Machine learning constitutive models of inelastic materials with microstructure.
Abstract not provided.
Jones, Reese↗
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Machine learning constitutive models of inelastic materials with microstructure
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Jones, Reese Edward [Sandia National Laboratories ↗
Simultaneous Development and Robust Optimization of a Microstructure Dependent Material Model
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Karl Garbrecht↗
Earth-abundant Li-ion cathode materials with nanoengineered microstructures
Manganese-based materials have tremendous potential to become the next-generation lithium-ion cathode as they are Earth abundant, low cost and stable. Here we show how the mobility of manganese cations can be used to obtain a unique nanosized microstructure in large-particle-sized cathode materials with enhanced electrochemical properties. By combining atomic-resolution scanning transmission electron microscopy, four-dimensional scanning electron nanodiffraction and in situ X-ray diffraction, we show that when a partially delithiated, high-manganese-content, disordered rocksalt cathode is slightly heated, it forms a nanomosaic of partially ordered spinel domains of 3–7 nm in size, which impinge on each other at antiphase boundaries. The short coherence length of these domains removes the detrimental two-phase lithiation reaction present near 3 V in a regular spinel and turns it into a solid solution. This nanodomain structure enables good rate performance and delivers 200 mAh g –1 discharge capacity in a (partially) disordered material with an average primary particle size of ~5 µm. The work not only expands the synthesis strategies available for developing high-performance Earth-abundant manganese-based cathodes but also offers structural insights into the ability to nanoengineer spinel-like phases.
36 MATERIALS SCIENCE↗
Probing Microstructure-induced Swelling & Thermal Property Changes in Ion-Irradiated Oxide Fuels using Laser-generated Surface Acoustic Waves
Radiation of fuel rods with neutrons during a fission reaction in a nuclear reactor is known to affect the material microstructure of the fuel as a result of fission fragment damage, and lead to the formation of atomic-level defects such as voids, dislocation loops, and lattice swelling from fission-gas release. A consequence of the radiation-induced damage in nuclear fuels is the drastic alteration of material properties, in particular, the reduction of the thermal conductivity – a key parameter that governs the transport of thermal energy released from fissile fuel atoms to the surrounding coolant. Swelling can induce high stresses and ultimate failure of the cladding. A fundamental understanding of the role of radiation-induced damage on the thermal transport and mechanical properties of nuclear fuels is therefore critical for efficient, reliable and safe operation of a nuclear power plant. As opposed to conventional post-irradiation examination techniques that are often time-consuming and require significant sample preparation, laser-based characterization methods have emerged as promising non-contact, non-destructive tools to measure the evolution of microstructure-induced material property changes. Moreover, laser methods offer the promise of in-situ characterization while the material is being irradiated. This project aims to develop an improved understanding of the impact of radiation-induced changes to material microstructure on the thermal and elastic properties of nuclear materials. A laser ultrasonic technique, known as the transient grating (TG) spectroscopy method, is used to simultaneously measure thermal diffusivity and elastic properties in unirradiated and ion-irradiated oxide nuclear fuel samples. The results will complement on-going investigations on electron- and phonon-mediated thermal transport in nuclear materials and will provide foundational work for incorporating the influence of defects in fuel performance codes.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Adoption of image-driven machine learning for microstructure characterization and materials design: A Perspective
Microstructure characterization enables the development of structure-processing-property relationships critical to several research areas within the broad field of materials science, from alloy design to the assessment of corrosion resistance, and failure analysis. Conventional approaches to material characterization have relied on either qualitative inference by the human ex-pert or software applications that can extract high-level features from images, such as boundary segmentation, average grain diameter, etc. Such approaches rely heavily on subject matter expert user intervention and knowledge of what phases or more generally, what microstructural features, are of interest. The recent surge in the adoption of machine learning techniques to address problems in materials engineering has brought with it an increased interest and application of Image Driven Machine Learning (IDML) approaches. In this work, we review the applications of IDML to the field of materials characterization. A canonical hierarchy of stages is defined, which when put sequentially together completes an IDML study: problem definition, dataset building, model selection and training, model evaluation, and integration with existing instrumentation or simulation workflow. The studies reviewed in this work are analyzed from the perspective of each of these stages. Such a review permits agranular assessment of the field, for example the impact of IDML on materials characterization at the nanoscale, the size of a typical dataset required to train a semantic segmentation model on electron microscopy images, ubiquitousness of transfer learning in the domain, etc. Finally, we discuss the importance of interpretability and explainability in the field of IDML for materials characterization, and provide an overview of two emerging techniques in the field: semantic segmentation and generative adversarial networks.
