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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Initial testing of Alfvén eigenmode feedback control with machine-learning observers on DIII-D

A first of its kind fully data-driven system has been developed and implemented into the DIII-D plasma control system to detect and control Alfvén eigenmodes (AE) in real-time. Susceptibility to fast ion-induced AE is a challenge in fully non-inductive tokamak operation, which significantly reduces fast-particle confinement and results in degraded fusion gain. Controlling AEs in real-time to improve fast-ion confinement is, hence, important for future advanced tokamak fusion reactors. The models were implemented and tested in experiments which showed that neural networks (NN) are highly effective in detecting 5 types of AE (BAE, EAE, LFM, RSAE, TAE) using high resolution ECE. To estimate the neutron deficit, a NN has been trained that outputs the classical neutron rate using similar inputs to NUBEAM. Also a preliminary ML-based proportional control has been designed and gone through initial testing in experiment to use feedback-control on the neutral beam power to achieve desired amplitude of AE modes and neutron deficits. The effect of AEs on fast-ion confinement is measured by analysing the gap in classical neutron rate from the proposed NN-based NUBEAM and the measured neutron rate.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Integrated Neutronics Modeling for Inertial Fusion Energy Systems: Development and Application to LD-FIRST

Lawrence Livermore National Laboratory (LLNL) is proposing a new Laser Driven Fusion Integration Research and Science Test Facility (LD-FIRST) with the goal of providing an experimental testbed for future Inertial Fusion Energy (IFE) systems. However, IFE systems require detailed and accurate multiphysics modeling to quantify material damage, thermal loading, and tritium breeding within complex chamber environments. This article presents the first step in an integrated multiphysics framework that couples meshed CAD-based geometry within Monte Carlo neutronic simulations to enable high-fidelity analysis of IFE chamber concepts, with future coupling to external codes. The neutronics workflow utilizes OpenMC and its third-party capability to use CAD-based geometries through DAGMC and tally on unstructured meshes with Libmesh to evaluate neutron transport behavior, geometric fidelity, and material performance under reactor-relevant conditions. The use of tailored tallies on unstructured meshes in this framework allows direct transfer without interpolating to CFD simulation tools. Two IFE chambers were evaluated, both conceived by LLNL: HYLIFE-II and Laser IFE (LIFE). This work produced high-fidelity conformal surface and volumetric meshes of the HYLIFE-II and LIFE chambers with mapped spatial insight into material damage, thermal loading, and tritium breeding. The HYLIFE-II model was built utilizing available resources and used as a test case to verify that the neutronics framework can handle complex geometries. The LIFE chamber CAD was provided by LLNL and was the main focus of this work. This work analyzes multiple ternary alloy breeding materials for the LIFE chamber, across different 6 Li enrichments to produce data relevant to the LD-FIRST project. This work also investigates the level of model fidelity for the LIFE chamber, and results show that inclusion of detailed first wall and coolant structures increased the predicted tritium breeding ratio (TBR) by ~30%, highlighting the sensitivity of tritium breeding and the need for a high-fidelity simulation framework for IFE chambers. These developments provide a scalable toolset for the design and optimization of next-generation IFE chambers, forming a solid foundation for future coupled multiphysics analysis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

ViBRANT Applied as a Closed Loop Flux and Thermal Surrogate

Surrogate testing platforms provide a means to develop a target aspect of a larger system. Some systems target single aspects while others are more wholistic. ViBRANT (Visual Benign Reactor as Analog for Nuclear Testing) aims to be a wholistic system by representing the primary reactor behaviors through an accessible LED driven reactor core, photodiodes, thermocouples, and high-performance software interfaces being developed to the standards required for actual microreactor deployment such as MARVEL. With the ability to drive control drums enabled with full reactor capability (e.g. speed/angle/torque capability/limits for operation and clutch/spring scram capability), drive ViBRANT’s high fidelity 36 pin hexagon core to represent phenomena via LEDs, and capture the analog response of distributed photodiodes/thermocouples, this paper will show the system’s ability to operate in a closed-loop fashion and do well at matching reactor power/temperature distribution in an accessible reactor surrogate environment.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Thermophilic site-specific recombination system for rapid insertion of heterologous DNA into the Clostridium thermocellum chromosome

