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At least 127 records · Page 7

A Percolating Path to Green Iron

About 1.9 gigatonnes of steel is produced every year emitting 7% (2.7 gigatonnes) of global CO 2 in the process. More than 50% of the CO 2 emissions come from a single step of steelmaking, known as ironmaking. Hydrogen based direct reduction (HyDR) of iron oxide to iron has emerged as an emissions free ironmaking alternative. However multi-scale phenomena ranging from nanometers to meters inside HyDR reactors exhibit detrimental microstructure evolution which resists gaseous transport of H 2 /H 2 O, slows reaction rates and disrupts continuous reactor operation. To resolve the conundrum between atomic and reactor scales, we devise a percolation-theory model to reconcile nanoscale porosity with macroscopic properties relevant to reactor design models. Using synchrotron nano X-ray computed-tomography, we quantify the evolution of pores in iron oxide pellets, and demonstrate how nano-scale pore networks influence micro and macro-scale flow properties such as permeability, diffusivity and tortuosity. Our new modeling framework bridges the gap between scales and offers the criteria to accelerate HyDR by at least 5x via feedstock-reactor synergies based on percolation.

Paul, Subhechchha

Microscopy X-ray imaging enriched with small angle X-ray scattering for few nanometer resolution reveals shock waves and compression in intense short pulse laser irradiation of solids

Understanding how laser pulses compress solids into high-energy-density states requires diagnostics that simultaneously resolve macroscopic geometry and nanometer-scale structure. Here we present a combined X-ray imaging (XRM) and small-angle X-ray scattering (SAXS) approach that bridges this diagnostic gap. Using the Matter in Extreme Conditions end station at LCLS, we irradiated 25 μm copper wires with 45 fs, 0.9 J, 800 nm pulses at 3.5 × 10 19 W/cm 2 while probing with 8.2 keV XFEL pulses. XRM visualizes the evolution of ablation, compression, and inward-propagating fronts with ∼ 200 nm resolution, while SAXS quantifies their nanometer-scale sharpness via the time-resolved evolution of scattering streaks. The joint analysis reveals that an initially smooth compression steepens into a nanometer-sharp shock front after t sh ≈ (18 ± 3) ps, consistent with an analytical steepening model and hydrodynamic simulations. The front reaches a velocity of c sh ≈ 25 k m / s and a lateral width of several tens of microns, demonstrating direct observation of shock formation and decay at solid density for the first time with few-nanometer precision. This integrated XRM–SAXS method establishes a quantitative, multi-scale diagnostic of laser-driven shocks in dense plasmas relevant to inertial confinement fusion, warm dense matter, and planetary physics.

Kluge, Thomas [Helmholtz-Zentrum Dresden-Rossendor

Topography and functional traits shape the distribution of key shrub plant functional types in low-Arctic tundra

The expansion of shrubs in the Arctic tundra fundamentally modifies land-atmosphere interactions. However, it remains unclear how shrub distribution and expansion differ across key species due to challenges with discriminating tundra plant species at regional scales. Here, we combined multi-scale, multi-platform remote sensing and in situ trait measurements to elucidate the distribution patterns and primary controls of two representative deciduous-tall-shrub (DTS) genera, Alnus and Salix, in low-Arctic tundra. We show that topographic features were a key control on DTSs, creating heterogeneous, but predictable distributions of Alnus and Salix fractional cover (fCover). Alnus was more tolerant of elevation and slope and was found on hilly uplands (slope >10°) within a specific elevational band (200–400 m above sea level [MSL]). In contrast, Salix occurred at lower elevations (50–300 m MSL) on gentler slopes (3-10°) and required adequate soil moisture associated with its profligate water use. We also show that niche differentiation between Alnus and Salix changed with patch size, where larger patches were more specialized in resource requirements than individual plants of Alnus and Salix. To understand what constrains the growth of DTSs at locations with low fCover, we developed environmental limiting factor models, which showed that topography limits the upper bound of Alnus and Salix fCover in 69.2% and 48.7% of the landscape, respectively. These findings highlight a critical need to better understand and represent topography-controlled processes and functional traits in regulating shrub distribution, as well as a need for more detailed species classification to predict shrubification in the Arctic.

alder

Progress Towards Developing Neutron Tolerant Magnetostrictive and Piezoelectric Transducers

