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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Navigating high-dimensional process-structure–property relations in nanocrystalline Pt-Au alloys with machine learning

For decades, materials scientists have relied on the process-structure–property paradigm to guide investigations into material behaviors. Traditional studies often examine a limited number of process-structure–property variables, striving to elucidate mechanisms governing material response. However, this approach is time consuming and can limit exploration, as well as the discovery of process-structure–property relations in novel materials. In this paper, we combined combinatorial sputter deposition and multi-modal high-throughput materials characterization with feedforward neural networks to establish high-dimensional process-structure–property relations in Pt-Au alloys, yielding nanocrystalline alloys with high hardness and low resistivity relevant to electrical contact switch applications. We mapped three indicators of process conditions (composition and two atomic deposition characteristics) onto four indicators of material structure (X-ray diffraction, film thickness, density, and surface roughness) and two indicators of material properties (hardness and resistivity), resulting in 784 unique combinations evaluated over a 13-dimensional space. The neural networks predicted Pt-Au alloys with 18–24 at.% Au, when deposited at specific conditions, to have a nanoindentation hardness up to 7.2 GPa. This high hardness value, comparable to some steels, represents a 3-fold improvement in hardness over “hard gold”, a commonly used electrical contact alloy, while maintaining requisite electrical conductivity. The neural network models provide an avenue to identify expected process windows capable of maximizing material performance.

Electrical contact materials↗
DOE OSTI2021

Process-Structure-Property Relations for As-Deposited Solid-State Additively Manufactured High-Strength Aluminum Alloy

Solid-state additive manufacturing methods provide innovative solutions to circumvent problems associated with materials susceptible to hot cracking by avoiding liquid solid phase transformations. In this work, the process parameter influence on microstructural evolution and mechanical response of a fully dense aluminum alloy 7050 (AA7050) component manufactured via a rapid, solid-state additive manufacturing process known as Additive Friction Stir Deposition (AFS-D) was quantified for the first time. Three sections (starting dwell, transient, crossover of roads) of the deposition that exhibit differing thermomechanical processing histories were evaluated for the resulting microstructure and mechanical response. The microstructural characterization was performed on the as-deposited AA7050 via Electron Backscatter Diffraction (EBSD), TEM, optical microscopy, and Scanning Electron Microscopy (SEM). The microstructural characterization revealed refined constituent particles and grains throughout the as-deposited AA7050 microstructure. Furthermore, quasi-static tensile experiments were conducted in both the build and transverse directions, in order to quantify the orientation influence on tensile properties of the as-deposited AA7050 build. Spatially dependent tensile properties were observed in the material due to heat input variation coarsening of secondary phases towards the initial layers of the AFS-D build. Post-mortem analysis revealed that voids nucleated and coalesced from the overgrowth of the strengthening precipitates present in the material, resulting in fracture.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Cluster 1: commercializing additive manufacturing—hurdles in materials characterization and testing

A major challenge in the commercialization of additive manufactured (AM) materials and processes is the ability to achieve acceptance of processes and products. There has been some progress towards acceptance has been made by adapting legacy qualification paradigms to match with the very limited process control and monitoring offered by AM machines. The opportunity for in-situ measurement can provide process monitoring and control perhaps changing the way we qualify parts however it is limited by lack of adequate process measurement methods. New measurement techniques, sensors and correlations to relevant phenomena are needed that enable process control and monitoring for consistently producing high quality articles. Beyond process data we need to characterize uncertainties of performance in all aspects of material, process and final part. These are prerequisites to achieving articles that are indeed worthy of materials characterization efforts that establish a microstructural reference of desirable performance through process-structure-property relations. Only then can industry apply physics based understanding of the material, part and process to probabilistically predict performance of an AM part. Our paper provides a brief overview, discussion of hurdles and key areas where R&D investment is needed.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Microstructure-sensitive mechanical behavior of an additively manufactured psuedoelastic shape memory alloy

