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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Species Transport Framework Development in SAM for System-Level Tritium Source Term Analysis

The SAM code is under development as a modern system-level modeling and simulation tool for advanced non–light water reactor safety analyses, with recent efforts to add capabilities to evaluate radiological source term risks in these novel reactor concepts. By leveraging the established system-level multiphysics thermal-hydraulic models in SAM, a framework for tightly coupled species transport modeling has been integrated into the code for engineering-scale source term evaluation. This species transport framework was first applied to the simulation of tritium, which is a well-known source term in conventional light water reactors. Tritium poses a unique risk in salt-cooled reactors, especially those with lithium-bearing salts such as the fluoride salt–cooled high-temperature reactor (FHR) concept, as tritium is generated in the salt coolant in significant quantities due to neutron interactions. A compounding factor is the increased mobility of tritium at high temperatures, which is able to permeate through metals while also potentially being retained in graphite pebbles and structures. Engineering-scale models for the tritium transport pathways in a FHR have been developed using the new species transport framework in SAM. The capabilities are assessed through analytical verification problems and validated with data from a graphite retention experiment. In conclusion, the system-level model is demonstrated by performing an initial estimate of baseline tritium generation and flows in a generic reference SAM FHR model, setting a foundation for future studies of source term transient analysis with the potential for further multiscale and multiphysics integration.

SAM↗
DOE OSTI2025

Simulations suggest offshore wind farms modify low-level jets

Abstract. Offshore wind farms are scheduled to be constructed along the East Coast of the US in the coming years. Low-level jets (LLJs) – layers of relatively fast winds at low altitudes – also occur frequently in this region. Because LLJs provide considerable wind resources, it is important to understand how LLJs might change with turbine construction. LLJs also influence moisture and pollution transport; thus, the effects of wind farms on LLJs could also affect the region’s meteorology. In the absence of observations or significant wind farm construction as yet, we compare 1 year of simulations from the Weather Research and Forecasting (WRF) model with and without wind farms incorporated, focusing on locations chosen by their proximity to future wind development areas. We develop and present an algorithm to detect LLJs at each hour of the year at each of these locations. We validate the algorithm to the extent possible by comparing LLJs identified by lidar, constrained to the lowest 200 m, to WRF simulations of these very low LLJs (vLLJs). In the NOW-WAKES simulation data set, we find offshore LLJs in this region occur about 25 % of the time, most frequently at night, in the spring and summer months, in stably stratified conditions, and when a southwesterly wind is blowing. LLJ wind speed maxima range from 10 m s−1 to over 40 m s−1. The altitude of maximum wind speed, or the jet “nose”, is typically 300 m above the surface, above the height of most profiling lidars, although several hours of vLLJs occur in each month in the data set. The diurnal cycle for vLLJs is less pronounced than for all LLJs. Wind farms erode LLJs, as LLJs occur less frequently (19 %–20 % of hours) in the wind farm simulations than in the no-wind-farm (NWF) simulation (25 % of hours). When LLJs do occur in the simulation with wind farms, their noses are higher than in the NWF simulation: the LLJ nose has a mean altitude near 300 m for the NWF jets, but that nose height moves higher in the presence of wind farms, to a mean altitude near 400 m. Rotor region (30–250 m) wind veer is reduced across almost all months of the year in the wind farm simulations, while rotor region wind shear is similar in both simulations.