Current generation light water reactors (LWRs), sodium cooled fast reactors (SFRs), small modular reactors (SMRs), and next generation nuclear plants (NGNPs) produce harsh environments in and near the reactor core that can severely tax material performance and limit component operational life. To address this issue, several Department of Energy Office of Nuclear Energy (DOE-NE) research programs are evaluating the long duration irradiation performance of fuel and structural materials used in existing and new reactors. In order to maximize the amount of information obtained from Material Testing Reactor (MTR) irradiations, DOE is also funding development of enhanced instrumentation that will be able to obtain in-situ, real-time data on key material characteristics and properties, with unprecedented accuracy and resolution. Such data are required to validate new multi-scale, multi-physics modeling tools under development as part of a science-based, engineering driven approach to reactor development. It is not feasible to obtain high resolution/microscale data with the current state of instrumentation technology. However, ultrasound-based sensors offer the ability to obtain such data if it is demonstrated that these sensors and their associated transducers are resistant to high neutron flux, high gamma radiation, and high temperature. To address this need, the Advanced Test Reactor National Scientific User Facility (ATR-NSUF) is funding an irradiation, led by PSU, at the Massachusetts Institute of Technology Research Reactor to test the survivability of ultrasound transducers. As part of this effort, PSU and collaborators have designed, fabricated, and provided piezoelectric and magnetostrictive transducers that are optimized to perform in harsh, high flux, environments. Four piezoelectric transducers were fabricated with either aluminum nitride, zinc oxide, or bismuth titanate as the active element that were coupled to either Kovar or aluminum waveguides and two magnetostrictive transducers were fabricated with Remendur or Galfenol as the active elements. Pulse-echo ultrasonic measurements of these transducers are made in-situ. This paper will present an overview of the test design including selection criteria for candidate materials and optimization of test assembly parameters, data obtained from both out-of-pile and in-pile testing at elevated temperatures, and an assessment based on initial data of the expected performance of ultrasonic devices in irradiation conditions

Reinhardt1, Brian

BM3DORNL

BM3DORNL is a high-performance, open-source library for removing streak and ring artifacts from computed-tomography (CT) data, developed for neutron imaging at Oak Ridge National Laboratory's Spallation Neutron Source (VENUS beamline) and applicable to X-ray CT as well. Ring artifacts — concentric rings in reconstructed slices caused by detector pixel-to-pixel response non-uniformities — appear as vertical streaks in the sinogram and degrade both image quality and quantitative analysis. BM3DORNL operates in the sinogram domain using an adaptation of the BM3D (block-matching and 3D collaborative filtering) algorithm (Dabov et al., 2007). It provides a dedicated streak-removal mode, a true multi-scale BM3D variant (after Mäkinen et al., 2021) that suppresses wide streaks single-scale methods miss, and an alternative Fourier–SVD method (~2.6× faster) combining FFT-based energy detection with rank-1 SVD. The computationally intensive core is implemented in Rust with parallel (Rayon) block matching, integral-image pre-screening, and optimized transforms, and is exposed through a simple Python API (with an optional GUI) so it integrates directly into existing tomography reconstruction pipelines. It processes both 2D sinograms and 3D sinogram stacks, is pip-installable for Linux and macOS, and is documented at https://bm3dornl.readthedocs.io.

Zhang, Chen [Oak Ridge National Laboratory (ORNL),

Investigating Radiation-Induced Actinide Species in Solution

Our fundamental understanding of actinide radiation-induced redox chemistry is crucial due to their unavoidable exposure to ionizing radiation fields, both inherent and from in-process applications. Plutonium (Pu) and americium (Am) both possess multiple oxidation states, the careful manipulation of which are essential in the study and utilization of their rich chemistry, developing new nuclear technologies, and securing the long-term sustainability of nuclear power. However, knowledge in this area is far from complete. Consequently, we have studied the radiation-induced chemistry of both Pu and Am through a variety of techniques. Temperature-controlled electron pulse radiolysis has been used to study Am for the first time, determining the feasibility of Am redox reactions under used nuclear fuel reprocessing conditions, (e.g. nitric acid, non-ambient temperature). Additionally, we developed an experimentally evaluated multi-scale computer model for the prediction of gamma radiation-induced Pu redox chemistry due to radiolysis and disproportionation reactions in concentrated nitric acid solutions.

