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DOE OSTI2024

Super Resolution for Renewable Energy Resource Data With Wind From Reanalysis Data (Sup3rWind) and Application to Ukraine [Slides]

In this work we present a novel deep learning-based downscaling method, using generative adversarial networks (GANs), for generating high-resolution wind resource data from ECMWF Reanalysis v5 data (ERA5). We show that by training a GAN model on ERA5, as opposed to coarsened high-resolution data, we achieve results that are competitive with conventional dynamical downscaling. This GAN-based downscaling method additionally reduces computational costs over dynamical downscaling by two orders of magnitude. All GANs are trained on data sampled from CONUS, selected to provide a diverse sampling of terrain conditions, and validated on observational data along with data held out from training. This cross-validation shows low error and high correlations with observations and excellent agreement with hold out data across physical distributions. Our approach is finally used to downscale 30km hourly ERA5 to 2-km 5-minute wind data, for January 2000 through December 2023, at multiple hub heights, over Ukraine, Moldova, and part of Romania. Comparisons against observational data from Meteorological Assimilation Data Ingest System (MADIS) and multiple wind farms show the same level of performance as for CONUS validation. This 24 year data record is the first member of the "super resolution for renewable energy resource data with wind from reanalysis data" dataset (Sup3rWind).

17 WIND ENERGY↗

DOE OSTI2025

Benchmark Exercise Report for Experimental Study of Bubble Scrubbing in Sodium Pool

Mechanistic source term (MST) analyses are likely to be an important part of advanced reactor licensing applications. For the purpose, an MST analysis code applicable to newly introduced advanced reactors, called SRT (Simplified Radionuclide Transport) code, has been developed by Argonne National Laboratory. SRT can track overall behaviors of radionuclides especially in metal fuel-based sodium fast reactors (SFRs) and microreactors. Throughout the simulation, migration inside fuel pins before failure, interaction with coolant (for SFR), removal/leakage in cover gas and containment (or confinement), and environmental dose impacts are considered alongside radioactive decay for short-lived nuclides. Among the postulated process, pool scrubbing phenomenon, especially under sodium pool condition, has been identified as high importance with limited supportive data. The phenomenon plays a crucial role in assessing the degree of radiological impacts as radioactive aerosols or vapors are efficiently and effectively removed during the process. To provide validation basis for SRT in assessing pool scrubbing performance inside sodium pools, the University of Wisconsin-Madison performed tests including extensive parametric effects. Separate effect tests were conducted to directly evaluate the SRT models and to estimate degree of contribution by each contributing factor. Specifically, bubble size, aerosol size, aerosol density, aerosol concentration, pool depth, system temperature, and bubble swarm effects were considered. According to the parametric effects, decontamination performance enhances with decreasing bubble size, large density, and deeper pool height. Aerosol concentration provides no effect for the whole range of interest, and pool temperature variation shows minor effects under the considered temperature condition. When multiple bubbles are injected generating a bubble swarm condition, DF performance further enhances by bubble interactions and turbulence characteristics. The measurement shows the exceptional importance of aerosol size range considered, with the lowest decontamination, where most radionuclides are expected to escape.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Field Emission Analysis in SRF Cavities for PIP-II Using GEANT4

Field emission (FE) remains a key limitation to achieving optimal performance and reliability in superconducting radiofrequency (SRF) cavities used in accelerator cryomodules. Addressing this challenge requires a detailed understanding of the generation and transport of FE-induced radiation; however, the absence of standardized measurement protocols complicates comparisons across different testing stages and facilities. This work presents initial steps toward a quantitative, systematic framework for analyzing FE-induced radiation using Geant4 simulations. We model four cavity types to be deployed in Fermilab’s Proton Improvement Plan-II (PIP-II) linac - HB650, LB650, SSR2, and SSR1 - to investigate radiation signatures and their dependence on emission conditions. By integrating simulation results with radiation diagnostics, our goal is to identify probable emission sites, enhance diagnostic accuracy, and optimize detector placement. Ultimately, this approach seeks to inform cavity preparation, assembly, and testing procedures, enabling the delivery of FE-free SRF cryomodules for PIP-II.

