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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Computational Investigation of Oxidative Etch Pitting in FiberForm and Its Impact on Material Properties

Oxidation-driven carbon erosion does not occur uniformly but rather through the development of localized etch pits at active surface sites. These active sites form due to atomic defects on the carbon surface, making them significantly more reactive than the surrounding, non-defective areas. As a result, these sites are the first to react during ablation, leading to their removal. This process creates new defects in neighboring atoms, increasing their reactivity and causing localized carbon removal around these active sites. In this way, the highly reactive defective areas serve as nucleation points for the formation and growth of etch pits, which can have adverse effects on structural integrity of FiberForm. To better understand how these etch pits impact the material properties of carbon fiber microstructures, we have developed a new capability within the direct simulation Monte Carlo (DSMC) framework to capture the etch pit formation process. This capability, integrated into the DSMC code SPARTA (Stochastic Parallel Rarefied-gas Time-accurate Analyzer), models material removal in the presence of active sites, leading to the formation of etch pits. The current work focuses on studying the effects of these etch pits on the material properties of FiberForm, a widely used base material in thermal protection systems (TPS). The microstructure of virgin FiberForm, obtained via X-ray microtomography, is imported into SPARTA to generate the ablated geometries with etch pits. These modified microstructures are then analyzed using the Porous Microstructure Analysis (PuMA) software to compute various material properties, including elasticity, thermal conductivity, and permeability. We investigate the variation of these properties due to the complex surface topology changes caused by etch pit formation. Additionally, we compare the effects of pitting with the conventional model of shrinking fibers, traditionally used to simulate the ablation of carbon structures. Significant differences emerge between the two approaches. Consequently, this physically realistic model of material removal through etch pit formation offers improved accuracy in predicting the degradation of carbon-based TPS during oxidation. It also provides insights into other mechanisms, such as spallation, where chunks of material are removed into the flow due to etch pit growth. Ultimately, this model enhances our understanding of failure modes in these materials during ablation.

PuMA↗
DOE OSTI2020

Modeling Time-Dependent Surrogates of Additive-Manufactured Nuclear Fuels Processes

Additive manufacturing (AM) technology is being increasingly adopted in a wide variety of application areas for its ability to rapidly produce, prototype, and customize designs. Recently, a hybrid AM technique was successfully developed at Idaho National Laboratory (INL) to manufacture nuclear fuels [1]. Despite the advantages, this AM technique needs optimization due to defects from a highly complex melting and sintering process. The complex metallurgical phenomena during AM processes are strongly related to parameters such as applied laser power, traveling speed, and scan style, which could lead to differences in density, residual stress, crystallographic texture, and mechanical properties. In addition, stochastic variations in laser energy interaction and associated multiscale/multiphysics phenomena cause variations in microstructure evolution and mechanical properties. Currently, researchers at INL are focusing on developing a comprehensive modeling framework, leveraging INL’s simulation tools MOOSE/MARMOT/BISON/RAVEN [2-4] to describe all steps of this AM process across multiple length scales. Although this advanced framework plays a critical role in enabling enhancements to traditional trial and error approaches for design and optimization of nuclear fuel materials, it remains computationally intense, limiting its use in sensitivity and optimization analysis. In this case, an accurate and inexpensive surrogate becomes an effective tool for providing a tractable approximation of the underlying underline physics. Surrogate models generally not based on the physics of a system are purely mathematical models used to capture the relationships between specific system inputs and outputs. Popular approaches, including neural networks [5], response surfaces [6], and subspace-based reduced order models [7], have been applied to a wide range of disciplines, such as nuclear reactor design, aerospace design and automotive design. In this summary, we employ advanced time-dependent surrogate models such as high-dimensional model representation (HDMR) [8] and physics-informed deep neural network (PINNs) [9] to accelerate the design and optimization of AM process.