Clostridium thermocellum is an anaerobic thermophile capable of producing ethanol and other commodity chemicals from lignocellulosic biomass. The insertion of heterologous DNA into the C. thermocellum chromosome is currently achieved via a time-consuming homologous recombination process, where a single stable insertion can take 2–4 weeks or more to construct. In this work, we developed a thermostable version of the Serine recombinase Assisted Genome Engineering (tSAGE) approach for gene insertion in C. thermocellum utilizing a site-specific recombinase from Geobacillus sp. Y412MC61, enabling quick and easy insertion of DNA into the chromosome for accelerated genetic tool screening and heterologous gene expression. Using tSAGE, chromosomal insertion of plasmid DNA occurred at a maximum transformation efficiency of 5 × 10 3 CFU/µg, which is comparable to the transformation efficiency of a replicating control plasmid in C. thermocellum. Using tSAGE, we chromosomally integrated and characterized 17 reporter genes, 15 homologous and 31 heterologous constitutive promoters of varying strengths, 4 inducible promoters, and 5 riboswitches in C. thermocellum. We also determined that a 6–7 nucleotide gap between the ribosome binding site (RBS) and the start codon is optimal for high expression by employing a library of superfolder green fluorescent protein expression constructs driven by our strongest tested promoter (P clo1313_1194 ) with different distances between the RBS and start codon. The tools developed here will aid in accelerating C. thermocellum strain engineering for producing sustainable fuels and chemicals directly from plant biomass.

Biofuels↗
DOE OSTI2026

Assessment of multi-stage telescopic control rods in a pebble bed reactor. Part II: corrosion and wear performance

To support High-Temperature Gas-Cooled Reactor designs, the effects of corrosion and wear on a SS316L telescopic control rod prototype were investigated in helium at 775 °C with up to 5 ppm oxygen for durations up to 500 h. Three deployment conditions were tested to assess their impact on wear and oxide formation: severe cycles, daily cycles, and gravity-driven deployments. Here, a chromium-rich oxide layer developed on regions exposed to the helium flow, reaching up to 2 µm in the most affected areas. No carburization or decarburization was observed. Oxide breakthrough occurred mainly at grooves and contact points during severe cycling of the telescopic rod, while in the other tests it was limited to contact points. Friction forces increased due to oxide growth and thermal expansion, the latter being the dominant factor, but never enough to hinder rod deployment significantly. Coaxial misalignment of stages was identified as the main potential risk to reliable operation under normal conditions.

Control rod↗
DOE OSTI2024

Deep Learning-Based Dynamic Modeling of Three-Phase Voltage Source Inverters

Inverter-based resource (IBR) models are necessary to analyze modern power system stability and create effective control strategies. Modeling IBRs in converter-rich power systems is crucial, yet challenging due to the lack of commercial information on converter topologies and control parameters. This paper proposes novel convolutional neural network (CNN)–based data-driven techniques for modeling IBRs, addressing adaptability and proprietary concerns without requiring internal system physics knowledge. The proposed method is tested using real grid-tied commercial IBR transient data and demonstrates effectiveness and accuracy. Furthermore, the developed modeling approach is integrated and implemented in the open-source power distribution simulation and analysis tool, GridLAB-D, to illustrate the potentiality of dynamic analysis of large-scale power systems with high IBRs.

deep learning, artificial intelligence↗
DOE OSTI2024

U.S. Department of Energy Collegiate Wind Competition 2025: Rules - Phases 2 and 3

The U.S. Department of Energy (DOE) Wind Energy Technologies Office's (WETO) Collegiate Wind Competition (CWC, also referred to as the "competition" in this rules document) invites interdisciplinary teams of undergraduate students from a variety of academic programs to solve complex wind energy challenges. Through the competition, WETO intends to offer students direct industry experience, valuable exposure to wind energy career pathways, and greater knowledge of wind energy's potential to contribute to a clean energy future. The competition will select up to 35 teams to start, making them eligible to compete for a cash prize pool of up to $280,000. Each year, the competition identifies a new challenge and set of activities that address real-world research questions, thus demonstrating skills that students will need to work in the wind or wider renewable energy industries. The Collegiate Wind Competition 2025 challenge requires participants to compete simultaneously in four contests: 1) Turbine Design Contest: Design, build, and present a unique, wind-driven power system. 2) Turbine Testing Contest: Test the wind turbine in a competition wind tunnel at the final event. 3) Project Development Contest: Research wind resource data, transmission infrastructure, and environmental factors to create a site plan and financial analysis for a hypothetical wind farm. 4) Connection Creation Contest: Partner with wind industry professionals, raise awareness of wind energy in your local community, and work with local media to promote your team's accomplishments. The competition does not prescribe a power system market or wind regime. It is expected that each team will participate in all four contests.