Baskaran, Arun↗
Mechanical evaluation of WC-Co materials with varying microstructures
Tungsten carbide–cobalt materials are useful in a variety of extreme applications due to a desirable blend of properties, yet the technology has not significantly changed since their initial development in the 1920s. The mechanical properties of this class of materials is highly dependent on two variables, the size of the tungsten carbide grains, and the amount of binder phase present in the final body. In this study, the amount of binder phase is isolated across three commercial materials from the same manufacturer with three different grain sizes to investigate the effect on mechanical properties. The mechanical properties investigated are indentation hardness, flexure and tensile strength, as well as fracture toughness. In general, an increase in hardness and tensile strength with decreasing grain size was observed, while the fracture toughness showed the opposite trend with toughness increasing with increasing grain size. The flexure strength results did not show a correlation to grain size. Fractographic analysis identified the dominant strength-limiting flaw for each sample, which largely were in the form of porosity. Other flaws types, such as inclusions from the milling process, clusters of large grains, and machining cracks from the surface finishing process, were also identified. Finally, Weibull analysis was performed and deemed appropriate for analysis of these materials, but strength-size scaling was not conducted due to the variability in the strength-limiting feature between the different specimen geometries.
36 MATERIALS SCIENCE↗
Longtime Durability of PMR-15 Matrix Polymer at 204, 260, 288, and 316 C
Isothermal weight loss studies at the Glenn (Lewis) Research Center were conducted at four temperatures (204, 260, 288, and 316 C) with specimens of varied geometric shapes to investigate the mechanisms involved in the thermal degradation of PMR-15. Both neat resin behavior and composite behaviors were studied. Two points of interest in these studies are the role(s) of oxygen in the mechanisms involved in the thermo-oxidative degradation of these composite materials and the dimensional changes that occur during their useable lifetime. Specimen dimensional changes and surface layer growth were measured and recorded. It was shown that physical and chemical changes take place as a function of time and location in PMR-15 neat resin and composites as aging takes place in air at elevated temperatures. These changes initiate at the outer surfaces of both materials and progress inward following the oxygen as it proceeds by diffusion into the central core of each material. Microstructural changes cause changes in density, material shrinkage (strains), glass transition temperature, dimension, dynamic shear modulus, and compression properties. These changes also occur slowly dividing the polymer material into two distinct parts: a visibly undamaged core section between two visibly damaged surface layers. The surface layer has a significant effect on compression properties of thinner specimens, but the visibly undamaged core material controls these properties for specimens having eight or more plies. It was demonstrated that there are three different mechanisms involved in the degradation of PMR-15 during aging at elevated temperatures. These are a weight gain, a small weight fraction bulk material weight loss, and a large mass fraction weight loss concentrated at the surface of the polymer or composite. At the higher temperatures (260 C and above), the surface loss predominates. Below 260 C, the surface loss and the bulk core loss become more equivalent. Between 175 and 260 C, the initial weight change is due to a weight gain mechanism with a visible lifetime that diminishes as the aging temperature increases.
Bowles, Kenneth J.↗
Exascale granular microstructure reconstruction in 3D volumes of arbitrary geometries with generative learning
Reconstructing 3D granular microstructures within volumes of arbitrary geometries from limited 2D image data is crucial for predicting the material properties, as well as performances of structural components accounting for material microstructural effects. We present a novel generative learning framework that enables exascale reconstruction of granular microstructures within complex 3D geometric volumes. Building upon existing transfer learning techniques using pre-trained convolutional neural networks (CNN), we introduce several key innovations to overcome the difficulties inherent in arbitrary geometries. Our framework incorporates periodic boundary conditions using circular padding techniques, ensuring continuity and representativeness of the reconstructed microstructures. We also introduce a novel seamless transition reconstruction (STR) method that creates statistically equivalent transition zones to integrate multiple pre-existing 3D microstructure volumes. Based on STR, we propose a cost-effective strategy for reconstructing microstructures within complex geometric volumes, minimizing computational waste. Validation through numerical experiments using kinetic Monte Carlo simulations demonstrates accurate reproduction of grain statistics, including grain size distributions and morphology. A case study involving the reconstruction of a 4-blade propeller microstructure illustrates the method’s capability to efficiently handle complex geometries. In conclusion, the proposed framework significantly reduces computational demands while maintaining high reconstruction quality, paving the way for scalable microstructure reconstruction in materials design and analysis.
