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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Predicting material microstructure evolution via data-driven machine learning

The recent rapid rise in data-driven practices in the materials science domain has led to the development of efficient, generalizable, and accurate approaches for several applications, including: material property prediction, mining (micro)structure- property and (micro)structure-processing relationships, and characterization of material microstructures. Central to the materials science domain is linking microstructure to properties and performance, and critical to building such linkages is understanding how microstructures evolve as a function of environmental exposure or processing conditions (e.g., time, temperature, applied stress or strain, irradiation). This preview article summarizes recent work on the development of recurrent neural networks for predicting spatio-temporal microstructure evolution detailed by Yang et al in their manuscript entitled 'Self-supervised Learning and Prediction of Microstructure Evolution with Recurrent Neural Networks'.

machine learning (ML), materials characterization↗
DOE OSTI2024

Microstructure Clones

Microstructure drives component behavior. Contemporary crystal plasticity studies compare strain measurements of polycrystal specimens to models. Because each specimen is unique, it is impossible to know which differences are significant. In this project, we invented microstructure clones and explored their use in understanding crystal plasticity. Microstructure clones are specimens with nearly identical microstructures, which allows for multiple destructive tests of a microstructure, insight into how a specimen will deform, variability quantification, and the ability to measure the effects of microstructural changes. Several sets of microstructure clones, pure nickel tensile bars, were tested. The techniques of digital image correlation, crystal plasticity finite element analysis, high resolution electron backscatter diffraction, transmission electron microscopy, and dislocation dynamics were used to understand the structural behavior of these microstructures. This work reshapes the fields of crystal plasticity and structure-property relationships by providing a technique to control for specific variables, quantify microstructural stochasticity, and replicate experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Evolution of Microstructure, Residual Stress, and Tensile Properties of Additively Manufactured Stainless Steel Under Heat Treatments

This study investigated the microstructure, residual stress and tensile properties of the directed energy deposited stainless steel 316L (SS316L) under thermal annealing. Microstructure characterization shows the as-printed sample has laser-generated patterns where dendritic structure is observed at the edge of the patterns and cellular structure dominates the interior region. Here, the thermal annealing at 983 and 1093°C effectively removes the dendritic/cellular structures. Synchrotron X-ray diffraction reveals that the as-fabricated SS316L exhibits compressive residual stress of -197.4 MPa. The stress is greatly relieved to -53.8 MPa after annealing at 1093°C. The room temperature tensile testing indicates that the yield strength and ultimate tensile strength drop from 378 and 502 MPa in the as-printed sample to 258 and 446 MPa in the annealed samples (1093°C), respectively. Our study provides insights into the relationship between microstructure, residual stress, and tensile properties of laser additive manufactured SS316L.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Recent progress on the mesoscale modeling of architected thin-films via phase-field formulations of physical vapor deposition

Thin-film coatings can be found everywhere in modern technological applications due to desirable electrical, mechanical, chemical, and optical properties. These properties directly depend upon the thin-film’s microstructural features, which are themselves influenced by the materials and vapor-deposition processing conditions used for fabrication. As such, understanding processing-microstructure relationships is essential to designing thin-films with optimized properties, and discovering new processing conditions that allow for novel thin-films with multifunctional microstructures. Here, a short review is presented on recent developments that utilize the phase-field method to simultaneously model the vapor-deposition process and corresponding microstructure formation at the mesoscale. Also phase-field-based vapor-deposition models that simulate thin-film growth of immiscible alloy and polycrystalline systems are highlighted in addition to machine-learning-based surrogate models that can facilitate accelerated high-fidelity simulations along with materials design and exploration studies.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Volumetric energy density impact on mechanical properties of additively manufactured 718 Ni alloy

The 718 Ni-based superalloy has gained enormous attention in the additive manufacturing community for its great weldability, allowing for complex geometries to be formed, and its superb mechanical strength. Here we explore the influence of a wide range of major build parameters on microstructures and mechanical properties of 718 Ni alloys. Volumetric energy density appears to be one of the major parameters that captures the relationship between energy input and measured mechanical properties and microstructures of the alloys. A threshold energy density was identified below which the properties of the additively manufactured specimen degrade substantially. Finally, the influence of energy density on the quality of the built specimen simulated using Flow3D AM correlates well with the experimental results.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Microstructure Clones

