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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Statistical study of favorable foreshock ion properties for the formation of hot flow anomalies and foreshock bubbles

Hot flow anomalies (HFAs) and foreshock bubbles (FBs) are frequently observed in Earth's foreshock, which can significantly disturb the bow shock and therefore the magnetosphere-ionosphere system and can accelerate particles. Previous statistical studies have identified the solar wind conditions (high solar wind speed and high Mach number, etc.) that favor their generation. However, backstreaming foreshock ions are expected to most directly control how HFAs and FBs form, whereas the solar wind may partake in the formation process indirectly by determining foreshock ion properties. Using Magnetospheric Multiscale mission and Time History of Events and Macroscale Interactions during Substorms mission, we perform a statistical study of foreshock ion properties around 275 HFAs and FBs. We show that foreshock ions with a high foreshock-to-solar wind density ratio (>∼3%), high kinetic energy (>∼600 eV), large ratio of kinetic energy to thermal energy (>∼0.1), and large ratio of perpendicular temperature to parallel temperature (>∼1.4) favor HFA and FB formation. We also examine how these properties are related to solar wind conditions: high solar wind speed and oblique bow shock (angle between the interplanetary magnetic field and the bow shock normal 𝜃Bn∼45°) favor high kinetic energy of foreshock ions; foreshock ions have large ratio of kinetic energy to thermal energy at large 𝐴𝐴𝐴𝐴Bn (>30°); small 𝐴𝐴𝐴𝐴Bn (<30°), high Mach number, and closeness to the bow shock favor a high foreshock-to-solar wind density ratio. Our results provide further understanding of HFA and FB formation.

Terry Z. Liu↗
NASA NTRSDate not supplied

Automation of the ICME Workflow Incorporating Material Digital Twins at Different Length Scales Within a Robust Information Management System

Recent successes in Integrated Computational Materials Engineering (ICME) have demonstrated the potential in designing fit-for-purpose materials for a given application in a cost and time efficient manner. However, the material design process must contain a level of automation in the material decision process, implementing some optimization algorithms, to truly enable the full benefits of ICME, particularly when considering materials at multiple length/time scales. In this work, we will demonstrate how the GRC ICME schema and Python framework automates a workflow that captures, analyzes, maintains, and disseminates the digital footprint in the context of tailoring resin material at the nanoscale of a woven composite Y-joint at the macroscale for an Aurora D8 double bubble fuselage. This digital footprint incorporates the interaction of both structural digital twins and material twins at various length scales.

Brandon L. Hearley↗
NASA NTRS2023

Structural Complexity Biases Vegetation Greenness Measures

Vegetation ‘greenness’ characterized by spectral vegetation indices (VIs) is an integrative measure of vegetation leaf abundance, biochemical properties and pigment composition. Surprisingly, satellite observations reveal that several major VIs over the US Corn Belt are higher than those over the Amazon rainforest, despite the forests having a greater leaf area. This contradicting pattern underscores the pressing need to understand the underlying drivers and their impacts to prevent misinterpretations. Here we show that macroscale shadows cast by complex forest structures result in lower greenness measures compared with those cast by structurally simple and homogeneous crops. The shadow-induced contradictory pattern of VIs is inevitable because most Earth-observing satellites do not view the Earth in the solar direction and thus view shadows due to the sun–sensor geometry. The shadow impacts have important implications for the interpretation of VIs and solar-induced chlorophyll fluorescence as measures of global vegetation changes. For instance, a land-conversion process from forests to crops over the Amazon shows notable increases in VIs despite a decrease in leaf area. Our findings highlight the importance of considering shadow impacts to accurately interpret remotely sensed VIs and solar-induced chlorophyll fluorescence for assessing global vegetation and its changes.

