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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Space Plasma Ion Processing of the Lunar Soil: Modeling of Radiation-Damaged Rim Widths on Lunar Grains

Chemically and microstructurally complex altered rims around grains in the finest size fraction (<20 micron) of the lunar regolith are the result of multi-stage processes involving both solar ion radiation damage and nanoscale deposition of impact or sputter-derived vapors. The formation of the rims is an important part of the space weathering process, and is closely linked to key changes in optical reflectance and other bulk properties of the lunar surface. Recent application of field-emission scanning transmission electron microscope techniques, including energy dispersive X-ray spectral imaging, is making it easier to unravel the "nano-stratigraphy" of grain rims, and to delineate the portions of rims that represent Radiation-Amorphized (RA) host grain from overlying amorphous material that represents vapor/sputter deposits. For the portion of rims formed by host grain amorphization (henceforth called RA rims), we have been investigating the feasibility of using Monte Carlo-type ion-atom collision models, combined with experimental ion irradiation data, to derive predictive numerical models linking the width of RA rims to the grain s integrated solar ion radiation exposure time.

Chamberlin, S.↗
DOE OSTI2025

Quantifying Impacts of Biomass Pelletization on Fast Pyrolysis Using a Single-Particle Reactor, X-ray Computed Tomography, and Computational Modeling

The pore structure and density of lignocellulosic feedstocks dictate intraparticle transport phenomena and thereby play an important role in thermochemical conversion processes such as fast pyrolysis for biofuel and biochemical production. Variations in microstructure are inherent from different biomass species and can be introduced by preprocessing techniques such as cutting and pelletization. Morphological changes also occur during conversion and lead to vastly different pore structures and behavior during pyrolysis, which impact required conversion times and product distributions. The current work presents a comprehensive comparison of fast pyrolysis of neat and pelletized pine feedstocks, which includes single-particle experiments, modeling, and 3D imaging by X-ray computed tomography (XCT). The particle-scale model included anisotropic heat and mass transport in a shrinking particle with pyrolysis reactions based on the CRECK mechanism with boundary conditions informed by reactor-scale simulations of the single-particle reactor. The models were validated by measurements of the temperature and mass loss from single-particle pyrolysis experiments of neat and pelletized pine. Quantitative analysis of XCT geometries revealed that pyrolytic conversion yielded chars with increased porosity and permeability compared to the unpyrolyzed materials, along with decreased tortuosity and anisotropy. Pelletization of the pine feedstock resulted in a much denser, less permeable material, which converted slower and produced more residual char after pyrolysis compared to neat pine. The results from particle modeling revealed that accounting for the dynamic and anisotropic heat and mass transport caused by differences in pore structure is critical to achieving agreement with experimental results. Overall, this study highlights the dramatic differences in conversion behavior imparted by pelletization and the importance of capturing microstructural attributes in computational models to guide the design and optimization of pyrolysis processes for specific biomass feedstocks.

09 BIOMASS FUELS↗
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 OSTI2017

Effect of nano-SiO{sub 2} particles and curing time on development of fiber-matrix bond properties and microstructure of ultra-high strength concrete

Bond properties between fibers and cementitious matrix have significant effect on the mechanical behavior of composite materials. In this study, the development of steel fiber-matrix interfacial bond properties in ultra-high strength concrete (UHSC) proportioned with nano-SiO{sub 2} varying between 0 and 2%, by mass of cementitious materials, was investigated. A statistical model relating either bond strength or pullout energy to curing time and nano-SiO{sub 2} content was proposed by using the response surface methodology. Mercury intrusion porosimetry (MIP) and backscatter scanning electron microscopy (BSEM) were used to characterize the microstructure of the matrix and the fiber-matrix interface, respectively. Micro-hardness around the embedded fiber and hydration products of the matrix were evaluated as well. Test results indicated that the optimal nano-SiO{sub 2} dosage was 1% in terms of the bond properties and the microstructure. The proposed quadratic model efficiently predicted the bond strength and pullout energy with consideration of curing time and nano-SiO{sub 2} content. The improvement in bond properties associated with nano-silica was correlated with denser matrix and/or interface and stronger bond and greater strength of hydration products based on microstructural analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2022