Background: A material’s microstructure drives its material performance. Contemporary crystal plasticity experiments compare full-field strain measurements of polycrystal specimens to models. Because each specimen is unique, it is impossible to know which features of the observed deformation are deterministic vs statistical; thus, differences between model and experiment may or may not be significant. Objective: This paper introduces the invention of microstructure clones. Microstructure clones are 2D oligocrystal specimens that have nearly identical microstructures to remedy the aforementioned experimental limitations. Having specimens with nearly identical microstructures will allow for multiple destructive tests of a microstructure (either as repeats or intentionally different experiments), an ability to “see the future” by providing insight into how a specimen will deform, variability quantification, and experimental investigations of response to small microstructural changes. Methods: This work introduces microstructure clones. Repeatability of these clones is demonstrated in tensile bars of pure nickel. Local strain measurements from digital image correlation are compared between clone specimens and compared to results from a crystal plasticity finite element model. Results: Two sets of microstructure clones were tested in this study and displayed very consistent deformation responses within each clone set. Small observed differences in deformation invite investigation into microstructure stochasticity and the effect of small microstructural and loading differences. Conclusions: Microstructure clones represent a significant shift in understanding structure–property relationships. This work reshapes experimental crystal plasticity to allow for experiments that control for specific variables, quantification of microstructural stochasticity (and other sources of stochasticity), and opportunities for replicating experiments.

Crystal Plasticity↗
DOE OSTI2024

A Case Study of Multimodal, Multi-institutional Data Management for the Combinatorial Materials Science Community

Although the convergence of high-performance computing, automation, and machine learning has significantly altered the materials design timeline, transformative advances in functional materials and acceleration of their design will require addressing the deficiencies that currently exist in materials informatics, particularly a lack of standardized experimental data management. The challenges associated with experimental data management are especially true for combinatorial materials science, where advancements in automation of experimental workflows have produced datasets that are often too large and too complex for human reasoning. The data management challenge is further compounded by the multimodal and multi-institutional nature of these datasets, as they tend to be distributed across multiple institutions and can vary substantially in format, size, and content. Furthermore, modern materials engineering requires the tuning of not only composition but also of phase and microstructure to elucidate processing–structure–property–performance relationships. To adequately map a materials design space from such datasets, an ideal materials data infrastructure would contain data and metadata describing (i) synthesis and processing conditions, (ii) characterization results, and (iii) property and performance measurements. In this work, we present a case study for the low-barrier development of such a dashboard that enables standardized organization, analysis, and visualization of a large data lake consisting of combinatorial datasets of synthesis and processing conditions, X-ray diffraction patterns, and materials property measurements generated at several different institutions. While this dashboard was developed specifically for data-driven thermoelectric materials discovery, we envision the adaptation of this prototype to other materials applications, and, more ambitiously, future integration into an all-encompassing materials data management infrastructure.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Solidification dynamics in metal additive manufacturing: analysis of model assumptions

Solidification characteristics in the meltpool drive the process-microstructure relationship which helps dictate the material properties of as-built parts in additive manufacturing; therefore, being able to accurately and quickly simulate the size, shape, and solidification characteristics in the melt pool is of great interest to the field. In this study, we investigate various important physical phenomena (dynamic material properties, fluid-flow, radiation and vaporization) which can either be included or neglected in a continuum finite volume model (FVM) and their effect on the solidification conditions. Additionally, since the simplest form of such a model (conduction only) has an analytic solution which is much faster, its viability is also considered. Since the inclusion of some of these physical phenomena will inherently change the net energy input as well as the amount of energy needed to achieve melting of a control volume, each set of included phenomena had an effective absorption efficiency which was calibrated to closely match the dimensions of the melt pool to that of the ground truth data. The ground truth data for this study was defined to be the output of the FVM which included all the physical phenomena (OF). This study then goes on to compare the effects on solidification conditions each of these calibrated models has. It was found that most of the change in solidification conditions comes from the inclusion of latent heat. A posterior correlation factor (PCF) is then introduced to enable an analytic model to predict similar solidification conditions to OF model.

