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Black body irradiation of metal surfaces to determine roughness effects on hemispherical and specular reflectance, for various temperatures and angles of incidence
Black body irradiation of metal surfaces to determine roughness effects on hemispherical and and specular reflectance for various temperatures and angles of incidence
U-10wt.% Zr (U-10Zr) based metallic fuel is the leading candidate for next-generation sodium cooled fast reactor in United States. Currently, Idaho National Laboratory (INL) has been the leading national laboratory for research, development, and demonstration (RD&D) on metallic fuel. Advanced post-irradiation characterization will help to understand fuel microstructure and property change during irradiation, benefiting fuel qualification for commercial application. Characterization capabilities ranging from sub-nanometer to micrometer, such as scanning electron microscopy (SEM), focused ion beam (FIB) sampling, transmission electron microscopy (TEM) characterization, and local thermal conductivity microscopy (TCM), have been utilized recently on irradiated U-10Zr fuel samples to gain a better understanding of nuclear fuel microstructure and property evolution inside a reactor. The FIB/SEM coupled with energy dispersive X-ray spectroscopy (EDS) can capture the essential information to achieve better understanding of fuel behaviors. Inside a nuclear reactor, the phase and microstructure of U-10Zr is constantly changing under neutron bombardment. For example, the gaseous fission product atoms have a limited solubility inside fuel matrix and tend to precipitate out in bubble form, which not only contribute to fuel thermal conductivity degradation but also provide a shortcut for movement of fission products, i.e. lanthanides. The resultant deposition of lanthanides at the cladding inner surface will potentially trigger a chemical reaction/interaction between nuclear fuel and cladding at reactor operational conditions, threatening fuel integrity and safety. FIB/SEM coupled with EDS can provide the fission bubble information as well as probe into phase separation or Zr redistribution, which is fundamental to predict the fuel performance. With high velocity image data generating method, such as FIB/SEM, an automatic way to extract the microstructural information quantitively can better serve the needs from post irradiation characterization. A trained machine learning model, named Decision Tree, is employed to generate a bubble classifier and to categorize bubbles into three categories: isolated bubble, connected without lanthanides, and connected with lanthanides bubbles[3]. This work presents a showcase of this approach on six regions of a fuel cross-section along the radial temperature gradient. We obtained distributions of bubble categories and porosity rates along the six regions. Moreover, a secondary phase U-Zr2 was determined and found on regions 5 and 6. The secondary phase fraction was increasing from 15.61% in region 5 to 34.79% in region 6 based on this approach . This quantitative data offers insights into the lanthanide migration and potentially thermal conductivity degradation. This information from machine learning will be fed into fuel design code for better prediction of fuel performance.
The U.S. DOE-NE’s Advanced Reactor Technologies (ART) Fast Reactor Program (FRP) and the NE-4 Advanced Fuels Campaign (AFC) have jointly undertaken efforts to qualify post-irradiation examination (PIE) data for Experimental Breeder Reactor II (EBR-II) and Fast Flux Test Facility (FFTF) metallic fuels. Among the thousands of PIE datasets in the Metallic Fuels Irradiation & Physics Database (FIPD), data from selected EBR-II experiments have been prioritized as the most valuable to the U.S. nuclear industry for licensing activities related to metallic fuel-based advanced fast reactors. This report summarizes the general data qualification progress of the EBR-II and FFTF experiments, with focus on the selected EBR-II experiments.
Grain boundaries (GBs) in polycrystalline materials are powerful sinks for irradiation defects. While standard theories assume that a GB’s efficiency as a sink is defined solely by its character before irradiation, recent evidence conclusively shows that the irradiation sink efficiency is a highly dynamic property controlled by the intrinsic metastability of GBs under far-from-equilibrium irradiation conditions. In this paper, we reveal that the denuded (i.e., defect-free) zone, typically the signature of a strong sink, can collapse as irradiation damage accumulates. We propose a radiation damage evolution model that captures this behavior based on the emergence of a series of irradiation defect-enabled metastable GB microstate changes that dynamically alter the ability of the GB to absorb further damage. We show that these microstate changes control further defect absorption and give rise to the formation of a defect network that manifests itself as a net Nye-tensor signal detectable via lattice curvature experiments.
Method uses radiation chemistry as practical tool for inorganic preparations and in particular deposition of metals by irradiation of their aqueous metal salt solutions with high energy electrons. Higher valence metal halide is dissolved in organic liquid and exposed to high energy electrons. This causes metal halide to be reduced to a lower valence metal halide.
