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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A Segregated Approach for Modeling the Electrochemistry in the 3-D Microstructure of Li-Ion Batteries and Its Acceleration Using Block Preconditioners

Abstract Battery performance is strongly correlated with electrode microstructure. Electrode materials for lithium-ion batteries have complex microstructure geometries that require millions of degrees of freedom to solve the electrochemical system at the microstructure scale. A fast-iterative solver with an appropriate preconditioner is then required to simulate large representative volume in a reasonable time. In this work, a finite element electrochemical model is developed to resolve the concentration and potential within the electrode active materials and the electrolyte domains at the microstructure scale, with an emphasis on numerical stability and scaling performances. The block Gauss-Seidel (BGS) numerical method is implemented because the system of equations within the electrodes is coupled only through the nonlinear Butler–Volmer equation, which governs the electrochemical reaction at the interface between the domains. The best solution strategy found in this work consists of splitting the system into two blocks—one for the concentration and one for the potential field—and then performing block generalized minimal residual preconditioned with algebraic multigrid, using the FEniCS and the Portable, Extensible Toolkit for Scientific Computation libraries. Significant improvements in terms of time to solution (six times faster) and memory usage (halving) are achieved compared with the MUltifrontal Massively Parallel sparse direct Solver. Additionally, BGS experiences decent strong parallel scaling within the electrode domains. Last, the system of equations is modified to specifically address numerical instability induced by electrolyte depletion, which is particularly valuable for simulating fast-charge scenarios relevant for automotive application.

25 ENERGY STORAGE↗
DOE OSTI2021

Microstructure-Based Estimation of Strength and Ductility Distributions for \(\alpha +\beta \) Titanium Alloys

Titanium alloys are processed to develop a wide range of microstructure configurations and therefore material properties. While these properties are typically measured experimentally, a framework for property prediction could greatly enhance alloy design and manufacturing. In this work, a microstructure-sensitive framework is presented for the prediction of strength and ductility as well as estimates of the bounds in variability for these properties. The framework explicitly considers distributions of microstructure via new approaches for instantiation of structure in synthetic samples. The parametric evaluation strategy, including the finite element simulation package FEpX, is used to create and test virtual polycrystalline samples to evaluate the variability bounds of mechanical properties in Ti-6Al-4V. Critical parameters for the property evaluation framework are provided by measurements of single crystal properties and advanced characterization of microstructure and slip system strengths in 2D and 3D. Property distributions for yield strength and ductility are presented, along with the validation and verification steps undertaken. Comparisons between strain localization and slip activity in virtual samples and in experimental grain-scale strain measurements are also discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2023

The Effects of Silicon and Niobium Concentration on the Solidification Behavior and Microstructure of Cast Monel Alloys

Cast Monel alloys are used in applications requiring a combination of good mechanical properties and excellent resistance to corrosion. Despite prevalent industrial use, relatively few studies have been conducted to investigate the relationships between composition, solidification behavior, and microstructure. Given that these alloys are used in the cast and welded conditions, these factors have a significant influence over the material properties. Here, in this work, microstructural characterization, electron probe microanalysis, X-ray diffraction, and differential scanning calorimetry were used to study how changes in Si and Nb concentrations affected the solidification path and microstructure of Monel alloys. It was found that increasing Nb concentration stabilized higher amounts of MC carbides and suppressed graphite formation during solidification. It was also found that the high nominal concentration and segregation of Si to the liquid led to the formation of Ni 31 Si 12 and other silicides via terminal eutectic reactions at the end of solidification. A pseudo-binary solidification diagram was constructed using experimental data and was applied to predict the mass fraction of solidified eutectic as a function of composition. The modeled microstructures were found to be in good agreement with experimentally measured phase fractions.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Effects of MnS Inclusions on the Banded Microstructure in Non-quenched and Tempered Steel

The effects of MnS inclusions in a non-quenched and tempered steel on banded ferrite/pearlite (F/P) microstructure have been studied in this investigation. The results show that the banded microstructure is mainly caused by fluctuations in the concentration of alloying elements such as Mn and Cr, which were determined by energy dispersive spectroscopy. As a manganese-rich precipitate, MnS inclusions might produce extra effects on the banded microstructure and diffusion behavior of alloying elements depending on their size and distribution. Large-sized MnS inclusions distributed within pearlite bands retard the diffusion process of alloying elements, and accordingly, aggregate the degree of banding. On the contrary, small-sized MnS inclusions are much smaller than average grain size, which have little effects on the diffusion of alloying elements on a large scale, and they can promote the nucleation of ferrite and lead to a uniform distribution of ferrite and pearlite. An extended homogenization treatment at high temperatures can convert large-sized MnS inclusions into evenly dispersed smaller inclusions, and accordingly reduce the effect of MnS inclusions on the final microstructure.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Effect of Mg-Bearing Oxide(+TiN) Inclusion on Microstructure of Fe-16 Mass Pct Cr Ferritic Alloy and Its Evolution During Heating at 1573 K (1300 °C)