ExaAM: Metal additive manufacturing simulation at the fidelity of the microstructure

Additive manufacturing (AM), or 3D printing, of metals is transforming the fabrication of components, in part by dramatically expanding the design space, allowing optimization of shape and topology. However, although the physical processes involved in AM are similar to those of welding, a field with decades of experimental, modeling, simulation, and characterization experience, qualification of AM parts remains a challenge. The availability of exascale computational systems, particularly when combined with data-driven approaches such as machine learning, enables topology and shape optimization as well as accelerated qualification by providing process-aware, locally accurate microstructure and mechanical property models. We describe the physics components comprising the Exascale Additive Manufacturing simulation environment and report progress using highly resolved melt pool simulations to inform part-scale finite element thermomechanics simulations, drive microstructure evolution, and determine constitutive mechanical property relationships based on those microstructures using polycrystal plasticity. We report on implementation of these components for exascale computing architectures, as well as the multi-stage simulation workflow that provides a unique high-fidelity model of process–structure–property relationships for AM parts. In addition, we discuss verification and validation through collaboration with efforts such as AM-Bench, a set of benchmark test problems under development by a team led by the National Institute of Standards and Technology.

3D printing↗
DOE OSTI2025

Physics-Informed Machine Learning Model for Ceramic Matrix Composite Creep

A physics-informed recurrent neural network (RNN) based surrogate model is developed to emulate the nonlinear, time-dependent constitutive behavior of ceramic matrix composites (CMCs) driven by matrix damage and constituent creep at the microscale. Physics-informed constraints are introduced into the surrogate model through regularization to ground the prediction in physics and improve its predictive capabilities. Training data is generated using the high-fidelity generalized method of cells (HFGMC) approach which calls appropriate creep and damage models for each of the constituents. This coupling permits simulating the nonlinear behavior of CMCs based on constituent response at the microscale along with microstructural features such as fiber and porosity volume fraction and fiber radius. The microscale repeating unit cell is loaded under creep fatigue conditions to replicate the material loading experienced in a turbine engine. Therefore, the RNN-based surrogate model is tasked with predicting, as a function of variable input stress sequence, temperature, and microstructural features, the resulting strain history response while satisfying physical constraints related to creep rate, isochoric inelastic deformation, and strain energy density. The trained surrogate model is shown to effectively match the strain history over quantified distributions of microstructural features and relevant loading regimes and temperatures. Neural network based surrogate models can offer efficient alternatives to running computationally intensive multiscale material models to simulate the nonlinear response of large structural models. Therefore, the presented work provides evidence towards the feasibility of developing, training, and running such models for CMCs with complex microstructures, nonlinear time-dependent material response, and under non-monotonic loading conditions.

ceramic matrix composites↗
DOE OSTI2025

Subvoxel Control of Fiber Orientation via Multidirectional Shearing in 3D Printing

Anisotropy, the characteristic of materials exhibiting different properties based on their direction, is widespread in nature. Conventional manufacturing techniques often fall short in recreating such complex anisotropy. While 3D printing allows for precise fiber deposition in anisotropic composites, previous studies have only achieved bulk reorientation of fibers at the voxel level in two dimensions. This limits the replication of localized 3D anisotropies found in natural materials. To address this, a novel 3D printing technique is presented that enables subvoxel control of fiber orientation in all three directions using multidirectional shearing via nozzle rotation and inclination. The fiber orientation control in these experimental tests is driven by a numerical model, enabling a fully digital approach to program microstructures within a strand. This programmability is demonstrated through mechanical and thermal tests, illustrating a localized and controllable response to external stimuli. Achieving such complex anisotropy holds potential across several fields, including wearables and biomedical implants, lightweight composite structures, and energy storage technologies such as batteries and supercapacitors.