The additive manufacturing of shape memory alloys into complex geometries enables fabrication of advanced functional systems across a variety of fields and domains. This work presents results focused on the mechanical behavior of additively manufactured shape memory pseudoelastic NiTi. The deformation induced solid state phase transformation from austenite to martensite allows this system to accommodate large recoverable strains. This deformation behavior is fundamentally driven by crystal-scale transformation physics. Laser powder bed fusion processing reveals that the resulting microstructure, both grain morphology and crystallographic texture, is strongly dependent on the manufacturing processing history. Exhaustive mechanical testing demonstrates that these microstructural factors strongly impact both tensile and cyclic stress–strain behavior. Cyclic dissipative behavior, however, is similar across all tested microstructures following an initial transient period. Remarkably, analysis of spatial strain fields during tensile loading reveals two distinctly different localization “modes”. The first is initiation of localized deformation bands which continuously propagate through the tensile bar during loading. In the second mode localization is observed but lacks propagation; instead additional localization cites nucleate during subsequent loading. The latter phenomena is suspected to be driven by grain-scale deformation physics as the localized band morphologies coincide with grain morphologies. These phenomena strongly impact the resulting aggregate stress–strain behavior. Hence, manufacturers and designers of psuedoelastic functional components must at the very least consider the potential variability in properties when considering additive manufacturing processing. More ideally the process–structure–property relations can be used to further tailor and optimize final functional performance.

Additive manufacturing↗
DOE OSTI2025

Micromechanical Surrogate Machine Learning Model for Creep Deformation Modeling

Process variability during the manufacture of gas turbine engine hot section components can significantly affect the material’s resulting microstructure. In casting, for instance, geometric variation within a component (thin sections versus thick sections, radial location) influences cooling rates and the resulting grain size. The high temperature creep response is known to be sensitive to grain size owing to a diffusional creep mechanism which occurs more readily along grain boundaries. Microstructural variation correspondingly drives mechanical behavior which propagates into component scale performance uncertainty. These factors are essential when planning inspection, maintenance, and repair strategies within a reliability framework. These benefits provide opportunities to increase overall energy efficiency through refined margins. Critically, there is an opportunity to bolster existing data-driven reliability models using physics-driven process-structure-property relations. Here we present recent work establishing a framework for evaluating the probabilistic creep performance of high-temperature materials. A novel microstructure-sensitive crystal plasticity finite element model is established that captures both grain boundary and crystallographic deformation effects. The computationally expensive physics model is calibrated using a statistical approach and this high-fidelity model is subsequently used to train a computationally efficient machine learning surrogate model. The surrogate model is essential for sampling a large ensemble of simulated structure-property pair results. The ensemble data are then mined to extract salient trends to be incorporated into a microstructure-sensitive reliability model. The proposed approach represents a novel way to capture microstructure-sensitive trends from physics-based models within a modern reliability framework.

Fernandez-Zelaia, Patxi [ORNL]↗
DOE OSTI2024

Deep-learning based artificial intelligence tool for melt pools and defect segmentation

Accelerating fabrication of additively manufactured components with precise microstructures is important for quality and qualification of built parts, as well as for a fundamental understanding of process improvement. Accomplishing this requires fast and robust characterization of melt pool geometries and structural defects in images. This paper proposes a pragmatic approach based on implementation of deep learning models and self-consistent workflow that enable systematic segmentation of defects and melt pools in optical images. Deep learning is based on an image-to-image translation–conditional generative adversarial neural network architecture. An artificial intelligence (AI) tool based on this deep learning model enables fast and incrementally more accurate predictions of the prevalent geometric features, including melt pool boundaries and printing-induced structural defects. We present statistical analysis of geometric features that is enabled by the AI tool, showing strong spatial correlation of defects and the melt pool boundaries. The correlations of widths and heights of melt pools with dataset processing parameters show the highest sensitivity to thermal influences resulting from laser passes in adjacent and subsequent layer passes. The presented models and tools are demonstrated on the aluminum alloy and datasets produced with different sets of processing parameters. However, they have universal quality and could easily be adapted to different material compositions. The method can be easily generalized to microstructural characterizations other than optical microscopy.

additive manufacturing↗
DOE OSTI2019

A review of computational modeling techniques in study and design of shape memory ceramics