Vegetation indices↗
NASA NTRSDate not supplied

In-situ Imaging of Pyrolyzing Aerospace Materials

Tracking morphological changes of materials during heating is crucial to understand its response in fire protection, biofuel production, thermal protection systems (TPS) for hypersonic flight. As materials are heated, they undergo physical and chemical changes due to water loss, stretching or shrinking, pyrolysis and chemical reactions in the ambient environment. The effects of these changes can have a profound impact on the material’s performance, indicated by changes in on the porosity and volume. While materials such as wood shrink as they pyrolyze and lose mass, others swell due to their inherent characteristics when exposed to heat [1]. This study focuses on experiments conducted at the Advanced Light Source (ALS) beamline 8.3.2, where in situ micro-computed tomography (µ-CT) is performed on materials as they are being pyrolyzed. Through in situ µ-CT, the change in total volume and porosity can be obtained in real-time, allowing for better understanding of the underlying thermophysical and chemical processes as a function of temperature. This study also focuses on the implementation of the Porous Microstructure Analysis software (PuMA) [2] to obtain thermal conductivity, permeability, and other properties of the material from the 3D tomographies. The information gained from these tomographies will supplement microscale model development of material morphological change and will aid macroscale modeling for high-temperature applications. For this study, Room Temperature Vulcanizing silicone (RTV) [3-5] is heated from room temperature to 1000°C using an infrared lamp heating system, and tomographies are continuously collected as the sample is heated. The tomographies are then segmented to obtain solid and void phases, from which estimates of pore size, porosity and total volume are extracted as a function of temperature. PuMA is deployed on the segmented tomographies to obtain thermal conductivity, permeability, and other properties as a function of temperature. Preliminary results show that RTV first intumesces (swells) as pyrolysis begins, due to build-up of pyrolysis gases in closed pores, and then shrinks significantly as more open pores are formed and the pyrolysis gases outgas. Pore network visualization of the tomographies using OpenPNM [6] showed the increase in pore connectivity with increase in temperature. Future work will focus on using PuMA to obtain macroscopic properties of RTV as a function of temperature.

Tomography↗
NASA NTRS2024

Automation of the ICME Workflow Incorporating Material Digital Twins at Different Length Scales Within a Robust Information Management System

Recent successes in Integrated Computational Materials Engineering (ICME) have demonstrated the potential in designing ‘fit-for-purpose’ materials for a given application in a cost and time efficient manner. However, the material design process must contain a level of judicious automation in the material decision process; that is implementing some optimization algorithms to truly enable the benefits of ICME, particularly when considering materials at multiple length and time scales. Furthermore, the ability to effectively store developed material models, experimental data used for validation, and link models at multiple length and time scales must be implemented to ensure traceability across the material design process, such that the data gathered can be leveraged towards efficient material design. To enable such an optimization scheme a robust framework must exist: (1) that can capture changes made at a given length scale, (2) automatically propagate changes upstream to the highest scale, and (3) evaluate the material’s performance at the structural level. In this work, a developed framework for tracking material changes, automatically running the necessary simulations to determine the properties at the next highest scale, and saving each iteration of the design process to maintain the application’s digital thread is presented for polymer matrix composites (PMCs). The Automated Information Management Across Organizations and Scales (AIMAOS) program offers users an interactive graphical user interface (GUI) for defining constituent materials, building lamina and laminates, and applying effective laminate properties to finite element and composite optimization third party software. At each length scale, the necessary input files are automatically written, and subsequent analysis tools are called to solve for effective properties at the next scale, which are then read by the AIMAOS tool and displayed to the user. As changes are made to the material at lower length scales, information is automatically propagated upstream to higher length scales, and changes made are automatically tracked and versioned to maintain traceability during the design process. The AIMAOS tools serves as the first step in enabling optimized design of composites from the nano to the macroscale for a given application.

Brandon L Hearley↗
NASA NTRS2024

Automatic Detection and Classification of Aurora in THEMIS All‐Sky Images

We report a novel machine-learning algorithm for automatically detecting and classifying aurora in all–sky images (ASI) that is largely trained without requiring ground–truth labels. By including a small number of labeled images, we are able to automatically label all of the approximately 700 million images in the Time History of Events and Macroscale Interactions during Substorms (THEMIS) ASI data set from 2008 to 2022. We use a two–stage approach. In the first stage, we adapt the Simple framework for Contrastive Learning of Representations (SimCLR) algorithm to learn latent representations of THEMIS all–sky images. We then finetune a classifier network on the latent representations our model learns of the manually labeled Oslo aurora THEMIS (OATH) data set. We demonstrate that this two–stage approach achieves excellent classification results on data for which there is no current ML classification benchmark. The outcome of this work will facilitate efficient information retrieval for researchers interested in specific categories of aurora and will enable large scale statistical studies and machine learning analyses of THEMIS all–sky images that have not previously been possible. To demonstrate possible ways to utilize this database, we performed a statistical analysis of the occurrence rates of auroral labels with respect to solar wind parameters, interplanetary magnetic field vector, and geomagnetic indices. We further investigate the occurrence rates of auroral phenomena in the annotated data set and their geoeffectiveness by utilizing the co–located THEMIS ground magnetometer data set.