Wijethunga, Sajini [Fermilab] (ORCID:0000000287659↗

DOE OSTI2026

Field Emission Analysis in SRF Cavities for PIP-II Using GEANT4

Wijethunga, Sajini [Fermilab] (ORCID:0000000287659↗

DOE OSTI2024

Experimental and Numerical Characterization of High Temperature Deformation Behavior of 347H Stainless Steel

This study investigates how temperature affects the plasticity and thermal creep behavior of 347H stainless steel under uniaxial tension. The research combined experimental testing with advanced computational modeling. Two types of experiments were conducted: uniaxial tensile tests at temperatures from 100°C to 750°C using strain rates of ~10⁻⁴ s⁻¹, and creep tests at temperatures between 600°C and 750°C under various stress levels. These experimental results were used to develop and validate a new integrated mechanistic model that can predict material behavior under any loading condition while accounting for both stress and temperature effects. The model was implemented using a polycrystalline microstructure simulation framework based on elasto-viscoplastic Fast Fourier Transform (EVPFFT). It incorporates three key deformation mechanisms: thermally activated dislocation glide, dislocation climb, and vacancy diffusional creep. The model accounts for internal stress distribution within single crystals and considers how precipitates and solute atoms (both interstitial and substitutional) affect dislocation movement. After validation against experimental data, the model was used to generate Ashby-Weertman deformation mechanism maps for 347H steel, providing new insights into how microstructure influences the activation of different creep mechanisms.

Joy, Jobin K.↗

DOE OSTI2024

Near-Field Passive Wireless Sensor for High-Temperature Metal Corrosion Monitoring

This work focuses on the fabrication and evaluation of a passive wireless sensor for the monitoring of the temperature and corrosion of a metal material at high temperatures. An inductor–capacitor (LC) resonator sensor was fabricated through the screen printing of Ag-based inks on dense polycrystalline Al 2 O 3 substrates. The LC design was modeled using the ANSYS HFSS modeling package, with the LC passive wireless sensors operating at frequencies from 70 to 100 MHz. The wireless response of the LC was interrogated and received by a radio frequency signal generator and spectrum analyzer at temperatures from 50 to 800 °C in real time. The corrosion kinetics of the Cu 110 was characterized through thermogravimetric (TGA) analysis and microscopy images, and the oxide thickness growth was then correlated to the wireless sensor signal under isothermal conditions at 800 °C. The results showed that the wireless signal was consistent with the corrosion kinetics and temperature, indicating that these two characteristics can be further deconvoluted in the future. In addition, the sensor also showed a magnitude- and frequency-dependent response to crack/spallation events in the oxide corrosion layer, permitting the in situ wireless identification of these catastrophic events on the metal surface at high temperatures.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Prediction of electric and magnetic fields from spectral data using machine learning algorithms for Doppler-free saturation spectroscopy diagnostics

The prediction of electric and magnetic field amplitudes from atomic spectral data is critical for plasma control in fusion devices such as tokamaks. Conventional approaches that rely on physics-based models are computationally expensive and unsuitable for real-time applications. In this work, we develop and benchmark three machine learning algorithms—simulation-based inference (SBI), fully connected neural networks (FCNN), and histogram-based gradient boosting regression (GBR-Hist)—to infer field intensities directly from Doppler-free saturation spectroscopy (DFSS) spectra. Synthetic datasets of spectra were generated using the EZSSS code and evaluated both with and without added Poisson noise to mimic experimental conditions. We find that SBI achieves the highest accuracy and robustness, FCNN provides a strong balance of accuracy and computational efficiency for real-time applications, and GBR-Hist offers the fastest inference but is more sensitive to noise. Furthermore, these results demonstrate the potential of machine learning to accelerate DFSS analysis and enhance its utility for plasma diagnostics and control.

