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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 OSTI2017

Formulation and validation of a reduced order model of 2D materials exhibiting a two-phase microstructure as applied to graphene oxide [Formulation and validation of a reduced order model of 2D materials exhibiting a two-phase microstructure]

Novel 2D materials, e.g., graphene oxide (GO), are attractive building blocks in the design of advanced materials due to their reactive chemistry, which can enhance interfacial interactions while providing good in-plane mechanical properties. Recent studies have hypothesized that the randomly distributed two-phase microstructure of GO, which arises due to its oxidized chemistry, leads to differences in nano- vs meso–scale mechanical responses. However, this effect has not been carefully studied using molecular dynamics due to computational limitations. Herein, a continuum mechanics model, formulated based on density functional based tight binding (DFTB) constitutive results for GO nano-flakes, is establish for capturing the effect of oxidation patterns on the material mechanical properties. GO is idealized as a continuum heterogeneous two-phase material, where the mechanical response of each phase, graphitic and oxidized, is informed from DFTB simulations. A finite element implementation of the model is validated via MD simulations and then used to investigate the existence of GO representative volume elements (RVE). We find that for the studied GO, an RVE behavior arises for monolayer sizes in excess to 40 nm. Moreover, we reveal that the response of monolayers with two main different functional chemistries, epoxide-rich and hydroxyl–rich, present distinct differences in mechanical behavior. In addition, we explored the role of defect density in GO, and validate the applicability of the model to larger length scales by predicting membrane deflection behavior, in close agreement with previous experimental and theoretical observations. As such the work presents a reduced order modeling framework applicable in the study of mechanical properties and deformation mechanisms in 2D multiphase materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Extreme shear-deformation-induced modification of defect structures and hierarchical microstructure in an Al–Si alloy

Abstract Extreme shear deformation is used for several material processing methods and is unavoidable in many engineering applications in which two surfaces are in relative motion against each other while in physical contact. The mechanistic understanding of the microstructural evolution of multi-phase metallic alloys under extreme shear deformation is still in its infancy. Here, we highlight the influence of shear deformation on the microstructural hierarchy and mechanical properties of a binary as-cast Al-4 at.% Si alloy. Shear-deformation-induced grain refinement, multiscale fragmentation of the eutectic Si-lamellae, and metastable solute saturated phases with distinctive defect structures led to a two-fold increase in the flow stresses determined by micropillar compression testing. These results highlight that shear deformation can achieve non-equilibrium microstructures with enhanced mechanical properties in Al–Si alloys. The experimental and computational insights obtained here are especially crucial for developing predictive models for microstructural evolution of metals under extreme shear deformation.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Microstructure optimization with constrained design objectives using machine learning-based feedback-aware data-generation

Microstructure sensitive design has a critical impact on the performance of engineering materials. The safety and performance requirements of critical components, as well as the cost of material and machining of Titanium components, make dovetailing of the microstructure imperative. This paper addresses the optimization of several microstructure design problems for Titanium components under specific design constraints using a feedback-aware data-driven solution methodology. In this study, the microstructure is modeled with an orientation distribution function (ODF) that measures the volumes of different crystallographic orientations. Two algorithms are used to sample the entire microstructure space followed by machine learning-aided identification of a minimal subset of ODF dimensions which is subsequently explored by targeted sampling. Conventional optimization methods lead to a unique microstructure rather than yielding a comprehensive space of optimal or near-optimal microstructures. Multiple solutions are crucial for the deployment of materials design for manufacturing as traditional manufacturing processes can only generate a limited set of microstructures. Our data sampling-based methodology not only outperforms or is on par with other optimization techniques in terms of the optimal property value, but also provides numerous near-optimal solutions, 3–4 orders of magnitude more than previous methods. Consequently, the proposed framework delivers a spectrum of optimal solutions in the microstructure space which can accelerate materials development and reduce manufacturing costs.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Report on the FY20 Characterization of Age Hardened Alloy 709 Materials