Jeremiah W Johnson↗
NASA NTRS2024

The Global Mapping of Electron Precipitation and Ionospheric Conductance From Whistler-Mode Chorus Waves

Auroral precipitation is the second major energy source after solar irradiation that ionizes the Earth’s upper atmosphere. Diffuse electron aurora caused by wave-particle interaction in the inner magnetosphere (L < 8) takes over 60% of total auroral energy flux, strongly contributing to the ionospheric conductance and thus to the ionosphere-thermosphere dynamics. This paper quantifies the impact of chorus waves on the diffuse aurora and the ionospheric conductance during quiet, medium, and strong geomagnetic activities, parameterized by AE <100, 100 < AE < 300, and AE > 300, respectively. Using chorus wave statistics and inner-magnetosphere plasma conditions from Timed History Events and Macroscale Interactions during Substorms (THEMIS) observations, we directly derive the energy spectrum of diffuse electron precipitation under quasi-linear theory. We then calculate the height-integrated conductance from the wave-driven aurora spectrum using the electron impact ionization model of Fang et al. (Geophys. Res. Lett., 2010, 37) and the MSIS atmosphere model. By utilizing Fang’s ionization model, the US Naval Research Laboratory Mass Spectrometer and Incoherent Scattar Radar (NRLMSISE-00) model from 2000s for the neutral atmosphere components, and the University of California, Los Angeles (UCLA) Full Diffusion Code, we improve upon the standard generalization of Maxwellian diffuse electron precipitation patterns and their resulting ionosphere conductance. Our study of global auroral precipitation and ionospheric conductance from chorus wave statistics is the first statistical model of its kind. We show that the total electron flux and conductance pattern from our results agree with those of Ovation Prime model over the pre-midnight to post-dawn sector as geomagnetic activity increases. Our study examines the relative contributions of upper band chorus (UBC) and lower band chorus wave (LBC) driven conductance in the ionosphere. We found LBC waves drove diffuse electron precipitation significantly more than UBC waves, however it is possible that THEMIS data may have underestimated the upper chorus band wave observations for magnetic latitudes below 65 degrees. ° .

diffuse auroral precipitation↗
NASA NTRSDate not supplied

Multiscale Modeling of Fracture Strength in Fibrous Thermal Protection System Materials

This work presents a multiscale modeling approach to predict the fracture strength of fibrous Thermal Protection System (TPS) materials. The model assumes that system failure is initiated at the joints between individual fibers. We investigated three distinct TPS compositions: amorphous silica, alumina and aluminosilicate fibers. Molecular dynamics (MD) simulations were employed to determine the fracture strength values of these fiber joints for both material systems. These fracture strength values were then integrated into simulations of 3D randomly populated fiber structures, where tensile load transfer occurs through the fiber joints. These microscale properties are upscaled through a renormalization approach [1] to predict macroscale tensile strength of 3D random fiber networks, accounting for joint-dominated failure and effective load-bearing area. The study concludes by demonstrating the resulting strength variation as a function of material composition, fiber density, and morphology. We also show validation of results by comparing them against explicit fiber finite element (FE) modeling [2] where fiber joint fracture is represented by cohesive elements.

Jaehyun Cho↗
DOE OSTI2010

Oriented hydroxyapatite in turkey tendon mineralized via the polymer-induced liquid-precursor (PILP) process

Bone is a hierarchically structured composite which imparts it with unique mechanical properties and bioresorptive potential. These properties are primarily influenced by the underlying nanostructure of bone, which consists of nanocrystals of hydroxyapatite embedded and uniaxially aligned within collagen fibrils. There is also a small fraction of non-collagenous proteins in bone, and these are thought to play an important role in bone's formation. In our in vitro model system of bone formation, polyanionic peptides are used to mimic the role of the non-collagenous proteins. In our prior studies, we have shown that intrafibrillar mineralization can be achieved in synthetic reconstituted collagen sponges using a polymer-induced liquid-precursor (PILP) mineralization process. This led to a nanostructured arrangement of hydroxyapatite crystals within the individual fibrils which closely mimics that of bone. This report demonstrates that biogenic collagen scaffolds obtained from turkey tendon, which consist of densely packed and oriented collagen fibrils, can also be mineralized by the PILP process. Synchrotron X-ray diffraction studies show that the mineralization process leads to a high degree of crystallographic orientation at the macroscale, thus emulating that found in the biological system of naturally mineralizing turkey tendon.