This study investigates the effects of electron beam (e-beam) irradiation on the mechanical and structural properties of eight bulk metallic samples, comprising both polycrystalline (PC) and single-crystalline (SC) forms of Ni, Cr, V, and Ti. These metals were evaluated as potential candidates for beam exit windows in high-power (MW-class) particle accelerators. The primary objective is to identify metals capable of withstanding the conditions of high-power/MW-class e-beam accelerators and serve effectively as exit windows. Selection criteria were based on each metal’s intrinsic properties, power dissipation capability, and irradiation-induced changes in mechanical behavior, including hardness, elastic modulus, and defect density. Comprehensive characterization was conducted using field-emission scanning electron microscopy (FESEM), atomic force microscopy (AFM), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM–EDS), X-ray diffraction (XRD), high-resolution transmission electron microscopy (HRTEM), selected area electron diffraction (SAED), and nanoindentation, performed both before and after exposure to a ~¿66 kGy dose from a 10 MeV e-beam accelerator. Results revealed that e-beam irradiation induced hardening in PC Ni, whereas PC Ti, commonly used in beam exit windows, exhibited softening. The observed softening in PC Ti is attributed to grain coarsening, elongation, and the formation of twins and twin boundaries, in contrast to the smaller, compressed grains in the pristine (Pr) PC Ti samples, consistent with the Hall–Petch relationship. The stresses due to twinning are small and insignificant in influencing the overall hardening of the PC Ti irradiated sample when compared to the stresses due to the dislocation density. Conversely, SC Ti samples exhibited irradiation-induced hardening. The SC Ti irradiated samples developed additional irradiation-induced modifications in crystallographic texture of (100), (101), (110), (200), (112), (004), and (211) as evidenced from the XRD results, which could probably explain the hardening effect that is caused by irradiation.
The U.S. DOE-NE’s Advanced Reactor Technologies (ART) Fast Reactor Program (FRP) and the NE-4 Advanced Fuels Campaign (AFC) have jointly undertaken the qualification of the legacy post-irradiation examination (PIE) data held in the Fuels Irradiation & Physics Database (FIPD), covering metallic fuel experiments conducted in the Experimental Breeder Reactor II (EBR-II) and the Fast Flux Test Facility (FFTF). In FY26 this effort reached a milestone: six major types of PIE data—contact profilometry, isotopic gamma scan, fission gas chemistry, fission gas release, laser profilometry, and neutron radiography—have been qualified for all available experiments in FIPD, and the U.S. nuclear industry can now draw on them with confidence in licensing activities for metallic fuel-based advanced fast reactors. This report documents the qualification process and the resulting status of the PIE data.
Metallic fuels have seen increased interest for future sodium fast reactors due to their material properties: high thermal conductivities and advantageous neutronic properties allow for greater fission densities. One drawback to typical metallic fuels is zirconium redistribution, which impacts this advantageous material and its neutronic properties. Unfortunately, the processes behind zirconium migration behavior are understood using first principles, so before these fuels are implemented in future fast reactors, characterization and fuel qualification regimes must be completed. These activities can be supported through the use of robust modeling using the most accurate empirical models currently available to fuel researchers around the world. The tool that allows researchers to model this complex coupled thermo-mechanical behavior and nuclear properties is BISON. Additionally, BISON model parameters need to be compared against PIE measurements. The current work utilizes two fuel pins from EBR-II experiment X441 to optimize various model parameters, including porosity correction factor, thermal conductivity, phase transition temperature, and diffusion coefficient multipliers, before implementing the final model for seven fuel pins with differing characteristics. To properly evaluate the BISON simulations, the results are compared to PIE metallography data for each fuel pin, to ensure the zirconium redistribution is properly reflected in the simulation results. Six out of seven analyzed fuel pins demonstrate good agreement between the metallography images and BISON results, showing alignment of the Zr-rich, Zr-depleted, and moderately Zr-enriched zones at various axial heights along the fuel pins. Further work is needed to refine the model parameters for general pin use.
