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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Nonlinear ultrasonic technique for the characterization of microstructure in additive materials

This study employs nonlinear ultrasonic techniques to track microstructural changes in additively manufactured metals. The second harmonic generation technique based on the transmission of Rayleigh surface waves is used to measure the acoustic nonlinearity parameter, β. Stainless steel specimens are made through three procedures: traditional wrought manufacturing, laser-powder bed fusion, and laser engineered net shaping. The β parameter is measured through successive steps of an annealing heat treatment intended to decrease dislocation density. Dislocation density is known to be sensitive to manufacturing variables. In agreement with fundamental material models for the dislocation-acoustic nonlinearity relationship in the second harmonic generation, β drops in each specimen throughout the heat treatment before recrystallization. Geometrically necessary dislocations (GNDs) are measured from electron back-scatter diffraction as a quantitative indicator of dislocations; average GND density and β are found to have a statistical correlation coefficient of 0.852 showing the sensitivity of β to dislocations in additively manufactured metals. Moreover, β shows an excellent correlation with hardness, which is a measure of the macroscopic effect of dislocations.

42 ENGINEERING↗
DOE OSTI2025

Prediction of Silicon Content in a Blast Furnace via Machine Learning: A Comprehensive Processing and Modeling Pipeline

Silicon content plays an important role in determining the operational efficiency of blast furnaces (BFs) and their downstream processes in integrated steelmaking; however, existing sampling methods and first-principles models are somewhat limited in their capability and flexibility. Current data-based prediction models primarily rely on a limited set of manually selected furnace parameters. Additionally, different BFs present a diverse set of operating parameters and state variables that are known to directly influence the hot metal’s silicon content, such as fuel injection, blast temperature, and raw material charge composition, among other process variables that have their own impacts. The expansiveness of the parameter set adds complexity to parameter selection and processing. This highlights the need for a comprehensive methodology to integrate and select from all relevant parameters for accurate silicon content prediction. Providing accurate silicon content predictions would enable operators to adjust furnace conditions dynamically, improving safety and reducing economic risk. To address these issues, a two-stage approach is proposed. First, a generalized data processing scheme is proposed to accommodate diverse furnace parameters. Second, a robust modeling pipeline is used to establish a machine learning (ML) model capable of predicting hot metal silicon content with reasonable accuracy. The method employed herein predicted the average Si content of the upcoming furnace cast with an accuracy of 91% among 200 target predictions for a specific furnace provisioned by the XGBoost model. This prediction is achieved using only the past shift’s operating conditions, which should be available in real time. This performance provides a strong baseline for the modeling approach with potential for further improvement through provision of real-time features.

Chemistry↗
DOE OSTI2022

Multiscale and multiphysics FEA simulation and materials optimization for laser ultrasound transducers

In this study, the relationship between the nanocomposite design and the laser ultrasound transducer (LUT) characteristics was investigated through simulations in multiple scale levels for material behavior, device response, and acoustic wave propagation in media. First, the effects of the nanoparticle size and concentration on the effective properties of composites were quantitatively investigated with the finite element analysis (FEA) method. Second, the effective properties of the nanocomposite were assigned to the layer, which is modeled as a homogeneous material, in the FEA for the LUT simulating the energy conversion from the incident laser to the acoustic wave. Finally, the ultrasound propagation in the water was calculated by a theoretical wave propagation model. The FEA-based prediction was compared with the experimental data in the literature and a theoretical analysis for LUT based on Thermal-Acoustic coupling. As a result, the ultrasound waves on the transducer surface and at a distance in the water could be predicted. Based on the hierarchically integrated prediction procedure, the optimal conditions of the photoacoustic nanocomposites were investigated through the parametric study with the particle size and concentration as variables. The results guide the material designs optimized for different device characteristics, such as high pressure and broad bandwidth.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The Functor system: a new on-the-fly take on Material Properties based on C++ functions

In the context of solving multiphysics problems, the discretization of the partial differential equations (PDE) at hand often takes the spotlight. However, for most engineering users and even application developers, the discretization of the equations has already been performed. Instead, they are tasked with implementing specific closure relations and material properties. MOOSE has long enabled this using the Materials system. This system relied on the pre-computation of all properties before they are used in the PDE or in postprocessing. In this talk we will introduce the Functor system, which was deployed in MOOSE in 2021, then present a few applications of functors in flow modeling simulations by the NEAMS program. Functors first offer great flexibility in their evaluation. Rather than storing various arrays for material properties, they are evaluated on the fly at the location and state, e.g. current or old value, requested. Unlike regular material properties, several operations such as the time derivative, the divergence and the curl can be requested from a functor. Similar to material properties, functors can be made to depend on arbitrary combinations of variables, functions, postprocessors and other properties. However, unlike material properties, any of these can be substituted for a functor material property. Thanks to this, objects no longer need to be duplicated based on the types of their parameters.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Dependence of the kinetic energy absorption capacity of bistable mechanical metamaterials on impactor mass and velocity