Doppler-free saturation spectroscopy↗

DOE OSTI2024

Impact of High-Reactivity Advanced Test Reactor Experiments on Photon Heating in Nearby Experiment Locations

The Advanced Test Reactor’s (ATR’s) distinctive ability to provide a wide range of irradiation conditions is attractive for programs pursuing fuel qualification experiments. These potentially high-fuel-load experiments are a relatively new development and produce unexplored effects on nearby experiments. Here, this paper explores how photon heating of such an experiment may affect other nearby experiment programs, ultimately serving to better inform decisions regarding experiment design and risks to programmatic goals. The MC21 (Monte Carlo for the 21st Century) code is used to model and study how gamma heat generation rates and axial effects impact different ATR positions. The results reveal that the proximity of a given experiment’s position to the high-fuel-load one can significantly alter that experiment’s expected axial profile.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

CLM5 Simulations of Soil Moisture and Gross Primary Productivity for CONUS at 0.125 degrees

This dataset provides 0.125-degree gridded simulations of soil moisture and gross primary productivity (GPP) for the Contiguous United States (CONUS), generated using the Community Land Model version 5 (CLM5) with the biogeochemistry module enabled. The data covers a historical baseline (1980-2015) and mid-century future projections (2020-2055). Future projections are organized into two sets of scenarios to distinguish the impacts of different drivers: (1) Atmospheric Only (ATM): These scenarios apply future atmospheric forcings while holding land use and land cover (LULC) at historical baseline levels. The atmospheric forcings represent moderately versus severely hotter/drier atmospheric conditions (dynamically downscaled perturbed thermodynamics simulations based on CMIP6 SSP245 and SSP585 warming signals), each with cooler versus hotter Earth System Model temperature sensitivity instantiations. These scenarios are identified in the folder names as rcp45_cooler_near, rcp45_hotter_near, rcp85_cooler_near, and rcp85_hotter_near. (2) Coupled Atmospheric and Land-Use (LAND+ATM): These scenarios apply future atmospheric forcing together with future LULC by pairing atmospheric pathways with lower versus higher population/economic growth scenarios representing Shared Socioeconomic Pathways 3 and 5 (SSP3 and SSP5). These scenarios are identified in the file names as ssp3_rcp45_cooler_near, ssp3_rcp45_hotter_near, ssp5_rcp85_cooler_near, and ssp5_rcp85_hotter_near. Please refer to the "README_first.md" file for detailed information on file structure, variables, units, and data formats.

drought↗

DOE OSTI2025

Tactical Analysis for Calculating Contextual Risk at Boundaries: Summary of Laboratory Directed Research & Development Effort

The Tactical Analysis for Calculating Contextual Risk at Boundaries (TACCRAB) tool is an innovative digital twin (DT) platform and automated risk algorithm designed to transform operational decision-making in structured screening environments, with an initial focus on Southern Border Land Ports of Entry (POEs). The invention provides integration points for advanced artificial intelligence, predictive modeling, and real-time data analysis to produce a comprehensive risk management tool that enables proactive, data-informed security strategies. The core inventive features of TACCRAB center on its unique risk algorithm, which dynamically calculates contextual risk by synthesizing historical data, near real-time streaming data from the checkpoints themselves, and AI-generated predictions. Unlike traditional risk assessment methods, TACCRAB utilizes a DT to provide comprehensive operational insights, allowing stakeholders to visualize, simulate, and optimize checkpoint configurations with unprecedented speed and contextual awareness. TACCRAB's key innovation lies in its ability to combine multiple complex inputs - including technology detection probabilities, resource availability, screening pathway characteristics, and threat actor behavioral patterns - into a unified risk calculation and update these inputs based on changing operational and environmental conditions. By leveraging a DT that continuously updates and learns from linked data, TACCRAB can suggest adaptive mitigation strategies that minimize risk while maintaining operational efficiency. Particularly novel is the platform's approach to decision support, which goes beyond static risk assessment. The DT provides dynamic metrics such as wait times, resource allocation effectiveness, and potential emerging threat scenarios, enabling users to view sophisticated, relevant what-if simulations and optimize checkpoint operations in near real-time. The system's architecture allows for generalized application across different screening environments, such as secure facilities, ports of entry, and soft targets, making it a versatile tool for security and operational management. The invention distinguishes itself through its comprehensive integration of predictive modeling, AI-driven pattern discovery, and user-friendly interface design. By combining these elements, TACCRAB transforms complex risk data into actionable insights, supporting decision-makers at various organizational levels - from booth agents making split-second screening decisions to checkpoint managers optimizing the day's resource allocation to strategic planners managing long-term investments.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2026