42 ENGINEERING↗
DOE OSTI2022

Context-Aware Learning for Inverse Design in Photovoltaics

This document describes progress in the ARPA-E DIFFERENTIATE project titled “Context-Aware Learning for Inverse Design in Photovoltaics” during the period of May 2019 to May 2022. This project is being performed at Iowa State University, New York University, Stanford University, and National Renewable Energy Laboratory. The project aims to develop a new machine learning (ML) framework to significantly accelerate the design of organic microstructures for improved organic photovoltaic performance. In this project, we had developed an inverse design framework using Deep Learning called InvNets for generating microstructures with desired physics-driven properties. As a preliminary product, in Milestone 3, we demonstrated how InvNets show 20% improvement in the performance of the microstructures and over 100X speedup in the performance compared to traditional processes for physics-driven inverse design. Later, in Milestone 6, we demonstrated that InvNets work for more complex physics properties, specifically, generating microstructures for organic photovoltaic cells with desired current-voltage characteristics. Further, in Milestone 4, we explored the idea of using physics-aware surrogates for obtaining solutions of partial differential equations(PDE) called as DiffNets(now called as NeuFENets to avoid ambiguity of names). The connection between both frameworks is that DiffNet surrogates form the physics-aware surrogate in the InvNet framework. Finally in Milestone 8, we extend our framework for other physics domains. Specifically, we explore building geometry-aware NeuFENets by developing physics surrogates that exploit ideas from traditional immersed boundary finite element methods. With these updates, we are able to achieve all the Milestones.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Microscale Constitutive Model Sensitivity on Multiscale Modeling of Fiber Reinforced Composites

Fiber reinforced composites are desirable in applications where low weight and high strength are needed, but are susceptible to microscale variability during manufacturing, making failure predictions difficult. The impact microscale variability has on macroscale mechanical response is difficult to predict due to the computational efficiency needed to simulate many, large, high fidelity, microscale models. In this study, a multiscale approach was taken to model 3-point bend, 4-point bend, and tensile experiments of a unidirectional composite from only having microstructure scans of these samples and constituent properties from literature. These scans were sampled with different sized windows, and statistically equivalent microstructures were generated, then simulated for stiffness, strength, and fracture toughness using an efficient micromechanical. Mesoscale models were created where element sizes equaled microstructure size, and properties were assigned through sampling of microscale simulation results. First, this study showed the effect of using Weibull scaling on constituent matrix strength on macroscale response. Then, a comparison was made between different element sizes and experiments. Finally, model dimensions were fixed, and the effect of randomly distributed local properties alone was examined. Results showed that the scatter of strength and stiffness in the experiments could be predicted well using images of the microscale fiber morphologies and that using stochastic properties produced a 3% coefficient of variation of strength for all experiments.

statistical microstructure↗
DOE OSTI2021

Comparison on the properties of ITZs in fly ash-based geopolymer and Portland cement concretes with equivalent flowability

Highlights: • Modelled ITZs can effectively avoid the effects caused by different factors and test locations of ITZ. • ITZ properties of PC and geopolymer concretes are compared based on equivalent flowability of binders. • Geopolymer ITZ exhibits stronger bonding than that of PC, and higher mechanical properties than geopolymer paste. • Strong ITZs in geopolymer concrete are attributed to the layer of gel-rich paste with dense microstructures. • Elastic modulus and hardness properties of ITZs in PC concrete are increased due to the CH crystals. This paper aims to compare the properties of interfacial transition zones (ITZs) in Portland cement (PC) concrete and geopolymer concrete. Portland cement and geopolymer pastes were designed with the equivalent flowability to provide similar mix and casting condition of ITZs. Two types of modelled ITZs were prepared to facilitate the nanoindentations across ITZs, microstructural characterization, and comparison on the properties of ITZs with less influential factors. The results showed that the interfacial bonding of ITZs between geopolymer matrix and aggregate is relatively stronger than the counterpart in the PC concrete. There is a high amount of crystalline hydration products in the ITZs of PC concrete, but a layer gel-rich paste with denser microstructures in the ITZs of geopolymer concrete. Additionally, the interface morphology and nanoindentation analysis indicate that the property of ITZs in the modelled geopolymer concrete is not poorer than that of the corresponding geopolymer paste.

