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All sources NASA NTRS DOE OSTI

NASA NTRS2017

Chemical, Structural, and Microstructural Changes in Metallic and Silicon-Based Coating Materials Exposed to Iodine Vapor

The chemical, structural and microstructural behavior of steels (304, 316 and A36), titanium-aluminum alloy (Ti-Al, (6Al-4V)), aluminum-magnesium alloy (Al-Mg, 6061), and coatings (Silcolloy and Dursan (SilcoTek Corporation)) were probed after exposure to iodine laminar flow. Exposures were carried out in a custom-built Iodine Vapor RIG (IVR) at 300 degrees C to an iodine laminar vapor flow of 1 mg min(exp. -1), carried by 145 mL-min(exp. -1) argon gas, for 5, 15 and 30 days. Samples were characterized before and after the experiment by gravimetric analysis, X-ray diffraction (XRD) and cross section electron microscopy analysis coupled with energy dispersive X-ray spectroscopy (EDS). All steels exposed for 30 days formed scales consisting mainly of metal (Cr, Fe, Ni) oxides showing different chemistry, microstructure and crystalline phases. Elemental iodine was only detected by EDS analysis in the scales of stainless steels 304 and 316. After 30 days, the Ti-Al exhibited no detectable scale, suggesting only a very thin film was formed. A scale consisting mainly of aluminum, iodine, and oxygen formed on the Al-Mg sample exposed to 30 days. Some pockets rich in magnesium, iodine and oxygen also formed in this Al-Mg alloy. Stainless steel 316, low carbon steel A36 and Ti-Al alloy coated with Silcolloy and stainless steel 304 coated with Dursan that were exposed for 30 days exhibited no oxidation. Stainless steel 304 coated with Silcolloy exposed for 30 days did not exhibit corrosion although the sample gained weight and the coating exhibited expansion. The weight gain per area performance of the materials exposed in iodine lamina flow containing oxygen at impurity level for 10, 15 and 30 days are reported from the lowest to the highest weight gain per area as follows: Steels: Less than 316 less than 304 less than A36; Ti-Al-Mg based alloys: Al-Mg less than Ti-Al: Considering the experimental uncertainties, no weight change was observed for Stainless steel 316, low carbon steel A36 and Ti-Al alloy coated with Silcolloy and stainless steel 304 coated with Dursan. The corrosion of the alloys is catalyzed by iodine in the presence of oxygen as impurity.

Costa, Gustavo C. C.↗

NASA NTRS1990

Quantitative characterization of spatial distribution of particles in materials: Application to materials processing

Most engineering materials contain second phase particles or fibers which serve to reinforce the matrix phase. The effect of reinforcements on material properties is usually analyzed in terms of the average volume fraction and spacing of reinforcements, quantities which are global microstructural characteristics. However, material properties can also depend on local microstructural characteristics; for example, on how uniformly the reinforcing phase is distributed in the material. The analysis method will then be applied to a materials processing problem to discover how processing parameters can be selected to maximize redistribution of the reinforcing phase during processing. Several mathematical analysis methods could be adapted to the problem of characterizing the distribution of particles in materials. A tessellation-based method was selected. In the first phase of the investigation, a software package was written to automate the analysis. Typical results are shown. The analysis technique allows the degree to which particles are clustered together, the size and spacing of particle clusters, and the particle density in clusters to be found. The analysis methods were applied to computer-generated distributions and to a few real particle-containing materials. Methods for analyzing a nonuniform particle distribution in a material can be applied to two broad classes of materials science problems: understanding how the resulting particle distribution affects properties. The analysis method described is applied to a materials processing problem: how to select extrusion conditions to maximize the redistribution of reinforcing particles that are initially nonuniformly distributed. In addition, the tessellation-based method to analyze star distributions in spiral galaxies was adapted, illustrating the diverse types of problems to which the analysis method can be applied.