Both the effects of Mg-bearing oxide and Mg-bearing oxide +TiN inclusions on the microstructures of Fe-16 mass pct Cr alloy should be clarified. In this paper, the effects of Mg-bearing oxide(+TiN) inclusions on the microstructures of Fe-16 mass pct Cr ferritic alloy and changes due to the inclusions during heating at 1573 K (1300 °C) were studied by manual and automatic SEM-EDS analyses. Three alloys were used as specimens. The inclusions were Al{sub 2}TiO{sub 4} +TiN with a small amount of TiN in alloy #1, Al{sub 2}(Mg, Ti)O{sub 4} in alloy #2 and Al{sub 2}(Mg, Ti)O{sub 4} +TiN in alloy #3. The average grain size was smaller, and the ratio of columnar grains was lower in the as-cast alloy #2 and as-cast alloy #3 than that in the as-cast alloy #1. This is attributed to the heterogeneous nucleation of delta ferrite on Al{sub 2}(Mg, Ti)O{sub 4} during solidification. Refinement of the microstructures of ferritic steel by Mg-bearing oxide + multi-TiN inclusions is proposed. Changes in composition, aspect ratio, and size distribution of inclusions in each alloy during heating were investigated systematically. Mg-bearing oxide(+TiN) was dispersed in both the as-cast alloys and heated alloys, which contributed to microstructural refinement of the heated alloys through the pinning effect.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Effect of Heating Rate during Continuous Annealing on Microstructure and Mechanical Properties of High-Strength Dual-Phase Steel

The study concerns the influence of heating rate during continuous annealing on microstructure and mechanical properties of high-strength ferrite–martensite dual-phase (DP) steel. The study suggests that the superheat (ΔT), carbon diffusion and distribution were strongly influenced by increasing the heating rate, leading to different microstructure and mechanical properties of DP steel. The morphology of martensite was observed to shift from network along ferrite grain boundaries to banded structure to uniformly dispersed, and the average ferrite grain size was refined from 5.3 to 2.0 µm. When the heating rate was 300 °C/s, the steel exhibited excellent mechanical properties with tensile strength (UTS) of 982 MPa, total elongation (TEL) of 17.5%, strength–elongation balance (UTS × TEL) of 17.2 GPa%, because of finer microstructure, uniform martensite distribution and low carbon content of martensite. Moreover, the precipitation strengthening effect of NbC is an important reason for the high-strength DP steels. In addition, the variation in strain-hardening behavior and fracture mechanism of steel subjected to different heating rates is discussed in relation to the microstructure.

36 MATERIALS SCIENCE↗
DOE OSTI2026

MoNbTi-based Refractory Multi-principal Element Alloy System: A Review of the Thermodynamic Phase Predictions, Formed Microstructures, and Mechanical Properties as a Function of the Fabrication Methods (AM versus SPS versus VAM)

Abstract Multi-principal element alloys, particularly of refractory compositions, are an increasingly popular candidate for extreme-environment applications, including for next-generation nuclear reactors and in other industries, such as biomedical and aerospace, due to their high strength. The ability to achieve solid-solution microstructures provides these alloys with improved mechanical properties; however, these microstructures are heavily composition- and processing-dependent. In this review, the multi-principal element alloy MoNbTi-based system (with Zr, V, and Cr additions) was selected to compare the effects of elemental composition and processing route on the resulting microstructures and mechanical properties. The review provides insight into identifying the optimal alloy composition and processing route for a balance of desired microstructure and mechanical properties.