36 MATERIALS SCIENCE↗
An approach to combine neutron and ion irradiation data to accelerate material qualification for nuclear reactors
Next-generation nuclear power plants are generally characterized by higher operating temperatures, increased neutron fluences and energies, and distinct corrosive coolant environments versus the existing light water reactor fleet. Whether using existing materials in new environments, newly developed materials tailored for these environments, or new manufacturing methods, the traditional decades-long approach for materials qualification does not facilitate rapid deployment. Ion irradiation has demonstrated success in reproducing material microstructure and select property evolution resulting from neutron irradiation with three to four orders of magnitude reduction in time and cost, making it an ideal candidate for accelerated irradiation testing. Because microstructure has a large impact on bulk material properties, limited neutron irradiation data at lower damage levels can in principle be combined with accelerated ion testing results and modeling and simulation to form an accurate prediction of microstructure evolution and select properties under different neutron irradiation conditions and at higher damage levels. The objective of this work is to present a conceptual framework of specific steps to fulfill several technical challenges associated with qualifying materials for performance in radiation environments on an accelerated time frame. A brief review of the regulatory landscape for materials in nuclear environments is presented, followed by additional overviews to understand the current state of the art for correlation of materials properties across radiation environments using experimental and computational methodologies. Finally, the roles of academia, national laboratories, and industry in the advancement of this accelerated materials qualification framework are discussed as a path forward, with possible case studies presented.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Quantitative ultrasonic evaluation of engineering properties in metals, composites, and ceramics
Ultrasonic techniques that have demonstrated potential for material characterization are reviewed. These techniques rely on physical acoustic properties of materials and the interaction of elastic stress waves with morphological factors in the ultrasonic regime. The speed of wave propagation and energy loss by interaction with material microstructure and geometrical factors underlie ultrasonic determination of material properties. Two categories of ultrasonic measurements are discussed: those related to material strengths (e.g., elastic moduli, tensile strength, and fracture toughness) and those related to morphology and material conditions that govern strength and performance (e.g., microstructure, void content, residual stress, fatigue damage). It is shown that large-scale industrial application of ultrasonic NDE will depend on advancement in such areas as theory development, instrumentation, system automation, standardization, and coordination with design.
Vary, A.↗
Long-term stability and properties of zirconia ceramics for heavy duty diesel engine components
Physical, mechanical, and thermal properties of commercially available transformation-toughened zirconia are measured. Behavior is related to the material microstructure and phase assemblage. The stability of the materials is assessed after long-term exposure appropriate for diesel engine application. Properties measured included flexure strength, elastic modulus, fracture toughness, creep, thermal shock, thermal expansion, internal friction, and thermal diffusivity. Stability is assessed by measuring the residual property after 1000 hr/1000C static exposure. Additionally static fatigue and thermal fatigue testing is performed. Both yttria-stabilized and magnesia-stabilized materials are compared and contrasted. The major limitations of these materials are short term loss of properties with increasing temperature as the metastable tetragonal phase becomes more stable. Fine grain yttria-stabilized material (TZP) is higher strength and has a more stable microstructure with respect to overaging phenomena. The long-term limitation of Y-TZP is excessive creep deformation. Magnesia-stabilized PSZ has relatively poor stability at elevated temperature. Overaging, decomposition, and/or destabilization effects are observed. The major limitation of Mg-PSZ is controlling unwanted phase changes at elevated temperature.
Larsen, D. C.↗
Quantifying Microstructure Variability in Laser Powder Bed Fusion 316 L Stainless Steel Microstructures with Spatial Statistics
Here, we have explored data-driven methods for material microstructure quantification that improve sensitivity to microstructural changes compared to traditional approaches. The methods integrate multiple microstructural properties, including grain morphology, crystallographic orientation, and material phase information. The simpler method employs maps of the Euclidean distance transformation metric to evaluate the morphology of grain boundary networks. The more intensive approach employs generalized spherical harmonic mapping for crystallographic orientations, per-pixel phase information, and a variational auto-encoder for dimensionality reduction and results in a multidimensional clustering of by microstructure similarity. Applied to an experimental dataset of additively manufactured steel, both methods detected slight variations in samples produced under nominally identical processing conditions. Both methods were able to distinguish between samples from multiple (nominally identical) builds, while the generalized spherical harmonics-based method could additionally cluster data samples rotated at two orientations on the build plate. The improved sensitivity of the methods, demonstrated through comparison with traditional microstructure characterization techniques, offers advantages for microstructure quantification and comparisons in advanced manufacturing applications.