36 MATERIALS SCIENCE↗
DOE OSTI2023

3D Printed eutectic aluminum alloy has facility for site-specific properties

Additive manufacturing (AM) has the ability to print structures with site-specific properties. Existing AM approaches for site-specific properties are, however, based on complex processing-microstructure relationships in conventional alloys that were not designed for this purpose. Here, in this work, we report a straightforward approach for achieving site-specific properties that takes advantage of eutectic solidification characteristics. We demonstrate that the yield strength of a eutectic Al-Cu-Ce-Zr alloy can be tuned by varying the laser scan speed in concert with hatch spacing in laser powder bed fusion AM. A faster speed increases solidification rate resulting in finer eutectic spacing and higher strength. The hatch spacing is reduced at faster speeds to ensure overlap between melt pools which become smaller with increasing scan speed. The yield strength and its anisotropy relative to build direction are further tunable with a heat treatment. The scan speed-eutectic spacing-strength relationship is successfully applied to print a complex pattern of site-specific hardness in the alloy. The generalized principle of using AM for a eutectic alloy to create site-specific properties and anisotropy in properties is demonstrated.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Chain rigidity controlled aggregation ability and solid-state microstructures for efficient stretchable conjugated polymer films

Stretchable conjugated polymer films with good electrical performance under mechanical deformation are highly desirable for soft electronics. However, the mechanical and electrical properties of these films, particularly in conjugated polymer:elastomer blends, are not fully understood at molecular level. Here, this study explores the relationships among molecular structure, aggregation ability, film microstructure, and the electrical/mechanical properties of three diketopyrrolopyrrole-based conjugated polymers (P1, P2, P3) with decreasing backbone rigidity and their corresponding polymer:elastomer blends. The most flexible polymer P3 shows strong aggregation, which forms highly crystalline fibers to produce fragile neat film and produces large isolated crystallites restricting charge transport in blend film. As chain rigidity increases, the P1 and P2 polymers show weaker aggregation, and produce smaller crystallites in neat films with enhanced ductility. P1 and P2 based blend films display nanocrystallites polymer networks with dispersed elastomer domains. As a result, we achieved near-constant charge mobility before and after stretching under 50 % strain for P2-based blend films with well-controlled pathways for both charge transport and energy dissipation. This study demonstrates the critical role of backbone rigidity in regulating the properties of stretchable conjugated polymer films, paving the way for more reliable and deformable materials in soft electronics.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Irradiation-Induced Structural Disorder and Its Influence on the Mechanical Response of Polycrystalline MoS2

Molybdenum disulfide (MoS2) thin films are widely used as dry-film lubricants and protective coatings in aerospace and other radiation-exposed environments. Conventional synthesis routes produce polycrystalline films whose grain boundaries and other native defects cause their mechanical and tribological behavior to differ substantially from that of ideal single crystals. Under irradiation, these films progressively evolve from polycrystalline structures, composed of layered MoS2 grains, into highly disordered and eventually amorphous structures, altering both their tribological performance and mechanical integrity. Here, we employ reactive atomistic simulations to investigate irradiation-driven structural evolution in bulk polycrystalline MoS2. Using controlled primary knock-on atom (PKA) events, we characterize the progressive transition from a polycrystalline microstructure to an amorphous network by tracking defect accumulation and structural disorder. We then establish how this transition modifies the dominant deformation mechanisms and the temperature-dependent tensile response. Specifically, irradiation suppresses interlayer sliding and delamination, mechanisms which facilitate the deformation of the pristine polycrystal, resulting in defect-induced hardening. Broadly, our results establish direct process–structure–property relationships linking irradiation-induced defect accumulation, microstructural evolution, deformation mechanisms, and mechanical behavior, providing an atomistic framework for understanding the structural integrity and long-term reliability of irradiated MoS2 coatings.