Using an alternative mechanism to dissipation or scattering, bistable structures and mechanical metamaterials have shown promise for mitigating the detrimental effects of impact by reversibly locking energy into strained material. Herein, we extend prior works on impact absorption via bistable metamaterials to computationally explore the dependence of kinetic energy transmission on the velocity and mass of the impactor, with strain rates exceeding 10 2 s –1 . We observe a large dependence on both impactor parameters, ranging from significantly better to worse performance than a comparative linear material. We then correlate the variability in performance to solitary wave formation in the system and give analytical estimates of idealized energy absorption capacity under dynamic loading. In addition, we find a significant dependence on damping accompanied by a qualitative difference in solitary wave propagation within the system. The complex dynamics revealed in this study offer potential future guidance for the application of bistable metamaterials to applications including human and engineered system shock and impact protection devices.

42 ENGINEERING↗
DOE OSTI2023

Light Water Reactor Sustainability Program: Materials Research Pathway: FY 23 Technical Program Plan

Components in operating commercial nuclear power plants must withstand very harsh environments that include extended time at neutron and gamma irradiation, stress, and temperature, as well as possible exposure to corrosive media. The many modes of materials degradation are complex and often include synergies between multiple environmental variables and conditions that vary depending on locations and materials. Understanding and managing materials degradation is a requirement for the continued safe and reliable operation of nuclear power plants.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Biomass Feedstock National User Facility--Improving Bale Deconstruction and Material Flow

The project aims to reduce feedstock variability using a quality-by-design approach beginning when the biomass is introduced to the process and will continue through the size reduction process, which will yield the results of fine generation reduction, contaminant removal, control of the physical and chemical critical material attributes in the process, and management of the flowability.

09 BIOMASS FUELS↗
DOE OSTI2021

Efficiency of natural pozzolans, ground glasses and coal bottom ashes in mitigating sulfate attack and alkali-silica reaction

The use of pozzolans to partially replace Portland cement in concrete has generally demonstrated beneficial impacts on the durability characteristics of concrete for decades. In this paper a diverse range of pozzolans including natural pozzolans, ground glasses and industrial by-products such as coal ash (fly ash and bottom ash) and silica fume were investigated for their synergistic potential in binary or ternary blends with Portland cement in improving resistance to chemical sulfate attack and alkali-silica reaction (ASR). It is generally considered that pozzolans improve most of the durability issues encountered in concrete, including reducing the risk of sulfate attack or ASR. But this is not always the case. For example, it was found that ground glasses were very efficient in improving sulfate resistance, but their ability to mitigate expansion due to ASR was dictated by the equivalent alkalis content (Na{sub 2}O{sub e}) of the glass and high-alkali soda glass was generally not effective in this role. On the other hand, metakaolin, a highly reactive pozzolan, was highly effective in reducing ASR expansion, but may actually increase the damage due to sulfate attack when used at moderate replacement levels. Most pozzolans, such as low-CaO coal fly ash and ground coal bottom ash, silica fume, and pumice, were effective in controlling expansion due to both ASR and sulfate attack. The results demonstrated that the extent of the positive impact of using natural pozzolans on both properties was variable. The pozzolanic reactivity of materials alone was an unreliable indicator to assess the ability of the pozzolan to suppress expansion due to sulfate attack or ASR.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Life cycle assessment of coir fiber-reinforced composites for automotive applications

Past decades have seen an increasing prevalence of natural fiber-reinforced composites (NFRCs) due to growing conscientiousness around sustainability and a push towards vehicle lightweighting. The environmentally friendly and sustainable claims of NFRCs need to be validated due to their large variability and variety, particularly where material substitutions are concerned, such as in substituting glass fiber with natural fiber. Additionally, the objective of this work is to determine the cumulative energy demand (CED) and greenhouse gas emissions (GHG) associated with an automotive part (of volume 0.001 m3) made from 40 wt% coir fiber-reinforced polypropylene (PP) and compared with a similar part made from 40 wt% glass fiber reinforced PP. SimaPro v. 9.0.0.49 was used for the analysis, whereas inventory data were collected from databases, such as Ecoinvent 3, Transportation Energy Databook, Greet model 2022, and published papers. The results showed that CED and GHG associated with the coir fiber-reinforced composite part were lower than the glass fiber-reinforced composite part for both cradle-to-gate (~34–40%) and cradle-to-grave (excluding end-of-life) (~24%) analysis.