17 WIND ENERGY↗
DOE OSTI2026

Enabling the Broader Use of MOOSE for Nuclear Energy and Other Simulation

This Final Scientific and Technical Report summarizes work performed under the Phase IIA SBIR project “Enabling the Broader Use of MOOSE for Nuclear Energy and Other Simulation” (DE-SC0020906) from August 2023 through August 2025. The objective of the Phase IIA effort was to mature and harden capabilities developed during Phase II, with the goal of enabling practical interoperability between Coreform’s isogeometric analysis (IGA) technologies and the Multiphysics Object-Oriented Simulation Environment (MOOSE), while improving robustness, performance, and scalability for complex, nuclear-relevant geometries. Over the course of Phase IIA, the project established and validated an extraction-based interoperability pathway between Coreform tools and MOOSE. A combined mesh and matrix format was defined collaboratively with MOOSE developers and integrated into the solver, enabling standard MOOSE workflows to operate on data exported from Coreform’s IGA and Flex Representation Method (FRM) pipelines. Early demonstrations validated architectural compatibility using linear solid mechanics problems, while later efforts focused on benchmark testing and external use. By the end of the project period, engineers at BWXT were able to independently set up and execute a simulation using the Coreform–MOOSE workflow and provide direct feedback that informed further refinement. In parallel, substantial effort was devoted to improving the robustness of trimmed U-spline construction for complex CAD geometries. A growing test suite of nuclear-relevant models was compiled through collaboration with multiple stakeholders and used to drive extensive bug fixing and reliability improvements. These efforts resulted in improved robustness and performance, including the addition of fallback capabilities that enhance reliability when the underlying commercial CAD kernel fails. Performance-oriented work progressed later in the project, with the development and demonstration of methods to decompose complex geometries into structured subregions and updated data representations to support more efficient solver processing. Additionally, extensive enhancements to threadsafe parallel data structures and trimming operations established a foundation for scalable processing of large assemblies. Collaboration with Sandia National Laboratories on the SGM geometric modeling kernel advanced to a functioning interface test case, positioning the workflow for future kernel integration. Overall, the Phase IIA effort successfully transitioned the project from architectural proof-of-concept to externally exercised, solver-integrated capability, while clarifying remaining technical challenges related to standardization, performance optimization, and kernel integration.

42 ENGINEERING↗
DOE OSTI2024

Assessment and validation of NEAMS tools for high-fidelity multiphysics transient modeling of microreactors: Application of NEAMS codes to perform multiphysics modeling analyses of micro-reactor concepts

The NEAMS Multiphysics Applications team aims at providing assessment of code useability and functionality for microreactor design and analyses, together with demonstration of their capabilities to properly capture the steady-state and time-dependent behavior of different microreactor concepts. In FY-24, significant progress was achieved in improving multi-physics models of several microreactors systems: HP-MR, GC-MR and KRUSTY. These efforts focused on solving more complex multiphysics problems enabled by enhanced tools capability, verifying and validating results obtained, providing feedback to developers for suggested improvements, and sharing these models to facilitate user training. A series of new multiphysics transients were completed on the HP-MR (using Griffin/BISON/Sockeye) with core startup transient, control drum inadvertent rotation accident, and hydrogen leakage from hydride moderator (also including SWIFT). On the GC-MR, a new full-core model was developed and analyzed through a series of new multiphysics (Griffin/BISON/SAM) transients to simulate moderator leakage (also including SWIFT), flow blockage and coolant depressurization. Additional and updated TRISO failure analyses were completed on the HP-MR unit-cell and GC-MR assembly models leveraging improved TRISO modeling capabilities. The amount of SiC failure following accidental transients at end-of-life was null. However, GC-MR assembly TRISO analysis highlighted Pd penetration rate can be problematic and may require design changes on the studied microreactor concept. The neutronics discrepancies observed on the KRUSTY model in previous years were resolved using hybrid set of Monte Carlo/Deterministic cross-sections. The multiphysics (Griffin neutronics / BISON thermal-mechanics) 15₵ insertion transient simulation displayed good agreement when comparing with experimental data. Initial modeling of the 30 ₵ reactivity insertion also displays promising results. Such close agreement provides important validation data that can be leveraged by the NEAMS program and by microreactor vendors to support licensing of their technology. Finally, important experience was gathered with the NEAMS tools leading to several user feedback shared with tools developers, especially with regards to MOOSE mesh generator and Griffin. This project led to many publications demonstrating modeling capabilities, and to three models shared on the Virtual Test Bed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Hydra: An AI-Based Framework for Interpretable and Portable Data Quality Monitoring

Hydra is an advanced framework designed for training and managing AI models for near real time data quality monitoring at Jefferson Lab. Deployed in all four experimental halls, Hydra has analyzed over 2 million images and has extended its capabilities to offline monitoring and validation. Hydra utilizes computer vision to continually analyze sets of images of monitoring plots generated 24/7 during experiments. Generally, these sets of images are produced at a rate and quantity that is exceedingly difficult for shift crews to effectively monitor. Significant effort has been devoted to enhancing Hydra’s user interface, to ensure that it provides clear, actionable insights for shift workers and other users. Gradient Weighted Class Activation Maps (GradCAM) provide added interpretability, allowing users to visualize important regions of the image for classification. Hydra has been containerized to enable the creation of portable demos and seamless integration with container-based technologies such as Kubernetes and Docker. With the user interface enhancements and containerization, Hydra can be rapidly deployed for new use cases and experiments. This talk will describe the Hydra framework, its user interface and experience, and the challenges inherent in its design and deployment.

