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DOE OSTI2026

Influence of Oxygen Flow and Stoichiometry on Optical Properties and Damage Resistance of Hafnium Oxide Thin Films

Hafnium oxide (HfO 2 ) is predominantly used as a high-index material in multi-layer dielectric coatings for high-peak- and high-average-power lasers, but laser damage often initiates within the HfO 2 layers despite their wide bandgap. Oxygen deficiency during deposition can introduce vacancy-related sub-bandgap states and absorptive defects, lowering damage resistance. This study investigates how oxygen flow during HfO 2 deposition with ion beam sputtering (IBS) affects its stoichiometry, defect formation, and nanosecond laser-induced damage threshold (LIDT) and whether single-layer trends predict multilayer performance. Single layers were deposited at varying oxygen flows, characterized for optical and structural properties, and tested for the LIDT at 1064 nm and 355 nm. Increasing oxygen flow drove the layer toward near-stoichiometric HfO 2 , reduced the refractive index, and altered the density of surface pinhole-like features. The single-layer LIDT at 355 nm increased with oxygen, whereas the 1064 nm LIDT was comparatively less sensitive to oxygen flow, consistent with the wavelength-dependent roles of absorptive precursors and microstructural defects. In contrast, a HfO 2 -based high-reflector (HR) showed a higher LIDT at lower oxygen flow, indicating that the family of damage precursors changes between single layers and multilayers; in stacks, structural properties such as stress, gas entrapment and thermal dissipation may outweigh the isolated absorptive defects found in single layers. These results demonstrate that the optimal oxygen flow condition depends on both LIDT wavelength and film architecture. We identified, for single layers, a 15–35 sccm window for maximizing the 1064 nm LIDT and a high-flow optimum (45 sccm) for the 355 nm LIDT and, for 355 nm HR stacks, a distinct lower-flow regime (~10 sccm).

Optics and optical instruments↗

DOE OSTI2024

Uncertainty-Aware, Structure-Preserving Machine Learning Approach for Domain Shift Detection From Nonlinear Dynamic Responses of Structural Systems

Complex structural systems deployed for aerospace, civil, or mechanical applications must operate reliably under varying operational conditions. Structural health monitoring (SHM) systems help ensure the reliability of these systems by providing continuous monitoring of the state of the structure. SHM relies on synthesizing measured data with a predictive model to make informed decisions about structural states. However, these models—which may be thought of as a form of a digital twin—need to be updated continuously as structural changes (e.g., due to damage) arise. We propose an uncertainty-aware machine learning model that enforces distance preservation of the original input state space and then encodes a distance-aware mechanism via a Gaussian process (GP) kernel. The proposed approach leverages the spectral-normalized neural GP algorithm to combine the flexibility of neural networks with the advantages of GP, subjected to structure-preserving constraints, to produce an uncertainty-aware model. This model is used to detect domain shift due to structural changes that cannot be observed directly because they may be spatially isolated (e.g., inside a joint or localized damage). This work leverages detection theory to detect domain shift systematically given statistical features of the prediction variance produced by the model. The proposed approach is demonstrated on a nonlinear structure being subjected to damage conditions. In conclusion, it is shown that the proposed approach is able to rely on distances of the transformed input state space to predict increased variance in shifted domains while being robust to normative changes.

Algorithms↗

DOE OSTI2024

An approach to combine neutron and ion irradiation data to accelerate material qualification for nuclear reactors

Next-generation nuclear power plants are generally characterized by higher operating temperatures, increased neutron fluences and energies, and distinct corrosive coolant environments versus the existing light water reactor fleet. Whether using existing materials in new environments, newly developed materials tailored for these environments, or new manufacturing methods, the traditional decades-long approach for materials qualification does not facilitate rapid deployment. Ion irradiation has demonstrated success in reproducing material microstructure and select property evolution resulting from neutron irradiation with three to four orders of magnitude reduction in time and cost, making it an ideal candidate for accelerated irradiation testing. Because microstructure has a large impact on bulk material properties, limited neutron irradiation data at lower damage levels can in principle be combined with accelerated ion testing results and modeling and simulation to form an accurate prediction of microstructure evolution and select properties under different neutron irradiation conditions and at higher damage levels. The objective of this work is to present a conceptual framework of specific steps to fulfill several technical challenges associated with qualifying materials for performance in radiation environments on an accelerated time frame. A brief review of the regulatory landscape for materials in nuclear environments is presented, followed by additional overviews to understand the current state of the art for correlation of materials properties across radiation environments using experimental and computational methodologies. Finally, the roles of academia, national laboratories, and industry in the advancement of this accelerated materials qualification framework are discussed as a path forward, with possible case studies presented.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Sprain energy consequences for damage localization and fracture mechanics