This report provides the results on microstructural characterization of the first commercial heat of Alloy 709 plate material undergone additional age hardening treatment. In FY19, through thermodynamic-kinetic modeling and microstructural characterization, it is determined that the 775°C/10 h age-hardening treatment can promote desired precipitation on dislocations and has the potential to further improve the mechanical performance of the material. In FY20, the treatment protocol was applied to the ESR-1150°C condition, and the microstructure was then compared with the ESR-1100°C condition. It was found that the 1150°C solution annealing (SA) dramatically reduced the dislocation density compared to the 1100°C SA, resulting a smaller number density of Nb-rich fine precipitates which grow on dislocations during the age hardening treatment. Other features are very similar between the two. The effect of age hardening on creep performance is discussed. In addition, TEM characterization of a creep-ruptured sample (600°C/330 MPa) was performed. Specimens from the grip region and the gauge region was studied. The grip region specimen was effectively aged at 600°C for extended period, while the gauge region specimen was mechanically deformed at 600°C. The microstructures are very different, implying the importance of a dynamical effect on precipitation.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Hardness Prediction by Incorporating Heat Transfer and Molten Pool Fluid Flow in a Multi-pass, Multi-layer Weld for Onsite Repair of Grade 91 steel

Introduction: In the current fleet of fossil-fired power plants, creep strength enhanced ferritic steels (CSEF) are used to sustain the harsh service conditions. Enhanced properties of Grade 91 steel result from tempered martensite with a fine distribution of MX and M23C6 carbides. Grade 91 steel is subjected to onsite welding repair to remedy their degradation due to extreme service condition. Knowledge of weld repairability of these steels, such as as-welded hardness distribution, is essential to establishing sound repair procedures. Experimental trial and error tests can consume a lot of time as many welding variables need to be studied. For numerical modelling, most of the multi-pass multi-layer models are based on finite element method, which are limited to solve the heat conduction equation and ignore convective heat transfer due to melt flow. Moreover, the mesh has to be pre-built based on a known or assumed weld cross-section geometry. These finite element based models thus have limited predictive capability as defects are not considered and nugget size are pre-assumed. This research aims at developing a thermal and microstructure evolution model incorporating molten pool dynamics in a multi-pass multi-layer material deposition to predict the as-welded hardness distribution. Technical Approach: All the thermal, physical, and metallurgical properties of Grade 91 as a function of temperature are collected from the literature and inputted into the thermo-fluid model based on Flow-3D, a computational fluid dynamics software. A multi-pass, multi-layer material deposition is simulated where the melting of filler wire into the molten pool is directly considered based on the volume of fluid (VOF) method. The flow behaviour of the molten pool is used to understand the formation of deposition geometry and defects. The temperature profiles during the multi-pass, multi-layer welding are calculated. The results computed using the new model are compared against the experimental data of fusion zone geometry and thermal cycles. Hardness prediction in the heat-affected zone (HAZ) are made using Johnson-Mehl-Avrami (JMA) equation for solid-state phase transformation kinetics. The JMA parameters are extracted from the experimental data available in the literature. For comparison, a standard finite element heat conduction model is also developed to predict the thermal cycles and hardness distribution in the multi-pass, multi-layer weld. Expected Result: Results obtained using the molten pool dynamic simulation versus the finite element heat conduction model are compared. Specifically, the effects of convective heat transfer on the accuracy of the calculated thermal history, bead shape and size, and HAZ hardness distribution are examined.

Modeling, Grade 91 steel, Multi-pass multi-layer d↗
DOE OSTI2017

Band Gaps for Elastic Wave Propagation in a Periodic Composite Beam Structure Incorporating Microstructure and Surface Energy Effects

Here, a new model for determining band gaps for elastic wave propagation in a periodic composite beam structure is developed using a non-classical Bernoulli–Euler beam model that incorporates the microstructure, surface energy and rotational inertia effects. The Bloch theorem and transfer matrix method for periodic structures are employed in the formulation. The new model reduces to the classical elasticity-based model when both the microstructure and surface energy effects are not considered. The band gaps predicted by the new model depend on the microstructure and surface elasticity of each constituent material, the unit cell size, the rotational inertia, and the volume fraction. To quantitatively illustrate the effects of these factors, a parametric study is conducted. The numerical results reveal that the band gap predicted by the current non-classical model is always larger than that predicted by the classical model when the beam thickness is very small, but the difference is diminishing as the thickness becomes large. Also, it is found that the first frequency for producing the band gap and the band gap size decrease with the increase of the unit cell length according to both the current and classical models. In addition, it is observed that the effect of the rotational inertia is larger when the exciting frequency is higher and the unit cell length is smaller. Furthermore, it is seen that the volume fraction has a significant effect on the band gap size, and large band gaps can be obtained by tailoring the volume fraction and material parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Brownian-like kinematics of ball milling for particulate structural modeling

