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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Preliminary Results on Process Modeling Tools for Determining Variability in Additively Manufactured Stainless Steel 316 Parts

The Advanced Materials and Manufacturing Technologies program aims to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy. However, the distinct characteristics of additive manufacturing (AM) materials, stemming from their unique processing history, microstructure, and properties, pose significant challenges for the qualification and certification of nuclear components. These challenges primarily arise from component-scale variations in microstructure and properties influenced by local process conditions and geometry, which affect thermal history, melt pool dynamics, and microstructure evolution. Computational modeling tools can play a crucial role in predicting and controlling this variability. This report presents preliminary results on process modeling tools designed to predict microstructure variability in additively manufactured stainless steel 316 parts. It details the software packages and physical modeling approaches employed to simulate an AM component within an automated process modeling workflow. Initial results are demonstrated through comparisons between predicted microstructures and experimental measurements across various representative processing conditions. The report concludes by discussing the challenges inherent in process modeling of AM components and outlines a plan for future development needs.

36 MATERIALS SCIENCE↗
DOE OSTI2018

An efficient algorithm for generating diverse microstructure sets and delineating properties closures

Properties closures delineate the theoretical objective space for materials design problems, allowing designers to make informed trade-offs between competing constraints and target properties. In this paper, we present a new algorithm called hierarchical simplex sampling (HSS) that approximates properties closures more efficiently and faithfully than traditional optimization based approaches. By construction, HSS generates samples of microstructure statistics that span the corresponding microstructure hull. As a result, we also find that HSS can be coupled with synthetic polycrystal generation software to generate diverse sets of microstructures for subsequent mesoscale simulations. Finally, by more broadly sampling the space of possible microstructures, it is anticipated that such diverse microstructure sets will expand our understanding of the influence of microstructure on macroscale effective properties and inform the construction of higher-fidelity mesoscale structure-property models.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Comprehensive Experimental Study on Microstructure‐Graded Graphite Anodes for Enhancing Fast‐Charging Capability of Lithium‐Ion Batteries

Lithium‐ion batteries with high gravimetric capacity density and improved cycle life performance under fast‐charging conditions are crucial for widespread electric vehicle (EV) adoption. This study investigates how designing graphite anode microstructure, specifically porosity, and particle‐size gradients, improves lithium‐ion (Li + ) transport during fast‐charging conditions. Three‐layered graphite anodes with varying porosity (24%, 36%, 46%) and particle size gradients (3, 5, 10 μm) were compared to a conventional single‐layered electrode in half‐cell configurations. At room temperature and high discharge rate (2C), both gradient structures showed significantly enhanced capacity retention (80% and 67% vs. 50%) compared to the conventional electrode, highlighting the effectiveness of microstructure engineering for fast charging. The study also investigated the temperature's impact on cycle life. After 200 cycles at 2°C and 45°C, all gradient structures demonstrated superior capacity retention (≈80%) compared to the conventional electrode (35%), suggesting the gradients mitigate degradation rate at high temperatures. Electrochemical impedance spectroscopy confirmed superior Li+ diffusion and lower resistivity in gradient electrodes. Simulations explored the influence of gradient profiles on reaction kinetics across the electrode thickness. Overall, this research demonstrates that the fast‐charging capability of graphite electrodes can be greatly enhanced by engineering the electrode microstructure, thereby making EV technology more accessible and appealing.

Ahmadi, Soma↗
DOE OSTI2024

Novel Materials R&D for Next-Generation Accelerator Target Facilities

High-Entropy Alloys and Electrospun Nanofiber materials are two classes of novel materials that can offer improved resistance to beam-induced radiation damage and thermal shock. Research to develop these new materials specifically for multi-megawatt accelerator target applications, such as beam windows and particle-production targets, are ongoing at Fermilab within the scope of a DOE Early Career Research Program. The research program combines in-beam experiments with complementary simulations to tailor the microstructures of these novel materials for use in next-generation accelerator target facilities. Iterative simulations to optimize the material composition, physics performance, and beam-induced thermomechanical response will guide the material design and fabrication processes based on established figures of merit. This will be followed by material irradiation experiments using low-energy ions and prototypic high-energy protons with extensive post-irradiation material characterization to assess and qualify the selected novel materials. This talk will describe the alloy design and synthesis, microstructural pre-characterization of the alloys, and plans for the eventual down selection following low-energy ion irradiation studies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Microstructure-sensitive modeling of high temperature creep in grade-91 alloy