Britton, Thomas [Thomas Jefferson National Acceler↗
DOE OSTI2026

Cosmological analysis of the DESI DR1 Lyα 1D power spectrum

We present the cosmological analysis of the one-dimensional Lyman-α flux power spectrum from the first data release of the Dark Energy Spectroscopic Instrument (DESI). We capture the dependence of the signal on cosmology and intergalactic medium physics using an emulator trained on a cosmological suite of hydrodynamical simulations, and we correct its predictions for the impact of astrophysical contaminants and systematics, many of these not considered in previous analyses. We employ this framework to constrain the amplitude and logarithmic slope of the linear matter power spectrum at k ★ = 0.009 km -1 s and redshift z = 3, obtaining Δ 2 ★ = 0.379 ± 0.032 and n ★ = -2.309 ± 0.019 https://github.com/igmhub/cobaya_lya_p1d. The robustness of these constraints is validated through the analysis of mocks and a large number of alternative data analysis variations, with cosmological parameters kept blinded throughout the validation process. We then combine our results with constraints from DESI BAO and temperature, polarization, and lensing measurements from Planck, ACT, and SPT-3G to set constraints on ΛCDM extensions. While our measurements do not significantly tighten the limits on the sum of neutrino masses from the combination of these probes, they sharpen the constraints on the effective number of relativistic species, N eff = 3.02 ± 0.10, the running of the spectral index, α s = 0.0014 ± 0.0041, and the running of the running, β s = -0.0006 ± 0.0048, by a factor of 1.18, 1.27, and 1.90, respectively. We conclude by outlining the improvements needed to fully reach the level of confidence implied by these uncertainties.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Synthesis of ARM User Facility Surface Rainfall Datasets to Construct a Best Estimate Value Added Product (PrecipBE)

Surface precipitation measurements are essential for Earth system model (ESM) evaluation and understanding cloud processes. An ever-growing need for robust, temporally evolving, and easy-to-use statistical datasets provides motivation for a baseline ground-based precipitation properties data product. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility operates an extensive suite of precipitation instruments with various sensitivities and operating mechanisms, which render the decision of which instrument to use based on one or more fixed thresholds challenging and prone to errors and bias. Using a long-term instrument inter-comparison from a unique per-precipitation event perspective, rather than instantaneous sample comparison, we demonstrate that ARM rainfall-measuring instruments are generally consistent with each other at the statistical level. Inter-instrument deviations at the single event level can be large, especially for specific rainfall event properties such as maximum precipitation rates. A machine-learning (ML) analysis using a random forest regressor indicates that in some cases, depending on instrument, local site climatology, and/or specific deployment configuration, certain atmospheric state variables influence the measured quantities in an unpredictable manner. Thus, a-priori weighting of different instruments does not necessarily lead to more accurate and less biased synthesis of instrument data. These results motivate the design of the ARM precipitation best-estimate (PrecipBE) value-added product, which incorporates all valid precipitation data while considering data quality and other instrument limitations. PrecipBE consists of time series and tabular statistics datasets in an easy-to-use and insightful per-precipitation event format. It provides a large set of precipitation event properties supplemented with ancillary data from ARM datasets that correspond to the detected precipitation events. We describe the PrecipBE algorithm and demonstrate its use via the examination of a single-day output as well as a long-term trend analysis of precipitation events at the ARM Southern Great Plains (SGP) site, covering more than 30 years of data. The trend analysis tentatively suggests a long-term temporal tendency for mainly shorter and less intense precipitation events at the SGP site, but a long-term increase in annual rainfall by more than 36 mm (5 %) per decade. This rainfall trend is catalyzed primarily by more extreme event properties of relatively rare, intense precipitation events, with event total and 1 min maximum precipitation rate at a 1 year timeframe increasing up to 5 mm and 9 mm h −1 (several percent) per decade, respectively. While the currently available PrecipBE datasets (at https://adc.arm.gov/discovery/, last access: 8 December 2025) cover rainfall from multiple ARM deployments up to March 2025, PrecipBE is planned to be expanded to include solid-phase precipitation and will soon become an operational product with a several-day lag from real-time. We invite the ARM user community to leverage this new product and welcome user feedback to further enhance the dataset.