Moore, Daniel [Sandia National Laboratories (SNL)]↗
DOE OSTI2024

The role of annealing and grain boundary controls on the mechanical properties of limestones and marbles

Chemical and mechanical processes are coupled in many geological and geochemical environments. Reactive processes anneal defects and restructure grain boundaries, modifying their elastic properties, levels of internal friction, wave propagation rates and fracture behaviors. The nature of these changes is, however, contingent on the initial state of the rock. Here, in this study, impulse excitation was used to measure changes in mechanical properties as a function of dry and steam heating time at 300 °C for three carbonate rocks: Carrara marble, Carthage marble (Burlington Limestone), and Texas Cream limestone (Edwards limestone), with initial porosities of about 1 %, 3 %, and 27 %, respectively. Frequency-dependent phenomena along with mineral recrystallization were observed. This was coupled with small-angle X-ray and neutron scattering analysis to determine the relationship between changes in bulk mechanical and microstructural properties. An observed decrease in both the Young's and shear moduli in the experimental limestones as a function of heating time reflects and quantifies a reduction in the stiffness of the rock due to annealing. The internal friction of the samples first increases then decreases with reaction time, reflecting defect annealing, but this was balanced against grain boundary dissolution apparently driven by condensation of steam in the confined grain-boundary environment. This suggests an increase in the boiling point under confinement leading to dissolution, increased porosity and widening of the grain boundaries. In addition, comparison of small-angle X-ray and neutron scattering results suggests that it is inappropriate to assume that pores are empty for quantitative analysis of typical small-angle scattering samples.

58 GEOSCIENCES↗
NASA NTRS1980

NBS: Nondestructive evaluation of nonuniformities in 2219 aluminum alloy plate: Relationship to processing

The compositional homogeneity, microstructure, hardness, electrical conductivity and mechanical properties of 2219 aluminum alloy plates are influenced by the process variables during casting, rolling and thermomechanical treatment. The details of these relationships wre investigated for correctly processed 2219 plate as well as for deviations caused by improper quenching after solution heat treatment. Primary emphasis was been placed on the reliability of eddy current electrical conductivity and hardness as NDE tools to detect variations in mechanical properties.

Swartzendruber, L.↗
DOE OSTI2023

Towards inverse microstructure-centered materials design using generative phase-field modeling and deep variational autoencoders

The field of Integrated Computational Materials Engineering (ICME) combines a broad range of methods to study materials’ responses over a spectrum of length scales. A relatively unexplored aspect of microstructure-sensitive materials design is uncertainty propagation and quantification (UP/UQ) of materials’ microstructure, as well as establishing process-structure–property (PSP) relationships for inverse material design. In this study, an efficient UP technique built on the idea of changing probability measures and a deep generative unsupervised representative machine learning method for microstructure-based design of thermal conductivity of materials is proposed. Probability measures are used to represent microstructure space, and Wasserstein metrics are used to test the efficiency of the UP method. By using deep Variational AutoEncoder (VAE), we identify the correlations between the material/process parameters and the thermal conductivity of heterogeneous dual-phase microstructures. Through high-throughput screening, UP, and the deep-generative VAE method, PSP relationships that are too complex can be revealed by exploiting the materials’ design space with an emphasis on microstructures. As a last point, we demonstrate generative machine learning serves as a useful tool for inverse microstructure-centered materials design, and we demonstrate this by examining the inverse design of thermal conductivity in nano-structured materials. Here, the results reveal the effects of morphology, volume fraction, characteristic length scale, and the individual thermal diffusivity of phases on the thermal conductivity of dual-phase alloys. Our findings emphasize the advantages of high-throughput phase-field modeling and generative deep learning for linking PSP and inverse microstructure-centered materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Frontal Polymerization of Stereoisomers: Distinct Microstructures and Properties in Endo- and Exo- Dicyclopentadiene Thermosets