Numerical Simulation of a Natural Convection–Driven Air-Cooled Reactor Cavity Cooling System Experiment

Ensuring the efficient removal of decay heat from the reactor vessel is essential for the safety of advanced reactor technologies. Several Generation-IV concepts incorporate variations in the reactor vessel cooling systems to achieve this objective. High-temperature gas-cooled reactors utilize a reactor cavity cooling system (RCCS), a passive ex-vessel system designed to operate without active components or external power during accident conditions. The RCCS removes decay heat primarily through radiative and convective heat transfer mechanisms. Here, this study presents a comprehensive validation of a computational fluid dynamics Reynolds-averaged Navier-Stokes model for the University of Wisconsin-Madison air-cooled RCCS facility. Validation was conducted for both high- and low-power natural convection cases under a uniform heating profile. Near-wall resolution was found to be critical for accurately modeling natural convection in the RCCS; employing an all-𝑦 + wall treatment resulted in wall temperature discrepancies exceeding 50 °⁢𝐶 compared to a wall-resolved mesh. Thermal-hydraulic behaviors under natural and forced convection conditions were compared within the heated cavity and RCCS. A turbulence model sensitivity analysis indicated that low-Reynolds number k-ɛ, k-ω shear stress transport (SST), and Reynolds stress transport models produce similar wall temperature predictions. A buoyancy modeling sensitivity study revealed that the Boussinesq approximation significantly underpredicted thermal-hydraulic behavior in the RCCS. Based on these findings, modeling recommendations are provided. The validated data set along with identified sensitivities refine the modeling of natural convection in the RCCS. The information produced by this study supports RCCS design, optimization, and safety evaluations, enabling the calibration and verification of reduced-order thermal-hydraulic models.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

De novo Materials Design of Catalytic Surface Motifs for Water-Gas-Shift (Final Progress Report DOE Grant DE-SC0019281)

This project was aimed at developing innovative theoretical methods and models to understand essential catalysis-relevant issues such as CO 2 conversion, fuel cells, and lithium batteries. Through collaborations with experimentalists, we strive to develop new quantum and machine learning methods for the understanding of surface and interfacial chemistry that can empower the design of energy and sustainability systems. Overall, our work under this grant brings cross-disciplinary insights into catalytic materials, microenvironments, and other conditions, which can serve to provide design rules for the next generation of catalysts.

25 ENERGY STORAGE↗

DOE OSTI2026

Microscopic theory of angular momentum distributions across the full range of fission fragments

Modern nuclear theory provides qualitative insights into the fundamental mechanisms of nuclear fission and is increasingly capable of making reliable quantitative predictions. Most quantities of interest pertain to the primary fission fragments, whose subsequent decay is typically modeled using statistical reaction models. Consequently, a key objective of fission theory is to inform these models by predicting the initial conditions of the primary fragments. Here, in this work, we employ a framework that combines joint angular momentum and particle number projection with time-dependent configuration mixing to calculate the angular momentum distributions of primary fragments. Focusing on the benchmark cases of neutron-induced fission of 235 U and 239 Pu , we predict—for the first time—microscopic angular momentum distributions for all fragments observed in experiments. Our results reveal a pronounced sawtooth pattern in the average angular momentum as a function of fragment mass, consistent with recent measurements. Additionally, we observe substantial variations in angular momentum distributions along isobaric chains, indicating that commonly used empirical formulas lack sufficient accuracy. We also quantify a strong correlation between the angular momentum and the deformation of the fragments at scission, and a weak correlation in the magnitude of the angular momentum between fragment partners. The generated data will enable estimation of the impact of microscopic distributions on fission spectra, paving the way toward fission modeling based on microscopic inputs.