36 MATERIALS SCIENCE↗
NASA NTRS1994

Improved construction materials for polar regions using microcellular thermoplastic foams

Microcellular polymer foams (MCF) are thermoplastic foams with very small cell diameters, less than 10 microns, and very large cell densities, 10(exp 9) to 10(exp 15) cells per cubic centimeter of unfoamed material. The concept of foaming polymers with microcellular voids was conceived to reduce the amount of material used for mass-produced items without compromising the mechanical properties. The reasoning behind this concept was that if voids smaller than the critical flaw size pre-existing in polymers were introduced into the matrix, they would not affect the overall strength of the product. MCF polycarbonate (PC), polystyrene (PS), and polyvinyl chloride (PVC) were examined to determine the effects of the microstructure towards the mechanical properties of the materials at room and arctic temperatures. Batch process parameters were discovered for these materials and foamed samples of three densities were produced for each material. To quantify the toughness and strength of these polymers, the tensile yield strength, tensile toughness, and impact resistance were measured at room and arctic temperatures. The feasibility of MCF polymers has been demonstrated by the consistent and repeatable MCF microstructures formed, but the improvements in the mechanical properties were not conclusive. Therefore the usefulness of the MCF polymers to replace other materials in arctic environments is questionable.

Cunningham, Daniel J.↗
NASA NTRSDate not supplied

Recent Developments to the Porous Microstructure Analysis (PuMA) Software

Introduction The Porous Microstructure Analysis (PuMA) software is an open source framework for image-based simulation, primarily used to determine effective properties based on material microstructure. PuMA was originally developed for the study of NASA thermal protection materials; however, many of the solvers in PuMA have applicability to a broad range of materials science applications. PuMA version 3.2 computes material surface area, pore diameters, effective thermal conductivity, continuum and rarefied tortuosity, and permeability. For anisotropic materials, PuMA can estimate material orientation and compute anisotropic thermal conductivity and elasticity. In this talk, a brief overview of the PuMA software and underlying methods will be presented, as well as some recent and ongoing developments, including the use of immersed boundary methods for image-based simulation and the development of a new weave segmentation tool, called TomoSAM. Cut-Cell method for heat and mass transfer For simulations on complex microstructures, traditional unstructured meshing techniques often prove to be difficult and time-intensive. Voxel-based solvers, which represent the surface as a staircase structure, are relatively simple to implement but can lose accuracy when feature resolution is poor. In this work, we present a novel 3D cut-cell method for solving the variable coefficient Poisson equation on complex microstructures, suitable for the determination of effective thermal conductivity or tortuosity of a material. The method uses a Marching Cubes/Marching Squares surface reconstruction to create cut-cells and determine geometric quantities. A flux-correction method is extended to 3D, with least squares gradient reconstruction, to solve for the boundary fluxes in the cut-cells. Verification cases show the solver achieves globally 2nd order accuracy on complex microstructures. TomoSAM TomoSAM, a module of the PuMA software, has been developed as a plugin for 3D Slicer, a software platform used for 3D image processing and visualization. It utilizes the Segment Anything Model (SAM), a deep learning model capable of identifying objects and generating image masks based on minimal user input. This feature enables efficient segmentation of complex 3D datasets, particularly of woven materials, from tomography or similar imaging methods, reducing the need for manual segmentation.