This study explores the relationship between monomer stereochemistry and the materials properties of thermosets generated by frontal ring-opening metathesis polymerization (FROMP). The exo- and endo-stereoisomers of dicyclopentadiene (DCPD) undergo rapid FROMP to fabricate robust thermosets within seconds. The exo-isomer provides significant benefits compared to the commonplace endo-DCPD resins; exo-DCPD supports faster fronts (≈3 times faster) than the endo-analogue and is an easier-to-handle liquid at room temperature. This work is among the first to explore the materials properties of p(exo-DCPD) thermosets. Here, we demonstrate a significant decrease in T g for products derived from exo-DCPD, despite having a similar rubbery plateau compared to p(endo-DCPD). Soluble oligomers were synthesized as models to probe the microstructural features affected by the monomer stereochemistry. Interestingly, hydrogenated oligomers of endo-DCPD exhibit slight differences in stereoregularity compared to their exo-counterparts. Finally, we incorporated a bisnorbornenyl cross-linker in exo-DCPD resins to boost the polymer T g , while also maintaining fronts that propagate faster than endo-DCPD.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Influence of Oxygen Uptake on Pitch Carbon Fiber

Carbon fiber precursor materials, such as polyacrylonitrile, pitch, and cellulose/rayon, require thermal stabilization to maintain structural integrity during conversion into carbon fiber. Thermal stabilization mitigates undesirable decomposition and liquification of the fibers during the carbonization process. Generally, the thermal stabilization of mesophase pitch consists of the attachment of oxygen-containing functional groups onto the polymeric structure. In this study, the oxidation of mesophase pitch precursor fibers at various weight percentage increases (1, 3.5, 5, 7.5 wt%) and temperatures (260, 280, 290 °C) using in situ differential scanning calorimetry and thermogravimetric analysis is investigated. The results are analyzed to determine the effect of temperature and weight percentage increase on the stabilization process of the fibers, and the fibers are subsequently carbonized and tested for tensile mechanical performance. Finally, the findings provide insight into the relationship between stabilization conditions, fiber microstructure, and mechanical properties of the resulting carbon fibers.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Segmentation method comparison for residual fiber length measurement across tiled microscopy images

Fiber length distribution (FLD), in part, governs mechanical properties in discontinuous fiber composites, yet manual measurement methods limit the high-throughput characterization needed for materials design optimization. This study compares deep learning segmentation approaches for automated FLD measurement in large-field microscopy, evaluating how method choice affects the microstructural descriptors used in structure-property-processing relationships. A critical challenge is that high-resolution microscopy images (10,000×10,000 pixels) must be tiled for deep learning analysis, fragmenting fibers at boundaries. We demonstrate that segmentation method proves crucial for measurement accuracy. For example, instance segmentation with Slicing Aided Hyper Inference (SAHI) preserves individual fiber integrity across tiles while semantic segmentation prioritizes speed. Comparing against manual measurement of extracted carbon fibers, YOLOv11-SAHI matched manual ground truth (238 μm weighted mean) with 40x speedup (4.5 vs 167 minutes per image). U-Net provides rapid quantification although it is at the cost of reduced accuracy due only reliably measuring stand-alone fibers. Our comparative analysis reveals that instance segmentation with SAHI better preserves length measurements while semantic segmentation prioritizes speed, providing empirical guidance for method selection. The characterization provides essential inputs for mechanical property prediction models and inverse design workflows, accelerating composite materials development cycles.

Additive manufacturing↗
NASA NTRS1996

Thermoplastic Ribbon-Ply Bonding Model

The aim of the present work was to identify key variables in rapid weldbonding of thermoplastic tow (ribbon) and their relationship to matrix polymer properties and to ribbon microstructure. Theoretical models for viscosity, establishment of ply-ply contact, instantaneous (Velcro) bonding, molecular interdiffusion (healing), void growth suppression, and gap filling were reviewed and synthesized. Consideration of the theoretical bonding mechanisms and length scales and of the experimental weld/peel data allow the prediction of such quantities as the time and pressure required to achieve good contact between a ribbon and a flat substrate, the time dependence of bond strength, pressures needed to prevent void growth from dissolved moisture and conditions for filling gaps and smoothing overlaps.

Hinkley, Jeffrey A.↗