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At least 757 records · Page 42

All sources NASA NTRS DOE OSTI

DOE OSTI2025

Effect of pore fluid chemistry on the mechanical behavior of a divalent compacted bentonite, an experimental and constitutive study

Ongoing research in isolating high-level nuclear waste and spent fuel has highlighted compacted bentonite as a suitable material for engineered barrier systems in deep geological repositories due to its extraordinary swelling and retention properties. This research focuses on the chemo-mechanical behavior of compacted bentonite exposed to different pore fluids with different concentrations and loading conditions. The study involves swelling pressure and compressibility experiments along with mineralogy analysis employing X-ray diffraction (XRD) and Cation exchange. The tests were conducted on BCV (a Mg/Ca- bentonite) compacted at a dry density of 1.48 ± .02 Mg/m 3 . An advanced chemical-mechanical constitutive model for unsaturated highly expansive clays was adopted to simulate the material response and better understand its behavior. The model is able to account for the main phenomena at both macro and microstructural levels and the interactions between them. The model successfully replicated experimental observations. The XRD analyses support the macroscopic observation, indicating that salinity impacts crystalline swelling as demonstrated by the reduction of basal spacing from 19.27 Å to 15.68 Å when the osmotic suction increases from 0 MPa to 33 MPa. The results suggested that the osmotic pressure generated by the concentration in the pore fluids promotes a reduction in swelling pressures, swelling strains, and crystalline swelling of clay minerals. Also, it affects the pre-consolidation stress and the compressibility of the compacted samples. In conclusion, it was also observed that both solution type and solution concentration impact the clay swelling pressure.

Chemo-mechanical constitutive model↗

DOE OSTI2023

Unraveling local structures of Salt-in-Water and Water-in-Salt electrolytes via ab initio molecular dynamics

Water-in-salt electrolytes (WiSE) are attractive for electrochemical energy storage applications owing to their wide electrochemical stability windows and inherent safety. The high concentration of salts is widely known to suppress the decomposition of water, which otherwise limits the stability of conventional aqueous salt-in-water electrolytes (SiWE). Nevertheless, the microstructural features of WiSE that lead to this enhanced stability have not yet been fully elucidated. In this work, ab initio molecular dynamics simulations were performed to study the energetic, structural, and spectroscopic properties of LiTFSI SiWE and WiSE solutions. A detailed mapping of water-water and water-anion hydrogen bonding and cation–anion electrostatic interactions is reported for both electrolytes. Structural features are presented in terms of both radial and spatial distribution functions. Analysis of IR and vibrational power spectra reveal key differences in the intermolecular interactions in SiWE and WiSE that arise from modified solvation shell structures. Finally, the results obtained herein reveal the most important structural and spectroscopic differences between the electrolytes in normal and superconcentrated concentrations.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Microstructure homogenization of concrete used in nuclear power plants

Almost all nuclear power plants in the United States are operating past their intended lifetimes or are requesting lifetime extensions. Therefore, understanding changes to the concrete containment structure over time is crucial to evaluate the structure’s continued viability. Concrete materials are heterogeneous particulate composites that exhibit viscoelastic material properties, which can lead to slow deformation over time, causing stress redistribution and the potential for creep cracking. A code to generate random, three dimensional (3D) concrete microstructures has been developed and paired with finite element analysis to predict the long-term viscoelastic properties of concrete. Data from these simulations are used to develop constitutive equations for the viscoelastic behavior of the homogenized concrete. The codes in this work are used to virtualize laboratory experiments, to obtain long-term creep data in a faster, cheaper manner. To validate this work, the simulated creep behavior of concrete is compared to 800 d of experimental data that has been extended to 27 y of data using the Time-Temperature superposition (TTS) principal. Excellent agreement between the simulation results and experimental data is seen.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

Si Microwire-Array Photocathodes Decorated with Cu Allow CO 2 Reduction with Minimal Parasitic Absorption of Sunlight