3D printing↗
DOE OSTI2019

Investigation on Microsegregation of IN718 Alloy During Additive Manufacturing via Integrated Phase-Field and Finite-Element Modeling

In this work, we apply a multi-scale model combining finite-element method (FEM) and phase-field model (PFM) to simulate the evolution of solidification microstructures at different locations within a molten pool of an additively manufactured IN718 alloy. Specifically, the FEM is used to calculate the shape of molten pool and the relative thermal gradient G at the macroscale. Then, the calculated thermal information is input into PFM for microstructure simulation. Finally, the morphology of solidification structures and formation of Laves phase at different sites are studied and compared. We found that the solidification site with a large angle between the temperature gradient and the preferred crystalline orientation could build up a high niobium (Nb) concentration in the liquid during solidification but has less possibility of forming continuous long chain morphology of Laves phase particles. This finding provides an understanding of the microstructure evolution inside the molten pool of IN718 alloy during solidification. Further, the finding indicates that the site with a large misorientation angle will have a good hot cracking resistance after solidification.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Lattice distortion in a strong and ductile refractory high-entropy alloy

Here, the maximization of the mixing entropy with the optimal range of enthalpy in high-entropy alloys (HEAs) can promote the formation of a stable single solid-solution phase with the absence of competing intermetallic compounds. The resultant effects, such as lattice distortion, can contribute to excellent mechanical properties, which has motivated numerous efforts to develop and design single-phase HEAs. However, challenges still remain, particularly on quantifying the lattice distortion and relating it to materials properties. In this study, we have developed a NbTaTiV refractory HEA with a single body-centered-cubic (BCC) structure using an integrated experimental and theoretical approach. The theoretical efforts include thermodynamic modeling, i.e., CALculation of PHAse Diagram (CALPHAD). The microstructural evolutions have been investigated by systematic heat-treatment processes. The typical dendrite microstructure was observed, which is caused by the elemental segregation during the solidification in the as-cast condition. The structural inhomogeneity and chemical segregation were completely eliminated by the proper homogenization treatment at 1200 °C for 3 days. The homogeneous elemental distribution was quantitatively verified by the Atom Probe Tomography (APT) technique. Importantly, results indicate that this HEA exhibits the high yield strength and ductility at both room and high temperatures (up to 900 °C). Furthermore, the effects of the high mixing entropy on the mechanical properties are discussed and quantified in terms of lattice distortions and interatomic interactions of the NbTaTiV HEA via first-principles calculations. It is found that the local severe lattice distortions are induced, due to the atomic interactions and atomic-size mismatch in the homogenization-treated NbTaTiV refractory HEA.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Modelling recrystallization textures driven by intragranular fluctuations implemented in the viscoplastic self-consistent formulation

This work presents a recrystallization model driven by intragranular orientation gradients and strain energy fields calculated by means of the viscoplastic self-consistent (VPSC) formulation. The VPSC model is extended for calculation of the coupling between intragranular stress fluctuations with corresponding second moments of lattice spin and misorientation fields in the grains. Access to these quantities allows modelling of transition bands and nucleation kinetics. In the proposed recrystallization model, grain growth is assumed to be proportional to the difference between the stored energy of each grain and that of the effective medium. Recrystallization textures for several cubic metals are simulated, showing good agreement with corresponding experiments. The model reveals the importance of considering appropriate, microstructurally-based and orientation-dependent recrystallization nucleation mechanisms. The recrystallization texture of heavily rolled copper with a strong cube texture component is found to be a consequence of nucleation at transition bands, which is also the cause of the recrystallization textures in compressed iron and drawn copper wire. Finally, in contrast, the recrystallization texture of rolled interstitial-free steel is found to be caused by grain boundary nucleation occurring in grains with the highest strain energy.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Elaborating the phases and mechanical properties of multiphase alloy: Experimental two-dimensional mapping combined with theoretical calculations