Irradiation damage drives complex and coupled phenomena in materials at far-from-equilibrium conditions. The self-organization of nanoscale defects in materials under irradiation shows great potential to tailor the physical properties of materials by controlling nanopatterned microstructures. Irradiation-induced gas bubble and void superlattices are two important ordered nanostructures of great scientific interest. Although both types of superlattices have been investigated extensively, a consensus has yet to be reached on their formation mechanisms. In this review article, the current research status of gas bubble and void superlattices in metals and alloys and their characterization, structural stability, and mechanistic modeling are summarized. The fundamental research goals to advance the mechanistic understanding of gas bubble and void superlattices are outlined.
A Np(V) neptunyl metal–organic framework (MOF) with rod-shaped secondary building units was synthesized, characterized, and irradiated with γ rays. Furthermore, single-crystal X-ray diffraction data revealed an anionic framework containing infinite helical chains of actinyl–actinyl interaction (AAI)-connected neptunyl ions linked together through tetratopic tetrahedral organic ligands (NSM). NSM exhibits an unprecedented net, demonstrating that AAIs may be exploited to give new MOFs and new topologies. To probe its radiation stability, we undertook the first irradiation study of a transuranic MOF and its organic linker building block using high doses of γ rays. Diffraction and spectroscopic data demonstrated that the radiation resistance of NSM is greater than that of its linker building block alone. Approximately 6 MGy of irradiation begins to induce notable changes in the long- and short-range order of the framework, whereas 3 MGy of irradiation induces total X-ray amorphization and changes in the local vibrational bands of the linker building block.
The continuous formation and growth of voids induced by radiations in metallic materials may lead to significant microstructure damage and degradation of mechanical properties. In sharp contrast to the void swelling commonly observed in irradiated metallic materials, nanovoids in nanoporous metallic materials are found to shrink during radiation and thus nanovoids enhance the radiation tolerance of metallic materials. This article reviews recent studies on size-dependent void shrinkage in metallic materials subject to in situ heavy ion irradiation. Furthermore, we demonstrate the capability of machine learning in identifying and tracking the evolution of nanovoids. Finally, the physical mechanisms of radiation induced void shrinkage revealed by simulation studies are briefly summarized.
The objectives of this project are to increase fundamental understanding of irradiation induced metallic U-Pu-Zr fuel behavior and to obtain data needed for the development of irradiation models for metallic fuels in MARMOT. The requested metallic fuel pins are from the X441A experiment irradiated in Experimental Breeder Reactor (EBR)-II and will be provided by the PIs for the duration of the experiment. The purpose of the pins irradiated in the X441A assembly was to vary Zr composition and fuel slug diameter in order to provide data for the metallic fuel performance code “LIFEMETAL” [ANL-IFR-125]. The fuels of interest to this project are metallic fuels with several Zr compositions (in wt.%): U-19Pu-6Zr, U-19Pu-10Zr, and U-19Pu-14Zr, which were irradiated to a peak burnup of approximately 11 at.%. The cladding was the same for all three fuel pins (austenitic stainless steel D9), which allows investigation of fuel-cladding interaction (FCI) phenomena. Varying Zr content in fuel pins enables investigation of the effect of Zr on fuel restructuring and fuel-cladding compatibility. Engineering and microstructure scale PIE activities will be focused on investigation of fundamental aspect of fuel performance such as species diffusion and migration, fission product behavior, and constituent redistribution. Obtained microstructural information will be used as the basis for the development of MARMOT models of U-Pu-Zr fuel performance at the mesoscale. Experimental data obtained through this proposal will be used to provide this fundamental understanding which will serve as the foundation of the development of radiation models for U-Pu-Zr in MARMOT. It will also provide a starting point for the design of new experiments to provide data for the validation of these MARMOT models.
U-Pu-Zr metallic fuels are important fuel candidate for future advanced and/or test reactors. To better understand fuel performance and guide future fuel development, a U-20Pu-10Zr (in weight) metallic fuel irradiated in the Experimental Breeder Reactor was revisited using advanced electron microscopies. This fuel was irradiated to a burnup of 7.6 % at a peak cladding temperature of 521 °C. In this research, several transmission electron microscopy samples were extracted from different but representative radial locations of the fuel cross section using focused ion beam technique. Phase identification and chemical analysis with sub-micron spatial resolution was carried out using scanning transmission electron microscopy. Multiple phenomena that are critical to fuel performance, such as Zr phase and its distribution in a different matrix, were revealed in an unpreceded and highly detailed manner. An improved understanding of fuel reconstruction in U-Pu-Zr metallic fuel under reactor irradiation is provided.