High loadings of Cu were integrated on the light-facing side of Si microwire arrays used under simulated sunlight for the photoelectrochemical reduction of CO 2 (aq) to hydrocarbons in 0.10 M KHCO 3 (aq). Radial-junction n + p-Si microwire arrays decorated with Cu exhibited absolute photocurrent densities comparable to an uncovered Si surface. Moreover, with respect to a Cu foil electrode, the positive shift in the onset potential for hydrocarbon formation at n + p-Si/Cu microwire arrays was equal to or greater than the photovoltage of the semiconductor alone. Selective electrodeposition of Cu on the tips and sidewalls of Si microwires was responsible for the minimal parasitic reflection and absorption exhibited by the catalyst, such that light-limited, absolute current densities >25 mA·cm –2 were sustained for 48 h under simulated sunlight. Photoelectrodes prepared from semiconductors with low diode quality factors and electrocatalysts with large Tafel slopes are shown to benefit from increased microstructured surface area. Si microwire arrays are thus suitable for photoelectrochemical reactions requiring high loadings of metallic and reflective electrocatalysts.

25 ENERGY STORAGE↗

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 OSTI2024

Efficient mapping between void shapes and stress fields using Deep Convolutional Neural Networks with sparse data

Establishing fast and accurate structure-to-property relationships is an important component in the design and discovery of advanced materials. Physics-based simulation models like the finite element method (FEM) are often used to predict deformation, stress, and strain fields as a function of material microstructure in material and structural systems. Such models may be computationally expensive and time intensive if the underlying physics of the system is complex. This limits their application to solve inverse design problems and identify structures that maximize performance. In such scenarios, surrogate models are employed to make the forward mapping computationally efficient to evaluate. However, the high dimensionality of the input microstructure and the output field of interest often renders such surrogate models inefficient, especially when dealing with sparse data. Deep convolutional neural network (CNN) based surrogate models have shown great promise in handling such high-dimensional problems. In this paper, a single ellipsoidal void structure under a uniaxial tensile load represented by a linear elastic, high-dimensional and expensive-to-query, FEM model. We consider two deep CNN architectures, a modified convolutional autoencoder framework with a fully connected bottleneck and a UNet CNN, and compare their accuracy in predicting the von Mises stress field for any given input void shape in the FEM model. Additionally, a sensitivity analysis study is performed using the two approaches, where the variation in the prediction accuracy on unseen test data is studied through numerical experiments by varying the number of training samples from 20 to 100.

surrogate modeling; convolutional neural networks;↗

DOE OSTI2024

Phase-Field Modeling of Mechanical Damages in Ceramic Matrix Composites

Developed a phase-field model for mechanical damages in CMCs, which incorporates the CMC microstructures, matrix cracking, fiber breakage, and interfacial sliding. Two types of mechanisms, fiber bridging and fiber pull-out, are considered. The obtained simulation results agree with experimental observations and an analytical solution. Simulation results suggest that the performance of CMCs would be enhanced with thicker fibers, longer fibers, and higher fiber density. Opposite trends of interfacial sliding resistances are suggested for the two types of situations. In reality, a mixture of the two situations may exist, and then an intermediate interfacial sliding resistance may be optimal.

