometric feature-based qualification framework for WAAM of SS 17-4PH components. This report outlines selection methodology of representative build geometries, optimization of WAAM process parameters, in-situ monitoring, microstructure-property evaluation, thermal simulations, as well as data visualization techniques incorporated in this project. The results from this project demonstrate a clear understanding of thermal history dependent phase evolution and consequent location-specific property variations in WAAM of SS 17-4PH. These results in conjunction with the data-driven methodologies used in this project are expected to reduce qualification timelines, improve predictability, and accelerate the development of reliable feature-based qualification strategies for part production via large-scale M-AM technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Leverage modern artificial intelligence (AI) enabled systems for waste reduction

Manufacturing industries continue to face challenges in reducing waste, as upstream strategies such as source reduction and product redesign require a deeper understanding of processes compared to conventional recycling methods. Recent advancements in artificial intelligence (AI) and machine learning (ML) have opened new opportunities to integrate modern computational techniques with traditional waste minimization strategies. This paper explores AI-enabled approaches for product redesign, source reduction, and recycling that can significantly reduce waste generation while improving efficiency and sustainability. AI-driven material substitution and lightweighting in product design enable discovery of novel materials with optimized properties, reducing waste without compromising performance. Reinforcement learning models optimize process parameters, raw material specifications, and machine sequencing to minimize production losses, while Industrial Internet of Things (IIoT) systems paired with AI analytics enhance real-time waste tracking, predictive maintenance, and quality inspection. Furthermore, AI-based demand forecasting and production planning reduce overproduction and excess inventory, as demonstrated in industrial applications. In recycling, ML-powered pattern recognition and robotic sorting technologies achieve higher accuracy in waste segregation, directly improving recycling efficiency. Complementary solutions such as smart bins and AI-enabled waste pickup scheduling optimize collection logistics, reducing both costs and emissions. Although implementation requires upfront investment in infrastructure and training, the long-term benefits include higher material efficiency, reduced waste, improved product quality, and stronger sustainability outcomes across the supply chain. By leveraging AI-enabled systems, manufacturers can align waste minimization efforts with circular economy principles, creating scalable solutions for both industry and society.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