Silber, Israel [Pacific Northwest National Laborat↗
DOE OSTI2026

A Centralized AI Lakehouse Framework for Brain Tumor MRI Classification and Segmentation, University KPI Forecasting, and Water Potability Prediction

In many university and healthcare projects, models are built for very different data types such as tables, institutional time series, and medical images, but they are deployed as separate applications. In this work, that separation made testing and maintenance difficult because each module had its own pipeline and runtime requirements. This paper presents an integrated AI lakehouse-style implementation that runs three model pipelines inside one containerized backend. For medical imaging, we used MRI datasets from IEEE DataPort: a four-class classification set with 7012 images (5708 train/1304 test) and a segmentation set with 3063 image–mask pairs. The classification model (ResNet50 transfer learning) is evaluated using a proper train–validation–test protocol across multiple splits (80/10/10, 70/10/20, 60/10/30, and 10/30/60), achieving a test accuracy of 99.00% under the standard 80/10/10 split. Additionally, a patient-level evaluation is conducted using an external glioma dataset to provide a more realistic assessment without data leakage. The segmentation model (DeepLabV3-ResNet50) achieved 83.09% validation mIoU and 88.79% Dice score. For university KPI forecasting, we used annual IPEDS and NSF HERD data from 2010 to 2023 for three universities (BSU, EOU, and UAB). To examine the effect of preprocessing on forecasting performance, two case studies are conducted. In the first case, linear interpolation is applied to generate semester-level data. In the second case, the original annual data is used directly without interpolation. Random Forest regression and ARIMA models are evaluated using MAE, RMSE, MAPE, and R 2 . The results showed that interpolation improved apparent forecasting performance due to smoothing, while evaluation on the original annual data provided a more realistic assessment of model behavior. To further validate the framework on a larger dataset, an additional case study is conducted using a student dropout dataset. For water potability, we trained and compared multiple tabular classifiers on a large dataset (1,048,575 samples). A Random Forest model (100 trees, max depth 10) achieved 85.86% test accuracy and high recall for unsafe samples (0.8447). All modules are served via FastAPI and deployed together using Docker, with workflow automation routing requests to the correct endpoint. System-level benchmarking indicates that the backend maintains stable throughput and latency under concurrent requests.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Design by analysis rules for ASME Section III, Division 5, Class B components

The current rules for elevated temperature ASME Section III, Division 5, Class B components, other than piping, were basically adapted from the Design-by-Rule approach for Section VIII, Division 1 vessels. A goal of the proposed new rules is to explicitly account for the design life and cyclic service while recognizing the less-rigorous requirements commensurate with lesser safety consideration. Further goals are to maximize the use of modern computational technology, for example finite element analysis in conjunction with reference stress concepts, and to avoid the need for stress classification. This paper summarizes the work done to address these issues. A summary of the design-by-analysis for primary loads, strain limit evaluation, and creep-fatigue damage assessment is presented. The proposed design-by-analysis creep-fatigue damage calculation approach uses a new elastic follow-up-based Isochronous Stress Strain Curve stress relaxation procedure. A set of sample problems are selected to validate the proposed design-by-analysis rules for creep-fatigue damage assessment. The proposed Class B rules are evaluated against the Class A elastic design rules and the experimental data obtained from a family of a simplified model test-based key-feature test results. The proposed Class B creep-fatigue damage assessment methodology yields conservative design cycles estimates compared to the experimental results.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A Climatology and Life‐Cycle Characteristics of Atmospheric Fronts and Their Associated Precipitation