Ball milling motion has been previously studied through computationally expensive, off-line experimental video processing and numerical simulations by the discrete element method. Here, this research establishes a more efficient formulation of the ball energetics and kinetics similar to the Brownian kinetic theory of statistical mechanics. Based on assumptions of thermomechanical equilibrium, negligible gravitational, aerodynamic and surface condition effects, and decoupled impact interaction among balls and with milled particulates, this model proposes mono-parametric spectral energy and velocity probability density functions akin to Maxwell-Boltzmann statistics, along with uniformly distributed impact directionality. The model predictions are calibrated and validated by comparison with published experimental measurements and computationally derived spectra. This descriptive Brownian-like motion model enables effective simulation of contact and impact, material deformation and micro-joining of ball milled bimetallic powders. A comprehensive simulation of the evolving internal fractal microstructure of the processed particulates is implemented at real-time computation speed, and its predictions are compared with experimental micrographs of ball milled Ni—Al particulates.

42 ENGINEERING↗
DOE OSTI2024

Phase Composition and Phase Transformation of Additively Manufactured Nickel Alloy 718 AM Bench Artifacts

Additive manufacturing (AM) technologies offer unprecedented design flexibility but are limited by a lack of understanding of the material microstructure formed under their extreme and transient processing conditions and its subsequent transformation during post-build processing. As part of the 2022 AM Bench Challenge, sponsored by the National Institute of Standards and Technology, this study focuses on the phase composition and phase evolution of AM nickel alloy 718, a nickel-based superalloy, to provide benchmark data essential for the validation of computational models for microstructural predictions. Here, we employed high-energy synchrotron X-ray diffraction, in situ synchrotron X-ray scattering, as well as high-resolution transmission electron microscopy for our analyses. The study uncovers critical aspects of the microstructure in its as-built state, its transformation during homogenization, and its phase evolution during subsequent aging heat treatment. Specifically, we identified secondary phases, monitored the dissolution and coarsening of microstructural elements, and observed the formation and stability of γ ’ and γ ” phases. The results provide the rigorous benchmark data required to understand the atomic and microstructural transformations of AM nickel alloy 718, thereby enhancing the reliability and applicability of AM models for predicting phase evolution and mechanical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Identification of crystal plasticity model parameters by multi-objective optimization integrating microstructural evolution and mechanical data

Crystal plasticity models evolve a polycrystalline yield surface using meso-scale descriptions of deformation mechanisms. The activation of deformation mechanisms is governed by crystallography and a set of model parameters, which are typically calibrated through the fitting of mechanical data such as stress–strain curves and elastic lattice strains. Microstructural data such as phase fractions and texture evolution are used for verifying crystal plasticity parameters. In this study, we use a multi-objective genetic algorithm to identify hardening parameters from flow stress curves with an option to incorporate texture into the optimization approach. Robust, generalized objective functions are developed and used to identify sets of parameters pertaining to dislocation density-based hardening laws in visco-plastic and elasto-plastic self-consistent (VPSC and EPSC) homogenization models. First, the parameters are identified for pure Nb directly from texture using an objective function based on generalized spherical harmonics. Since texture evolution is driven by the relative contribution of active slip systems, the parameters governing the evolution of slip resistance ratios can be recovered from fitting discrete textures at a series of strains. Next, a comprehensive set of load reversal data for dual phase (DP) 780 steel is used to fit a hardening law and a back-stress law in EPSC. Finally, parameters pertaining to a complex hardening law for the evolution of slip and twinning in pure α-Ti are identified. Remarkably, using texture as an objective in combination with stress–strain objectives constrains the model of Ti to fully reproduce not only stress–strain and texture evolution but also hierarchical twinning measurements as a function of initial grain size and texture. Furthermore, given an appropriate model fit to representative experimental texture evolution, underlying twin volume fractions contributing to texture evolution can be predicted.

42 ENGINEERING↗
DOE OSTI2018

Interfacial diffusion reactions and mechanical properties of Ti3Al/Ni-based superalloy joints brazed with AgCuPd filler metal

