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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Microstructure-process relationships in monolithic U-10Mo fuel foil single-pass rolling: A parametric simulation study

In this work, a previously validated coupling of Kinetic Monte Carlo (KMC) Potts Model and finite element method (FEM) simulations was implemented to investigate the effects of microstructural features in as-cast and homogenized monolithic U-10Mo foils on the emergent microstructure after rolling and reheating. Parameters that could potentially affect recrystallization behavior of the rolled U-10Mo foils were considered: grain size distribution, uranium carbide (UC) size distribution, UC volume fraction, spatial distribution of UC, and rolling reduction magnitude. Grain structure and the magnitude of rolling reduction have the strongest influence on recrystallization kinetics and the fabricated grain size distribution. The UC distribution had only a weak effect on the recrystallization kinetics and final microstructures. While particle-stimulated nucleation (PSN) occurred in simulation more frequently as grain size increased, its incidence did not appear to considerably affect the recrystallization kinetics or grain size distribution.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Microstructure characterization and elastic-plastic self-consistent simulation studies of anisotropic deformation of β -tin

Uniaxial compression tests of 99.9% pure polycrystalline $β$-tin (Sn) were conducted at various strain rates (10 -3 /s, 10 -1 /s and 1/s) and temperatures (294 K and 193 K) to understand the effects of these variables on the stress–strain response, microstructure evolution, and stress relaxation behavior. Multiple Sn specimens were subjected to complex compressive loading/unloading/reloading paths at different strain rates. Specimens initially compressed at higher strain rates showed strain rate-dependent texture evolution and more pronounced relaxation upon reloading, as compared to those pre-strained at lower rates at room temperature (RT). Compression tests conducted at low temperature (193 K) revealed increased strength, similar to the enhanced strength observed when the strain rate was increased at RT. Neutron diffraction was employed to characterize the initial and final bulk textures of the RT specimens. Electron backscatter diffraction was utilized to examine the crystallographic grain orientation and morphology, thereby identifying the signatures of dislocation-mediated deformation, grain refinement, recrystallization, and twinning behaviors. Elastic-plastic self-consistent simulations were performed to investigate the deformation modes responsible for the strain rate-dependent macroscopic stress–strain response and texture evolution. Effects of crystallographic orientation on stress relaxation behavior was also examined. In conclusion, the model predictions are in reasonable agreement with experimental observations.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Model of Grain Boundary Complexion Transitions and Grain Growth in Yttria-Doped Alumina

In this work, we present a physically-parameterized microstructure evolution model for the Yttria-doped alumina system. Yttria-doped alumina is a well-known ceramic system which undergoes first-order phase-like transitions at grain boundaries, which can radically alter interface properties. The change in interfacial properties in turn can radically change microstructure outcomes during processing, including the induction of abnormal grain growth modes. In this work, we develop a simulation that evolves alumina microstructure as a function of yttria concentration and temperature. In the window studied, we achieve strong agreement with reviewed experimental results in identifying the windows for large grains, small grains, abnormal grain growth, and complexion transition kinetics. We then apply the model to study and demonstrate how the possible inclusion of second-phase particles or uneven solute distribution profiles will impact microstructure evolution. It is found that particles do not significantly affect abnormal grain growth in the window studied (but do lead to reduced grain size through pinning effects). It is found that even modest amounts of solute inhomogeneity will result in substantial changes in microstructure outcomes, frequently leading to clusters of abnormal grains. This model largely corroborates the expectations and hypotheses made from recent experimental studies in oxide-doped alumina systems. Further, it is found that there exists a peak transition fraction for the system at which abnormal grain size tends to be maximized.

Grain Growth, Grain Boundary Complexion, abnormal ↗
DOE OSTI2022

Simulation-guided analysis of resonant soft X-ray scattering for determining the microstructure of triblock copolymers

Resonant soft X-ray scattering (RSoXS) probes structure with chemical sensitivity that is useful for determining the morphology of multiblock copolymers. However, the hyperspectral scattering data produced by this technique can be challenging to interpret. Here, in this study, we use computational scattering simulations to extract the microstructure of a model triblock copolymer from the energy-dependent scattering from RSoXS. An ABC triblock terpolymer formed from poly(4-methylcaprolactone) (P4MCL), poly(2,2,2-trifluoroethylacrylate) (PTFEA), and poly (dodecylacrylate) (PDDA), P4MCL-block-PTFEA-block-PDDA, was synthesized as the model triblock system. Through quantitative evaluation of simulated scattering data from a physics-informed set of candidate structure models against experimental RSoXS data, we find the best agreement with hexagonally packed core–shell cylinders. This result is also consistent with electron-density reconstruction from hard X-ray scattering data evaluated against electron-density maps generated with the same model set. These results demonstrate the utility of simulation-guided scattering analysis to study complex microstructures that are challenging to image by microscopy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Combined crystal plasticity and grain boundary modeling of creep in ferritic-martensitic steels: II. The effect of stress and temperature on engineering and microstructural properties