Highlights: • Mechanical modulus and hardness distribution mapping is obtained for the first time by NanoBlitz 3D. • Phases mapping and exact element ratio are characterized for the nano multielement carbides by EPMA. • Carbides types are confirmed by acid etching and XRD refinement. • Young's modulus of multiple carbides is verified by DFT calculations and Voigt-Reuss-Hill model. - Abstract: The relationship between types of phases, microstructure and mechanical properties is always a critical challenge for the research and application of multiphase alloys. We report for the first time that NanoBlitz 3D method implemented in iNano is used to investigate the distribution of micro mechanical modulus and hardness for the entire complex multiphase alloy Fe-6.0W-5.0Mo-4.0Cr-2.0V-0.83C by constructing the two-dimensional mapping. In order to build the correlation between the microstructure and properties, the carbides in this tempering state steel are confirmed to be Fe{sub 3}(W, Mo){sub 3}C, Cr{sub 15.58}Fe{sub 7.42}C{sub 6} and V{sub 8}C{sub 7} by acid etching and XRD refinement. The phases mapping is obtained by electron probe micro-analysis. Exact element ratio is characterized for the nano-scale multielement carbides precipitates. The Young's modulus and hardness of the precipitated carbides are up to 330 GPa and 22 GPa, respectively. The distribution range of Young's modulus and hardness is during 150 to 250 GPa and 2.5 to 7.0 GPa for the matrix phases, respectively. The experimental Young's modulus and hardness results are verified by first-principles calculations combined with Voigt-Reuss-Hill approximation. In fact, this work provides a successful demonstration to three-dimensionally characterize the micro mechanical properties for all the ceramics and alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Radiation response of a Fe–20Cr–25Ni austenitic stainless steel under Fe 2+ irradiation at 500 °C

The radiation response of a Fe–20Cr–25Ni austenitic stainless steel under self-ion irradiation at 500 °C was systematically investigated. The steel was irradiated at 500 °C by 3.5 MeV Fe 2+ ions to 10, 50, and 150 peak dpa, respectively. In the 200–400 nm depth region, radiation-induced Frank loops were relatively stable in both size and number density from 10 to 150 peak dpa. Anisotropic distribution of Frank loops was observed in the 50 and 150 peak dpa specimens, possibly due to interaction of Frank loops and network dislocations with preferred orientations. Coarse voids were found only in the 50 and 150 peak dpa specimens in depths less than 750 nm, suggesting that injected interstitials at deeper regions suppressed the void nucleation. The peak swelling was very low (~0.4%) for both 50 and 150 peak dpa irradiation. Radiation also led to the formation of intragranular plate-like Cr-rich carbides. Radiation-induced segregation of Ni and Si was found at various sinks: dislocation loops, void surfaces, and carbide-matrix interfaces. Finally, irradiation hardening was measured by nanoindentation and the results are consistent with microstructure-based calculations using the dispersed barrier hardening model. The major contributor to irradiation hardening changed from Frank loops at the lowest dose to network dislocations at the highest dose.

dislocation loops↗
DOE OSTI2018

4D nano-tomography of electrochemical energy devices using lab-based X-ray imaging

Electrochemical energy devices offer a variety of alternate means for low-carbon, multi-scale energy conversion and storage. Reactions in these devices are supported by electrodes with characteristically complex microstructures. To meet the increasing capacity and lifetime demands across a range of applications, it is essential to understand microstructural evolutions at a cell and electrode level which are thought to be critical aspects influencing material and device lifetime and performance. X-ray computed tomography (CT) has become a highly employed method for non-destructive characterisation of such microstructures with high spatial resolution. However, sub-micron resolutions present significant challenges for sample preparation and handling particularly in 4D studies, (three spatial dimensions plus time). Here, microstructural information is collected from the same region of interest within two electrode materials: a solid oxide fuel cell and the positive electrode from a lithium-ion battery. Using a lab-based X-ray instrument, tomograms with sub-micron resolutions were obtained between thermal cycling. The intricate microstructural evolutions captured within these two materials provide model examples of 4D X-ray nano-CT capabilities in tracking challenging degradation mechanisms. Furthermore, this technique is valuable in the advancement of electrochemical research as well as broader applications for materials characterisation.

25 ENERGY STORAGE↗
DOE OSTI2021

Utilizing a Dynamic Segmentation Convolutional Neural Network for Microstructure Analysis of Additively Manufactured Superalloy 718

Additive manufacturing (AM) is revolutionizing almost all industries through the production of intricate geometries previously prohibited by cost or machinability. Ni-based superalloys form a primary alloy class for high temperature applications in the petrochemical, aerospace, and nuclear industries because of their intrinsic resistance to creep and the ability to heat treat the superalloy for high strength. Despite these attractive properties, the extreme work hardening of Ni-based superalloys makes traditional manufacturing of complex shapes difficult and thus, these alloys are an attractive target for AM. Superalloy 718 was chosen as an example superalloy because of the wide variety of precipitates that can form within its composition space from the repetitive heating and cooling cycles of the AM process. The precipitates and other microstructure features, such as grain boundaries and dislocations, will dictate the mechanical properties and thus, there is an extensive challenge to characterize the size, number density, composition, and volume fraction of each microstructural feature from AM fabrication using analytical electron microscopy. This work focused on the application of a pixel-wise classification machine learning (ML) model called a dynamic segmentation convolutional neural network (DSCNN) to identify the microstructural features of an as-fabricated additively manufactured superalloy 718.