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.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Hypercomplex Automatic Differentiation in the Eulerian Hydrocode PAGOSA

Enabling the computation of partial derivatives or sensitivities in production hydrocodes is beneficial for design, optimization, sensitivity analysis, and uncertainty quantification. Traditional finite difference approximations of these sensitivities are inefficient since convergence studies of the step size is required for each parameter of interest. For these reasons, HYPercomplex Automatic Differentiation (HYPAD) was implemented in the Eulerian hydrocode PAGOSA. HYPAD is analogous to forward-mode automatic differentiation except hypercomplex numbers (numbers with multiple imaginary parts) are used instead of dual numbers. Accurate partial derivatives can be computed of all state variables with respect to multiple input variables in a single run. The method was implemented using operator overloading to handle hypercomplex algebra. HYPAD was demonstrated and verified on Sod’s shock tube problem to compute derivatives of the state variables with respect to a material parameter, initial conditions, and geometry.

97 MATHEMATICS AND COMPUTING↗
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

A Review of the Research and Development of Brayton Cycle Technology in Nuclear Power Applications with a Focus on Compressor Technology

This study reviews the integration of Brayton Cycle (BC) systems in nuclear power generation, emphasizing their potential to enhance thermal efficiency and operational flexibility over traditional Rankine Cycle (RC) systems. Key working fluids, such as helium (He), supercritical carbon dioxide (sCO 2 ), nitrogen (N 2 ), and air, are evaluated for their performance, efficiency, and compatibility with nuclear systems. He is recognized for its high thermal conductivity and inertness at elevated temperatures, while sCO 2 demonstrates advantages in compactness and efficiency in midrange temperatures. This article also highlights the importance of compressor designs in optimizing BC performance and reviews, available compressor technologies. Axial and centrifugal compressor designs enable efficient gas compression while managing the thermal and mechanical stresses associated with high-pressure operations in nuclear systems. Combined with variable geometry components and advanced materials, these technologies address the challenges posed by varying load conditions. Despite the promising features of BC systems, several challenges persist, including high leakage rates and material degradation under extreme conditions, which necessitate robust sealing technologies and thorough testing. The insights gained from operational experiences at facilities, such as the Oberhausen II plant and the High-Temperature He Test Facility (HHV), underscore the complexities involved in designing high-temperature gas turbines for nuclear applications. This review concludes that as the nuclear industry evolves, BC systems hold significant promise for contributing to a sustainable energy future, particularly in the context of small modular reactors (SMRs) and microreactors. Further exploration of combined cycle configurations that combine BCs with RCs may enhance overall efficiency and flexibility in power generation.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

ASME Design Code Rule Changes for Nuclear Graphite

The American Society of Mechanical Engineers Boiler Pressure and Vessel Code (ASME BPVC) Section III, Division 5, Article HHA-3000 outlines graphite core component and graphite core assembly design guidelines. Graphite core components are defined as ?components manufactured from graphite that are installed to form a graphite core assembly within the reactor pressure vessel of a high temperature, graphite moderated fission reactor.? (p. 413) Graphites? inherent defect distributions do not allow for deterministic material reliability. Rather, graphite has variable strength distributions which change by grade. Article HHA-3000 outlines two semi-probabilistic methods, the full and simplified assessments, which set design load limit targets for each of three component structural reliability classes. The Design Task Group was officially recognized as a specialized task group within ASME November of 2023, though we?ve been collaborating since 2022. The purpose of the Design Task Group is to correct, clarify, and make HHA-3000 function as intended. The Design Task Group will sunset once we?ve achieved our objectives. The Design Task Group was specifically told to not write new Code. While there may be more precise and more accurate methods to determine reliability targets, the current methods are conservative, relatively simple to implement, and have thus far been considered satisfactory for setting design reliability targets. Much of the ground-work to write proposal files and background documents for records to make the changes needed to achieve our objective have been completed. The Design Task Group has documented much of their work through papers, presentations, and memorandums. Three memorandums in which INL team members had substantial contributions are found in the Appendices: FEA Modeling for the Baseline Program, Evaluating the Effects on Margin of Updating the Threshold and Shape Parameters in the Full Assessment, and Interpretations of the Full and Simplified Assessments in ASME BPVC. Most of the on-going work to achieve the Design Task Group?s objective will be addressing comments on existing records and moving records through the balloting process. The Design Task Group met bi-weekly mostly through the end of FY2023. Since February 2024, the Design Task Group has mostly been completed with solving and documenting the technical issues associated with the assessments. Unless new tasks are identified, the remaining work of the Design Task Group will be political and editorial.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

High Carrier Lifetimes in Epitaxial Germanium-Tin/Al(In)As Heterostructures with Variable Tin Compositions