Predicting the effects of microstructure on high-temperature creep responses of steel components is critical to minimize the risks of failure and maximize economic viability in the energy sector. In this research, a recently developed advanced mechanistic constitutive model is employed to study the effect of microstructure on the creep responses and to rationalize experimentally reported variability in the performances of grade-91 alloy. Hundreds of experimental creep tests from the literature are used to assess the predictability of the model. The model proposed is shown to accurately predict the steady-state creep rates and also the less commonly considered, yet important, primary-to-secondary transients for a wide range of creep conditions. Further, the model is capable of extrapolating the creep response of grade 91 alloy even outside of the calibration regime. Using this model, the roles of initial microstructure described in terms of grain size, dislocation density, and precipitate content, on the creep responses are investigated. The effect of initial microstructure on the steady-state creep rates and creep-strain is found to vary with stress and temperature. Grain size plays a significant role in the low-stress creep, where the diffusional creep is dominant. On the other hand, the effects of dislocation densities and precipitate content are significant in the dislocation-plasticity-dominated high-stress and temperature regimes. Furthermore, by comparing model predictions against a large experimental creep database, it is found that variability in the creep responses can be rationalized on the basis of differences in the initial microstructure. Overall, this work provides a robust pathway for microstructure-aware engineering design, verification, and validation of metallic components for energy applications.

36 MATERIALS SCIENCE↗
DOE OSTI2019

An Approach to Upscaling SPPARKS Generated Synthetic Microstructures of Additively Manufactured Metals

Additive manufacturing (AM) of metal parts can save time, energy, and produce parts that cannot otherwise be made with traditional machining methods. Near final part geometry is the goal for AM, but material microstructures are inherently different from those of wrought materials as they arise from a complex temperature history associated with the additive process. It is well known that strength and other properties of interest in engineering design follow from microstructure and temperature history. Because of complex microstructure morphologies and spatial heterogeneities, properties are heterogeneous and reflect underlying microstructure. This report describes a method for distributing properties across a finite element mesh so that effects of complex heterogeneous microstructures arising from additive manufacturing can be systematically incorporated into engineering scale calculations without the need for conducting a nearly impossible and time consuming effort of meshing material details. Furthermore, the method reflects the inherent variability in AM materials by making use of kinetic Monte Carlo calculations to model the AM process associated with a build.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Strain-rate sensitivity of strength dependence on the softening factor c b in Ti-alloy wires

Titanium alloys such as Ti-6Al-4V and Ti-6Al-7Nb are widely used in the biomedical industry as structural implant materials. The strain-rate sensitivity of tensile strength is now assessed using a modified Kocks-Mecking formulation for hardening. The operative scale of the microstructural strengthening is designated by the coefficieint c b that can be determined from measurements of plastic strain, yield and ultimate strength. It is found that although that strength varies slightly with strain rate, the scale of the microstructure c b remains nearly constant for each material. Finally, a low c b -value of 14 is computed for Ti-6Al-4V that is consistent with the refined twophase microstructure needed to enhance both ductility beyond yielding and its ultimate strength.

36 MATERIALS SCIENCE↗
DOE OSTI2024

AOI [1] Advanced Manufacturing of Ceramic Anchors with Embedded Sensors for Process and Health Monitoring of Coal Boilers

Researchers at West Virginia University (WVU) developed methods to fabricate and test ceramic anchors with an embedded sensor technology for monitoring the health and processing conditions within pulverized coal (PC) and fluidized-bed combustion (FBC) boiler systems. The technology included the development of advanced manufacturing processes for 2D/3D printing electroceramic (conductive ceramic) sensor designs within the ceramic anchor microstructure during the manufacturing process. This advanced manufacturing process would allow for the precise control of local microstructure and composition in order to engineer layer-by-layer any protective and electrically active materials within the refractory anchor. This 3D printing technology would permit the rapid and controlled design of the refractory microstructure and embedded sensor design throughout the volume of the ceramic anchor. The work also included a method to interconnect the sensors to boiler shell through the anchor clamp, where the sensor signals will be processed by low-power electronics and transmitted wirelessly to a central processing hub. The end-goal of the program was to produce a ceramic anchor sensor system which would be ready for implementation within a coal boiler, and/or other similar refractory liner systems (such as that in the glass and metal manufacturing areas). The project objectives were to: 1) Define the chemical and microstructural stability, in addition to the electrical properties, of oxide and non-oxide ceramic composites to be embedded within the ceramic anchor compositions that may operate up to 1400ºC; 2) Develop and implement the 2D/3D printing technology to pattern and control the microstructure of the ceramic anchor and embedded sensor circuits; 3) Develop an interconnect technology which will permit easy installation of the ceramic anchors and signal collection at the boiler shell; 4) Develop low power analog electronics and wireless communication hardware to efficiently collect the sensor signal at each processing unit and transmit data to a central hub for data analysis; 5) Demonstrate the smart ceramic anchor system for temperature and liner fracture within a high-temperature processing unit, such as a boiler furnace or glass melting furnace floor/wall liner.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS1982