actinide

Meso-scale modeling of UO2 nuclear fuel to high burnup

To improve the economics of light water reactors for commercial nuclear energy generation, utility operators are seeking to obtain regulatory approval to run UO2 fuel to higher levels of burnup. One potential impediment to obtaining this approval is the phenomenon of fuel fragmentation, relocation, and dispersal (FFRD). FFRD can result when fuel experiences a rapid temperature transient, such as that occurring during a Loss Of Coolant Accident (LOCA). FFRD has historically been most associated with the rim region in UO2 fuel pellets, where the phenomenon of fragmentation is also referred to as pulverization due to the small size of the fragments. More recent evidence suggests that the so-called “dark zone” (due to its appearance in micrographs) that can be observed in the mid-radial regions of high burnup fuel is also susceptible to FFRD. Although empirical fuel performance models have been developed that can adequately predict pulverization in the rim region under typical LWR conditions, a scientific understanding of what underlies fuel restructuring and subsequent FFRD is lacking even in the rim region, and no models are currently available for the behavior the dark zone. To address these challenges, the U.S. Department of Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program has employed a multi-scale modeling approach to improve scientific understanding and develop new fuel performance models. In this talk, I will focus on meso-scale efforts, which form a crucial link between atomic-scale and engineering-scale models. Phase-field modeling combined with cluster dynamics is used to predict the restructuring process in the rim region. Phase-field fracture modeling, informed by atomistic simulations, is used to predict the onset of pulverization in the rim region. Combining these techniques together allows the extent of rim pulverization to be predicted. The formation and evolution of the dark zone has also been simulated with the phase-field method, using an improved approach to vacancy source term parameterization. The work shows the important impact of microstructure on fuel performance.

fracture

SQuaD: Smart Quantum Detection for Photon Recognition and Dark Count Elimination

Quantum detectors of single photons are an essential component for quantum information processing across computing, communication and networking. Today's quantum detection system, which consists of single photon detectors, timing electronics, control and data processing software, is primarily used for counting the number of single photon detection events. However, it is largely incapable of extracting other rich physical characteristics of the detected photons, such as their wavelengths, polarization states, photon numbers, or temporal waveforms. This work, for the first time, demonstrates a smart quantum detection system, SQuaD, which integrates a field programmable gate array (FPGA) with a neural network model, and is designed to recognize the features of photons and to eliminate detector dark-count. The SQuaD is a fully integrated quantum system with high timing-resolution data acquisition, onboard multi-scale data analysis, intelligent feature recognition and extraction, and feedback-driven system control. Our \name experimentally demonstrates 1) reliable photon counting on par with the state-of-the art commercial systems; 2) high-throughput data processing for each individual detection events; 3) efficient dark count recognition and elimination; 4) up to 100% accurate feature recognition of photon wavelength and polarization. Additionally, we deploy the SQuaD to an atomic (erbium ion) photon emitter source to realize noise-free control and readout of a spin qubit in the telecom band, enabling critical advances in quantum networks and distributed quantum information processing.

Linne, Karl C. [U. Chicago (main)] (ORCID:00090009

REIMAGINING HEAT EXCHANGERS FOR NEXT GENERATION ENVIRONMENTAL SYSTEMS

Air-to-refrigerant heat exchangers (HXs) are essential components in space conditioning, refrigeration, and power systems, and recent efforts have focused on making these devices more compact, reducing refrigerant charge and lowering manufacturing costs. Historically, HX innovation has been limited by available computational resources, design tools, and manufacturing constraints. The best available technologies utilize tube-fin and micro- or macro-channel tubes with fins, which are not necessarily the optimal designs achievable with current technology. In this paper, we highlight the latest advancements in air-to-refrigerant HXs, specifically emphasizing innovations achieved through shape and topology optimization. A multi-scale design optimization approach is introduced, alongside similar methods in literature, which enable highly sophisticated shape-optimized tube designs with more than 50% reduction in size and 25% reduction in refrigerant charge, essential for A3 refrigerant charge limit compliance. The frameworks integrate traditional heat and mass transfer science with state-of-the-art machine learning, genetic algorithms, and adjoint algorithms to create novel designs. While many of these innovative designs may not be manufacturable using conventional methods, they allow us explore the boundaries of what is possible. These novel air-to-refrigerant HXs are key enablers for ultra-low-refrigerant charge heat pump and refrigeration systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI

SCM overview & the EBR-II shutdown heat removal tests validation.