SS316L↗
Characterization of a Structural Transformation of Carbon Nanotube Materials by Raman Spectroscopy
Raman spectra of carbon nanotubes and carbon microstructure materials synthesized on Si substrates by pulsed laser vaporization have been measured in the range of 50/cm to 4500/cm with the excitation of He-Ne laser. It is found that the formation of nanotubes depends strongly on the growth temperatures and high quality multi-wall and single-wall nanotubes were produced at 700 and 990 C, respectively. The Raman spectra of one sample grown at 700 C were found to be dependent on the excitation intensity. The spectra of the sample suggest that the structure is similar to that of multi-wall nanotubes at low excitation intensity (2.5 kW/sq cm) and it converts to the structure of single-wall nanotubes at higher intensity (25 kW/sq cm). Measurements taken while cycling the light intensity indicate a reversible structural transition.
Cui, Y.↗
Microstructurally based model of fatigue initiation and growth
A model to calculate fatigue life is developed based on the assumption that fatigue life is entirely composed of crack growth from an initial microstructural inhomogeneity. Specifically, growth is considered to start from either an ellipsoidal void, a cracked particle, or a debonded particle. The capability of predicting fatigue life from material microstructure is based on linear elastic fracture mechanics principles, the sizes of the crack-initiating microstructural inhomogeneities, and an initiation parameter that is proportional to the cyclic plastic zone size. A key aspect of this modeling approach is that it is linked with a general purpose probability program to analyze the effect of the distribution of controlling microstructural features within the material. This enables prediction of fatigue stress versus life curves for various specimen geometries using distributional statistics obtained from characterizations of the microstructure. Results are compared to experimental fatigue data from an aluminum alloy.
Brockenbrough, J. R.↗
Promoting the regulatory acceptance of combined ion and neutron irradiation for material degradation in nuclear reactors
The Advanced Materials and Manufacturing Technologies (AMMT) program within the Department of Energy (DOE) Office of Nuclear Energy has developed its current recommendation for promoting the use of combined ion irradiation and neutron irradiation for the accelerated qualification of materials to be deployed in nuclear reactors. This plan is intended to provide a collaborative path forward that can be adopted by academia, national laboratories, and industry, and has been developed with input from the regulatory research arm of the U.S. Nuclear Regulatory Commission (NRC). To deploy new materials or materials manufactured with new technologies, such as additive manufacturing, materials must be evaluated for reactor-induced degradation from the combination of harsh temperatures, corrosive environments, and radiation fields. However, rapid deployment of materials necessitates accelerated testing methods rather than relying on years of neutron irradiation in a material test reactor. Ion irradiation has demonstrated success in reproducing material microstructure and select property evolution resulting from neutron irradiation with three to four orders of magnitude reduction in time and cost, making it an ideal candidate for accelerated irradiation testing. This presentation provides context governing both the scientific and regulatory aspects of the proposed goal. The discussion is aimed at a broad audience including researchers from industry, national laboratories, and academia. The recommended path forward is presented as a conceptual framework of specific steps. In brief, the strategy entails developing an integrated ion and neutron irradiation test plan for the material property of interest based on the fundamental tenet of the linkage of microstructure and properties in materials. Physics-based modeling interprets ion irradiation data and predicts neutron irradiation microstructure and properties with uncertainty bounds. The first round of testing is sufficient for an initial licensing application using a risk-informed approach, while a minimum required neutron irradiation test plan reduces cost and time requirements. A surveillance program with witness specimens in-reactor provides additional data over time to improve model predictions to higher damage levels and further reduce uncertainty bounds, which can be used for license extensions or longer lifetimes in new license applications.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Multiscale Analysis of Structurally-Graded Microstructures Using Molecular Dynamics, Discrete Dislocation Dynamics and Continuum Crystal Plasticity
A multiscale modeling methodology is developed for structurally-graded material microstructures. Molecular dynamic (MD) simulations are performed at the nanoscale to determine fundamental failure mechanisms and quantify material constitutive parameters. These parameters are used to calibrate material processes at the mesoscale using discrete dislocation dynamics (DD). Different grain boundary interactions with dislocations are analyzed using DD to predict grain-size dependent stress-strain behavior. These relationships are mapped into crystal plasticity (CP) parameters to develop a computationally efficient finite element-based DD/CP model for continuum-level simulations and complete the multiscale analysis by predicting the behavior of macroscopic physical specimens. The present analysis is focused on simulating the behavior of a graded microstructure in which grain sizes are on the order of nanometers in the exterior region and transition to larger, multi-micron size in the interior domain. This microstructural configuration has been shown to offer improved mechanical properties over homogeneous coarse-grained materials by increasing yield stress while maintaining ductility. Various mesoscopic polycrystal models of structurally-graded microstructures are generated, analyzed and used as a benchmark for comparison between multiscale DD/CP model and DD predictions. A final series of simulations utilize the DD/CP analysis method exclusively to study macroscopic models that cannot be analyzed by MD or DD methods alone due to the model size.
Saether, Erik↗