Tomography↗
NASA NTRSDate not supplied

Recent Developments to the Porous Microstructure Analysis (PuMA) Software

The Porous Microstructure Analysis (PuMA) software is an open source framework for image-based simulation, primarily used to determine effective properties based on material microstructure. PuMA was originally developed for the study of NASA thermal protection materials; however, many of the solvers in PuMA have applicability to a broad range of materials science applications. PuMA version 3.2 computes material surface area, pore diameters, effective thermal conductivity, continuum and rarefied tortuosity, and permeability. For anisotropic materials, PuMA can estimate material orientation and compute anisotropic thermal conductivity and elasticity. In this talk, a brief overview of the PuMA software and underlying methods will be presented, as well as some recent and ongoing developments, including the use of immersed boundary methods for image-based simulation and the development of a new weave segmentation tool, called TomoSAM. Cut-Cell method for heat and mass transfer For simulations on complex microstructures, traditional unstructured meshing techniques often prove to be difficult and time-intensive. Voxel-based solvers, which represent the surface as a staircase structure, are relatively simple to implement but can lose accuracy when feature resolution is poor. In this work, we present a novel 3D cut-cell method for solving the variable coefficient Poisson equation on complex microstructures, suitable for the determination of effective thermal conductivity or tortuosity of a material. The method uses a Marching Cubes/Marching Squares surface reconstruction to create cut-cells and determine geometric quantities. A flux-correction method is extended to 3D, with least squares gradient reconstruction, to solve for the boundary fluxes in the cut-cells. Verification cases show the solver achieves globally 2nd order accuracy on complex microstructures. TomoSAM TomoSAM, a module of the PuMA software, has been developed as a plugin for 3D Slicer, a software platform used for 3D image processing and visualization. It utilizes the Segment Anything Model (SAM), a deep learning model capable of identifying objects and generating image masks based on minimal user input. This feature enables efficient segmentation of complex 3D datasets, particularly of woven materials, from tomography or similar imaging methods, reducing the need for manual segmentation.

Tomography↗
DOE OSTI2021

Microstructural Progression of Shear-Induced Mixing in a CuNi Alloy

Shear deformation has been highlighted in multiple research efforts for its ability to impart mechanical properties improvements and unique microstructures. When used to process and densify powdered material, these shear-based consolidation techniques are commonly referred to as friction consolidation (FC). In this paper, we have examined the microstructural evolution from compacted Cu and Ni powders to a consolidated Cu0.5Ni0.5 alloy. We have shown various stages of porosity reduction and preferential deformation being accommodated by the more ductile material early in the process, leading to formation of a tortuous microstructural zone. Porosity reduction was extensive, decreasing from ~65% in the unprocessed powder to ~1% in the fully consolidated alloy. The final consolidated alloy showed roughly a 2X hardness improvement over the unalloyed, compacted material. Unique aspects of this work include demonstration of the ability to use FC processing to produce a submicron and equiaxed grain size in samples within a 0.5 to 2 minute processing time.

Overman, Nicole R.↗
DOE OSTI2021

Microstructural-Level Fuel Performance Modeling of U-Mo Monolithic Fuel

As the physics that governs the microstructural evolution of nuclear fuel span various time and spatial scales, to fully understand the fuel behavior inevitably involves atomic to mesoscale resolution that can be difficult to determine experimentally. Microstructural-level modeling and simulations can be used to develop physics-based materials models that can provide physical understanding to inform fabrication process control, as well as a valuable feedback mechanism between post-irradiation examination (PIE) results and fabrication parameters. In accordance with the program schedule, the primary goals of the microstructure modeling effort are to: 1. Address critical microstructural questions and provide practical guidance to the fabricator via the fuel product specification; 2. Provide mechanistic inputs for the existing fuel performance code to improve its descriptive and predictive capability at the macroscopic scale. In fiscal year (FY)-21, the work scope consisted of six main facets: (1) the effect of carbides on fuel performance; (2) gas diffusivity in different phases; (3) integration of microstructural fuel performance modeling; (4) property degradation; (5) irradiation creep; and (6) historical analysis of microstructure data. Brief summaries of each are included below.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2004