This paper describes a series of physically-based crystal plasticity finite element method (CPFEM) simulations of long-term creep and creep rupture of Grade 91 steel. It is Part 2 of a two part series of papers. Part 1 describes the simulation framework; this part focuses on specific simulations and on how the predicted long-term creep properties of Grade 91 compare to the assumptions used in current high temperature design practices. This work extends the model developed in Part 1 to look at creep properties at different temperatures, principal stresses, and multiaxial stress states. The simulations show that empirically extrapolating creep rupture stresses from short-term experimental data may substantially over predict the actual long-term creep properties of Grade 91. Additionally, the CPFEM calculations predict a transition from notch strengthening creep behavior for high values of maximum principal stress and moderate notch severity to notch weakening behavior for low principal stresses and more severe notches. The latter regime better categorizes conditions in engineering components designed for long term elevated temperature use, which implies Grade 91 may be a notch weakening material in actual service. This would have a significant impact on high temperature design practices, though confirmatory test data on long-life, low stress notched specimens is difficult to obtain. Finally, one advantage of the physically-based modeling approach adopted here is that the simulation results also elucidate the microstructural mechanisms causing the macroscopic trends in engineering properties predicted by the simulations. This paper shows that the detailed micromechanical mechanisms predicted by the CPFEM simulations can be abstracted with a simple micromechanical model that can be used to both explain the detailed results and make improved predictions of engineering properties from experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Comparison of Solidification Structures and Submicroscale Cellular Segregation in Rapidly Solidified Stainless Steels Produced via Two-Piston Splat Quenching and Laser Powder Bed Fusion

Fusion-based additive manufacturing techniques leverage rapid solidification (RS) conditions to create parts with complex geometries, unique microscale/nanoscale morphological features, and elemental segregation. Three custom composition stainless steel alloys with varying chromium equivalence to nickel equivalence ratio (Cr eq /Ni eq ) between 1.53 and 1.95 were processed using laser powder bed fusion (LPBF) and/or two-piston splat quenching (SQ) to produce solidification rates estimated between 0.4 and 0.8 m/s. Both scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were utilized to collect high-resolution images, electron backscatter diffraction (EBSD) phase identification, and measure cellular segregation. Similar features were observed in both LPBF and SQ samples including phase and microstructure, nanoscale oxide particles, cell size, and segregation behavior. However, dislocation pileup was observed along the cell boundaries only in the LPBF austenite solidified microstructure. Targeted adjustment of the SQ feedstock Cr and Ni concentrations, within the ASTM A240 specification for 316L resulted in no observable impact on the cell size, oxide particle size, or magnitude of segregation. Also, the amount of Ni segregation in the ferrite solidified microstructures did not significantly differ, regardless of Cr/Ni eq or processing technique. Here, SQ is demonstrated as capable of simulating RS rates and microstructures similar to LPBF for use as an alternative screening tool for new RS alloy compositions.

42 ENGINEERING↗
DOE OSTI2021

A mesoscopic digital twin that bridges length and time scales for control of additively manufactured metal microstructures

We present our recent development of an integrated mesoscale digital twin (DT) framework for relating processing conditions, microstructures, and mechanical responses of additively manufactured (AM) metals. In particular, focusing on the laser powder bed fusion technique, we describe how individual modeling and simulation capabilities are coupled to investigate and control AM microstructural features at multiple length and time scales. We review our prior case studies that demonstrate the integrated modeling schemes, in which high-fidelity melt pool dynamics simulations provide accurate local thermal profiles and histories to subsequent AM microstructure simulations. We also report our new mechanical response modeling results for predicted AM microstructures. In addition, we illustrate how our DT framework has been validated through modeling–experiment integration, as well as how it has been practically utilized to guide and analyze AM experiments. Finally, we share our perspectives on future directions of further development of the DT framework for more efficient, accurate predictions and wider ranges of applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Active- and transfer-learning applied to microscale-macroscale coupling to simulate viscoelastic flows