Collegiate Wind Competition↗
DOE OSTI2025

An efficient hybrid downscaling framework to estimate high-resolution river hydrodynamics

Flow depth and velocity are the most important hydrodynamic variables that govern various river functions, including water resources, navigation, sediment transport, and biogeochemical cycling. Existing high-resolution flow depth simulations rely on either computationally expensive river hydrodynamic models (RHMs) or data-driven models with formidable training costs, whereas data-driven modeling of flow velocity has rarely been explored. Here, using the hybrid Low-fidelity, Spatial analysis, and Gaussian process learning (LSG) model, we developed a downscaling approach to construct high-resolution flow depth and velocity from a two-dimensional (2-D) RHM simulation at coarse resolution. The LSG models were trained and tested in an urban watershed in Houston using two different hurricane-driven flood events. The high-resolution (as fine as 30 m resolution) and low-resolution (mostly 1000 m resolution) meshes include 664 724 and 14 536 grid cells, respectively. The results showed that through downscaling, the simulation errors were reduced to less than one-fourth and one-third of the errors of the low-resolution 2-D RHM for flow depth and velocity, respectively. Our analysis further revealed that the dominant uncertainty sources of the downscaled hydrodynamics are different, with flow velocity dominated by the dimensionality reduction error, which we reduced by using a regionalized training procedure. The downscaling approach achieves an 84-fold acceleration in computational time compared to the high-resolution 2-D RHM, making high-fidelity ensemble flood modeling feasible. More importantly, the developed method provides an opportunity to couple large-scale hydrodynamical processes with local physical, chemical, and biological processes in river models.

Tan, Zeli [Pacific Northwest National Laboratory (↗
DOE OSTI2025

Cyclic loading–unloading impacts on geomechanical stability of multiple salt caverns for underground hydrogen storage

Underground caverns in a salt dome are promising geologic features to store hydrogen because of salt’s extremely low permeability and self-healing behavior. The salt cavern storage community, however, has not fully understood the geomechanical behaviors of salt rock driven by quick operation cycles of injection–production, which may significantly impact the cost-effective storage-recovery performance of multiple caverns. Our field-scale generic model captures the impact of cyclic loading–unloading on the salt creep behavior and deformation under different cycle frequencies, operating pressure, and spatial order of operating cavern(s). This systematic simulation study indicates that the initial operation cycle and arrangement of multiple caverns play a significant role in the creep-driven loss of cavern volumes and cavern deformation. Finally, our future study will develop a new salt constitutive model based on geomechanical tests of site-specific salt rock to probe the cyclic behaviors of salt precisely both beneath and above the dilatancy boundary, including reverse (inverse transient) creep, the Bauschinger effect, and damage-healing mechanism.

08 HYDROGEN↗
DOE OSTI2025

Performance Evaluation of Intelligent Solar Control Software Through Hardware-in-the-Loop (CRADA Final Report)