Highlights: • Ti{sub 3}Al alloy to Ni-based superalloy was successfully brazed using Ag-21Cu-25Pd filler metal. • Phases TiPd (fcc), Ti{sub 3}Pd{sub 5} (bct) and AlPd (hcp) formed within the Ti{sub 3}Al/Ni-based superalloy joint. • Diffusion behavior of elements and its influence on joint interfacial microstructure were revealed. • A formation model of interfacial microstructure has been established. • The joint tensile strength at room temperature reached 404MPa, and the strength of 212MPa was maintained at 773 K. - Abstract: Dissimilar joining of a Ti{sub 3}Al-based alloy to a Ni-based superalloy has been conducted using Ag-21Cu-25Pd filler metal. The joint microstructures were analyzed using SEM, EPMA, XRD and TEM. The reaction behaviors between different elements were discussed. Due to the strong affinity, element Pd mainly reacted with Ti{sub 3}Al substrate and formed intermetallic compounds of TiPd, Ti{sub 3}Pd{sub 5} and AlPd. Elements Ni and Ti reacted with each other and caused the formation of Ni{sub 3}Ti in the centre of the joint. Owing to the diffusion effect, element Ag was enriched in the joint central area as (Ag,Cu){sub ss}. With the prolonging of the brazing time varying from 3 min to 15 min at 1253 K, the tensile strength of the joint firstly increased and then decreased. The joints brazed at 1253 K for 10 min presented the maximum strength of 404 MPa at room temperature, and the joint strength of 212 MPa was maintained at 773 K. The total fracture surface covered the most of the reaction zones from the (Ag,Cu){sub ss} to the diffusion effect zone at the Ti{sub 3}Al side.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Generalizable Image Segmentation for Microstructure Characterization Through Integrated SEM and EBSD Analysis

We demonstrate generalizable semantic segmentation using minimal ground truth data. Correlated scanning electron microscopy (SEM) images and electron backscatter diffraction (EBSD) measurements of frictionstir processed 316L stainless steel plates were used to train deep learning models for grain boundary segmentation. Secondary electron (SE) imaging taken at an accelerating voltage of 10 keV correlated to EBSD-derived grain boundaries produced the best performing model. Notably, an ensemble of three models trained on a single SE image produced accurate segmentation over a series of BSE images of samples manufactured under different processing parameters, with a resultant mean absolute error in grain size of 0.34 µm. The striking generalizability of the models likely results from the similar escape depths of the SE training input and the EBSD training output and the reduced probability of dislocation artifacts appearing in the image. This finding highlights the importance of considering the physical principles behind imaging in the development of robust segmentation models for microstructure characterization.

Taufique, Mohammad Fuad Nur↗
DOE OSTI2023

Self-supervised learning of spatiotemporal thermal signatures in additive manufacturing using reduced order physics models and transformers

Microstructure control via additive manufacturing has enormous potential as manufacturers, materials scientists, and designers alike seek to exploit novel fabrication technologies to improve component performance. Recent works have demonstrated the feasibility of producing materials with controlled microstructures across various length scales. However, the experimental approach towards exploring the process-structure space can be laborious and costly. This is particularly true if also considering scan pattern optimization which is well suited for processes such as powder bed fusion electron beam melting. In this work we propose an approach for encoding additive manufacturing layer-wise thermal response signatures using self-supervised representation learning. Thermal simulations from a reduced order model are utilized to estimate the spatiotemporal response during printing. A machine learning framework, using video-transformers, is utilized to efficiently distill spatiotemporal patterns into a compact latent space representation. This latent state representation encodes the relevant physics which is then utilized to establish a data-driven process-structure model for an additively manufactured Ni-based superalloy. In conclusion, the proposed methodology could potentially be used towards in-situ process monitoring, scan pattern experimental design, and component qualification.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Data-driven high-fidelity 2D microstructure reconstruction via non-local patch-based image inpainting

Microstructure reconstruction problems are usually limited to the representation with finitely many number of phases, e.g. binary and ternary. However, images of microstructure obtained through experimental, for example, using microscope, are often represented as a RGB or grayscale image. Because the phase-based representation is discrete, more rigid, and provides less flexibility in modeling the microstructure, as compared to RGB or grayscale image, there is a loss of information in the conversion. In this paper, a microstructure reconstruction method, which produces images at the fidelity of experimental microscopy, i.e. RGB or grayscale image, is proposed without introducing any physics-based microstructure descriptor. Furthermore, the image texture is preserved and the microstructure image is represented with continuous variables (as in RGB or grayscale images), instead of binary or categorical variables, which results in a high-fidelity image of microstructure reconstruction. The advantage of the proposed method is its quality of reconstruction, which can be applied to any other binary or multiphase 2D microstructure. The proposed method can be thought of as a subsampling approach to expand the microstructure dataset, while preserving its image texture. Moreover, the size of the reconstructed image is more flexible, compared to other machine learning microstructure reconstruction method, where the size must be fixed beforehand. In addition, the proposed method is capable of joining the microstructure images taken at different locations to reconstruct a larger microstructure image. A significant advantage of the proposed method is to remedy the data scarcity problem in materials science, where experimental data is scare and hard to obtain. The proposed method can also be applied to generate statistically equivalent microstructures, which has a strong implication in microstructure-related uncertainty quantification applications. The proposed microstructure reconstruction method is demonstrated with the UltraHigh Carbon Steel micrograph DataBase (UHCSDB).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Effects of secondary void-initiating particles on the steady-state crack growth resistance of high-strength steel