Parse, J. B.↗

DOE OSTI2022

Effect of grain size on damage and failure in two-phase materials: Homogenized CuPb

It is well known that spall failure strongly depends on the microstructure of a material. There have been numerous studies to study the effect of grain size on the overall spall strength and the total amount of damage in single element metals like copper. However, such systematic studies remain rare in two-phase materials and alloys. In this work, two incipient spall experiments were performed on a Cu–1%Pb alloy to understand the effect of grain size on the damage and failure in a two-phase material. Overall, these results showed that even though the spall strength did not change as a function of grain size, there were significant differences in the total amount of damage as a function of grain size. A clear increase in the total damage present in the material was seen as the grain size was increased from 32 to either 70 or 75 μm in either of the experiments. Furthermore, this difference was attributed to variations in the void growth rate as the grain size was increased.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Effect of void positioning on the detonation sensitivity of a heterogeneous energetic material

We show although it is well-established that voids profoundly influence the initiation and reaction behaviors of heterogeneous energetic materials such as polymer-bonded explosives (PBX) and propellants, there has been little study of how void location in different constituents in the microstructures of such materials affect the macroscale behavior. Here, we use three-dimensional (3D) mesoscale simulations to study how void placement within the reactive grains versus the polymer binder influences the shock-to-detonation transition (SDT) in a polymer-bonded explosive. The material studied here has a microstructure comprised of 75% PETN (pentaerythritol tetranitrate) grains and 25% HTPB (hydroxyl-terminated polybutadiene) polymer binder by volume. Porosities up to 10% in the form of spherical voids distributed in both the grains and polymer are considered. An Arrhenius reactive burn relation is used to model the chemical kinetics of the PETN grains under shock loading, thereby resolving the heterogeneous detonation behavior of the PBX. The influence of void location on the shock initiation sensitivity of the material is quantitatively ranked by comparing the predicted run distance to detonation (RDD) for each sample. The analysis includes inherent quantification of uncertainties arising from the stochastic variations in the microstructure morphologies and void distributions by using statistically equivalent microstructure sample sets (SEMSS), leading to probabilistic formulations for the RDD as a function of shock pressure. The calculations reveal that the location of voids in the composite microstructure significantly affects the RDD. Specifically, voids exclusively within the grains cause the PBX to be more sensitive (having shorter RDD) than voids in the polymer binder. Unique probabilistic relationships are derived to map the probability of observing RDD for each void location material case, allowing for prediction of initiation behavior anywhere in the shock pressure – RDD space. These findings agree with trends reported in the literature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

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

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

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2019

Additive Manufacturing of Multi-Material Systems for Aerospace Applications

Additive manufacturing methods for producing single materials are rapidly improving. The resulting material properties and microstructures are becoming more comparable to those of conventionally fabricated materials. However, the need for multi-functional and complex structures and components requires additional innovations in manufacturing such as multi-material and hybrid additive manufacturing approaches. Additive manufacturing machines with multiple print capabilities and combinations of AM, machining, and conventional processing methods will further open up design spaces and possibilities. In this presentation, several examples of the needs and methods for multi-material fabrication will be discussed with a focus on aerospace applications. Direct printing of silver coils in conjunction with fused deposition modeling, machined parts, and, binder jetting is being developed for innovative stator designs. Binder jetting of silicon-based materials with powder bed additions is being developed for heat exchanger applications. Additive manufacturing of bi-material systems is being pursued to fabricate lightweight, integrated, multifunctional structures.

multi-materials↗

DOE OSTI2022

Process-microstructure relationship of laser processed thermoelectric material Bi2Te3

Additive manufacturing allows fabrication of custom-shaped thermoelectric materials while minimizing waste, reducing processing steps, and maximizing integration compared to conventional methods. Establishing the process-structure-property relationship of laser additive manufactured thermoelectric materials facilitates enhanced process control and thermoelectric performance. This research focuses on laser processing of bismuth telluride (Bi 2 Te 3 ), a well-established thermoelectric material for low temperature applications. Single melt tracks under various parameters (laser power, scan speed and number of scans) were processed on Bi 2 Te 3 powder compacts. A detailed analysis of the transition in the melting mode, grain growth, balling formation, and elemental composition is provided. Rapid melting and solidification of Bi 2 Te 3 resulted in fine-grained microstructure with preferential grain growth along the direction of the temperature gradient. Experimental results were corroborated with simulations for melt pool dimensions as well as grain morphology transitions resulting from the relationship between temperature gradient and solidification rate. Samples processed at 25 W, 350 mm/s with 5 scans resulted in minimized balling and porosity, along with columnar grains having a high density of dislocations.