X-ray and extreme ultraviolet imaging using layered synthetic microstructures

Up to now designers of X-ray optical instruments such as microscopes and telescopes have been compelled to use mirrors operating at small glancing angles. However, using vapor deposition techniques, it is now possible to build multilayered thin film structures in which the individual layers have thicknesses comparable with a wavelength of X-rays. These devices operate in a manner exactly analogous to multilayer dielectric coatings at visible wavelengths and can be used to increase the X-ray and extreme ultraviolet reflectivity of surfaces at large glancing angles. These X-ray mirrors have numerous potential applications in X-ray optics. Attention is given to X-ray images with a normal incidence mirror, and applications of multilayer optics. Single-mirror telescopes for solar/stellar astronomy are discussed, taking into account solar corona and transition region studies, and X-ray and extreme ultraviolet astronomy. Hybrid X-ray telescopes are also considered.

Underwood, J. H.↗
NASA NTRS1984

Theoretical design and analysis of the layered synthetic microstructure optic for the dual path X-ray telescope

A ray tracing analysis was performed for several configurations for the inner channel of the dual path X-ray telescope, which is proposed to use the second mirror of the Stanford/MSFC Wolter-Schwarzchild telescope and a normal incident layered synthetic microstructure (LSM) mirror to form a secondary image near the front of the telescope. The LSM mirror shapes considered were spherical, ellipsoid, hyperboloid, and constant optical path length (OPL) aspheric. Only the constant OPL case gave good axial resolution. All cases had poor off axis resolution as judged by the RMS blur circle radius.

Shealy, D. L.↗
DOE OSTI2011

Numerical Modeling of the Distributed Electrochemistry and Performance of Solid Oxide Fuel Cells

A cell-level distributed electrochemistry (DEC) modeling tool has been developed to enable prediction of solid oxide fuel cell performance by considering the coupled and spatially varying multi-physics that occur within the tri-layer. The approach calculates the distributed electrochemistry within the electrodes, which includes the charge transfer and electric potential fields, ion transport throughout the tri-layer, and gas distributions within the composite and porous electrodes. The thickness of the electrochemically active regions within the electrodes is calculated along with the distributions of charge transfer. The DEC modeling tool can examine the overall SOFC performance based on electrode microstructural parameters, such as particle size, pore size, porosity factor, electrolyte and electrode phase volume fractions, and triple-phase-boundary length. Recent developments in electrode fabrication methods have lead to increased interest in using graded and nano-structured electrodes to improve the electrochemical performance of SOFCs. This paper demonstrates how the DEC modeling tool can be used to help design novel electrode microstructures by optimizing a graded anode for high electrochemical performance.

30 DIRECT ENERGY CONVERSION↗
NASA NTRSDate not supplied

The Porous Microstructure Analysis (PuMA) software

The open-source Porous Microstructure Analysis (PuMA) software was implemented to offer an efficient framework for determining material characteristics from 3D microstructures. Its development was inspired by progress in X-ray microtomography, an imaging technology that captures the internal structure of materials in 3D, and even in a 4D temporal context. Over recent years, this method has transformed the domain of materials science due to its capability to non-destructively examine material microstructures while presenting digital data about their geometrical details. It has provided insights into materials relevant to several NASA missions, including heatshields, parachute fabrics, meteorites, and other advanced composites. PuMA, in its current version 3, delivers an array of features, spanning from basic geometric insights of a microstructure to intricate anisotropic thermo-elastic and chemical behavior. Specifically, the software evaluates morphological attributes (specific surface area, volume fractions, mean intercept lengths, orientation) and physical characteristics (conductivity, elasticity, permeability, and tortuosity). Additionally, it can model material degradation processes, such as oxidation and surface chemistry interactions. The software can generate synthetic microstructures, from straightforward geometrical designs to intricate woven and non-woven geometries. Coupling material generation and characterization enable parametric studies and sensitivity analysis to optimize the microstructural performance and inform design decisions and reliability assessment based on uncertainty quantification. A recent addition to PuMA includes the TomoSAM plugin, devised to incorporate the cutting-edge Segment Anything Model (SAM). SAM is a promptable deep learning model that can identify objects and create image masks in a zero-shot manner, based only on a few user clicks. The synergy between these tools aids in the segmentation of complex 3D datasets from tomography or other imaging techniques, which would otherwise require a laborious manual segmentation process.