Metallic fuels, particularly U—10Zr, are promising candidates for next-generation sodium-cooled fast reactors. Irradiation of nuclear fuels in reactors can lead to the formation of solid and gas fission product which subsequently forms microstructural pores, deteriorating fuel performance. Due to the massive amount of pores and complex phases formed, a quantitative description of fission gas pores is not yet available, preventing the development of microstructure-informed fuel performance modeling for fuel qualification. This paper applied a pre-trained deep learning model to ~10,260 high magnification scanning electron microscopy images. This method increased the accuracy of fission gas pore segmentation and allows statistical features to be extracted which cannot be achieved manually. A pre-trained decision tree model worked on the segemenation results and further classified the pores into different categories to produce a correlation between the pores, movement of lanthanides, and temperature gradient during irradiation. Finally, this paper emphasizes the potentials of machine learning models to accelerate fuel research, development, and qualification for advanced reactors.
Metallic fuels, particularly U-10Zr and its performance in reactor irradiation conditions, have been thoroughly investigated and are a promising candidate for next-generation sodium-cooled fast spectrum nuclear reactors. Irradiation in reactors can lead to the formation of fission gas and increased pore formation which can significantly impact fuel performance. Due to the large number of pores and various phases formed in metallic fuel during irradiation, a quantitative description of fission gas pores as a function of irradiation conditions is not yet available, undermining the fidelity of fuel performance modeling to support fuel qualification. It has been difficult to clearly detect pore boundaries and distinguish matrix phases from fission gas pores using optical microscopy by using simple threshold methods working with low magnification images. The pre-trained deep learning model for fission gas pore detection was applied to ~10,260 high magnification scanning electron microscopy images. The model increased the accuracy of fission gas pore segmentation to obtain statistical features, which cannot be processed manually. A pre-trained decision tree model was used to classify pores as isolated or connected pores, providing new insight into the correlation between the movement of lanthanides, solid fission products, and the radial temperature gradient developed in fuel irradiation conditions. This paper emphasizes the potential that artificial intelligence-based machine learning models have to accelerate qualification and support nuclear fuel development.
Understanding the microstructural and phase changes occurring during irradiation and their impact on metallic fuel behavior is integral to research and development of nuclear fuel programs. This paper reports systematic analysis of as-fabricated and irradiated low-enriched U-Mo (uranium-molybdenum metal alloy) fuel using atom probe tomography (APT). This study is carried out on U-7 wt.% Mo fuel particles coated with a ZrN layer contained within an Al matrix during irradiation. The dispersion fuel plates from which the fuel samples were extracted are irradiated at Belgian Nuclear Research Centre (SCK CEN) with burn-up of 52% and 66% in the framework of the SELENIUM (Surface Engineering of Low ENrIched Uranium-Molybdenum) project. The APT studies on U-Mo particles from as-fabricated fuel plates enriched to 19.8% revealed predominantly γ-phase U-Mo, along with a network of the cell boundary decorated with α-U, γ’-U2Mo, and UC precipitates along the grain boundaries. The corresponding APT characterization of irradiated fuel samples showed formation of fission gas bubbles enriched with solid fission products. The intermediate burnup sample showed a uniform distribution of the typical bubble superlattice with a radius of 2 nm arranged in a regular lattice, while the high burnup sample showed a non-uniform distribution of bubbles in grain-refined regions. There was no evidence of remnant α-U, γ’-U2Mo, and UC phases in the irradiated U-7 wt.% Mo samples.
Experiments indicated that metallic fuel in sodium-cooled fast reactors (SFRs) rapidly swells radially and axially at low burnup. Despite that, prior studies have been focused on describing high burnup axial fuel elongation. With recent conventional and non-conventional metallic fuel concepts being considered for license applications, understanding multidimensional fuel swelling at a wide range of burnup levels is important to fuel analysis and qualification activities. Here, we report the development and demonstration efforts of a low-burnup SFR metallic fuel swelling model evaluation framework using the BISON advanced fuel performance code. The framework leverages the Integral Fast Reactor (IFR) program X423 experiment data through the ongoing integration project to enable standardized and automated use of legacy metallic fuel irradiation data maintained in the SFR fuel irradiation and physics database (FIPD) for BISON metallic fuel model verification and validation. In conclusion, the performance of the framework was demonstrated using the two representative metallic fuel swelling model sets implemented in BISON, with a series of insights about future advanced swelling model development.