The 2023 smooth Lagrangian Crack-Band Model (slCBM), inspired by the 2020 invention of the gap test, prevented spurious damage localization during fracture growth by introducing the second gradient of the displacement field vector, named the “sprain,” as the localization limiter. The key idea was that, in the finite element implementation, the displacement vector and its gradient should be treated as independent fields with the lowest ( C 0 ) continuity, constrained by a second-order Lagrange multiplier tensor. Coupled with a realistic constitutive law for triaxial softening damage, such as microplane model M7, the known limitations of the classical Crack Band Model were eliminated. Here, we show that the slCBM closely reproduces the size effect revealed by the gap test at various crack-parallel stresses. To describe it, we present an approximate corrective formula, although a strong loading-path dependence limits its applicability. Except for the rare case of zero crack-parallel stresses, the fracture predictions of the line crack models (linear elastic fracture mechanics, phase-field, extended finite element method (XFEM), cohesive crack models) can be as much as 100% in error. We argue that the localization limiter concept must be extended by including the resistance to material rotation gradients. We also show that, without this resistance, the existing strain-gradient damage theories may predict a wrong fracture pattern and have, for Mode II and III fractures, a load capacity error as much as 55%. Finally, we argue that the crack-parallel stress effect must occur in all materials, ranging from concrete to atomistically sharp cracks in crystals.

Science & Technology - Other Topics↗

DOE OSTI2020

Quantitative assessment of environmental phenomena on maximum pit size predictions in marine environments

Maximum pit sizes were predicted for dilute and concentrated NaCl and MgCl 2 solutions as well as sea-salt brine solutions corresponding to 40% relative humidity (RH) (MgCl 2 -rich) and 76% RH (NaCl-rich) at 25 °C. A quantitative method was developed to capture the effects of various cathode evolution phenomena including precipitation and dehydration reactions. Additionally, the sensitivity of the model to input parameters was explored. Despite one's intuition, the highest chloride concentration (roughly 10.3 M Cl – ) did not produce the largest predicted pit size as the ohmic drop was more severe in concentrated MgCl 2 solutions. Therefore, the largest predicted pits were calculated for saturated NaCl (roughly 5 M Cl – ). Next, it was determined that pit size predictions are most sensitive to model input parameters for concentrated brines. However, when the effects of cathodic reactions on brine chemistry are considered, the sensitivity to input parameters is decreased. Although there was not one main input parameter that influenced pit size predictions, two main categories were identified. Under similar chloride concentrations (similar RH), the water layer thickness (WL), and pit stability product, (i · x) sf , are the most influential factors. When varying chloride concentrations (RH), changes in WL, the brine specific cathodic kinetics on the external surface (captured in the equivalent current density (i eq )), and conductivity (k o ) are the most influential parameters. Finally, it was noted that dehydration reactions coupled with precipitation in the cathode will have the largest effect on predicted pit size, and cause the most significant inhibition of corrosion damage.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Deep Learning Provides Substantial Improvements to County‐Level Fire Weather Forecasting Over the Western United States