Recent research has highlighted the potential for solar to act as a zero-marginal-cost and zero-emission flexibility resource on the bulk power system when operated with advanced control systems. To increase the performance of these systems, leading technologies, including machine learning (ML) and hierarchical inverter set point allocation, have been developed by Latimer Controls, Inc. to estimate the headroom of large PV plants for grid operation and control; however, these technologies lack comprehensive validation under real-world application scenarios. Latimer Controls, Inc. received two voucher awards for research at a national laboratory from the Department of Energy American Made Solar Prize Round 6. The National Renewable Energy Laboratory (NREL) was selected to collaborate with Latimer staff to conduct a performance evaluation of Latimer PV control software. The NREL team will develop a hardware-in-the-loop (HIL) testbed to perform testing and validation of the Latimer PV control technology in a de-risked yet realistic testbed environment. Latimer and NREL worked together to analyze the test data, draw conclusions from the results, and disseminate the resulting scientific findings. In this CRADA work, we propose to test and validate the real-world application of the Latimer Control solution in an HIL environment. We evaluate the performance of different flexible solar technologies in responding to automatic generation control signals in a closed-loop fashion. In particular, a data-driven potential high limit (PHL) estimation is developed for large solar plants to accurately estimate their headroom so that they have fast and short-time regulation and control capability to participate in grid services and respond to grid signals in real time (e.g., AGC). This PHL estimation algorithm is embedded in a hardware power plant controller (PPC) and tested with an IEEE-39 bus system model developed in RTDS. To account for the varying cloud conditions and diverse inverter dispatches, we developed a 135-MW PV plant with detailed modeling of 27 individual PV modules and inverters using RTDS. The real-world communications used in such big plants, such as ModBus TCP/IP for inverter level and DNP3 for plant level, were developed to emulate the real-world applications in big PV plants. The ML-based PHL estimation method is tested under nine separate weather scenarios against the ‘reference-control’ solution, hereafter referred to as the baseline solution. The baseline method reserves a subset of inverters (reference group) to operate at their PHL at all times and dispatches only the remaining inverters (control group) at curtailed levels to fulfill the flexibility need. Despite being successfully piloted by NREL in California in 2017 and Chile in 2020, there exist two gaps in the state of the art to fully unlock the flexibility of PV plants: a. There is a trade-off between the PHL estimation accuracy and the flexibility range. b. There lacks granularity in the PHL estimation to capture the variation across inverters. The Latimer solution seeks to address these gaps by applying machine learning methods to improve PHL estimation accuracy while accounting for variability at every inverter. Performance metrics were taken from the 2023 Georgia Power CARES utility-scale RFP. The results demonstrate that the ML-based approach outperforms the traditional baseline method in PHL estimation accuracy for 7 of 9 scenarios. The average PHL error across the nine scenarios was 7.40% for the ML-based method, 2.06% less than the 9.46% PHL error average across scenarios that was exhibited by the baseline method. Additionally, the PHL error was below 5% for at least 95% of the testing interval for 3 of 9 tested intervals with the ML approach, whereas it did not achieve this metric for any of the baseline tests. Overall, simulation results indicate the superior performance of an ML-based approach compared to the conventional baseline reference-control approach, showcasing its potential to support grid stability and operational efficiency. This laboratory HIL testing using real PPC, representative power system simulation models in real-time with detailed PV plant and inverter models, and real-world communication protocols gives us confidence that this machine learning based PHL estimation algorithm works well in the hardware PPC and therefore de-risks future field commissioning. The end goal of this project is to advance grid technology to address the grid operation challenges brought by solar plant’s variability and uncertainties in power generation.

14 SOLAR ENERGY↗
DOE OSTI2025

A Tale from the Trenches: Applying Metamorphic and Differential Testing to Bioinformatics Software

Metamorphic and differential testing have been proposed as best practices for testing software that is difficult to test, such as for programs in scientific domains. An assumption is that these approaches can be easily customized and applied to almost any domain. However, scientific software is often data-driven, and metamorphic relations may require significant domain knowledge to develop. In addition, tools are often written for ad-hoc experimentation by the scientists and often embed many assumptions about the importance and representation of different natural phenomena. In this paper, we present our experience applying both metamorphic and differential testing to a set of four computational biology tools that predict the growth of an organism. While our original goal was to evaluate these techniques to improve our system-level testing, we encountered multiple roadblocks along the way. Although we did find faults (some confirmed by developers), we also uncovered a set of challenges, including the considerable manual effort required for (a) defining domain-specific tests, (b) validating correctness, and (c) distinguishing between issues stemming from poor data and those arising from incorrect software.

Marsh, Alexis L [Iowa State University/Ames Labora↗
DOE OSTI2026

Extending Component Lifetime And Improving Inverter Reliability (ECLAIIR)