Shape memory ceramics are a unique family of shape memory materials with a wide variety of applications, such as ultra-high energy dissipation and high-temperature actuation. Along with significant progress in the experimental study of zirconia-based shape memory ceramics in recent years, computational simulations have exhibited powerful capabilities in revealing nano/microstructure-dependent deformation and failure mechanisms in these materials. In this work, we review the recent progress in understanding the phase transformation behavior in shape memory ceramics, focusing on computational modeling of zirconia-based ceramics. Electronic structure calculations have provided new data on the phase stability and surface properties of shape memory ceramics. At the nanometer length scale, molecular dynamics simulations have captured fundamental information about martensitic phase transformation and the effects of grain boundaries and defects on mechanical response of bi- and polycrystalline zirconia-based ceramics. At the micrometer length scale, advanced phase-field models have shown the ability to predict morphological evolution of microstructures and the corresponding mechanical responses in good agreement with experimental observations. Despite the recent multiscale computational advancements, further developments are required to establish processing-structure-property-performance relations that will lead to reliable and practical strategies for designing zirconia-based (or other) ceramics with improved and sustained shape memory responses. This article critically reviews current computational modeling techniques and provides an outlook for the study and design of the next generation of shape memory ceramics.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Microstructural heterogeneity of the buffer layer of TRISO nuclear fuel particles

Tristructural isotropic (TRISO) nuclear fuel particles contain a layered spherical shell designed to retain fission products; however, failure occurs in rare cases—commonly initiated in the porous pyrocarbon buffer layer. Achieving a comprehensive understanding of the buffer-initiated failure mechanisms requires detailed characterization of the buffer porosity and its heterogeneous distribution across multiple length scales. Here we performed FIB-SEM tomography across the buffer layer thickness (~100 µm) to produce 3D reconstructions of the buffer microstructure with 50 nm spatial resolution. We found an average overall porosity of ~14%, which does not solely account for the low density of the buffer (50% of the theoretical density). Additionally, the local porosity and its fluctuation increase from the kernel interface towards the inner pyrocarbon (IPyC) layer, which we attribute to the chemical vapor deposition process conditions during the TRISO particle fabrication. Detailed characterization of the porous microstructure—including analysis of the pore size, distribution, shape, and orientation—provides insight into the process-structure-property-performance relations of TRISO nuclear fuel particles and will inform multiscale models designed to predict the failure of TRISO particles under irradiation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Sparse Data Machine Learning Integration with Theory, Experiment and Uncertainty Quantification: Process-Structure-Property-Performance of Friction Deformation Processing

Computer vision and deep learning tools that advance the ability to establish processing-structure-property-performance (PSPP) relations are presented. The Bayesian binning method for image segmentation enables quantitative analysis of microstructural features in an automated way, while the analysis of shapes and relative orientation of these features reveals local deformation maps indicative of both, material flow and residual stresses due to materials processing. The deep learning method leads to the previous knowledge agnostic mapping of empirically observed microstructural zones in friction stir welding (FSW) process and synthetic microstructure generation capability that is statistically equivalent to experimentally collected data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Elucidating microstructural evolution and hardness variation across friction self-piercing riveted Al-7055 using synchrotron X-ray scattering and advanced microscopy techniques

Friction self-piercing riveting (FSPR) is a unique hybrid joining technique that combines the advantages of mechanical interlocking, frictional heat, and solid-state joining (if metallurgically compatible) to produce crack free joints in high strength and/or low-ductility alloys at room temperature. Here, in the current study, Al-7055 sheets were joined using FSPR for lightweight automotive applications and significant microhardness variations were observed across the joint cross-section. A detailed microstructural characterization at multiple length scales was carried out using advanced electron microscopy and X-ray scattering techniques to provide a fundamental understanding of the process-structure-property relationships. The relative contributions of microstructural characteristics at various length scales (i.e., grain size, dislocation density, solute concentration, precipitate nature) to strengthening were estimated using existent formulations (i.e., Hall-Petch, Taylor, precipitate bypass/shear equations) and correlated to the observed microhardness values across different regions. Small-angle X-ray scattering and scanning transmission electron microscopy revealed significant changes in the size and volume fraction of precipitate species, i.e., GP-I Zones, η′, and Mg/Zn solute co-clusters, depending on the process region. It was observed that the dissolution of the small η′/GP-I zones (T ∼ 150–200 °C) in the heat-affected zone were the key reason for the hardness drop. Further, it was shown that solid-solution, dislocation, grain size and solute co-cluster strengthening played a key role in the thermo-mechanically affected zone and grain-refined zone (GRZ). Finally, these observations were leveraged along with the Zener-Holloman relationship and grain size in the GRZ to estimate the peak joining temperature of the GRZ (∼ 350 °C) near the steel rivet.