Oztan, Cagri↗

DOE OSTI2021

Modified microstructures in proton irradiated dual phase 308L weldment filler material

In this study, the effect of proton irradiation on the microstructure of δ ferrite—γ austenite mixed phase 308L filler material in a 508–304 dissimilar metal weldment was investigated over a depth of 0 to 10 µm. Ni–Si–Mn G-phase precipitates were observed with SEM and TEM in δ ferrite but not in γ austenite. Our density functional theory based calculations show that the G/Fe interface energy in δ-Fe is significantly lower than that in γ-Fe (0.35 versus 1.25 J/m 2 ), which provides a thermodynamics-based explanation for our experimental observations of preferential formation of G-phase in δ ferrite. STEM-EDS, TEM dark field imaging, and diffraction patterns confirmed the Ni–Si–Mn enriched precipitates were G-phase precipitates with a stoichiometry of Mn 6 Ni 16 Si 7 . Intragranular voids and Ni–Si enriched clusters were observed in irradiated γ austenite. Additionally, Ni and Si segregation was observed along the void interfaces. In both cases, Ni–Si clusters and segregation to voids, selected area diffraction patterns did not reveal the existence of a second phase. Proton irradiation induced Cr depletion and Si and Ni enrichment at γ-γ austenite grain boundaries that was characterized with STEM/EDS.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Corrosion testing needs and considerations for additively manufactured materials in nuclear reactors

Metal additive manufacturing holds significant promise as an enabling technology for the 21st century nuclear energy industry. Metal additive manufacturing (MAM) can allow the fabrication of novel materials and innovative component designs that are not achievable through conventional manufacturing. Due to its very different fabrication methods, as-fabricated MAM components are characteristically different from conventionally manufactured components. MAM materials exhibit very different microstructures from conventional cast or wrought materials. For example, austenitic stainless steels fabricated by laser powder bed fusion exhibit columnar grain structures, dislocation cell structures, and melt pool fingerprints. In addition, MAM fabrication may result in defects such as porosity, incomplete processing of the feedstock (e.g., lack of fusion in melt-based methods), and oxide inclusions. Heat treatments may further evolve the microstructure, microsegregation, and stresses within the component. Furthermore, MAM components have a rough surface with feature sizes on the order of the feedstock material, as opposed to smooth surfaces resulting from conventional machining and forming operations. As a result, the corrosion behavior of MAM components will likely be significantly different from that of conventionally formed components. Corrosive environments for structural materials within advanced reactor environments include molten fluoride and chloride salts, liquid sodium and lead-bismuth, and high-temperature helium. The Advanced Materials and Manufacturing Technologies program within the Department of Nuclear Energy in the United States Department of Energy is assessing the unique concerns of MAM component corrosion and testing methodologies in advanced nuclear reactor environments. We discuss these concerns and testing strategies in this paper.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

A laser ultrasonics-based approach for rapid screening of high entropy alloys

This project demonstrates a laser ultrasonics-based characterization methodology for rapid metallic materials design and discovery via in situ determination of phases, microstructures and elastic properties with respect to temperature. Ultrasonic waves are strongly affected by material microstructure, and therefore serve as a facile means to probe elastic properties, phase content and their size distributions and volume fractions. In this study, a laser ultrasonic technique is used to systematically study the evolution of properties in a set of interrelated simple binary alloys and high entropy alloys (HEA). Phase transformations and microstructural changes inferred from the ultrasonic signals will be correlated with electron microscopy data and predictions using calculations of phase diagrams (CALPHAD). The non-contact and non-destructive ultrasonic testing approach developed in this study could overcome several limitations associated with current material characterization methods for materials discovery. It is expected that laser ultrasonics-based methodology developed in this work will be utilized to evaluate novel graded composition HEAs currently being developed at the Idaho National Laboratory (INL) using advanced manufacturing methods based on direct energy deposition and spark plasma sintering processes, and contribute to accelerate the discovery of HEAs.