Abstract Atmospheric fronts are one of the main sources of mid‐latitude variability. We employ a novel method for identifying and tracking fronts and frontal precipitation. Thermal and dynamical variables are used to identify fronts as areal objects in space, which are tracked in time using the open‐source TempestExtremes software package. Precipitation objects are co‐located to identify frontal precipitation. The method is subjected to validation and sensitivity tests using manually curated data from the National Weather Service. Climatologies of fronts and frontal precipitation are computed from reanalysis and observations; fronts are present upwards of 14% of the time in the storm tracks, and represent the majority (up to 90%) of total and extreme precipitation. Novel aspects of the method are showcased through the lifetime characteristics of fronts across North America. Three sets of warm and cold fronts were discovered, and their duration, distance‐traveled, and translation velocity are examined. Plain Language Summary Mid‐latitude low‐pressure systems and weather fronts are important for our day‐to‐day experience of weather events, particularly in the mid‐latitudes. This work makes use of standardized atmospheric data and creates a method of automatically tracking these important atmospheric features and their precipitation to quantify their relative role in global precipitation. Weather fronts are persistent in the mid‐latitudes and are associated with the majority of precipitation–particularly the most intense precipitation. Trajectories of fronts over North America are categorized to create a set of archetypal fronts that occur in that region. The differences between these types of fronts are characterized. Key Points An automated, efficient, and skillful frontal detection algorithm is developed and validated Fronts contribute a larger fraction of extreme precipitation than all precipitation in mid‐latitude storm tracks Fronts across North America have substantial variation in characteristics depending on their origin location

extratropical cyclone↗
DOE OSTI2026

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC

A novel solution is presented for the problem of estimating the backgrounds of a signal search using observed data while simultaneously maximizing the sensitivity of the search to the signal. The 'ABCD method' provides a reliable framework for background estimation by partitioning events into one signal-enhanced region (A) and three background-enhanced control regions (B, C, and D) via two smoothly varying, statistically independent variables. In practice, even slight correlations between the two variables can significantly undermine the method's performance. Thus, choosing appropriate variables by hand can present a formidable challenge, especially when background and signal differ only subtly. To address this issue, the ABCD with distance correlation (ABCDisCo) method was developed to construct two learned variables via a neural network trained to provide strong signal-background discrimination with small values of the distance correlation (DisCo) measure between the two learned variables. However, relying solely on minimizing the DisCo can result in learned variables that may not have distributions of background events that are smoothly varying and localized at extreme values, as necessary for the validity of the background estimation. The ABCDisCo training enhanced with closure (ABCDisCoTEC) method is introduced to solve this issue by directly minimizing the nonclosure, expressed as a dedicated differentiable loss term. This extended method is applied to a data set of proton-proton collisions at a center-of-mass energy of 13 TeV recorded by the CMS detector at the CERN Large Hadron Collider. Additionally, given the complexity of the minimization problem with constraints on multiple loss terms, the modified differential method of multipliers is applied and shown to greatly improve the stability and robustness of the ABCDisCoTEC method, compared to grid search hyperparameter optimization procedures.

Hayrapetyan, Aram [Yerevan Phys. Inst.]↗
DOE OSTI2025

Strategies for community-sourced biocuration in bioinformatics: a case study on MIBiG 4.0

Biocuration is essential to transform molecular sequence data into standardized, machine-readable resources. Such curated datasets enable comparative analysis, predictive modeling, and data integration across bioinformatics platforms. While professional biocuration is resource-intensive and usually limited to institutional settings, community-driven approaches can mobilize large-scale annotation of specialized datasets and are more resilient to disruptions in scientific funding. Here, we present a model for community-powered curation applied to the Minimum Information about a Biosynthetic Gene Cluster (MIBiG) repository. Through a framework of workflows for metadata capture, annotation validation, and contributor coordination, the MIBiG 4.0 initiative recruited 267 scientists across 178 institutions from 33 countries, volunteering an estimated 4000 h of work. These efforts expanded the MIBiG repository by 22% and enhanced its usability in downstream molecular data analyses in comparative genomic analyses, natural product discovery, and machine learning applications. We provide strategies and actionable lessons for adopting this model, supporting the sustainability of curated bioinformatics resources central to nucleic acid research and related fields.

biocuration↗
DOE OSTI2024

Developing Asparagaceae1726: An Asparagaceae‐specific probe set targeting 1726 loci for Hyb‐Seq and phylogenomics in the family