aluminum 7xxx alloy↗
DOE OSTI2022

Defects and anomalies in powder bed fusion metal additive manufacturing

Metal additive manufacturing is a disruptive technology that is revolutionizing the manufacturing industry. Despite its unrivaled capability for directly fabricating metal parts with complex geometries, the wide realization of the technology is currently limited by microstructural defects and anomalies, which could significantly degrade the structural integrity and service performance of the product. Accurate detection, characterization, and prediction of these defects and anomalies have an important and immediate impact in manufacturing fully-dense and defect-free builds. As such, this review seeks to elucidate common defects/anomalies and their formation mechanisms in powder bed fusion additive manufacturing processes. They could arise from raw materials, processing conditions, and post-processing. While defects/anomalies in laser welding have been studied extensively, their formation and evolution remain unclear. Additionally, the existence of powder in powder bed fusion techniques may generate new types of defects, e.g., porosity transferring from powder to builds. Practical strategies to mitigate defects are also addressed through fundamental understanding of their formation. Such explorations enable the validation and calibration of models and ease the process qualification without costly trial-and-error experimentation.

42 ENGINEERING↗
DOE OSTI2020

The 2020 Skyrmionics Roadmap

The notion of non-trivial topological winding in condensed matter systems represents a major area of present-day theoretical and experimental research. Magnetic materials offer a versatile platform that is particularly amenable for the exploration of topological spin solitons in real space such as skyrmions. First identified in non-centrosymmetric bulk materials, the rapidly growing zoology of materials systems hosting skyrmions and related topological spin solitons includes bulk compounds, surfaces, thin films, heterostructures, nano-wires and nano-dots. This underscores an exceptional potential for major breakthroughs ranging from fundamental questions to applications as driven by an interdisciplinary exchange of ideas between areas in magnetism which traditionally have been pursued rather independently. The skyrmionics roadmap provides a review of the present state of the art and the wide range of research directions and strategies currently under way. These are, for instance, motivated by the identification of the fundamental structural properties of skyrmions and related textures, processes of nucleation and annihilation in the presence of non-trivial topological winding, an exceptionally efficient coupling to spin currents generating spin transfer torques at tiny current densities, as well as the capability to purpose- design broad-band spin dynamic and logic devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Curing process and pore structure of metakaolin-based geopolymers: Liquid-state 1H NMR investigation

Geopolymers are emerging construction materials with lower carbon dioxide emissions compared to the conventional cementitious materials. The knowledge of the curing process and the related pore structures are important for optimizing the properties of these materials for different applications. The curing process and final pore structure are sensitive to the amount of used water, however the specifics are unclear. The curing process and pore structures of metakaolin-based geopolymers with a narrow water-to-solid (w/s) ratio (0.59–0.66) were monitored by nuclear magnetic resonance (NMR) relaxometry and cryoporometry. The 14-day curing process was investigated by monitoring the change of T{sub 2} and T{sub 1} relaxation times and water signal intensity. After the curing, the pore structures were characterized by 2D T{sub 1}-T{sub 2} correlation and T{sub 2}-T{sub 2} exchange measurements of absorbed water. The pore size distributions (PSDs) were measured with NMR cryoporometry and compared to nitrogen physisorption and mercury intrusion porosimetry (MIP) results. We found that the relaxation times decreased as the pore structure of the geopolymers matured during the curing while the dissolution and the condensation periods of the curing were distinguished by the changes in signal amplitude reflecting the proton density. After the curing, three distinct pore sizes and connectivity between pores were identified from T{sub 1}-T{sub 2} and T{sub 2}-T{sub 2} spectra. Their PSDs were measured, and they were found to correspond to two different pore sizes originating from the arrangement of clusters and defective pores. In the narrow w/s ratio (0.59–0.66), the curing times were the same for all samples when cured at 24 °C while the pore sizes were observed to increase as a function of the w/s ratio.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Microstructure and high temperature properties of tungsten processed via electron beam melting additive manufacturing