Abstract The recent wildfires in the western United States during 2018 and 2020 caused record‐breaking fire damage and casualties. Despite remarkable advances in fire modeling and weather forecasting, it remains challenging to anticipate catastrophic wildfire events and associated damage. One key missing component is a fire weather prediction system with sufficiently long lead time capable of providing useful regional details. Here, we develop a hybrid prediction model of wildfire danger called CFS with super resolution (CFS‐SR) as a proof of concept to fill that void. The CFS‐SR model is constructed by integrating the Climate Forecast System version 2 with a deep learning (DL) technique from Single Image Super Resolution, a method widely used in enhancing image resolution. We show that for the 2018–2019 fire season, the CFS‐SR model significantly improves accuracy in forecasting fire weather at lead times of up to 7 days with an enhanced spatial resolution up to 4 km. This level of high resolution provides county‐level fire weather forecast, making it more practical for allocating resources to mitigate wildfire danger. Our study demonstrates that a proper combination of ensemble climate predictions with DL techniques can boost predictability at finer spatial scales, increasing the utility of fire weather forecasts for practical applications.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Novel method to control explosive shock sensitivity: Formulations, experiments and modeling

The microstructure of a heterogeneous high explosive (HE) affects its shock initiation sensitivity and detonation performance. Alteration of the void content and/or void structure (i.e., bulk heating or mechanical damage) therefore changes the shock initiation behavior. Controlling and predicting the change in shock sensitivity after an HE has undergone microstructural changes addresses an important and challenging goal for the design and understanding of novel energetic material formulations. In this study, we aimed to develop HE systems to precisely tune shock sensitivity by a thermal treatment prior to use. Specifically, we incorporated a small fraction (1 wt% or less) of thermally expandable microspheres (TEMs) during the formulation process of various plastic-bonded explosives (PBX). TEMs typically consist of a thermoplastic acrylonitrile shell (10-50 µm diameter), which encapsulates an inert low boiling hydrocarbon. Upon heating, the TEMs expand as the shell softens while the hydrocarbon gasifies, increasing the internal pressure and expanding the particle by as much as 120 vol% (irreversibly). Here, we present our progress on shock sensitivity comparisons of HE formulations doped with TEMs after heating and expansion. Experiments, using Composition C-4 thermally cycled to 120°C, showed an increase in shock sensitivity. Additionally, mesoscale modeling revealed that the TEM itself does not act as a hotspot, but instead has a secondary effect on the response of nearby voids. We conclude that TEMs indeed do provide a method to tune shock sensitivity. Further, our results emphasize the non-uniformity of shock waves at the mesoscale, and that upstream defects influence the reactivity of downstream defects.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Integrating chromosome conformation and DNA repair in a computational framework to assess cell radiosensitivity

Objective. The arrangement of chromosomes in the cell nucleus has implications for cell radiosensitivity. The development of new tools to utilize Hi-C chromosome conformation data in nanoscale radiation track structure simulations allows for in silico investigation of this phenomenon. We have developed a framework employing Hi-C-based cell nucleus models in Monte Carlo radiation simulations, in conjunction with mechanistic models of DNA repair, to predict not only the initial radiation-induced DNA damage, but also the repair outcomes resulting from this damage, allowing us to investigate the role chromosome conformation plays in the biological outcome of radiation exposure. Approach. In this study, we used this framework to generate cell nucleus models based on Hi-C data from fibroblast and lymphoblastoid cells and explore the effects of cell type-specific chromosome structure on radiation response. The models were used to simulate external beam irradiation including DNA damage and subsequent DNA repair. The kinetics of the simulated DNA repair were compared with previous results. Main results. We found that the fibroblast models resulted in a higher rate of inter-chromosome misrepair than the lymphoblastoid model, despite having similar amounts of initial DNA damage and total misrepairs for each irradiation scenario. Significance. This framework represents a step forward in radiobiological modeling and simulation allowing for more realistic investigation of radiosensitivity in different types of cells.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Impact of Vegetation Assimilation on Flash Drought Characteristics across the Continental United States