Active- and transfer-learning are applied to microscale dynamics of polymer flows for the multiscale discovery of effective constitutive approximations required in viscoelastic flow simulation. The result is macroscopic rheology directly connected to a microstructural model. Micro and macroscale simulations are adaptively coupled by means of Gaussian process regression (GPR) to run the expensive microscale computations only as necessary. This multiscale method is demonstrated with flows of a polymer solution as a model system. At the microscale level dissipative particle dynamics (DPD) is employed to model the fluid as a suspension of bead-spring micro-structures subjected to steady shear flow. The results yield the non-Newtonian viscosity and the first normal stress difference at strain rates as training data used in a GPR model. DPD parameters are calibrated with respect to experimental data for a real polymer solution. Compliance with these data requires adjustment of the DPD model's cutoff radius, which then becomes a function of the second invariant of the strain rate tensor. The FENE-P model is chosen for the macroscale description using the spectral element method (SEM) to simulate channel flow and flow past a circular cylinder. The DPD results at the lowest possible shear strain rate yield an estimate of the zero-shear rate viscosity, which allows the initiation of the macroscale flow by SEM as a Newtonian fluid. The resulting strain-rate field is surveyed to determine additional shear strain rate sampling points for the DPD system. This new information allows an initial fitting of parameters of the constitutive equation followed by new SEM simulations at the macroscale. Additionally, guided by active-learning GPR to select new sampling points, this process continues until convergence is achieved. The effectiveness of this new simulation paradigm for viscoelastic flows is tested with different macroscale operating conditions. The effective closure learned in the channel simulation is then transferred directly to the flow past a circular cylinder at low Reynolds number, where the results show that only two additional DPD simulations are required to achieve a satisfactory constitutive model. With an increase of the Reynolds number, the active-learning scheme automatically detects the inaccuracy of the learned constitutive model, and initiates additional DPD simulations for the extra data needed to once again close the microscale-macroscale coupled system. This new paradigm of active- and transfer-learning for multiscale modeling is readily applicable to other microscale-macroscale coupled simulations of complex fluids and other materials. Furthermore, the coupling between microscale and macroscale solvers can be seamlessly implemented with our open source multiscale universal interface (MUI) library.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Mesoscale Modeling of the Effects of Accelerated Burnup on UO2 Microstructural Evolution

Accelerating the nuclear fuel qualification process will rely on some combination of advanced modeling and simulation techniques with accelerated irradiation testing and separate effects experiments to enable the development of new fuel concepts in a shorter time frame. One of the key challenges to successfully leveraging accelerated irradiation tests will be understanding the artifacts that may be introduced with accelerated accumulation of dose and/or burnup. This work presents phase field (MARMOT) simulations of the evolution of representative 2D UO2 microstructures up to 40 MWd/kgU. Simulations were performed under both commercial light water reactor fuel conditions as well as those that would be expected for highly accelerated (~10x) burnup conditions similar to those used in the MiniFuel irradiations in Oak Ridge National Laboratory’s High Flux Isotope Reactor. The phase field model was coupled with a discrete nucleation algorithm to model re- structuring at high burnup. The effect of the different fission rates in both microstructures was investigated at two temperatures: 650?C and 800?C. The lower temperature simulations both showed an onset of restructuring at nearly 60 MWd/kgU. More extensive restructuring was obtained in the MiniFuel microstructure compared with that of the PWR fuel. At 800?C, no restructuring was obtained as a result of the thermally activated diffusion of Xe atoms and U vacancies to fission gas bubbles, which reduces the nucleation driving force. These results highlight the importance of using modeling and simulation tools to inform the environmental conditions during targeted accelerated irradiation tests to extract the most useful fuel performance data.

accelerated fuel qualification, Phase Field, Restr↗
NASA NTRSDate not supplied

Towards Integrated Computational Materials Engineering for Quantifying Performance Impacts of Microstructure and Defect Interactions in Powder Bed Fusion Parts