36 MATERIALS SCIENCE↗
DOE OSTI2010

A mesoscale analysis of the Rayleigh-Plateau instability.

Capillary pinch-off results carried out with the Many-Body Dissipative Particle Dynamics (MDPD) method are compared with the two-phase continuum discretization of hydrodynamics. The MDPD method provides a mesoscale description of the liquid-gas interface -- molecules can be thought of as grouped in particles with modeled Brownian and dissipative effects. No liquid-gas interface is explicitly defined; surface properties, such as surface tension, result from the MDPD interaction parameters. In side-to-side comparisons, the behavior of the MDPD liquid is demonstrated to replicate the macroscale behavior (thin interface assumption) calculated by the Combined Level Set-Volume of Fluid (CLSVOF) method. For instance, in both the continuum and mesoscale discretizations the most unstable wavelength perturbation leads to pinch-off, whereas a smaller wavelength-to-diameter ratio, as expected, does not. The behavior of the virial pressure in MDPD will be discussed in relation to the hydrodynamic capillary pressure that results from the thin interface assumption.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2010

The effect of error models in the multiscale inversion of binary permeability fields.

We present results from a recently developed multiscale inversion technique for binary media, with emphasis on the effect of subgrid model errors on the inversion. Binary media are a useful fine-scale representation of heterogeneous porous media. Averaged properties of the binary field representations can be used to characterize flow through the porous medium at the macroscale. Both direct measurements of the averaged properties and upscaling are complicated and may not provide accurate results. However, it may be possible to infer upscaled properties of the binary medium from indirect measurements at the coarse scale. Multiscale inversion, performed with a subgrid model to connect disparate scales together, can also yield information on the fine-scale properties. We model the binary medium using truncated Gaussian fields, and develop a subgrid model for the upscaled permeability based on excursion sets of those fields. The subgrid model requires an estimate of the proportion of inclusions at the block scale as well as some geometrical parameters of the inclusions as inputs, and predicts the effective permeability. The inclusion proportion is assumed to be spatially varying, modeled using Gaussian processes and represented using a truncated Karhunen-Louve (KL) expansion. This expansion is used, along with the subgrid model, to pose as a Bayesian inverse problem for the KL weights and the geometrical parameters of the inclusions. The model error is represented in two different ways: (1) as a homoscedastic error and (2) as a heteroscedastic error, dependent on inclusion proportionality and geometry. The error models impact the form of the likelihood function in the expression for the posterior density of the objects of inference. The problem is solved using an adaptive Markov Chain Monte Carlo method, and joint posterior distributions are developed for the KL weights and inclusion geometry. Effective permeabilities and tracer breakthrough times at a few 'sensor' locations (obtained by simulating a pump test) form the observables used in the inversion. The inferred quantities can be used to generate an ensemble of permeability fields, both upscaled and fine-scale, which are consistent with the observations. We compare the inferences developed using the two error models, in terms of the KL weights and fine-scale realizations that could be supported by the coarse-scale inferences. Permeability differences are observed mainly in regions where the inclusions proportion is near the percolation threshold, and the subgrid model incurs its largest approximation. These differences also reflected in the tracer breakthrough times and the geometry of flow streamlines, as obtained from a permeameter simulation. The uncertainty due to subgrid model error is also compared to the uncertainty in the inversion due to incomplete data.