Inverter reliability remains one of the most persistent challenges limiting the performance, availability, and economic viability of utility‑scale photovoltaic (PV) plants. Industry data consistently show that inverters account for the highest share of corrective maintenance events and unplanned outages across PV fleets. These failures result in energy losses, increased O&M costs, and reduced confidence in long‑term solar asset performance. Motivated by these challenges, this project—Extending Component Lifetime and Improving Inverter Reliability (ECLAIIR)—was undertaken to systematically investigate inverter degradation and failure mechanisms, develop predictive maintenance capabilities, and establish data‑driven pathways to improve service life and reduce the Levelized Cost of Energy (LCOE) for large‑scale PV systems. The primary goal of the project was to identify pre‑failure signatures in string inverters using both lab‑based accelerated lifetime testing and field‑based data and to develop predictive maintenance algorithms that can anticipate inverter faults before they occur. Through collaboration with inverter testing laboratory, solar PV plant owner, and failure‑analysis experts, the project advanced the technical understanding of inverter reliability. By instrumenting inverters with thermistors, humidity sensors, power‑quality meters, and acoustic sensors, the research established how multiple sensing modalities can reliably detect deviations from normal behavior hours to days before failure. These findings substantially enhance scientific understanding of inverter failure kinetics and provide the PV industry with the most comprehensive cross‑OEM characterization of early‑stage failure indicators reported to date. Technically, the project demonstrated the effectiveness of predictive maintenance by developing and validating the PreDICT (Predictive Diagnostics of PV Inverters Using Condition Monitoring and Trend Analysis) framework—a multi‑layer diagnostic architecture combining peer‑to‑peer analytics, historical trend modeling, and advanced machine‑learning techniques such as the Sequential Conditional Variational Autoencoder (SCVAE). This predictive model achieved more than 90% accuracy in detecting pre‑failure conditions and provided up to four days of lead time before inverter failure in field scenarios. Economically, the project’s LCOE analysis showed that predictive maintenance can reduce lifetime energy losses and minimize corrective maintenance interventions. Modeling indicated that, depending on inverter failure rates and replacement timelines, predictive maintenance can significantly reduce LCOE impacts associated with inverter downtime: from as high as 19.4% under conventional maintenance strategies to 0.1%–10.17% when predictive analytics are adopted. These results confirm that predictive maintenance is both technically feasible and economically advantageous for utilities and plant operators. The project’s findings also have broad public benefit. By improving inverter reliability and reducing downtime, predictive maintenance directly increases electricity generation from existing PV assets. Enhanced reliability lowers operational costs for utilities, which can translate over time into lower energy costs for consumers. Furthermore, the project’s technical publications, conference presentations, and industry workshops ensure that knowledge gained is shared broadly across the solar industry, supporting workforce development and enabling utilities of all sizes to adopt modern asset‑health monitoring practices. The retrofitting case study and service‑life prediction framework further support informed decision‑making for aging PV fleets, helping operators extend system life and reduce electronic waste. In summary, the ECLAIIR project significantly advanced the state of knowledge on inverter degradation, demonstrated the technical and economic value of predictive maintenance, and delivered actionable tools and insights that support more reliable, cost‑effective, and sustainable PV plant operation. The outcomes of this project will continue to inform utility practices, guide inverter design improvements, and strengthen the long‑term performance of solar assets nationwide.

14 SOLAR ENERGY↗
DOE OSTI2024

Who will be making wave energy? A community-driven design approach toward just and sustainable energy futures in Alaska

Renewable energy transition, once thought of as a singular, large-scale switch in industrial energy generating technologies from fossil fuels to renewables, have gained a broader set of requirements, including the need for justice and environmental protection. While discourse around policy and process has grown to consider these requirements, the focus of design of new renewable energy projects and technologies remains dominantly fixed on maximizing energy production, and renewables are falling short of meeting the requirements of just energy transitions. Meanwhile, in design studies, participatory and co-design approaches which explicitly consider justice in both process and outcomes have long been theorized and applied. However, applications of these approaches to renewable energy project design are under-theorized. In this paper, we explore the effectiveness of the adoption and adaptation of a co-design approach called Community-Driven Design (CDD) in enabling meaningful participation prior to technological maturity and bringing the attention of designers to a more diverse set of energy futures focused on justice and sustainability. Here, we do so by developing and testing the approach with community members in Sitka, AK, USA. We conclude that the approach has the potential to increase meaningful participation. Furthermore, we conclude that through diverse understandings of energy and the emergence of design narratives that engage with the complexity of vulnerability, accountability, and resilience, CDD led designers to consider justice, sustainability, and a diverse set of possible energy futures both within and beyond Sitka.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