Xue, Fei↗

NASA NTRS1989

Identification of a cast iron alloy containing nonstrategic elements

A program was performed to address the mechanical and environmental needs of Stirling engine heater head and regenerator housing components, while reducing the dependence on strategic materials. An alloy was developed which contained no strategic elemental additions per se. The base is iron with additions of manganese, molybdenum, carbon, silicon, niobium, and ferro-chromium. Such an alloy should be producible on a large scale at very low cost. The resulting alloy, designated as NASAUT 4G-Al, contained 15 Mn, 15 Cr, 2 Mo, 1.5 C, 1.0 Si, 1.0 Nb (in weight percent) with a balance of Fe. This alloy was optimized for chemistry, based upon tensile strength, creep-rupture strength, fracture behavior, and fatigue resistance up to 800 C. Alloys were also tested for environmental compatibility. The microstructure and mechanic properties (including hardness) were assessed in the as-cast condition and following several heat treatments, including one designed to simulate a required braze cycle. The alloy was fabricated and characterized in the form of both equiaxed and columnar-grained castings. The columnar grains were produced by directional solidification, and the properties were characterized in both the longitudinal and transverse orientations. The NASAUT 4G-Al alloy was found to be good in cyclic-oxidation resistance and excellent in both hydrogen and hot-corrosion resistance, especially in comparison to the baseline XF-818 alloy. The mechanical properties of yield strength, stress-rupture life, high-cycle-fatigue resistance, and low-cycle-fatigue resistance were good to excellent in comparison to the current alloy for this application, HS-31 (X-40), with precise results depending in a complex manner on grain orientation and temperature. If required, the ductility could be improved by lowering the carbon content.

Cooper, C. V.↗

NASA NTRS1990

Demo of three ways to use a computer to assist in lab

The objective is to help the slow learner and students with a language problem, or to challenge the advanced student. Technology has advanced to the point where images generated on a computer can easily be recorded on a VCR and used as a video tutorial. This transfer can be as simple as pointing a video camera at the screen and recording the image. For more clarity and professional results, a board may be inserted into a computer which will convert the signals directly to the TV standard. Using a computer program that generates movies one can animate various principles which would normally be impossible to show or would require time-lapse photography. For example, you might show the change in shape of grains as a piece of metal is cold worked and then show the recrystallization and grain growth as heat is applied. More imaginative titles and graphics are also possible using this technique. Remedial help may also be offered via computer to those who find a specific concept difficult. A printout of specific data, details of the theory or equipment set-up can be offered. Programs are now available that will help as well as test the student in specific areas so that a Keller type approach can be used with each student to insure each knows the subject before going on to the next topic. A computer can serve as an information source and contain the microstructures, physical data and availability of each material tested in the lab. With this source present unknowns can be evaluated and various tests simulated to create a simple or complex case study lab assignment.

Neville, J. P.↗

DOE OSTI2025

Meso-scale modeling of UO2 nuclear fuel to high burnup

To improve the economics of light water reactors for commercial nuclear energy generation, utility operators are seeking to obtain regulatory approval to run UO2 fuel to higher levels of burnup. One potential impediment to obtaining this approval is the phenomenon of fuel fragmentation, relocation, and dispersal (FFRD). FFRD can result when fuel experiences a rapid temperature transient, such as that occurring during a Loss Of Coolant Accident (LOCA). FFRD has historically been most associated with the rim region in UO2 fuel pellets, where the phenomenon of fragmentation is also referred to as pulverization due to the small size of the fragments. More recent evidence suggests that the so-called “dark zone” (due to its appearance in micrographs) that can be observed in the mid-radial regions of high burnup fuel is also susceptible to FFRD. Although empirical fuel performance models have been developed that can adequately predict pulverization in the rim region under typical LWR conditions, a scientific understanding of what underlies fuel restructuring and subsequent FFRD is lacking even in the rim region, and no models are currently available for the behavior the dark zone. To address these challenges, the U.S. Department of Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program has employed a multi-scale modeling approach to improve scientific understanding and develop new fuel performance models. In this talk, I will focus on meso-scale efforts, which form a crucial link between atomic-scale and engineering-scale models. Phase-field modeling combined with cluster dynamics is used to predict the restructuring process in the rim region. Phase-field fracture modeling, informed by atomistic simulations, is used to predict the onset of pulverization in the rim region. Combining these techniques together allows the extent of rim pulverization to be predicted. The formation and evolution of the dark zone has also been simulated with the phase-field method, using an improved approach to vacancy source term parameterization. The work shows the important impact of microstructure on fuel performance.

fracture↗

DOE OSTI2021

A Review: Microstructural and Phase Evolution in Alloys during Extended Plastic Deformation