Taller, Stephen↗
DOE OSTI2020

Grain boundary facilitated dissolution of nanocrystalline NpO 2 (s) from legacy waste processing

Dissolution of actinide dioxides, including neptunium dioxide (NpO 2 (s)), is paramount for the prediction of the environmental fate of nuclear materials. Quantifying dissolution rates, as well as understanding qualitative dissolution mechanisms, informs performance assessment for geologic disposal of spent nuclear fuel and management of legacy radioactive waste. The aim of this research was to measure the dissolution rate of nanocrystalline NpO 2 (s), produced through legacy nuclear waste processing, under oxidizing conditions, as well as to characterize surface alteration to the material. The solid phase was characterized using electron microscopy techniques (SEM/STEM) and X-ray photoelectron spectroscopy (XPS), indicating preferential dissolution of Np-hydroxide contained in the grain boundaries of NpO 2 (s) and fragmentation of grains from the matrix. The oxidative dissolution was monitored over 40 weeks, yielding a two-step kinetic dissolution model involving hydration of NpO 2 (s) and subsequent oxidation and dissolution of the hydroxide phase. Here, the proposed dissolution models for nanocrystalline NpO 2 (s) suggest that microstructural features such as grain boundaries are key factors affecting dissolution, including release of colloidal particles, and ultimately, environmental fate and transport of nuclear materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Review on the growth, properties and applications of self-assembled oxide–metal vertically aligned nanocomposite thin films—current and future perspectives

Self-assembled oxide–metal nanocomposite thin films have aroused great research interest owing to their wide range of functionalities, including metamaterials with plasmonic and hyperbolic optical properties, and ferromagnetic, ferroelectric and multiferroic behaviors. Oxide–metal nanocomposites typically self-assemble as metal particles in an oxide matrix or as a vertically aligned nanocomposite (VAN) with metal nanopillars embedded in an oxide matrix. Among them, the VAN architecture is particularly interesting due to the vertical strain control and highly anisotropic structure, enabling the epitaxial growth of materials with large lattice mismatch. In this review, the driving forces behind the formation of self-assembled oxide–metal VAN structures are discussed. Specifically, an updated in-plane strain compensation model based on the areal strain compensation concept has been proposed in this review, inspired by the prior linear strain compensation model. It provides a guideline for material selection for designing VAN systems, especially those involving complex orientation matching relationships. Based on the model, several case studies are discussed, comparing the microstructure and morphology of different oxide–metal nanocomposites by varying the oxide phase. Specific examples highlighting the coupling between the electrical, magnetic and optical properties are also discussed in the context of oxide–metal nanocomposites. Finally, future research directions and needs are also discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

In-service Oxidation and IASCC in High Fluence Baffle-Former Bolts Retrieved from a Westinghouse PWR

Baffle-former bolts (BFBs) are subjected to significant mechanical stress and neutron irradiation from the reactor core during the plant operation. Over the long operation period, these conditions lead to potential degradation and reduced load-carrying capacity of the bolts, and life extension of existing pressurized water reactors (PWRs) would only cause more damage to the bolt material. To this end, the Light Water Reactor Sustainability (LWRS) Program Materials Research Pathway (MRP) successfully harvested two high fluence BFBs from a Westinghouse two-loop downflow type PWR in 2016. In the same year, the two BFBs were received at the Westinghouse Churchill facility for inspection and specimen fabrication. The fabrication was completed in 2017 with specimens shipped to Oak Ridge National Laboratory (ORNL) for further testing. The objective of this project is to provide information that is integral to evaluating end of life microstructure and properties as a benchmark of international models developed for predicting radiation-induced swelling, segregation, precipitation, mechanical property degradation, and with the latest findings, susceptibility to irradiation-assisted stress corrosion cracking (IASCC).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