Krogh, Kara↗
DOE OSTI2022

Impact of the plastic deformation microstructure in metals on the kinetics of recrystallization: A phase-field study

The sensitivity of recrystallization kinetics in metals to the heterogeneity of microstructure and deformation history is a widely accepted experimental fact. However, most of the available recrystallization models employ either a mean field approach or use grain-averaged parameters, and thus neglecting the mesoscopic heterogeneity induced by prior deformation. Here in the present study, we investigate the impact of deformation-induced dislocation (subgrain) structure on the kinetics of recrystallization in metals using the phase-field approach. The primary focus here is upon the role of dislocation cell boundaries. The free energy formulation of the phase-field model accounts for the heterogeneity of the microstructure by assigning localized energy to the resulting dislocation microstructure realizations generated from experimental data. These microstructure realizations are created using the universal scaling laws for the spacing and the misorientation angles of both the geometrically necessary and incidental dislocation boundaries. The resulting free energy is used into an Allen-Cahn based model of recrystallization kinetics, which are solved using the finite element method. The solutions thus obtained shed light on the critical role of the spatial heterogeneity of deformation in the non-smooth growth of recrystallization nuclei and on the final grain structure. The results showed that, in agreement with experiment, the morphology of recrystallization front exhibits protrusions and retrusions. By resolving the subgrain structure, the presented algorithm paves the way for developing predictive kinetic models that fully account for the deformed state of recrystallizing metals.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Microstructure effects on high velocity microparticle impacts of copper

Constitutive models can fail to predict high-rate deformation behavior due to their inability to account for microstructural effects. In part, this is because of a dearth of experimental benchmarking data in the high strain-rate, low pressure regime, since many high-rate experiments also probe a region of strong shockwaves, at which point microstructure effects no longer play a primary role. This work uses laser-induced particle impact testing to quantitatively study high velocity impacts of small, rigid alumina microspheres on flat copper substrates with varying amounts of initial cold work in the weak shock regime, but at very high strain rates up to ~10 7 s –1 . Through paired experiments and numerical simulations, this work shows that the initial microstructure condition can have significant influence on dynamical mechanical properties in this range. Specifically, prior work hardening of the copper substrate leads to increased rebounding of the microparticles (i.e., less plastic dissipation in the impact) as well as smaller craters. Each of these experimental measurables can be converted into a strength measure, i.e., the dynamic yield strength or dynamic hardness, respectively, neither of which is well predicted consistently by existing constitutive laws. The general trend of hardening can be captured by such models by incorporating an existing “pre-strain,” suggesting that future calibration of the materials parameters may yield a good fit over a broader range of conditions. Our results emphasize the importance of reporting the microstructural condition in dynamic studies, as well as the necessity of accounting for these factors when formulating and optimizing constitutive models.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Microstructural modification of additively manufactured metals by electropulsing

Additive manufacturing (AM) promises rapid development cycles and fabrication of ready-to-use, geometrically-complex parts. The metallic parts produced by AM often contain highly non-equilibrium microstructures, e.g. chemical microsegregation and residual dislocation networks. While such microstructures can enhance some material properties, they are often undesirable. Many AM parts are thus heat-treated after fabrication, a process that significantly slows production. This study investigated if electropulsing, the process of sending high-current-density electrical pulses through a metallic part, could be used to modify the microstructures of AM 316 L stainless steel (SS) and AlSi10Mg parts fabricated by selective laser melting (SLM) more rapidly than thermal annealing. Electropulsing has shown promise as a rapid postprocessing method for materials fabricated using conventional methods, e.g. casting and rolling, but has never been applied to AM materials. For both the materials used in this study, as-fabricated SLM parts contained significant chemical heterogeneity, either chemical microsegregation (316 L SS) or a cellular interdendritic phase (AlSi10Mg). In both cases, annealing times on the order of hours at high homologous temperatures are necessary for homogenization. Using electropulsing, chemical microsegregation was eliminated in 316 L SS samples after 10, 16 ms electrical pulses. In AlSi10Mg parts, electropulsing produced spheroidized Si-rich particles after as few as 15, 16 ms electrical pulses with a corresponding increase in ductility. Finally, this study demonstrated that electropulsing can be used to modify the microstructures of AM metals.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Effect of heat treatment on microstructural evolution and hardness homogeneity in laser powder bed fusion of alloy 718