36 MATERIALS SCIENCE↗

NASA NTRSDate not supplied

Predictive Modeling of Carbon Ablators

Efforts to build a Predictive Material Modeling (PMM) framework from the micro-scale to the macro-scale are presented in this abstract. To reduce the need for extensive testing, accelerate the design cycle process, and reduce uncertainty margins applied to final designs, NASA is developing simulation and modeling tools that enable characterization of material properties and response to high-enthalpy environments. The Porous Microstructure Analysis (PuMA) code has been developed for computing macroscale (volume averaged) properties of porous materials using microscale images from micro-computed tomography (micro-CT). Microscale modeling requires a realistic representation of a material microstructure; these are obtained either synthetically during the design of the material or through X-ray micro-CT. Volume averaged properties are then used to inform macroscale material response models, such as those implemented in the Porous-material Analysis Toolbox based on OpenFOAM (PATO) software, also actively developed by NASA. The computational model in PATO is a generic heat and mass transfer model for porous reactive materials containing several solid phases and a single gas phase. The detailed chemical interactions occurring between the solid phases and the gas phase are modeled at the pore scale assuming local thermal equilibrium. These tools were developed to efficiently interface with other pre-existing codes such as SPARTA (direct simulation Monte Carlo), DPLR (hypersonic CFD), NEQAIR (radiative transport) and DAKOTA (uncertainty quantification and optimization). Detailed flight data (Mars Science Laboratory [MSL] Entry Descent and Landing Instrument [MEDLI]) is critical for validating these computational tools for NASA applications. Examples of modeling ablative material response using these codes will be presented including 3D simulations of the full-scale heatshield of the MSL capsule. The simulations demonstrate the ability of the modern material response code, PATO, to handle the material response of geometrically complex and large domains, through the use of massively parallel computations.

Thermal Protection Systems↗

NASA NTRSDate not supplied

Predictive Modeling of Carbon Ablators Using Micro and Macro-Scale Modeling

Thermal Protection Systems↗

DOE OSTI2024

Machine-learning-assisted deciphering of microstructural effects on ionic transport in composite materials: A case study of Li 7 La 3 Zr 2 O 12 -LiCoO 2

The effective diffusivity of ionic species in multiphase materials is critical for the design and function of composite materials for electrochemical energy storage. In practice, effective diffusivity depends sensitively not only on the intrinsic diffusivities of constituting materials but also on their topological arrangement; nevertheless, these coupled contributions are oversimplified in most analytical models. Here, we combine atomistically informed mesoscale modeling and machine learning (ML) analysis to unravel how such features affect effective diffusivity in two-phase composites. Using the Li 7 La 3 Zr 2 O 12 -LiCoO 2 composite solid-state battery cathode as a model system, we compute effective diffusivity for 600 distinct dense polycrystalline microstructures with different topological configurations of grains, grain boundaries, and heterointerfaces. We verify that in addition to atomic-scale variabilities, microstructural feature diversity can significantly impact effective transport properties. Across the ensemble of test microstructures, this often results in bimodal distributions of effective diffusivity that encompass two qualitatively distinct operating mechanisms, which we identify via flux analysis. An ML approach reveals that the most critical determining factors for effective diffusivity are the connectivity of bulk phases and their heterointerfaces. The role of ionic mobility at the heterointerfaces is also discussed. These insights highlight the combined importance of microstructure and interface engineering in tuning the transport properties of ionic species in composite materials. In conclusion, our framework can also be extended for understanding generic microstructure-property relationships in other complex multiphase materials.

25 ENERGY STORAGE↗

NASA NTRS1991

Physical and chemical analysis of a Ni/H2 cell

A cycled aerospace nickel hydrogen (Ni/H2) cell was subjected to destructive physical analysis to determine the reason for a capacity loss after 5,967 cycles at 60 percent depth of discharge. The positive plates in the cell were analyzed in terms of chemical composition, active material utilization, charge efficiency, and thickness increase. The microstructure of a cross section of the positive plate was determined by backscattered electron image analysis. The results suggest that the capacity loss in the cell is caused by low charge acceptance and low active material utilization at the positive plate. The oxidized nickel species content of the positive plate increased due to corrosion of the nickel sintered skeleton. This appears to circumvent the orderly reaction of the active material. Microstructural analysis has indicated that a new phase of active material is formed with cycling.