Abstract Premise Target sequence capture (Hyb‐Seq) is a cost‐effective sequencing strategy that employs RNA probes to enrich for specific genomic sequences. By targeting conserved low‐copy orthologs, Hyb‐Seq enables efficient phylogenomic investigations. Here, we present Asparagaceae1726—a Hyb‐Seq probe set targeting 1726 low‐copy nuclear genes for phylogenomics in the angiosperm family Asparagaceae—which will aid the often‐challenging delineation and resolution of evolutionary relationships within Asparagaceae. Methods Here we describe and validate the Asparagaceae1726 probe set (https://github.com/bentzpc/Asparagaceae1726) in six of the seven subfamilies of Asparagaceae. We perform phylogenomic analyses with these 1726 loci and evaluate how inclusion of paralogs and bycatch plastome sequences can enhance phylogenomic inference with target‐enriched data sets. Results We recovered at least 82% of target orthologs from all sampled taxa, and phylogenomic analyses resulted in strong support for all subfamilial relationships. Additionally, topology and branch support were congruent between analyses with and without inclusion of target paralogs, suggesting that paralogs had limited effect on phylogenomic inference. Discussion Asparagaceae1726 is effective across the family and enables the generation of robust data sets for phylogenomics of any Asparagaceae taxon. Asparagaceae1726 establishes a standardized set of loci for phylogenomic analysis in Asparagaceae, which we hope will be widely used for extensible and reproducible investigations of diversification in the family.

Plant Sciences↗
DOE OSTI2024

The NASA ISRO SAR (NISAR) Mission - Validation of Science Measurement Requirements

The NASA ISRO Synthetic Aperture Radar (NISAR) is scheduled for launch early in 2024 from the Satish Dhawan Space Centre (SDSC), at Sriharikota, near Chennai, India. This mission is the result of a collaboration between NASA and Indian Space Research Organization (ISRO), where NASA has contributed elements of the mission such as an L-band SAR, and ISRO has contributed other elements, such as an S-band SAR. After successful launch, the NISAR mission will collect left-looking L-band SAR data over most of the Earth’s land areas twice during every 12-day exact repeat orbit. (once while in an ascending orbit direction and once while in a descending orbit direction). NASA and ISRO have individual and joint requirements on the mission that include the performance of the imaging radars onboard the spacecraft. For example, NASA must demonstrate that this L-band SAR will achieve a set of identified science measurement accuracy requirements that span Ecosystem science, Solid Earth science, and Cryosphere science disciplines. Likewise, ISRO has several applications objectives on both the L-band and S-band data from NISAR that the ISRO science team and project will be developing and testing. Pre-launch and post-launch activities have been planned to validate that these requirements are met. Here, we will discuss how the NASA plans are being executed and will present any initial results at the conference.

Chapman, Bruce↗
DOE OSTI2026

Statistics of base polytopes in F-theory

We propose a new statistical ensemble of toric bases for elliptic Calabi-Yaus used in F-theory models, by focusing on only the convex hull of the base, i.e., the base polytope. This physically motivated coarse-graining greatly simplifies the combinatorial complexity of the part of the 4d F-theory landscape with toric bases. We develop a Monte Carlo approach that randomly samples the base polytopes within fixed boxes, with proper statistical weights. We first apply the algorithm to the set of 2d base polytopes, generating an enlarged set of toric 2d bases that include certain types of codimension-two (4,6) points, and we validate our approach against exact numbers. We then explore the set of 3d base polytopes which fit in a set of “maximal” 3d boxes, and estimate the total number of inequivalent 3d base polytopes to be 10 85 –10 90 . We provide statistical data such as the distribution of non-Higgsable gauge groups on these bases. Amusingly, a similar method can also be applied to generate reflexive polytopes in various dimensions. In both the reflexive and base polytope cases, the number of relevant polytopes obeys a Gaussian distribution as a function of the number of vertices, which can be understood in terms of other results on random polytopes in the math literature.