58 GEOSCIENCES↗
DOE OSTI2012

Covalently bonded three-dimensional carbon nanotube solids via boron induced nanojunctions

The establishment of covalent junctions between carbon nanotubes (CNTs) and the modification of their straight tubular morphology are two strategies needed to successfully synthesize nanotube-based three-dimensional (3D) frameworks exhibiting superior material properties. Engineering such 3D structures in scalable synthetic processes still remains a challenge. This work pioneers the bulk synthesis of 3D macroscale nanotube elastic solids directly via a boron-doping strategy during chemical vapor deposition, which influences the formation of atomic-scale elbow junctions and nanotube covalent interconnections. Detailed elemental analysis revealed that the elbow junctions are preferred sites for excess boron atoms, indicating the role of boron and curvature in the junction formation mechanism, in agreement with our first principle theoretical calculations. Exploiting this material s ultra-light weight, super-hydrophobicity, high porosity, thermal stability, and mechanical flexibility, the strongly oleophilic sponge-like solids are demonstrated as unique reusable sorbent scaffolds able to efficiently remove oil from contaminated seawater even after repeated use.

36 MATERIALS SCIENCE↗
DOE OSTI2010

Thermal hydraulics development for CASL

This talk will describe the technical direction of the Thermal-Hydraulics (T-H) Project within the Consortium for Advanced Simulation of Light Water Reactors (CASL) Department of Energy Innovation Hub. CASL is focused on developing a 'virtual reactor', that will simulate the physical processes that occur within a light-water reactor. These simulations will address several challenge problems, defined by laboratory, university, and industrial partners that make up CASL. CASL's T-H efforts are encompassed in two sub-projects: (1) Computational Fluid Dynamics (CFD), (2) Interface Treatment Methods (ITM). The CFD subproject will develop non-proprietary, scalable, verified and validated macroscale CFD simulation tools. These tools typically require closures for their turbulence and boiling models, which will be provided by the ITM sub-project, via experiments and microscale (such as DNS) simulation results. The near-term milestones and longer term plans of these two sub-projects will be discussed.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2012

Carbon Nanotube Assemblies for Transparent Conducting Electrodes

The goal of this chapter is to introduce readers to the fundamental and practical aspects of nanotube assemblies made into transparent conducting networks and discuss some practical aspects of their characterization. Transparent conducting coatings (TCC) are an essential part of electro-optical devices, from photovoltaics and light emitting devices to electromagnetic shielding and electrochromic widows. The market for organic materials (including nanomaterials and polymers) based TCCs is expected to show a growth rate of 56.9% to reach nearly 20.3billionin2015,whilethemarketfortraditionalinorganictransparentelectronicswillexperiencegrowthwithratesof6.7103 billion in 2015. Emerging flexible electronic applications have brought additional requirements of flexibility and low cost for TCC. However, the price of indium (the major component in indium tin oxide TCC) continues to increase. On the other hand, the price of nanomaterials has continued to decrease due to development of high volume, quality production processes. Additional benefits come from the low cost, nonvacuum deposition of nanomaterials based TCC, compared to traditional coatings requiring energy intensive vacuum deposition. Among the materials actively researched as alternative TCC are nanoparticles, nanowires, and nanotubes with high aspect ratio as well as their composites. The figure of merit (FOM) can be used to compare TCCs made from dissimilar materials and with different transmittance and conductivity values. In the first part of this manuscript, we will discuss the seven FOM parameters that have been proposed, including one specifically intended for flexible applications. The approach for how to measure TCE electrical properties, including frequency dependence, will also be discussed. We will relate the macroscale electrical characteristics of TCCs to the nanoscale parameters of conducting networks. The fundamental aspects of nanomaterial assemblies in conducting networks will also be addressed. We will review recent literature on TCCs composed of carbon nanotubes of different types in terms of the FOM.

Garrett, Matthew P↗
DOE OSTI2015

Text Stream Trend Analysis using Multiscale Visual Analytics with Applications to Social Media Systems

In this paper, we introduce a new visual analytics system, called Matisse, that allows exploration of global trends in textual information streams with specific application to social media platforms. Despite the potential for real-time situational awareness using these services, interactive analysis of such semi-structured textual information is a challenge due to the high-throughput and high-velocity properties. Matisse addresses these challenges through the following contributions: (1) robust stream data management, (2) automated sen- timent/emotion analytics, (3) inferential temporal, geospatial, and term-frequency visualizations, and (4) a flexible drill-down interaction scheme that progresses from macroscale to microscale views. In addition to describing these contributions, our work-in-progress paper concludes with a practical case study focused on the analysis of Twitter 1% sample stream information captured during the week of the Boston Marathon bombings.