Predicting and managing the impacts of flash droughts is difficult owing to their rapid onset and intensification. Flash drought monitoring often relies on assessing changes in root-zone soil moisture. However, the lack of widespread soil moisture measurements means that flash drought assessments often use process-based model data like that from the North American Land Data Assimilation System (NLDAS). Such reliance opens flash drought assessment to model biases, particularly from vegetation processes. Here, we examine the influence of vegetation on NLDAS-simulated flash drought characteristics by comparing two experiments covering 1981–2017: open loop (OL), which uses NLDAS surface meteorological forcing to drive a land surface model using prognostic vegetation, and data assimilation (DA), which instead assimilates near-real-time satellite-derived leaf area index (LAI) into the land surface model. The OL simulation consistently underestimates LAI across the United States, causing relatively high soil moisture values. Both experiments produce similar geographic patterns of flash droughts, but OL produces shorter duration events and regional trends in flash drought occurrence that are sometimes opposite to those in DA. Across the Midwest and Southern United States, flash droughts are 4 weeks (about 70%) longer on average in DA than OL. Moreover, across much of the Great Plains, flash drought occurrence has trended upward according to the DA experiment, opposite to the trend in OL. This sensitivity of flash drought to the representation of vegetation suggests that representing plants with greater fidelity could aid in monitoring flash droughts and improve the prediction of flash drought transitions to more persistent and damaging long-term droughts.

Drought↗

DOE OSTI2023

Multiphysics Pebble-Bed Reactor Control Rod Withdrawal Study

This work studied the responses of both a generic gas- and a fluoride-cooled PBR concept---the gPBR-200 and gFHR, respectively---during reactivity insertion accidents. Both models rely on 2-D axisymmetric simulations to solve the neutron flux distribution, nuclide concentrations, and temperature across the core---in addition to numerous representative pebble and TRISO particle simulations for determining fuel and moderator temperatures. This not only allows for computing maximum temperatures in the core---thus enabling estimation of how near the fuel is to peak operational and safety limits---as prescribed by specified acceptable fuel design limits, which are determined in such a way that fuel is not damaged during operational or anticipated abnormal occurrences---but also predicting how much of the core exceeds a given temperature limit, as well as determining the local energy deposition rate. These models consider both control rod withdrawal and ejection events. The former introduces a great deal more reactivity, as all the control rods are withdrawn (as opposed to a single one in the latter case), though at a much slower pace. In addition, for the gPBR-200, two limiting cases were considered: one with the core starting under hot full-power conditions and one with it starting under cold zero-power conditions. While the amount of reactivity added in the latter case is much higher (due to the far lower temperatures and the lack of neutron poisons such as Xe-135), the margin to temperature limits is also much more significant. Overall, for the design considered, none of the accidents resulted in the maximum fuel temperature reaching values close to the TRISO limit. However, the methodology presented herein could be very relevant if some designs consider reduced margins (e.g., higher temperatures) to achieve enhanced economics. Further model improvement is needed to better capture control rod worth, both in terms of cusping effects (as the rods are slowly withdrawn) and differential worth, especially as the tips of the rods near the upper cavity.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Sound speed measurements in shock compressed cemented tungsten carbide: Evolution of elastic moduli with damage at pressures to 100 GPa

The motivation of the present study is to gain insights into the evolution of elastic properties of cemented tungsten carbides (WC) shock compressed to 100 GPa. Seven plate impact experiments—two front surface impact and five release wave overtake—are conducted to make simultaneous measurements of Hugoniot states and longitudinal sound speeds in shocked WC with 3.7wt.% cobalt binder. The sound speeds along with estimates for bulk sound speeds, obtained using the Birch–Murnaghan EoS, are analyzed to determine the elastic moduli—longitudinal, bulk, and shear—as a function of Hugoniot stress. The longitudinal and bulk sound speeds at Hugoniot states of interest are found to increase linearly with longitudinal stress. Consistent with the increase in sound speeds, the longitudinal and bulk moduli also increase with Hugoniot stress; however, the increase in longitudinal modulus is modest when compared to predictions of theoretical models that account for pressure and temperature dependence of elastic moduli, but with no damage. The shear moduli remain nearly constant at ~318 GPa over the range of Hugoniot states investigated. These values are, however, much lower than those predicted by the Steinberg–Guinan model with no damage. Poisson’s ratio decreases initially from its ambient value of 0.208 to ~0.199 for Hugoniot stress ≤10 GPa indicating consolidation of the WC microstructure with low initial stress; however, with an increase in Hugoniot stress to ~100 GPa, Poisson’s ratio increases to ~0.317, indicating degradation of shear moduli with increasing stress. The product of density and Grüneisen parameter (ρΓ), after an initial spike, remains nearly constant for volumetric strains ≥0.07. The maximum average temperature rise is estimated to be ~286°C at the highest Hugoniot stress employed in the study.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Multitask graph neural networks for elastoplastic response prediction in dual-phase polycrystals