Differential and algebraic geometry↗
DOE OSTI2024

G EANT 4 atomic relaxation data for transfermium nuclei (Z = 101–104)

Advanced theoretical methods can accurately calculate various atomic observables and predict electronic structure. Still, systematic computations of the radiative and non-radiative transition probabilities and energies are missing for the actinides and all the transfermium elements. However, these compilations are needed for comprehensive Monte-Carlo simulations (such as GEANT4) of the radioactive decay of transfermium nuclei. These simulations can forma basis for data analysis of experiments, especially with complex detection setups. Investigation of the transfermium nuclei is crucial for understanding the nature of the nuclear force. In this study, simulations based on data from the Jena Atomic Calculator (JAC) and the data from the Evaluated Atomic Data Library (EADL) present in GEANT4 were found compatible for the three elements Ba(Z = 56), U(Z = 92), and Fm(Z = 100), thus, validating the JAC calculations. For Z> 100, we also found sound agreement between simulations that used data generated with JAC and experimental results involving No(Z = 102) and Rf(Z = 104) isotopes. In conclusion, these results demonstrate that JAC can produce reliable atomic data sets for transfermium elements, which will assist in analyzing nuclear-decay-spectroscopy experiments.

GEANT4↗
DOE OSTI2025

Validation and demonstration of the AEFC as a practical safeguards tool for inventory verification

The Advanced Experimental Fuel Counter (AEFC) is a nondestructive assay (NDA) instrument designed to determine the residual fissile mass in irradiated fuel assemblies for safeguards verification purposes. This is done by actively interrogating an assembly with a neutron source and measuring the total (Singles) and correlated (Doubles) neutron count rates resulting from induced fissions in the irradiated nuclear fuel and relating those rates to the residual fissile mass using calibration curves. Comprehensive NDA measurements of the irradiated fuel inventory at Israeli Research Reactor 1 (IRR-1) were taken with the AEFC to validate a set of previously developed calibration curves. During the campaign, measurements were acquired of 32 standard fuel assemblies and three control assemblies in just nine days. This is a significant majority of the research reactor's irradiated fuel inventory and the largest data set gathered by the AEFC to date. Many of the fuel assemblies measured during the campaign had much shorter cooling times than those assemblies previously measured with the instrument. Calibration curves developed from previous AEFC deployments were used to determine the residual 235 U mass in the measured standard fuel assemblies. The results of the campaign demonstrated that the AEFC can be used to estimate the 235 U mass remaining in a large number of irradiated fuel assemblies with 1%–5% uncertainty in a reasonable amount of time despite operating in a high dose environment.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2026

Application of artificial intelligence methods in the international roughness index prediction of rigid and composite pavements: a systematic review

The International Roughness Index (IRI) is a widely adopted metric for quantifying pavement roughness, directly influencing vehicle safety, ride comfort, and overall roadway performance. In recent years, the use of Machine Learning (ML) models for IRI prediction has gained momentum, with the goal of improving the allocation of maintenance and rehabilitation resources by enabling accurate assessments of pavement conditions. Most prior reviews, however, have concentrated on flexible pavements, leaving a notable gap regarding rigid and composite pavements. To address this gap, the present study conducts a systematic review of Artificial Intelligence (AI) methods applied to IRI prediction for rigid and composite pavements. Literature published between 2004 and 2025 is synthesized to highlight prevailing trends, methodological contributions, and directions for future research. Particular attention is given to the types of models employed, the datasets used for training and validation, and the role of input variables and data-processing strategies. Across the included studies, ensemble learning methods (especially gradient boosting variants such as XGBoost), artificial neural networks, and hybrid architectures frequently achieved high predictive skill, with several models reporting test-set coefficients of determination approaching 0.9–0.96, indicating strong potential for capturing the influence of traffic, pavement structure, and climatic factors. Since these results are obtained from heterogeneous datasets and evaluation protocols, they are interpreted qualitatively rather than as strict cross-study rankings. Analysis of input variables revealed that pavement age and initial IRI were included in 91% (21 of 23) and 78% (18 of 23) of studies, respectively. Climatic variables such as the freezing index appeared in 57% (13 of 23), while traffic-related factors were considered in 65% (15 of 23). The findings underscore the importance of standardized, high-quality datasets, such as those from the Long-Term Pavement Performance (LTPP) program, along with data consistency, model interpretability, computational efficiency, and replicability in enhancing IRI prediction. Future research should focus on incorporating input variable selection techniques to identify the most influential predictors, thereby improving accuracy and robustness. Integrating these approaches with advanced non-linear data-driven models, coupled with robust hyperparameter optimization, holds considerable promise for strengthening the reliability of IRI prediction and supporting resilient pavement management strategies.

42 ENGINEERING↗