There is considerable interest in the adoption of additive manufacturing for processing refractory metals. The layer-wise fabrication approach enables opportunities for producing complex geometries which cannot be otherwise be achieved via powder metallurgy. However, the processing science is still in its nascent stages and structure–property relations are relatively unexplored. Fundamental research is needed to further develop the technology and enable the fabrication of refractory metals for high temperature applications. Here we focus on the processing of pure tungsten using electron beam melting additive manufacturing. Experimentally we develop a suitable processing window for achieving high density crack free material. Microstructural analysis reveals that the microstructure generally consists of a columnar structure with a (111) build direction fiber preference, although, fiber switching was observed. Process induced deformation is believed to drive the formation of subgrains whose boundaries exhibit a high dislocation density. Additionally, high temperature tensile testing reveals that the material exhibits excellent properties closer to that of annealed tungsten. Significant mechanical anisotropy was observed to be present which is likely driven by strong crystallographic texture.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Materials path toward improved energy efficiency for High Energy Physics accelerators

For nearly fifty years researchers working with particle accelerators have envisioned reducing their initial and operating costs by substituting the intermetallic compound Nb 3 Sn for Nb as the cavity interior surface. The goal has remained elusive, stymied especially by the fall of cavity quality factor (representing increasing energy consumption) with increasing gradient (the amount of accelerator need to reach an energy goal). While one research group reported making a few cavities free of the problem, no one has reported how this comes about or has reproduced the improvement. We believe that insufficient understanding is responsible. We report here findings of a detailed, systematic study of the formation of Nb 3 Sn coatings on Nb, relating structure and properties of the coating to the process parameters and substrate materials on the one hand, and RF surface impedance and SRF cavity performance on the other was carried out, focusing on the two step (nucleation – deposition) tin vapor diffusion process. Major outcomes are: The choice of initial surface preparation (BCP etc) has no discernable impact on any quality of the final coating; Though tin chloride serves as the coating nucleant, no trace could be found by any means of chlorine after nucleation or coating; No evidence was found for any substantive impact of the details of the nucleation on the ultimate coating, as long as it was done within the frequently used range; The coating forms as Nb 3 Sn crystals by tin from the vapor diffusing down grain boundaries to the Nb interface and growing there. Higher temperature and longer time lead to larger, but otherwise substantially identical crystals; All coatings exhibited roughness on the micron scale. Testing the effect of coating surface modification technologies in use by the community, only a carefully tailored electropolish showed significant benefit; and, Preliminary results indicate that Ti contamination may be associated with falling gradient. Two particular item appear promising for future work. The effect of surface topography in the range seen here on performance is significant for niobium. The nature and impact on Nb 3 Sn, if any, needs to be understood and managed. At what level Ti, Ta or other impurities affects the performance of Nb 3 Sn needs to be understood.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Novel High Temperature Carbide and Boride Ceramics for Direct Power Extraction Electrode Applications. Final report

The current project aims to develop new carbide and boride ceramic materials that incorporate different metal elements (e.g., (Hf 1-x Zr x )B 2 , also called ceramic solid solutions) and understand the fundamental composition-processing-structure-property relationships for such materials. Specifically, nano powders and related nano-composites for selected mixed metal carbide and boride materials are synthesized from nano-scale mixed precursors obtained from solution-based processing via heat treatment in inert atmosphere. The obtained fine powders were then consolidated into dense ceramics via a novel flash sintering process. The mechanical properties and thermal oxidation resistance for the obtained materials were evaluated for these materials. It was found that different synthesis routes produced powders with somewhat different characteristics in terms of composition and microstructure, which then led to different properties for the final ceramics. The synthesis and processing methods demonstrated in this study might be adopted for developing other similar high temperature ceramics, while the mixed metal or solid solution ceramic materials obtained from this study might be used as hot electrodes for direction power extraction and other energy-related applications.

20 FOSSIL-FUELED POWER PLANTS↗