Materials in service and during processing are often subjected to plastic deformation. For multi-phase metallic alloys, simple geometric models and atomistic simulations suggest that two distinctive regimes in these materials’ evolution during deformation exist. At low strains, evolutions are often dominated by the kinetic roughening of interfaces, which results from the superdiffusive transport of matter in sheared crystals. Furthermore, at high strains and temperatures where thermal diffusion is sluggish, on the other hand, shearing- induced forced atomic mixing dominates these evolutions, resulting in significantly enhanced solubility. Distinguishing these two regimes is shown to provide a convenient framework for rationalizing and analyzing recent experiments and simulations on wear of layered structures in the low strain-regime and nonequilibrium phase co-existence and self-organization in highly immiscible or reactive alloy systems in the high-strain regime.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Virtual texture analysis to understand microstructure effects on deformation twinning and detwinning behavior in BCC metals

Understanding and predicting deformation twinning contributions to plastic deformation in BCC metals has been a long-standing challenge due to the interplay with dislocation slip and non-Schmid effects that render an asymmetry under tension and compression. This paper uses molecular dynamics simulations to understand the effect of unloading and grain orientation on deformation twinning in a nanocrystalline Fe (nc-Fe) system as a model BCC metal. The nc-Fe system is loaded under uniaxial stress tension and compression to understand the effect of grain orientation (Schmid effects) on deformation twinning behavior and the tension/compression asymmetry (non-Schmid effects). A new virtual texture analysis “VirTex” tool is used to understand the role of grain orientation on the nucleation of twins and their contributions to the observed stress–strain response. For certain grain orientations, the twinnability is observed to be different in tension and compression. In addition, the flow stress accommodation from twins in certain grains is observed to be different in tension and compression and different from that for the grains. Subsequent unloading leads to detwinning in the deformed microstructures, where the extent of detwinning depends on the strain from which the system is unloaded and on the morphology of the twin. Lastly, the simulations are carried out to analyze the role of the Schmid factor on the twinnability and asymmetry in tension and compression.

Kannan, Aadhithyan [University of Connecticut, Sto↗

DOE OSTI2021

Segregation competition and complexion coexistence within a polycrystalline grain boundary network

Interfacial segregation can stabilize grain structures and even lead to grain boundary complexion transitions. However, understanding of the complexity of such phenomena in polycrystalline materials is limited, as most studies focus on bicrystal geometries. In this work, we investigate interfacial segregation and subsequent complexion transitions in polycrystalline Cu-Zr alloys using hybrid Monte Carlo/molecular dynamics simulations. No significant change in the grain size or structure is observed upon Zr dopant addition to a pure Cu polycrystal at moderate temperature, where grain boundary segregation is the dominant behavior. Segregation within the boundary network is inhomogeneous, with some boundaries having local concentrations that are an order of magnitude larger than the global value and others having almost no segregation, and changes to physical parameters such as boundary free volume and energy are found to correlate with dopant concentration. Further, another alloy sample is investigated at a higher temperature to probe the occurrence of widespread transitions in interfacial structure, where a significant fraction of the originally ordered boundaries transition to amorphous complexions, demonstrating the coexistence of multiple complexion types, each with their own distribution of boundary chemical composition. Overall, this work highlights that interfacial segregation and complexion structure can be diverse in a polycrystalline network. Here, the findings shown here complement existing computational and experimental studies of individual interfaces and help pave the way for unraveling the complexity of interfacial structure in realistic microstructures.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Laser‐Powder Melt Pool Solidification Dynamics and Microstructural Engineering of Ti‐5553 Microlattices