Pronghorn is an engineering-scale, coarse-mesh, thermal-hydraulics tool for supporting reactor-core simulations of advanced nuclear reactors. Most of the current efforts in Pronghorn have been devoted in developing porous finite-volume capabilities and adapting closure correlations for coarse-mesh thermal-hydraulics modeling. However, for liquid-metal reactors (LMRs) with wire-wrapped fuel pin assemblies, a pin-level thermal-hydraulic resolution is required for most safety case studies (pin rupture, channel blockage, etc.). For this purpose, a new Subchannel application is developed in MOOSE, which affords the required flow field resolution, while still preserving an engineering-scale approach. This new solver can be natively coupled to Pronghorn and other MOOSE objects to enable full-core, multi-physics, multi-scale engineering studies. This presentation presents the main features of the SCM code and demonstrates a validation case based on the EBR-II SHRT tests.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN

Surface heterogeneity propagation and homogenization for pouch cell-scale Li metal anodes

Li metal anode is a promising candidate for next-generation energy storage systems and is widely explored in Li-ion and solid-state batteries. Despite its potential, Li metal anodes face instabilities during long-term cycling, especially when paired with NMC or sulfur-based cathodes, where Li is cycled at high capacities of 6-8 mAh/cm2, equivalent to a thickness of 30-40 µm. During such extensive utilization, degradation mechanisms such as excessive solid electrolyte interphase (SEI), dendrites, and dead Li emerge, and ultimately lead to sudden failure and reduced cycle life. The origin of the degradation mechanisms stem from surface heterogeneities introduced during Li foil manufacturing and storage [1, 2]. As cycling progresses, the local heterogeneities propagate, resulting in uneven Li utilization and degradation-prone regions across the electrode [3], which consequently induce performance variations at the cell level. This heterogeneity propagation is particularly pronounced in large-format pouch cells in practical applications, where the surface effects are magnified. Without a clear understanding of the multi-scale heterogeneities and the development of surface homogenization methods, the performance consistency will be compromised, hindering the commercialization of Li metal batteries. In this presentation, we investigate the evolution of surface heterogeneity propagation on commercially available Li foils. We discover that inhomogeneous Li utilization appears as early as the first half-cycle of formation, manifesting as localized clusters and pits. In pouch cell configurations, these features exhibit areal density variations across the electrode on a scale of millimeters. To improve Li utilization homogeneity, a scalable mechanical brushing method is introduced to remove the chemically heterogeneous surface passivation layer. Furtherore, the influence of utilization homogeneity on cell-to-cell consistency is evaluated using 32 Li-NMC811 pouch cells divided into as-received and brushed Li groups. On the brushed Li, clusters and pits are no longer observable, and the cells exhibit significantly improved consistency in discharge capacity trajectories and cycle lifetime. Overall, this study highlights the role of Li surface utilization homogeneity on long-term cycling performance. Our research provides a pathway for improving large-area electrode uniformity and establishing evaluation methods for cell-to-cell consistency, both are key steps toward the commercialization of Li metal batteries and beyond. [1] Otto, Svenja-K., et al. "In-depth characterization of lithium-metal surfaces with XPS and ToF-SIMS: toward better understanding of the passivation layer." Chemistry of Materials 33.3 (2021): 859-867. [2] Hatzell, Kelsey, et al. "Aligning lithium metal battery research and development across academia and industry." Joule (2024). [3] Kim, Sangwook, et al. "Calendar life of lithium metal batteries: Accelerated aging and failure analysis." Energy Storage Materials 65 (2024): 103147.

25 - ENERGY STORAGE

Advanced Fuels Campaign Execution Plan

The Advanced Fuels Campaign (AFC) Execution Plan details the strategy, mission, scope, and goals—both near-term and long-term—along with the structure and organization of nuclear fuels and materials research, development, and demonstration (RD&D) activities within the Fuel Cycle Technologies (FCT) program. The FCT program, tasked by the U.S. Department of Energy (DOE), employs a science-based approach to advance fuel technologies. This approach integrates theory, experiments, and multi-scale modeling and simulation (M&S) to develop a predictive understanding of fuel fabrication processes and fuel/cladding performance under irradiation, moving beyond traditional empirical methods. The long-term goals of the AFC are guided by the AFC Strategic Plan and align with the DOE Office of Nuclear Energy (NE) Roadmap [1], which outlines a multi-decade vision for demonstrating and qualifying advanced fuel forms to support diverse fuel cycle options. Near-term goals focus on enhancing accident tolerant fuels (ATF) for Light Water Reactors (LWR), a significant challenge that demands balancing immediate objectives with ongoing progress toward advanced reactor missions. Accelerating the traditional fuel qualification process to meet ATF objectives is another critical challenge. A detailed set of 5-year goals, summarized below, has been developed in line with the overarching science-based fuel development approach: • Advanced LWR Fuel Technologies: By 2027, support the development of advanced LWR fuel technologies with improved performance and enhanced accident tolerance. This includes high burnup (HBu), low enriched uranium (LEU)+, coated cladding, and doped fuel, aimed at complementing industry-led significant LWR uprates and plant refurbishments. • Tristructural Isotropic (TRISO) Fuel: Achieve qualification by 2028 and develop improved designs for emerging markets. • Metal Fuel: Achieve qualification by 2028 and develop improved designs for emerging markets. • Molten Salt Fuel: By 2027, deploy a robust program that enables fuel salt qualification technologies needed to support fuel salt research and development (R&D), focusing on emergent needs to derisk fuel salt production and utilization in advanced reactors. • Long-Term ATF: Develop fuel technologies that enable significant power uprates (~50%) in refurbished or new LWRs while optimizing fissile material utilization and waste disposal. The 5-year milestones in the AFC Execution Plan are contingent on an assumed budget. This Execution Plan will be updated annually to reflect actual funding profiles as budget guidance becomes available, ensuring milestones are adjusted accordingly. In summary, the AFC Execution Plan presents a comprehensive strategy to advance nuclear fuel technologies through a science-based approach, addressing both near-term and long-term goals while adapting to funding realities.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS

Artificial Intelligence–Enabled Digital Twin for U.S. Cities

Over 50 participants—including national laboratory researchers, academic scholars, industry representatives, and stakeholders from the City of Chicago—convened in person and online to assess the readiness and potential of an Artificial Intelligence–Enabled Digital Twin (AIDT) for urban systems, with the Greater Chicago area serving as the benchmark location. The workshop underscored that the Chicago Urban Integrated Field Laboratory provides an unparalleled testbed for developing and validating urban DTs—combining dense, multiscale observations, advanced physics-based and AI modeling capabilities, and strong stakeholder and industry engagement. Discussions highlighted available datasets, AI architectures for high resolution, multipurpose urban DTs, key applications, and near- and long-term priorities for scaling this framework within Chicago and to other U.S. and global cities.

AIDT

Graph-Based Modeling and Decomposition of Hierarchical Optimization Problems

We present a graph-theoretic modeling approach for hierarchical optimization that leverages the OptiGraph abstraction implemented in the Julia package Plasmo.jl. We show that the abstraction is flexible and can effectively capture complex hierarchical connectivity that arises from decision-making over multiple spatial and temporal scales (e.g., integration of planning, scheduling, and operations in manufacturing and infrastructures). We also show that the graph abstraction facilitates the conceptualization and implementation of decomposition and approximation schemes. Specifically, we propose a graph-based Benders decomposition (gBD) framework that enables the exploitation of hierarchical (nested) structures and that uses graph aggregation/partitioning procedures to discover such structures. In addition, we provide a Julia implementation of gBD, which we call PlasmoBenders.jl. We illustrate the capabilities using examples arising in the context of energy and power systems.

97 MATHEMATICS AND COMPUTING

Convective heat transfer enhancement through additively built multiscale micro-tetrahedron features

Use of Additive Manufacturing (AM) to improve the heat transfer characteristics of tip shrouds in high-pressure turbines is being considered by industries. Existing designs of these components integrate micro-cooling channels to reduce the bulk temperature for improved life. In this research, closely packed micro tetrahedron features in addition to AM roughness has been considered. Further, this multiscale surface characteristics increased surface area per unit volume available for heat exchange. Micro-tet features were designed, manufactured, characterized, and evaluated systematically while increasing their height. An enormous increase in the overall wetted surface area by 200 % was measured. The convective heat transfer enhancement was ~3.72 times EDM rough coupon, and friction factor enhancement was ~5.5 times EDM rough coupon. Furthermore, the proposed design offers 2.5 times enhanced heat transfer for a given 2 W pumping power compared to our EDM rough coupon. Heat transfer enhancement was observed to not vary strongly with increased Reynolds number. Such complex designs are only possible through additive manufacturing for increased heat transfer with little pressure penalty. Finally, increasing the micro-tet height for increased surface area and improved heat exchange beyond an upper limit might not be a significant benefit as it gets compensated by increasing skin friction.