Advanced Constituents and Processes for Ceramic Composite Engine Components

The successful replacement of metal alloys by ceramic matrix composites (CMC) in hot-section engine components will depend strongly on optimizing the processes and properties of the CMC microstructural constituents so that they can synergistically provide the total CMC system with improved temperature capability and with the key properties required by the components for long-term structural service. This presentation provides the results of recent activities at NASA aimed at developing advanced silicon carbide (Sic) fiber-reinforced hybrid Sic matrix composite systems that can operate under mechanical loading and oxidizing conditions for hundreds of hours at 2400 and 2600 F, temperatures well above current metal capability. These SiC/SiC composite systems are lightweight (-30% metal density) and, in comparison to monolithic ceramics and carbon fiber-reinforced ceramic composites, are able to reliably retain their structural properties for long times under aggressive engine environments. It is shown that the improved temperature capability of the SiC/SiC systems is related first to the NASA development of the Sylramic-iBN Sic fiber, which displays high thermal stability, creep resistance, rupture resistance, and thermal conductivity, and possesses an in-situ grown BN surface layer for added environmental durability. This fiber is simply derived from Sylramic Sic fiber type that is currently produced at ATK COI Ceramics. Further capability is then derived by using chemical vapor infiltration (CVI) to form the initial portion of the hybrid Sic matrix. Because of its high creep resistance and thermal conductivity, the CVI Sic matrix is a required base constituent for all the high temperature SiC/SiC systems. By subsequently thermo- mechanical-treating the CMC preform, which consists of the S ylramic-iBN fibers and CVI Sic matrix, process-related defects in the matrix are removed, further improving matrix and CMC creep resistance and conductivity.

Yun, H. M.↗
DOE OSTI2020

Effect of 3D printing conditions on the micro- and macrostructure and properties of high-performance thermoplastic composites

Within the scope of extrusion based additive manufacturing, this chapter provides a review of the research in understanding the physical relationships between microstructure variables and macrostructure properties of high-performance thermoplastic polymers. The development of interlayer strength has been discussed based on the theory of inter-diffusion and re-entanglement of polymer chains at interface. The observations from the study in joining and direct-bonding of polymer surfaces provide insights into the mechanisms of the formation of interface between adjacent layers. Quantitative prediction of interfacial strength as a function of processing temperature and contact time is presented. The mechanisms that lead to reduced mechanical strength at the interface are discussed. The second half of the chapter outlined a set of printing conditions and their influence on structural properties.

Liu, Peng↗
DOE OSTI2026

Structure–Property Relationships of Recycled Lithium-Ion Battery Cathodes: Microstructure Optimization Using Virtual Materials Testing

The increasing demand for sustainable battery technologies requires effective recycling strategies for end-of-life lithium-ion battery cathodes. In this study, virtual materials testing, a well-established framework for modeling conventionally manufactured NMC-based cathodes, is applied to partially recycled cathodes. To this end, virtual cathodes consisting of mixtures of pristine and recycled NMC particles are utilized to systematically analyze structure–property relationships depending on mixing ratios and different spatial arrangement strategies. For this purpose, a stochastic 3D model is developed that is capable of generating virtual cathodes with arbitrary volume fractions of active materials and mixing ratios of pristine and recycled NMC particles. Particularly, the stochastic 3D model can mimic the different size distributions of pristine and recycled particles that are observed in image data. Additionally, the model allows the structuring of pristine and recycled NMC either uniformly mixed or layer-wise arranged, mimicking single- and dual-layer cathodes. Subsequently, a systematic computational analysis is conducted to assess the influence of increasing active material ratios of recycled particles, ranging from 0 % to 100 %, while maintaining a constant overall active material volume fraction. The impact of particle mixing on cathode performance is evaluated by examining transport-relevant geometrical descriptors and effective properties, such as geodesic tortuosity, specific surface area, and tortuosity factor.

25 ENERGY STORAGE↗
DOE OSTI2025

Tailoring mechanical properties of a multi-principal element alloy through a multi-length-scale approach