Physics - Nuclear physics and radiation physics↗

DOE OSTI2026

Hourly PM 2.5 Estimates across California from 2018 to 2023

This study presents a new data set of hourly PM 2.5 concentrations across California from 2018 to 2023 at a three-kilometer resolution. This data set was developed by assimilating observations from PurpleAir and the U.S. EPA Air Quality System monitors into wildfire smoke forecasts from the High-Resolution Rapid Refresh Smoke (HRRR-Smoke) model using the Gridpoint Statistical Interpolation (GSI) three-dimensional variational data assimilation framework. Archived forecasts of modeled wildfire smoke PM 2.5 from HRRR-Smoke create the background field for assimilation, which is then corrected using surface observations of total PM 2.5 . The resulting reanalysis from GSI provides an estimate of total PM 2.5 that minimizes error from both the observational and the model data. Validation results indicate strong performance, with monthly R 2 values ranging from 0.73 to 0.91 across the six-year data set, comparable to other PM 2.5 data sets. Case studies are presented for three major fire events, the 2018 Camp Fire, 2019 Kincade Fire, and 2020 Lightning Complex Fires to demonstrate the data set’s fidelity in resolving plume dynamics and local exposure patterns. Root-mean-squared error averaged over each month scales with average PM 2.5 concentrations, resulting in a low error under typical conditions but higher absolute errors during extreme smoke events. This is the first long-term, hourly PM 2.5 data set of its kind for California and enables the generation of subdaily exposure metrics, such as peak hourly concentrations, exceedance durations, and time-of-day exposure peaks. The novelty and strong validation of this data set make it a compelling resource for future studies on the impact and significance of subdaily PM 2.5 exposure.

PM2.5↗

DOE OSTI2026

A single-zone zero-dimensional study of HCCI combustion of methanol dehydration products to enable ignition of direct-injected methanol

Methanol is an alternative fuel gaining traction in the maritime sector. Its direct adoption, however, is accompanied by a unique set of technical challenges, such as low cetane number and high latent heat of vaporization. An approach to overcome these challenges is being developed at the US Department of Energy’s Oak Ridge National Laboratory, where onboard generation of dimethyl ether (DME) via catalytic dehydration of methanol can be used to assist in the mixing controlled combustion of direct-injected (DI) methanol. The generated mixture from this dehydration process can be premixed with intake air to condition the cylinder via. homogeneous charge compression ignition (HCCI) for subsequent DI methanol. In this preliminary work, various catalyst or reactor conversion efficiencies were simulated (using bottles) at constant DME and water flow at low load on a single-cylinder marine-variant of a CAT® C18 18 L engine with a 145 mm bore. To substantiate the experimental findings, a zero-dimensional engine model was developed in Cantera using a DME mechanism with 79 species and 658 reactions. Results presented include experimental and simulation heat release rate comparisons, species evolution information, and constant volume ignition delay (ID) for DI methanol with and without background species from HCCI of the premixed products from different reactor efficiencies. The results suggest that thermal effects dominate the DI methanol ignition process, and this work provides a chemical kinetic foundation or guideline for developing future control schemes.