interaction↗
DOE OSTI2017

3D Microstructures for Materials and Damage Models

Many challenges exist with regard to understanding and representing complex physical processes involved with ductile damage and failure in polycrystalline metallic materials. Currently, the ability to accurately predict the macroscale ductile damage and failure response of metallic materials is lacking. Research at Los Alamos National Laboratory (LANL) is aimed at building a coupled experimental and computational methodology that supports the development of predictive damage capabilities by: capturing real distributions of microstructural features from real material and implementing them as digitally generated microstructures in damage model development; and, distilling structure-property information to link microstructural details to damage evolution under a multitude of loading states.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Southern Great Plains (SGP) Three-Dimensional Observations of Fair-Weather Cumuli Field Campaign Report

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) Three-Dimensional (3D) Observations of Fair-Weather Cumuli (SGP3DOBS) experiment employed scanning Doppler lidar and the recently installed stereo camera ring to conduct a one-month intensive operational period (IOP) of fair-weather cumuli in a 3D domain at the ARM SGP Central Facility (CF). The SGP3DOBS is part of the Climate Model Development and Validation (CMDV) “Coupling Mechanistically the Convective Motions and Cloud Macrophysics in a Climate Model” (CM4) project and provides advanced observational analyses for improving physical understanding of shallow cumulus and supporting model parameterization evaluation. For the CM4 project, cloud macroscale and dynamical properties are determined using vertically pointing active remote-sensing measurements (e.g., lidar and radar), which provide a 2D view of fair-weather cumuli (FWC). It is critical to evaluate how well the 2D observations represent 3D cloud fields. The objectives of SGP3DOBS include: 1) evaluate the 3D gridded cloud field from stereo photogrammetry with scanning Doppler lidar measurements; 2) retrieve sub-cloud-layer turbulence (vertical velocity and mass flux) in a 3D domain; 3) estimate shallow cumulus lateral entrainment rate and its relation to environment conditions. The SGP3DOBS IOP was held at the SGP Central Facility from August 31 to September 30, 2017, as the fair-weather nonprecipitating shallow cumuli are prevalent during summertime over land (Lamer and Kollias, 2015). A weather forecast team conducted a short-range forecast of weather conditions in the early morning each day to determine whether it was favorable for FWC. For each IOP day, the Doppler lidar at the SGP Central Facility ran under the range height indicator (RHI) scanning mode in the cross-wind direction. Occasionally, plan position indicator (PPI) scanning mode was run for updated wind directions. During each RHI scanning, the Doppler lidar run continuously between 30- and 150-degree elevation angles. The scanning rate was approximately 10 degree/s, corresponding to a transverse resolution of 111 m at 2 km of a typical FWC height. The newly established three pairs of stereo cameras ran concurrently during the IOP.

Southern Great Plains, Doppler lidar, fair-weather↗
DOE OSTI2018

Advanced Characterization of Irradiated Fuels and Materials at INL

Advanced characterization of the irradiated fuels and materials is crucial to help understanding the material and fuel property changes under irradiation. Microstructural defect evolution down to nanometer scale could strongly affect the material and fuel performance on macroscale. Radiation-induced material degradation include dimensional instability, hardening and embrittlement, intergranular cracking, radiation-enhanced chemical interaction at the interfaces and breakaway swelling. To mitigate the radiation damage, advanced characterization on irradiated microstructure, microchemistry, micromechanical property and local thermal property is necessary to identify the controlling mechanisms that are responsible to the degradations. This presentation provides a summary of the advanced characterization capabilities at the Irradiation Materials Characterization Laboratory (IMCL), Materials and Fuels Complex (MFC), Idaho National Laboratory (INL). It highlights the recently established capabilities including shield sample preparation area (SSPA), shield confinement cells for two focused ion beam (FIB) systems, a shielded electron probe microanalysis (EPMA), the thermal property microscopy (TPM) along with non-shielded instruments like scanning electron microscopy (SEM), X-ray diffractometer, and transmission electron microscopy (TEM). Some examples on advanced characterizations on irradiated structural materials and fuels will be presented. It demonstrates the power of combining sample preparation of highly radioactive materials with the advanced characterization down to atomic resolutions within one facility.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