In this investigation, we focus on the dependence of stable crack growth resistance, as measured by the short-rod fracture toughness test, on secondary void-initiating particles such as AlN and Ti-based grain-refining precipitates in a variety of high-strength steels with tempered martensitic microstructures. The model developed by Ritchie and Thompson is modified to illustrate the significant amounts of toughening that can result from the refinement of secondary particles. Analysis of the data suggests that material strength is a predominant factor in increasing the short-rod fracture toughness relative to linear-elastic measures of initiation fracture toughness, but the extent of toughening is limited by the size and number density of secondary particles in the microstructure. The variation in estimates of secondary microvoid initiation and growth strains with precipitate size reinforce the notion that primary fracture at non-metallic inclusions and secondary fracture at smaller particles occur as sequential processes with a degree of concurrence that is dependent on the state of precipitation in both particle dispersions. Toughening in this connection is maximized by increases in microvoid growth strain that result from decreases in the size and areal number density of secondary void-initiating particles. Finally, the occurrence of transient instabilities during crack extension in short-rod specimens is explained with a phenomenological model that relates crack growth stability to natural variations in the dispersion of secondary void-initiating particles in the microstructure.

42 ENGINEERING↗
DOE OSTI2013

Hybrid models for the simulation of microstructural evolution influenced by coupled, multiple physical processes

Most materials microstructural evolution processes progress with multiple processes occurring simultaneously. In this work, we have concentrated on the processes that are active in nuclear materials, in particular, nuclear fuels. These processes are coarsening, nucleation, differential diffusion, phase transformation, radiation-induced defect formation and swelling, often with temperature gradients present. All these couple and contribute to evolution that is unique to nuclear fuels and materials. Hybrid model that combines elements from the Potts Monte Carlo, phase-field models and others have been developed to address these multiple physical processes. These models are described and applied to several processes in this report. An important feature of the models developed are that they are coded as applications within SPPARKS, a Sandiadeveloped framework for simulation at the mesoscale of microstructural evolution processes by kinetic Monte Carlo methods. This makes these codes readily accessible and adaptable for future applications.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Predicting extreme anisotropy and shape variations in impact testing of tantalum single crystals

We report recent Taylor cylinder impact tests carried out for Ta single crystals showed strong variations in dimensional changes for different crystallographic directions aligned with the cylindrical axis. In order to capture the effect of crystallography on the deformation characteristics and final shapes of the impacted cylinders, a single crystal material subroutine is adapted and embedded in the solid mechanics/dynamics Finite Element solver Abaqus to simulate the aforementioned single crystal Ta Taylor impact experiments. Details of the coupled model implementation, and insights on the role played by single crystal anisotropic flow on the deformation behavior across a broad range of strain rates and temperatures for different single crystal orientations are presented and discussed. We demonstrate the predictive capability of the adopted crystal plasticity model to capture the significant role played by crystal orientation-induced anisotropy, as well as strain hardening and adiabatic heating, on the dynamic deformation response of crystalline materials. This re-emphasizes the need of microstructure-aware models to improve the accuracy of simulations for high-consequence engineering design.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Parallel simulation via SPPARKS of on-lattice kinetic and Metropolis Monte Carlo models for materials processing

Abstract SPPARKS is an open-source parallel simulation code for developing and running various kinds of on-lattice Monte Carlo models at the atomic or meso scales. It can be used to study the properties of solid-state materials as well as model their dynamic evolution during processing. The modular nature of the code allows new models and diagnostic computations to be added without modification to its core functionality, including its parallel algorithms. A variety of models for microstructural evolution (grain growth), solid-state diffusion, thin film deposition, and additive manufacturing (AM) processes are included in the code. SPPARKS can also be used to implement grid-based algorithms such as phase field or cellular automata models, to run either in tandem with a Monte Carlo method or independently. For very large systems such as AM applications, the Stitch I/O library is included, which enables only a small portion of a huge system to be resident in memory. In this paper we describe SPPARKS and its parallel algorithms and performance, explain how new Monte Carlo models can be added, and highlight a variety of applications which have been developed within the code.

36 MATERIALS SCIENCE↗