Tomography↗
NASA NTRSDate not supplied

The Porous Microstructure Analysis (PuMA) software

The open-source Porous Microstructure Analysis (PuMA) software was created to offer an efficient framework for determining material properties from 3D microstructures. Its development was inspired by progress in X-ray microtomography, an imaging technology that captures the internal structure of materials in 3D, and even in a 4D temporal context. Over recent years, this method has transformed the domain of materials science due to its capability to non-destructively examine material microstructures while presenting digital data about their geometrical details. It has provided insights into materials relevant to several NASA missions, including heatshields, parachute fabrics, meteorites, and other advanced composites. PuMA, in its current version 3, delivers an array of features, spanning from basic geometric insights of a microstructure to intricate anisotropic thermo-elastic and chemical behavior. Specifically, the software evaluates morphological attributes (specific surface area, volume fractions, mean intercept lengths, orientation) and physical characteristics (conductivity, elasticity, permeability, and tortuosity). Additionally, it can model material degradation processes, such as oxidation and surface chemistry interactions. The software can generate synthetic microstructures, from straightforward geometrical designs to intricate woven and non-woven geometries. Coupling material generation and characterization enables parametric studies and sensitivity analysis to optimize the microstructural performance and inform design decisions and reliability assessment based on uncertainty quantification. A recent addition to PuMA includes the TomoSAM plugin, devised to incorporate the cutting-edge Segment Anything Model (SAM) into our image segmentation workflow. SAM is a promptable deep learning model that can identify objects and create image masks in a zero-shot manner, based only on a few user clicks. The synergy between these tools aids in the segmentation of complex 3D datasets from tomography and other imaging techniques, which would otherwise require a laborious manual segmentation process.

Tomography↗
DOE OSTI2019

Modeling mechanical behavior of an additively manufactured metal structure with local texture variations: a study on model form error

Here, the repetitive rapid solidification that occurs in metal additive manufacturing (AM) processes creates microstructures distinctly different from wrought materials. Local variability in AM microstructures (either by design or unintentional) raises questions as to how AM structures should be modeled at the part-scale to minimize modeling error. The key goal of this work is to demonstrate a posteriori error estimation applied to an AM part. It is assumed that the actual microstructure is unknown and an approximate, spatially uniform material model is used. Error bounds are calculated for many reference models based on AM microstructures with elongated grain morphology and localized or global fiber textures during a post-processing step. The current findings promote confidence that a posteriori model form error estimation could be used effectively in mechanical performance simulations of AM parts to quickly obtain quantitative error metrics between an approximate model result and many microstructure-based reference models. The a posteriori error estimation introduces significant time savings compared to computing the full reference model solutions. Tight bounds on model form error are obtained when texture variations in the reference models occur on large length scales. For materials with property variation at small length scales, multi-scale error estimation techniques are needed to properly account for the many interfaces present between areas with different properties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Assessment of Microstructure Prediction Capabilities for Powder Bed Fusion Stainless Steel 316

The Advanced Materials and Manufacturing Technologies program aims to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy. However, the characteristic process-structure-property relationships of additive manufacturing (AM) materials pose challenges for the qualification and certification of AM nuclear components. In particular, component-scale variations in microstructure and properties can be driven by localized changes in melt pool dynamics due to how process parameters interact with different part geometries. Computational modeling tools can play a crucial role in predicting and controlling this variability. This report presents final results on process modeling tools designed to predict microstructure variability in additively manufactured stainless steel 316 parts. It details the software packages and physical modeling approaches employed to simulate an AM component within an automated process modeling workflow. Results are demonstrated through comparisons between predicted microstructures and experimental measurements across various representative processing conditions. The report concludes by discussing identified challenges and future opportunities for connecting the developed simulation workflow with mechanics simulations for prediction of part performance.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Rational Design of Capacitive Pressure Sensors Based on Pyramidal Microstructures for Specialized Monitoring of Biosignals

Abstract There is an increasing demand for specialized pressure sensors in various applications. Previously, capacitive pressure sensors have been shown to have wide versatility in use, with a high degree of potential tuning possible through manipulating the geometry or material selection of the dielectric layer. However, in order to make sensors that are tunable and predictable, the design and fabrication method first needs to be developed. Presented here is an improved fabrication method to achieve tunable, consistent, and reproducible pressure sensors by using a pyramid microstructured dielectric layer along with a lamination layer. The as‐produced sensor performance is able to match predicted trends. Further, a simple model based on this system is developed and its efficacy is experimentally confirmed. Then, the model to predict a wide range of material and microstructure geometric properties prior to device fabrication is used to provide trends on sensor performance. Finally, it is demonstrated that the new method can be used to targetedly design a pressure sensor for a specific application—in vitro pulse sensing.

Ruth, Sara Rachel Arussy↗