In this study, a multi-length-scale strengthening approach was used to tailor the microstructure and the mechanical properties of a NiCoCr-based multi-principal element alloy (MPEA). Grain size refinement, severe lattice distortion, and stacking fault energy (SFE) reduction with Mo addition (up to 10 at.%) enhance yield strength by 85% with only 10% reduction in ductility in as-annealed MPEAs. A pronounced increase in the strain hardening rate was observed with the addition of Mo, which is ascribed to the promotion of complex stacking fault (SF) interaction and intersection, accompanied by Lomer-Cottrell (L-C) and Hirth locks inhibiting dislocation motion and substantial increase in the accumulation of back stress. To push the limit of the yield strength further, the Suzuki segregation phenomenon was manipulated by a careful control of SF density by pre-straining and a subsequent 500 °C heat treatment. The stress-strain responses of the pre-strained and heat treated MPEAs showed an obvious SF density and Mo concentration dependence. Furthermore, the yield strength of the pre-strained Mo-added MPEAs with subsequent heat treatment was increased up to true stress of 2.3 GPa with a corresponding fracture elongation of 12% true strain. SFs formed during pre-straining served as Cr segregation sites during subsequent heat treatment, which substantially varies the local SFE within the SF, presenting a roughened landscape and frustrating the dislocation dynamics.

mechanical properties↗
DOE OSTI2023

Property-Graded Stainless Steel 316L by Selective Laser Melting: Characterization & Design

Abstract The purpose of this research work is to characterize and inform the design of (mechanical) property-graded bulk structures made from a single metallic alloy via a laser powder bed fusion (LPBF) process, with an end goal of creating repeatable/reproducible functionally-graded additively manufactured (FGAM) parts. This paper specifically investigates the manufacture of stainless steel (SS) 316L structures via a pulsed selective laser melting (SLM) process, and the underlying causes of property variations (within a functionally-acceptable range) through various material characterization techniques. For this, a design of experiments spanning the volumetric energy density (VED) based process parameter design space was utilized to investigate the range of functionally-acceptable physical/mechanical properties achievable in SS 316L. Five sample conditions (made via different process parameter combinations) were down-selected for in-depth microstructure analysis and mechanical/physical property characterization; these were suitably selected to impart a wide and controllable property range (209–318 HV hardness, 90–99.9% relative density, and 154–211 GPa modulus). It was observed that property variations resulted from combinations of porosity types/amounts, martensitic phase fractions, and grain sizes. Based on these findings, property-graded standard test specimens were designed and manufactured for further investigation—tensile specimens having a monotonic hardness change along its gauge length, four-point bending specimens with varying elastic moduli as a function of the distance from the neutral axis, and Moore’s rotating beam fatigue specimens with moduli variations based on the distance from the center. Altogether, this work lays the foundation for understanding and designing the local and global mechanical performance of FGAM bulk structures.

Engineering↗
DOE OSTI2025

Methods and Models for the Study of Aging Effects in Palladium Tritide

We have developed methods to expose milligram-scale samples of metals to tritium for studies of aging effects on material properties, primarily intended for metals that reversibly form hydrides. The tritium decays to helium within the solid, leading to profound changes in microstructural features and macroscopic properties. We present our combined experimental and modeling approach, and provide an example of a palladium foil aged 3 months.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Quantitative assessment of particle dispersion in polymeric composites and its effect on mechanical properties

In this work, an automated image analysis tool is developed to establish quantitative correlations between the particle/cluster size distribution and the mechanical properties of particle reinforced polymer composites (PRPC).This automated image analysis tool is developed within python programming software to process and analyze the microstructural images of the polymerbased composite materials. The spent coffee bean powder (SCBP) reinforced poly-propylene carbonate (PPC) polymer composite with differing wt.% of the filler is selected for the analysis. Detailed statistical analysis of the microstructural images reveals that ‘clustering of clusters’ is also presented in addition to the most commonly reported ‘clustering of particles’, and the distribution of particle/clusters is bimodal. Based on these findings, an effective volume fraction for the filler material is proposed to mainly capture the agglomeration effect. With this effective volume fraction, the standard rule-of-mixture model correctly captures the experimentally measured tensile strength and modulus as a function of filler wt.%. Further, the applicability of this effective volume fraction for other theoretical models is also analyzed. The detailed statistical analysis of the microstructure and the proposed effective volume fraction helps to develop a deeper quantitative understanding of the PRPC than the conventional qualitative correlation of microstructural features with the properties and failure processes.

36 MATERIALS SCIENCE↗