Microstructure-sensitive prediction of elastoplastic response remains a recurring bottleneck in multiscale damage and fatigue modeling, where large ensembles of statistically distinct polycrystals are required to quantify variability and extreme-value behavior. In this work, we develop a multitask graph neural network (GNN) surrogate that maps dual-phase ferrite–martensite polycrystal microstructures to Statistical Volume Element (SVE)-level elastoplastic Quantities of Interest (QoIs). Each SVE is represented as a grain-adjacency graph, with node features encoding phase, geometry, and crystallographic orientation, and edge features encoding relative misorientation. A message-passing graph convolution generates node embeddings, which are pooled into a graph representation and passed to a multitask regression head that jointly predicts 10 scalar QoIs and vector-valued stress–strain responses in orthogonal loading directions across multiple martensite volume fractions and SVE sizes. Results show high accuracy for scalar QoIs and strong agreement for full stress–strain trajectories, with population envelopes reproducing both median behavior and finite-SVE variability across compositions and partition scales. A unified model trained on pooled volume-fraction data preserves most within-regime accuracy relative to regime-specific models while also capturing the broader cross-regime variation reflected in the pooled test set. Distributional comparisons further demonstrate that the surrogate preserves heterogeneity under SVE partitioning, enabling statistically consistent block-wise random-field construction for mesoscale analyses. Overall, the proposed grain-graph surrogate provides a practical pathway to accelerate ensemble-based studies of SVE-level constitutive variability in dual-phase polycrystals.

Crystal plasticity↗

DOE OSTI2023

Continuum Damage Mechanics Modeling of High-Temperature Flaw Propagation: Application to Creep Crack Growth In 316H Standardized Specimens and Nuclear Reactor Components

Predicting creep crack growth (CCG) of flaws found during operation in high-temperature alloy components is essential for assessing the remaining lifetime of those components. While defect assessment procedures are available for this purpose in design codes, these are limited in their range of applicability. This study assesses the application of a local damage-based finite-element methodology as a more general technique for the prediction of CCG at high temperatures on a variety of structural configurations. Numerical results for stainless steel 316H, which are validated against experimental data, show the promise of this approach. This integration of continuum damage mechanics (CDM) based methodologies, together with adequate inelastic models, into assessment procedures can therefore inform the characterization of CCG under complex operating conditions, while avoiding excessive conservatism. This article shows that such modeling frameworks can be calibrated to experimental data and used to demonstrate that the degree of tri-axiality ahead of a growing creep crack affects its rate of growth. The framework is also successfully employed in characterizing CCG in a realistic reactor pressure vessel geometry under an arbitrary loading condition. These results are particularly relevant to the nuclear power industry for defect assessment and inspections as part of codified practices of structural components with flaws in high-temperature reactors.

42 ENGINEERING↗

DOE OSTI2020

Oxidation and microstructure of SiC f /SiC composites in moist air up to 1600°C by X‐ray tomographic characterization

Abstract SiC f /SiC composites that possess PyC or BN interface layers were fabricated and then oxidized in moist air at 1000, 1200, 1400, and 1600°C. High‐resolution CT was used for capturing 3D images and quantifying the SiC phase, mesophase, and voids. The oxidation behavior and microstructural evolution of SiC f /SiC with PyC or BN interface are discussed in this study. The microstructure of the SiC f /SiC with a PyC layer was seriously damaged in moist air at high temperature, whereas the BN interface layer enhanced the oxidation resistance of the SiC f /SiC. These results are also confirmed by using XRD, oxidation mass gain, tensile testing, and SEM measurements. The results of the oxidation behavior and microstructural evolution for SiC f /SiC oxidized in dry air are also compared with the results of this study. Comparing the SiC f /SiC with a PyC interface layer, the composite with a BN interface layer oxidized in moist air exhibits a high void growth rate and a low SiO 2 grain growth rate from 1000 to 1600°C. This work will provide guidance for predicting the service life of SiC f /SiC for multiscale damage rate models of materials at a local scale and will also provide guidance on the life service design of SiC f /SiC materials.