Powder bed fusion (PBF) additive manufacturing (AM) enables the creation of parts with complexity and functionality levels that were previously impossible with traditional manufacturing methods. By modifying the laser power, hatch spacing, or the numerous other processing parameters, the PBF process supports the production of a wide set of materials and geometries. However, that same process parameter design flexibility causes the process-design space of PBF to be massive and expensive to explore experimentally. Another challenge is quality variation across a build. As a part is being built, geometric variance between locations, such as at a thin-wall section vs. the bulk material, may cause the specified processing parameters to no longer be acceptable for producing defect-free printing. Furthermore, if the processing parameters deviate during the print process, it is difficult to assess if the part will still perform satisfactorily. Integrated Computational Materials Engineering (ICME) provides a way to understand and address these various challenges. This talk will present advancements in process-structure simulations of PBF at NASA Langley Research Center. The ability to simulate grain-scale PBF microstructures using the Physically Based Monte Carlo method will be demonstrated and compared to experimental measurements. Techniques for simulating three-dimensional lack-of-fusion and keyhole porosity defects based on the specific processing conditions and approaches for integrating the two porosity prediction techniques alongside the computational microstructure evolution models will be shown. Finally, the integration of simulated PBF microstructures, embedded process defects, and crystal plasticity finite element models to elucidate the interaction of porosity and microstructure on micromechanical fields will be demonstrated. These integrated techniques demonstrate an example of using ICME to relate processing to performance for PBF AM materials. With continued maturity, it is hoped that such ICME approaches will lead to next-generation computational-materials supported qualification and certification of AM parts.

Additive manufacturing↗
DOE OSTI2018

Comparison of three-dimensional morphologies of abnormally growing grains between Monte Carlo simulations and experiments of Fe-3% Si steel

Highlights: • Grains with sub-boundaries grew abnormally in the MC simulation with realistic grain boundary energy. • The experimental 3-D morphology of Goss grain was reconstructed from serially sectioned images. • 2-D sectioned microstructures of the MC simulation and experiment have highly irregular shape. • Both simulated and experimental 3-D morphologies of AGG have similar complicated shapes. - Abstract: Three-dimensional (3-D) Monte Carlo simulations of grain growth with sub-boundaries in the presence of precipitates were performed based on the distribution of realistic grain boundary energy data. The simulated grains with sub-boundaries grew abnormally. The simulated and experimental morphologies of the abnormally growing grains in Fe-3%Si steel were compared. The experimental 3-D morphology was reconstructed from serially sectioned images. Both the simulated and experimental 3-D morphologies of abnormally growing grains have highly irregular and complicated shapes whereas both the simulated and experimental 3-D morphologies of normally growing grains have relatively regular and simple shapes. Growth by wetting is responsible for the highly irregular shape of the experimental abnormally growing grains of Fe-3%Si steel as well as for the highly irregular shape of the simulated abnormally growing grains.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Predicting Mechanical Properties from Microstructure Images in Fiber-Reinforced Polymers Using Convolutional Neural Networks

Evaluating the mechanical response of fiber-reinforced composites can be extremely time-consuming and expensive. Machine learning (ML) techniques offer a means for faster predictions via models trained on existing input–output pairs and have exhibited success in composite research. This paper explores a fully convolutional neural network modified from StressNet, which was originally used for linear elastic materials, and extended here for a non-linear finite element (FE) simulation to predict the stress field in 2D slices of segmented tomography images of a fiber-reinforced polymer specimen. The network was trained and evaluated on data generated from the FE simulations of the exact microstructure. The testing results show that the trained network accurately captures the characteristics of the stress distribution, especially on fibers, solely from the segmented microstructure images. The trained model can make predictions within seconds in a single forward pass on an ordinary laptop, given the input microstructure, compared to 92.5 h to run the full FE simulation on a high-performance computing cluster. These results show promise in using ML techniques to conduct fast structural analysis for fiber-reinforced composites and suggest a corollary that the trained model can be used to identify the location of potential damage sites in fiber-reinforced polymers.

Sun, Yixuan (ORCID:0000000311093380)↗
DOE OSTI2022

Heterogenous activation of dynamic recrystallization and twinning during friction stir processing of a Cu-4Nb alloy

An interplay between high degree of shear deformation and deformation-induced heating occurs during friction stir processing (FSP) of metals. In medium-to-low stacking fault energy Cu alloys, this can lead to a complex spatially heterogenous activation of dynamic recrystallization (DRX) and twinning mechanisms. Within the Cu-Nb system, the presence of Nb is further expected to influence the DRX mechanism of the Cu matrix. However, the microstructural changes induced by the co-deformation of Nb during FSP are still not well understood. So, this study uses a combination of multimodal microstructural characterization, solution thermodynamics-based predictions, and computational crystal plasticity simulation to reveal the various microstructural evolution mechanisms that can occur during FSP of a Cu-4at.%Nb binary model alloy. The formation of softer DRX zones, and harder shear localization regions are revealed using electron backscatter diffraction, transmission electron microscopy, atom probe tomography, and crystal plasticity modeling.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Understanding Microstructural Evolution During Rapid Heat Treatment of Microalloyed Steels Through Computational Modeling, Advanced Physical Simulation, and Multiscale Characterization Techniques