Tyrewala, Daanish [ORNL] (ORCID:0000000208599324)↗

DOE OSTI2024

Spectroscopic Measurements and Models of Energy Deposition in the Substrate of Quantum Circuits by Natural Ionizing Radiation

Naturally occurring background radiation is a potential source of correlated decoherence events in superconducting qubits that will challenge error-correction schemes. In order to characterize the radiation environment in an unshielded laboratory representative of superconducting qubits’ environments, we performed broadband, spectroscopic measurements of background radiation events inside a millikelvin refrigerator. The spectrometer was designed to mimic the size and composition of a quantum circuit. Specifically, we measured the background radiation spectra in silicon substrates of two thicknesses, 500 and 1500 µm, and one area, 25 mm 2 . The observed spectra span energies from a few kilo-electron-volts up to nearly 10 MeV, are nearly featureless, and decrease in intensity by a factor of 40 000 between 100 keV and 3 MeV for the 500-µm substrate. We integrate the spectra to obtain the average event rates and deposited power levels. These quantities correspond to a rate of 0.023 events per second and a power of 4.9 keV s -1 , when counting events that deposit at least 40 keV for the 500-µm-thick substrate. We find that the cryogenic measurements are in good agreement with predictions based on simple measurements of the terrestrial gamma-ray flux outside the refrigerator, published models of cosmic-ray fluxes, a crude model of the cryostat, and radiation-transport simulations. This model requires no free parameters to predict the background radiation spectra in the silicon substrates. The agreement between measurements and predictions demonstrates that the model we present can be used to assess the relative contributions of terrestrial and cosmic-ray sources to background radiation interactions in silicon substrates of varying thickness. These spectroscopic measurements are performed with a novel combination of superconducting microresonators located on micromachined silicon islands that define the interaction volume with background radiation. The resonators transduce deposited energy to a readily detectable electrical signal. Microresonator readout closely resembles dispersive superconducting qubit readout, so similar devices—with or without micromachined islands—are suitable for integration with superconducting quantum circuits as detectors for background radiation events. For our specific laboratory conditions, we find that gamma-ray emissions from radioisotopes are responsible for the majority of events that deposit E < 1 ⁢Me⁢V. We present results demonstrating that the background radiation spectrum contains relevant contributions from cosmic-ray particles other than muons, particularly a tail of multi-mega-electron-volt events due to protons and neutrons. These observations suggest several paths to reducing the impact of background radiation on quantum circuits, supported by an empirically validated model for generating reliable predictions of radiation interactions with silicon substrates.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Next Generation Heat Transfer Fluids for Two-Phase Immersion Cooling of Data Centers

The purpose of this study is to evaluate the performance of next generation dielectric fluids in a Two-Phase Immersion Cooling (2PIC) system, which was designed for use in data centers. Hence, this report contains the performance evaluations of a new developmental dielectric fluid, Opteon™ 2P50, in a commercially available small-scale 2PIC system under typical and off-design range of operating conditions. Accordingly, ambient temperature and thermal loads were varied to simulate different ambient conditions. Additionally, this research report describes the development of a semi-empirical lumped model to predict the energy efficiency of the 2PIC system using Opteon™ 2P50 across a wide range of conditions. The model aims to offer a comprehensive understanding of the system’s efficiency and potential improvements. The outcomes of this study are expected to contribute to the adoption of sustainable 2PIC cooling technologies in data centers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

MCOR User Guide

User guide for the MCOR software package which is currently publicly hosted on Github (https://github.com/pnnl/MCOR). The Microgrid Component Optimization for Resilience (MCOR) tool simulates the operation of a renewable energy, battery, and back-up generator microgrid under a large range of outage conditions to understand how a potential system can meet the resilience goals of a particular site. It is an open-source, command line, Python-based tool that produces an output Excel spreadsheet as well as several types of plots to enable a user to compare different microgrid system sizes and costs. It is intended for high-level system planning and opportunity identification, and not for detailed electric system modeling and design. The tool includes a range of input parameters that can be adjusted or tuned to provide a more custom analysis as needed.

24 POWER TRANSMISSION AND DISTRIBUTION↗