Lu, Zilong↗

DOE OSTI2022

Understanding Material Damage and Degradation through Thermal Property Instruments

Quantifying material damage and degradation under reactor conditions enables researchers to make important predictions regarding reactor safety and efficiency. More specifically, thermal property instruments serve as a powerful tool in quantifying the evolution of thermal properties under irradiation, and hence, are necessary in qualifying fuels and materials for reactor use. In addition to providing insight into thermal properties, some instruments may provide a new means of quantifying radiation damage, a challenge in modern nuclear materials research.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Revealing hidden defects through stored energy measurements of radiation damage

With full knowledge of a material’s atomistic structure, it is possible to predict any macroscopic property of interest. In practice, this is hindered by limitations of the chosen characterization techniques. For example, electron microscopy is unable to detect the smallest and most numerous defects in irradiated materials. Instead of spatial characterization, we propose to detect and quantify defects through their excess energy. Differential scanning calorimetry of irradiated Ti measures defect densities five times greater than those determined using transmission electron microscopy. Our experiments also reveal two energetically distinct processes where the established annealing model predicts one. Molecular dynamics simulations discover the defects responsible and inform a new mechanism for the recovery of irradiation-induced defects. The combination of annealing experiments and simulations can reveal defects hidden to other characterization techniques and has the potential to uncover new mechanisms behind the evolution of defects in materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Woven ceramic matrix composite surrogate model based on physics-informed recurrent neural network

A recurrent neural network (RNN) based surrogate model is developed to emulate the nonlinear constitutive behavior of woven ceramic matrix composites (CMCs) driven by matrix damage at multiple length scales. Physics-informed constraints are introduced into the surrogate model through regularization to ground the prediction in physics and improve its predictive capabilities. Training data is generated using the multiscale generalized method of cells (MSGMC) approach coupled with a matrix damage model. This coupling permits simulating the nonlinear behavior of woven CMCs based on constituent response at the micro-, meso-, and macroscales. The multiscale repeating unit cell is loaded under non-monotonic conditions including multiple load / unload cycles and tension / compression. The fiber volume fraction as well as the intra- and intertow void volume fractions are also varied in the generation of training data. Therefore, the RNN-based surrogate model is tasked with predicting, as a function of variable input strain sequence and fiber and void volume fractions, the resulting stress versus strain response while satisfying physical constraints such as positive semi-definiteness of the tangent stiffness matrix and linear elastic unloading. Further, the trained surrogate model effectively matches the stress versus strain response and successfully predicts the tangent modulus throughout the loading regime. Neural network based surrogate models can offer efficient alternatives to running computationally intensive multiscale material models to simulate the nonlinear response of large structural models. Therefore the presented work provides evidence towards the feasibility of developing, training, and running such models for CMCs with complex architectures, nonlinear multiaxial material response, and under non-monotonic loading conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Current distribution monitoring enables quench and damage detection in superconducting fusion magnets

Abstract Fusion magnets made from high temperature superconducting ReBCO CORC ® cables are typically protected with quench detection systems that use voltage or temperature measurements to trigger current extraction processes. Although small coils with low inductances have been demonstrated, magnet protection remains a challenge and magnets are typically operated with little knowledge of the intrinsic performance parameters. We propose a protection framework based on current distribution monitoring in fusion cables with limited inter-cable current sharing. By employing inverse Biot-Savart techniques to distributed Hall probe arrays around CORC ® Cable-In-Conduit-Conductor (CICC) terminations, individual cable currents are recreated and used to extract the parameters of a predictive model. These parameters are shown to be of value for detecting conductor damage and defining safe magnet operating limits. The trained model is then used to predict cable current distributions in real-time, and departures between predictions and inverse Biot-Savart recreated current distributions are used to generate quench triggers. The methodology shows promise for quality control, operational planning and real-time quench detection in bundled CORC ® cables for compact fusion reactors.

47 OTHER INSTRUMENTATION↗