An AISI 1045 steel modified with vanadium (V) and niobium (Nb) was studied to evaluate microstructural conditioning prior to and throughout a rapid heat treat process. In order to accomplish this, both computational and physical simulation techniques have been employed with the goal of assessing the microstructural evolution in a medium-carbon bar steel during the rapid austenitization and quenching procedures involved in an induction hardening process. The appropriate thermal profiles for induction hardening were obtained through finite element modeling using Flux 2D software. Physical simulations of the induction hardening process were carried out using a Gleeble{sup ®} 3500. Analysis of prior austenite grain size is complemented by observation of nanoscale carbonitride precipitation via transmission electron microscopy, scanning transmission electron microscopy, and high-energy synchrotron small-angle x-ray scattering. Through a combination of characterization techniques, this study presents a deeper understanding of nano- and microstructural changes occurring in a microalloyed steel during an induction hardening process.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Influence of Thermal History on the Hot Ductility of a Continuously Cast Low Alloyed Cr-Mo Steel

The hot ductility of a low alloyed Cr-Mo steel has been investigated to evaluate the surface cracking sensitivity within the straightening or unbending regime during the continuous casting process. Tensile samples were subjected to various thermal treatments, including melting and solidification, and were tested at deforming temperatures ranging between 600 and 1100 °C using a strain rate of 10{sup −3} s{sup −1}. Hot ductility was evaluated based on reduction in area measurement and metallographic investigations. The investigated steel exhibits a drop in ductility at around 800 °C due to intergranular cracking. Microstructural examinations and supplementary thermokinetic computer simulations were carried out to describe the evolution of the microstructure during solidification and cooling.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Prediction of grain structure after thermomechanical processing of U-10Mo alloy using sensitivity analysis and machine learning surrogate model

Abstract Hot rolling and annealing are critical intermediate steps for controlling microstructures and thickness variations when fabricating uranium alloyed with 10% molybdenum (U-10Mo), which is highly relevant to worldwide nuclear non-proliferation efforts. This work proposes a machine-learning surrogate model combined with sensitivity analysis to identify and predict U-10Mo microstructure development during thermomechanical processing. Over 200 simulations were collected using physics-based microstructure models covering a wide range of thermomechanical processing routes and initial alloy grain features. Based on the sensitivity analysis, we determined that an increase in rolling reduction percentage at each processing pass has the strongest effect in reducing the grain size. Multi-pass rolling and annealing can significantly improve recrystallization regardless of the reduction percentage. With a volume fraction below 2%, uranium carbide particles were found to have marginal effects on the average grain size and distribution. The proposed stratified stacking ensemble surrogate predicts the U-10Mo grain size with a mean square error four times smaller than a standard single deep neural network. At the same time, with a significant speedup (1000×) compared to the physics-based model, the machine learning surrogate shows good potential for U-10Mo fabrication process optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Multiphysics Modeling of Nuclear Materials

The operating environment of nuclear reactors presents many unique challenges both for the materials comprising fuel elements and other structural components within and surrounding the reactor. These environmental challenges are due to the simultaneous exposure to conditions from multiple physical systems, including high temperatures, high radiation fluxes, aggressive chemical environments, and mechanical loading. Numerical simulation has been used for decades as a tool for understanding the behavior of various components in this environment. As these tools have been developed to increasing levels of sophistication, they have been used increasingly both for engineering analysis of components, as well as for simulating the processes of microstructure evolution, which lead to changes in the engineering properties of interest in the materials used in these components. This document provides an overview of the methods used for multiphysics simulation of nuclear materials, and provides examples of the application of these techniques.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

PRISMS-Fatigue computational framework for fatigue analysis in polycrystalline metals and alloys

Abstract The PRISMS-Fatigue open-source framework for simulation-based analysis of microstructural influences on fatigue resistance for polycrystalline metals and alloys is presented here. The framework uses the crystal plasticity finite element method as its microstructure analysis tool and provides a highly efficient, scalable, flexible, and easy-to-use ICME community platform. The PRISMS-Fatigue framework is linked to different open-source software to instantiate microstructures, compute the material response, and assess fatigue indicator parameters. The performance of PRISMS-Fatigue is benchmarked against a similar framework implemented using ABAQUS. Results indicate that the multilevel parallelism scheme of PRISMS-Fatigue is more efficient and scalable than ABAQUS for large-scale fatigue simulations. The performance and flexibility of this framework is demonstrated with various examples that assess the driving force for fatigue crack formation of microstructures with different crystallographic textures, grain morphologies, and grain numbers, and under different multiaxial strain states, strain magnitudes, and boundary conditions.

Chemistry↗