H Vaidyanathan↗

DOE OSTI2024

Multiphysics Degradation Modeling of Energy Storage Materials via RKPM with a Neural Network-Enhancement

In energy storage materials, strong electrochemical-mechanical coupling and highly anisotropic material properties contribute to the formation and propagation of micro-cracking during charge/discharge cycling, resulting in reduced performance and service life. A coupled electro-chemo-mechanical reproducing kernel particle method (RKPM) formulation is developed, and a patch-test is formulated to certify optimal convergence of the proposed RKPM method for the coupled physics system. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based model construction by RKPM is then used to represent the complex material microstructures for modeling the coupled physics of these systems. Further, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1, 2] is introduced to effectively model damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022. [2] Baek, J., Chen, J. S., "A Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture", Computer Methods in Applied Mechanics and Engineering Vol. 410, 116590, 2024.

electro-chemo-mechanical coupling↗

DOE OSTI2022

A laser ultrasonics-based approach for rapid screening of high entropy alloys

The primary objective of this seed project is to develop a laser ultrasonics-based characterization methodology for rapid metallic materials design and discovery via in situ determination of phases, microstructures and elastic properties with respect to temperature. Ultrasonic waves are strongly affected by material microstructure, and therefore, serve as a facile means to probe elastic properties, phase content and their size distributions and volume fractions. In this study, a laser ultrasonic technique will be used to systematically study the evolution of properties in a set of interrelated simple binary alloys and high entropy alloys (HEA). Phase transformations and microstructural changes inferred from the ultrasonic signals will be correlated with electron microscopy data and predictions using calculations of phase diagrams (CALPHAD). The non-contact and non-destructive ultrasonic testing approach developed in this study could overcome several limitations associated with current material characterization methods for materials discovery. It is expected that the products of the proposed work will be utilized to evaluate novel graded composition HEAs currently being developed at the Idaho National Laboratory (INL) using advanced manufacturing methods based on direct energy deposition and spark plasma sintering processes, and contribute to accelerate the discovery of HEAs.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Transmitted wave measurements in cold sprayed materials under dynamic compression

Spray-formed materials have complex microstructures which pose challenges for microscale and mesoscale modeling. To constrain these models, experimental measurements of wave profiles when subjecting the material to dynamic compression are necessary. The use of a gas gun to launch a shock into a material is a traditional method to understand wave propagation and provide information of time-dependent stress variations due to complex microstructures. This data contains information on wave reverberations within a material and provides a boundary condition for simulation. Here we present measurements of the wavespeed and wave profile at the rear surface of tantalum, niobium, and a tantalum/niobium blend subjected to plate impact. Measurements of the Hugoniot elastic limit are compared to previous work and wavespeeds are compared to longitudinal sound velocity measurements to examine wave damping due to the porous microstructure.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Nanoscale Imaging and Measurements of Grain Boundary Thermal Resistance in Ceramics with Scanning Thermal Wave Microscopy

Material thermal conductivity is a key factor in various applications, from thermal management to energy harvesting. With microstructure engineering being a widely used method for customizing material properties, including thermal properties, understanding and controlling the role of extended phonon-scattering defects, like grain boundaries, is crucial for efficient material design. However, systematic studies are still lacking primarily due to limited tools. In this study, we demonstrate an approach for measuring grain boundary thermal resistance by probing the propagation of thermal waves across grain boundaries with a temperature-sensitive scanning probe. The method, implemented with a spatial resolution of about 100 nm on finely grained Nb-substituted SrTiO 3 ceramics, achieves a detectability of about 2 × 10 –8 K m 2 W –1 , suitable for chalcogenide-based thermoelectrics. The measurements indicated that the thermal resistance of the majority of grain boundaries in the STiO 3 ceramics is below this value. While there are challenges in improving sensitivity, considering spatial resolution and the amount of material involved in the detection, the sensitivity of the scanning probe method is comparable to that of optical thermoreflectance techniques, and the method opens up an avenue to characterize thermal resistance at the level of single grain boundaries and domain walls in a spectrum of microstructured materials.

36 MATERIALS SCIENCE↗