The thermal history developed in laser powder bed fusion (LPBF) processes has been shown to be complex resulting in equally complex microstructures and mechanical properties. Microscopic observations and Vickers micro-hardness mapping measurements were carried out on diff ;erent section planes of LPBF alloy 718 cuboids. Three-dimensional finite element analysis was used to simulate thermal history and to predict the residual stress distribution in the as-built material. Computational thermodynamics was used to predict the micro-segregation and nucleation driving force of various phases in the bulk and in segregated regions. Varied heat-treatments such as simulated hot isostatic pressing, and double aging were applied. Their influence on the microstructure, microsegregation, precipitate formation, and micro-hardness variations of LPBF alloy 718 were investigated. Hardness map results showed heterogeneous micro-hardness on the xy- and xz-planes of the as-built parts where the bottom plane and center regions had larger hardness of ~315 HV 0.5 while the top plane and contours showed hardness of ~300 HV 0.5 . It was found that the aging treatment increased the overall hardness of the as-built condition from ~310 HV 0.5 to 470 HV 0.5 but also increased the hardness gradient throughout the coupon. After simulated hot isostatic pressing process (i.e., without applied pressure) at 1020 °C for 4 h followed by water quench (HIPWQ), the hardness gradient and hardness was minimized (~210 HV 0.5 ) as the microstructure transitioned from heterogeneous columnar grains in the as-built condition to more uniform recrystallized grains. A double aging treatment was applied to enhance hardness from ~210 HV 0.5 to ~440 HV 0.5 . Finally, HIPWQ followed by double aging produced a homogeneous microstructure and more uniform hardness map with enhanced mechanical properties in LPBF alloy 718 coupons.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Equivariant graph convolutional neural networks for the representation of homogenized anisotropic microstructural mechanical response

Composite materials with different microstructural material symmetries are common in engineering applications where grain structure, alloying and particle/fiber packing are optimized via controlled manufacturing. In fact these microstructural tunings can be done throughout a part to achieve functional gradation and optimization at a structural level. To predict the performance of particular microstructural configuration and thereby overall performance, constitutive models of materials with microstructure are needed. In this work we provide neural network architectures that provide effective homogenization models of materials with anisotropic components. These models satisfy equivariance and material symmetry principles inherently through a combination of equivariant and tensor basis operations. We demonstrate them on datasets of stochastic volume elements with different textures and phases where the material undergoes elastic and plastic deformation, and show that the these network architectures provide significant performance improvements.

anisotropy↗
DOE OSTI2021

Evolution of microstructures in radiation fields using a coupled binary-collision Monte Carlo phase field approach

The simulation of radiation effects in materials broadly falls into two categories. At short time and length scales lies the modeling of primary radiation damage, such as point defect creation, energy deposition, and ballistic mixing. This is followed by the modeling at longer time scales of thermally activated microstructure evolution and defect reactions, such as recombination, clustering, and coarsening. The binary collision Monte Carlo method is an established, numerically efficient method for the computation of primary radiation damage. Conversely, the phase field method is a state of the art method for microstructure evolution on longer time and length scales. Here we present a concurrent coupling of these two methods, overcoming the difference between the discrete object Monte Carlo paradigm for primary radiation damage and the continuum field variable approach for microstructure evolution. The coupling is bidirectional, in which the microstructure evolution in the MOOSE finite element frame- work provides the spatial scattering data set for the charged particle trans- port and receives point defect, mass transport, and heat source terms from the simulated collision cascades that contribute to the field variable evolution. The concurrent coupling scheme is implemented in the code Magpie and demonstrated by investigating patterning for a model irradiated immiscible binary alloy. The results from the coupled binary collision Monte Carlo/phase field simulations reproduce the results of analytical models for phase separation, phase mixing, and patterning, supporting the approach and indicating its utility for modeling real materials systems.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Using machine-learning to understand complex microstructural effects on the mechanical behavior of Ti-6Al-4V alloys

Structural materials properties are highly dependent on their microstructure. Their microstructure is in turn affected by multiple fabrication and thermo-mechanical treatment parameters, all of which conform a highly-dimensional parametric space with often hidden correlations that are difficult to extract by experimentation alone. This is particularly true for alloys of the dual-phase Ti-6Al-4V family, with their greatly complex and rich microstructures, which combine several intrinsic length scales associated with multiple grain and subgrain structures, grains with different crystal lattices (α and β phases), and complex chemistry. In this paper we use a comprehensive set of machine learning techniques to develop predictive tools relating the yield strength and hardening rate of these alloys to a set of input parameters covering extensive ranges. The data generator is a finite-element crystal plasticity model for polycrystal deformation that takes into account slip anisotropy and employs standard dislocation evolution models for the α and β phases of Ti-based alloys. Our dataset includes over two thousand independent simulations and is used to train the machine learning models, which are then used to establish correlations between microstructural parameters and the alloys’ mechanical response. Our results point to the most influential parameters affecting yield strength and hardening rate, information that can then be used to guide experimental synthesis and characterization efforts to save time and resources.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A scalable framework for efficient coupling of thermal and microstructural simulations in additive manufacturing