Group IV-based germanium-tin (Ge1-ySny) compositional materials have recently shown great promise for infrared detection, light emission and ultra-low power transistors. High carrier lifetimes are desirable for enhancing the detection limit and efficiency of photodetectors, low threshold current density in lasers, and low tunneling barrier height by lowering defects and dislocations at the heterointerface of a source and a channel. Here, carrier lifetimes in epitaxial germanium (Ge) and variable tin (Sn) compositional Ge1-ySny materials were experimentally determined on GaAs substrates using the contactless microwave photoconductive decay (u-PCD) technique at an excitation wavelength of 1500 nm. Sharp (2 x 2) reflection high energy electron diffraction patterns and low surface roughness were observed from the surface of the Ge0.97Sn0.03 epilayer. X-ray rocking curves from Ge0.97Sn0.03 and Ge0.94Sn0.06 layers demonstrated the pseudomorphic and lattice-matched growth on AlAs and In0.12Al0.88As buffers, respectively, further substantiated by reciprocal space maps and abrupt heterointerfaces evident from the presence of Pendellosung oscillations. High effective carrier lifetimes of 150 ns to 450 ns were measured for Ge1-ySny epilayers as a function of Sn composition, surface roughness, growth temperature, and layer thickness. The observed increase in the carrier lifetime with an increasing Ge layer thickness and a reducing surface roughness, by incorporating Sn, were explained. The enhancement of the carrier lifetime with an increasing Sn concentration was achieved by controlling the defects with lattice-matched Ge0.94Sn0.06/In0.12Al0.88As heterointerfaces or the pseudomorphic growth of Ge0.94Sn0.06 on GaAs. Therefore, our monolithic integration of variable Sn alloy compositional Ge1-ySny materials with high carrier lifetimes opens avenues to realize electronic and optoelectronic devices.

carrier lifetimes↗
DOE OSTI2025

Horn Module Mounting: Kinematic Mount

This research will focus on the kinematic mount that is supporting the horn and horn module in the target hall, LBNF-20, at Fermilab. It will see if the current, ball-in-cup design will be the most optimal for the application as the horn module mount, and the main concerns that were considered were the ball diameter, cup angle, materials, and coating. There were many ways that information was gathered and analyzed such as: hand calculations, a spreadsheet calculator, and various graphs. The results will show if there is an exact cup angle, ball diameter, material, or coating that is the strongest candidate out of all the other options for all of these variables. It was determined that two dissimilar materials should be chosen for the materials, so the materials are 7Al-4Mo Ti and 4340 steel. Alongside those materials, the coating was determined to be tungsten disulfide. The cup angle and ball diameter can have a range of angles and diameters that will work. The ball diameter should be 7.5”, but it can be as small as 6”. Also, the cup angle can be 45° ± 5°. In the future, a Finite Element Analysis on the cup will provide additional confirmation that the assumptions made in the hand-calculations are correct, and there will be a prototype built to determine if the design can take loads under real conditions.

Malovski, Azra [Unlisted, US]↗
DOE OSTI2026

Interfacial Chemistry Involved in Selective Separation of NMC/LMO and LCO/LMO Binary Cathode Materials by Froth Flotation Using Oleic Acid

The variability in cathode compositions within recycled lithium-ion battery (LIB) feedstocks poses a significant challenge to efficient downstream refining processes. This study demonstrates the feasibility of using froth flotation with oleic acid as a collector to selectively separate lithium nickel-manganese-cobalt oxide (NMC) and lithium cobalt oxide (LCO) from lithium manganese oxide (LMO) materials. Laboratory-scale flotation tests achieved an 80% separation efficiency in a single stage, producing a froth product with >90% purity of NMC/LCO at approximately 90% yield. Concurrently, the LMO materials were enriched in the sink product with ∼90% purity and ∼90% yield. This approach was further validated using recycled cathode materials, confirming its applicability to realistic feedstocks. The underlying mechanism governing the selective separation of NMC/LCO from LMO was investigated using ζ-potential measurements, contact angle measurements, bubble-particle attachment experiments, and X-ray photoelectron spectroscopy (XPS) analysis. Both contact angle and bubble-particle attachment results confirmed that oleic acid adsorption rendered NMC and LCO surfaces hydrophobic, thereby enhancing flotation recovery. At pH 5, oleic acid adsorbed preferentially onto NMC and LCO surfaces via electrostatic interactions, while exhibiting minimal adsorption on LMO surfaces. However, separation efficiency deteriorated at higher pH, which was attributed to the co-flotation of LMO materials caused by oleate chemisorption on MnOH + species. This work establishes froth flotation as a viable cathode/cathode separation strategy, providing a low-cost, scalable pathway to preconcentrate and enrich nickel-rich and cobalt-rich cathode active materials from incompatible cathode chemistries for direct recycling or hydrometallurgical processing. Furthermore, this study reveals, for the first time, the mechanism of oleate adsorption on the surface of different cathode materials.

PH↗