42 ENGINEERING

A mechanistic, multiscale model for predicting Pd penetration in TRISO fuels using BISON

TRistructural ISOtropic (TRISO) particles use silicon carbide (SiC) as the primary structural member and barrier against metallic fission product (FP) release. palladiums (PDs), produced by fission in the fuel kernel, can diffuse to and chemically interact with the SiC layer, degrading its structural integrity and ability to contain radioactive FPs. Existing temperature-dependent correlations for predicting Pd penetration rely on experimental data with significant scatter due to varying conditions, potentially complicating ongoing fuel qualification and licensing efforts for advanced reactors that would subject TRISO fuels to operating conditions outside of those examined in the experiments. A mechanistic model of Pd production, transport, and reaction is developed in this work to better understand and predict PD attack of SiC in TRISO particles. molecular dynamicss (MDs) simulations are utilized to calculate the Pd diffusivity in SiC grain bulk and grain boundaries. A mesoscale phase-field diffusion model, informed by the MD diffusivities, is used to develop a reduced order model (ROM) for the effect of SiC microstructure on the Pd penetration rate. The engineering scale BISON model calculates Pd production and transport, utilizing the ROM to predict penetration rates consistent with experimental data. This novel mechanistic ROM captures the effect of temperature, microstructure, and irradiation history on Pd penetration. In conclusion, these new capabilities are expected to support ongoing qualification and licensing efforts associated with near-term TRISO-fueled reactor applications.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS

CoRE MOF DB: A curated experimental metal-organic framework database with machine-learned properties for integrated material-process screening

Here, we present an updated version of the Computation-Ready, Experimental (CoRE) Metal-Organic Framework (MOF) database, which includes a curated set of computation-ready MOF crystal structures designed for high-throughput computational materials discovery. Data collection and curation procedures were improved from the previous version to enable more frequent updates in the future. Machine-learning-predicted properties, such as stability metrics and heat capacities, are included in the dataset to streamline screening activities. An updated version of MOFid was developed to provide detailed information on metal nodes, organic linkers, and topologies of an MOF structure. DDEC6 partial atomic charges of MOFs were assigned based on a machine-learning model. Gibbs ensemble Monte Carlo simulations were used to classify the hydrophobicity of MOFs. The finalized dataset was subsequently used to perform integrated material-process screening for various carbon-capture conditions using high-fidelity temperature-swing adsorption (TSA) simulations. Our workflow identified multiple MOF candidates that are predicted to outperform CALF-20 for these applications.

CoRE MOF database

Radiation damage effects in beryllium for next generation neutrino beam targetry (Final Technical Report)

Current and future high-power accelerators put severe requirements on materials used for target and beam windows and target facilities have been recognized as a critical challenge in development of future particle accelerators. In accelerators, window and target materials are exposed to extreme conditions, which include bombardment with very high energy protons (1- 100 GeV) and thermomechanical shock waves. Radiation can cause direct damage in the material, and it leads to production of transmutation products (especially helium), both phenomena having a potential adverse effect on the stability and durability of the target/window material. At high enough temperatures, He can aggregate to form gas bubbles, which in turn cause significant dimensional changes (swelling), enable easy crack propagation, and eventually cause failure by fracture. On the other hand, if the temperature is too low, radiation damage accumulates in the form of internal defects (e.g., dislocations), leading to hardening and a decreased ductility of the material. In this project, we will focus on beryllium since it is considered to be one of the candidate materials for beam windows and targets in the next-generation proton accelerators, e.g., the Long Baseline Neutrino Facility (LBNF). Radiation effects in Be have been studied in the context of nuclear fusion reactor applications. However, key differences exist between reactor and accelerator conditions, including neutron vs. proton irradiation, continuous vs. pulsed beam flux, much higher energies of bombarding particles in accelerators, and higher operating temperatures for typical reactors. For example, the impact of beam pulsing on the radiation damage and the He bubble kinetics is largely unknown. While results obtained on Be from fusion research might not be directly transferrable to understanding target materials, there is an opportunity to bring state-of-the-art tools from materials research in nuclear reactors to aid design of target and beam window materials in high-power accelerators. To this end, the overarching goal of this project are to develop an experimentally-validated computational framework capable of predicting radiation damage evolution in beryllium relevant to beam window and target conditions, focusing on He bubble formation and growth as a function of irradiation temperature. Our model will be based on the cluster dynamics formalism, where size distribution of defects and He bubbles is simulated as a function of time, temperature, and radiation dose. Parameters for the model will be taken from published experiments and from high-fidelity atomistic simulations proposed in this project. In addition, we will carry out a series of targeted ex-situ and in-situ dual-beam experiments using low-energy protons to provide critical data for validation of the model on the effects of radiation on He clustering, He bubble distribution, and dislocation loop density/size in proton irradiated Be.

36 MATERIALS SCIENCE