Predicting microstructure evolution in metal additive manufacturing (AM) is important for process optimization, but spatiotemporal scale disparities between thermal transport and microstructure evolution create significant challenges for efficient data transfer between simulation codes. To address this, we present Stork, a scalable framework for coupling thermal and microstructural simulations. Stork uses a sparse data representation to identify and store active solidification sub-volumes, enabling highly parallel quad-linear interpolation from coarse thermal grids to fine microstructure grids without large intermediate storage. We demonstrate the framework by coupling the semi-analytic heat transfer code 3DThesis with the time-parallel cellular automata code Toucan. This approach achieves over two orders of magnitude reduction in data generation time and file size compared to prior workflows. Numerical studies show that quad-linear interpolation preserves grain morphology and crystallographic texture in laser powder bed fusion (LPBF) simulations for coarsening ratios up to 16. Overall, Stork provides a scalable pathway for high-throughput, component-scale AM simulations on modern high-performance computing systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multi deep learning-based stochastic microstructure reconstruction and high-fidelity micromechanics simulation of time-dependent ceramic matrix composite response

A multi deep learning-based framework is developed for efficient, automated microstructure reconstruction and generation of stochastic representative volume elements (SRVEs) with periodic boundary conditions (PBCs) for accurate modeling of ceramic matrix composite (CMC) response. The methodology comprises a convolutional neural network coupled with regression layers to act as a vanilla regression network for semantic segmentation of the microstructure, allowing accurate characterization of the phases and their distributions at the microscale. Scanning electron microscope and confocal microscope are used to obtain C/SiNC and SiC/SiNC CMCs micrographs for vanilla regression testing. Microstructure variability in terms of fiber volume fraction and porosity are quantified through the output regression layer, ensuring accurate representation of material variability in SRVE construction. Generative adversarial network (GAN) and its variants are designed to produce high-fidelity SRVE, spanning CMCs microstructure variability space. A circular padding algorithm is developed to generate SRVEs with PBCs during training of GANs. The accuracy of the generated SRVEs is established through micromechanics simulations, where an efficient formulation of the high-fidelity generalized methods of cells (HFGMC) approach is used to compute the effective mechanical properties. Furthermore, an iterative algorithm is implemented in the HFGMC solver to simulate time-dependent deformation of SiC/SiNC subjected to creep loading conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The effect of microstructure on the relationship between grain boundary sliding and slip transmission in high purity aluminum

The relationship between grain boundary sliding (GBS) and slip transmission is investigated experimentally at grain boundaries in 99.99% aluminum with a through-thickness, coarse grained microstructure deformed in tension at 190 °C. Using scanning electron microscope enabled digital image correlation (SEM-DIC) and electron backscatter diffraction (EBSD), high resolution strain fields and microstructural information were measured to examine the influence of microstructural neighborhoods on interactions between GBS and slip transmission and strain localization. Several distinct cases are presented that highlight important microstructural factors that govern deformation near grain boundaries. The findings include (1) direct transmission and GBS were anti-compatible and facilitated by opposing boundary types (low misorientation and high energy grain boundaries respectively); (2) increased GBS activity was correlated with decreased indirect transmission behavior; (3) GBS accommodation at triple junctions was enabled by intragranular plasticity; and (4) the local intragranular plastic strain discontinuity between grains determined the magnitude of GBS gradients. Finally, this work provides insight into the nature of these mechanisms and can be used to identify strain transfer criteria that can lead to improved GBS-sensitive crystal plasticity models.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Strength-ductility synergy through microstructural and compositional heterogeneity in directed energy deposition additive manufacturing of face-centered cubic materials

Directed energy deposition (DED) is an additive manufacturing (AM) process based on welding technology and offers the advantages of large build volume, high deposition rate, and ability to fabricate multi-material parts. Epitaxial continuous columnar grain growth is a characteristic microstructural feature of DED processed alloys. In this study, a bamboo-like microstructure (periodic alternation of equiaxed and columnar structure) was produced by adopting an intermittent deposition strategy in 316L stainless steel and Inconel 625. The formation of a bamboo-like alternating microstructure was confirmed through electron backscattered diffraction (EBSD) analysis. Hardness mapping showed that the columnar to equiaxed transition (CET) occurred at the region right below the fusion line. A finite element (FE) model was used to investigate the relationship between the temperature gradient (G) and the solidification rate (R). The FE model showed a low G/R ratio at the region right below the interface promoting the CET. The grain size and material-dependent deformation behaviors are analyzed using digital image correlation (DIC). The lower deformation on the fine-grain regions observed in DIC analysis is attributed to a higher strain hardening rate, which is confirmed through dislocation density analysis on a tensile-interrupted specimen. The periodically alternating grain size coupled with the microstructural changes caused by intermittent deposition strategy result in a better strength-ductility synergy in both single-material and bimetallic specimens.

36 MATERIALS SCIENCE↗