The fine geometric and topological control afforded by additive manufacturing technologies has enabled the manufacture of architected materials across length scales, and enabling tunable mechanical performance as a function of local and global design. Progress has been made to tune the mechanical response of architected materials through geometry, but understanding how the geometry and processing conditions will inform the microstructure remains a challenge due to the rapid solidification in laser powder bed fusion. This study uses in situ X-ray imaging and electron backscatter diffraction microscopy to demonstrate that the melt pool size, microstructure morphology, and elastic strain distribution is influenced by a combination of lattice geometry and laser processing conditions. These results indicate that within larger melt pools the local thermal gradients are sufficient to enable a columnar-to-equiaxed transition across the melt pool. Furthermore, the solidification mechanisms producing these microstructures are examined across the first 5 ms of melting and solidification, described via in situ high-speed X-ray imaging and mirrored via multiphysics simulation.

additive manufacturing↗

NASA NTRSDate not supplied

Fast Assessment of Metal Performance through Dislocation Physics and Machine Learning

The microstructure of metals is key to their mechanical properties. The types, density, composition and morphology of crystal defects all have pronounced impact on the properties. Changes to the microstructure occurring during processing and use can be very striking. The emerging technology additive manufacturing (AM) has the potential to improve performance by allowing optimized designs, but the process and environments can lead to unusual microscale features whose properties must be understood and characterized to enable higher technological readiness levels and application. Experimentally, an extensive evaluation of mechanical properties of 3D printed metals is a challenge, and anomalous effects related to the AM process add complexity. We present a new machine learning (ML) model predicting mechanical response based on dislocation mediated plasticity simulations. A large set of 3D discrete dislocation dynamics simulations with wide ranges of loading conditions is transformed to preprocessed data ready for training with the ML model. The trained model can predict the mechanical response of Mo30W for a given microstructure evolution, providing key information essential for optimization of AM processing.

Jaehyun Cho↗

NASA NTRSDate not supplied

Fast Assessment of Metal Performance through Dislocation Physics and Machine Learning

Jaehyun Cho↗

DOE OSTI2025

Best of both worlds: Enforcing detailed balance in machine learning models of transition rates

The slow microstructural evolution of materials often plays a key role in determining material properties. When the unit steps of the evolution process are slow, direct simulation approaches such as molecular dynamics become prohibitive and Kinetic Monte-Carlo (kMC) algorithms, where the state-to-state evolution of the system is represented in terms of a continuous-time Markov chain, are instead frequently relied upon to efficiently predict long-time evolution. The accuracy of kMC simulations however relies on the complete and accurate knowledge of reaction pathways and corresponding kinetics. This requirement becomes extremely stringent in complex systems such as concentrated alloys where the astronomical number of local atomic configurations makes the a priori tabulation of all possible transitions impractical. Machine learning models of transition kinetics have been used to mitigate this problem by enabling the efficient on-the-fly prediction of kinetic parameters. While conventional KMC methods based on transition state theory naturally yield reversible dynamics that exactly obey the detailed balance criterion, providing strong guarantees on the properties of the stationary distribution, many recently-proposed ML-based approaches to barrier predictions provide no such guarantees. In this study, we derive conditions under which physics-informed ML architectures exactly enforce the detailed balance condition by construction, even when relying on non-extensive descriptions of states in terms of local environments around mobile defects. In conclusion, using the diffusion of a vacancy in a concentrated alloy as an example, we show that such ML architectures also exhibit superior performance in terms of prediction accuracy, demonstrating that the imposition of physical constraints can facilitate the accurate learning of barriers at no increase in computational cost.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Indentation investigation of 304L stainless steel friction stir weld simulated crack repair

Simulated cracks were repaired in 304L stainless steel using low temperature friction stir welding. Indentation studies were carried out to understand the effect of microstructural features on the mechanical property variation across the weld and to measure the size of the weld zones with a quantitative technique. Microhardness and nanoindentation hardness profiles were constructed on a transverse section across the weld. The data obtained were correlated by extrapolating the nanoindentation hardness to greater depths which showed that the nanoindentation hardness closely reflects the microhardness values throughout the weld. Finally, grain size in the stir zone (SZ) was found to vary with the tool temperature which, in turn, alters the nanoindentation modulus variability and higher tool temperature resulted in widening of the SZ.

36 MATERIALS SCIENCE↗