A compact electromagnetic neutron nutator for precise neutron spin manipulation

A superconducting electromagnetic nutator (EMN) capable of generating a magnetic field vector along an arbitrary direction on a 2D plane has been designed. Its performance in precisely manipulating the neutron polarization vector has been tested at the HB2-D polarized development beamline at the High Flux Isotope Reactor. Unlike mechanical nutators that require physical handling or motor-driven actuation to rotate the magnetic field, the magnitude and orientation of the magnetic field produced by the EMN can be controlled electromagnetically. Further, the compact design (~15 mm depth, not including cryogenic housing) of this device ensures ease of coupling within existing superconducting neutron spin manipulation devices, such as magnetic Wollaston prisms (MWP), resonant radio frequency (RF) flippers, spherical neutron polarimetry (SNP) devices, etc.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Report on RIA Relevant Modified Burst Testing of ATF Cladding Materials

The mechanical performance of accident-tolerant fuel (ATF) cladding candidates in light-water reactors (LWRs) must be similar to or better than that of current conventional nuclear fuel claddings to reduce dose to the public and ensure a that the core coolable geometry is maintained during a postulated reactivity-initiated accident (RIA) in light-water reactors (LWRs). During an RIA event, the rapid thermal expansion of nuclear fuel can deform the cladding once the fuel–cladding gap closes. In some cases, the pellet–cladding mechanical interaction (PCMI) can induce mechanical failure in ATF candidates. Thus, the mechanical response of ATF cladding must be investigated by mimicking the conditions of RIA and potentially performing Transient Reactor Test (TREAT) experiments to establish or verify the safety envelope. The work presented in this report investigated the failure behavior of as-received, hydrided, and chromium-coated (Cr-coated) Zircaloy-4 (Zry-4) cladding tube under strain-driven mechanical conditions, mimicking postulated RIA loading conditions. Mechanical testing was performed at 300°C via modified burst test (MBT) equipment with pulse width control previously developed under the Department of Energy’s (DOE’s) Advanced Fuel Campaign (AFC). The mechanical strains were determined using 2D digital image correlation (DIC) techniques. The base Zry-4 acquired by Cameco Inc. was in stress-relieved annealed (SRA) condition. Because of the observed large deformation of the cladding tubes, the failure strain definition was updated for the MBT, which can also be applied to other tube tests where significant bulging (out-of-plane deformation) is present. The failure strain was determined to be affected by the speed of the test or the RIA event. As the RIA-like event duration decreased from 75 to 15 ms, the failure strain decreased 5, 7 and 1% for as-received, hydrided, and Cr-coated specimens, respectively. Fractography on the Cr-coated specimens indicated the presence of two failure mechanisms: (i) crenulation at the outer surface of the coating due to the tensile tractional forces along with coatings grain microstructure and (ii) formation of critical defect at the coating/cladding interface that initiated coating rupture after severe plastic deformation of the Zry- 4 substrate. Based on the MBT results and fractography observations, performing mechanical property testing at high strain rates—in particular on Cr-coated tubes—and semi-integral TREAT experiments are required future efforts to ensure ATF cladding performance during transients. The testing recommended would also inform the development of long-term generalized cladding technologies.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Irradiation of UC1+x kernels using the MiniFuel vehicle: Microstructure, phase analysis, and initial post-irradiation examination

Uranium carbide is a candidate fuel form for a wide range of advanced reactors, including larger Generation IV reactors as well as small modular reactors and microreactors. However, its commercial deployment timeline faces challenges via traditional qualification approaches. To address this issue, an accelerated fission rate irradiation test was performed to collect basic fuel performance data to inform fuel performance models and potential future integral tests. Hyperstoichiometric UC (UC1+x) kernels were irradiated in the High Flux Isotope Reactor using the MiniFuel irradiation vehicle. The test matrix spanned two temperature regimes (700 °C and 800 °C) and burnup levels (1.8 % FIMA and 2.8 % FIMA). Between 21 and 63 kernels were tested at each unique temperature and burnup condition. As-fabricated microstructural analysis revealed a multiphase composition with UC, UC2, UC2−y, and U-C-O bearing phases for the irradiated kernels. Following irradiation, fission gas release, swelling, and microstructures were analyzed. Measured fission gas release was below 5 % for all irradiation conditions, reaching a maximum at the highest temperature and burnup condition. A binary swelling response was observed; the lower burnup and temperature conditions resulted in negligible swelling, but the higher burnup and temperature conditions produced significant anisotropic swelling and densification in a subset of kernels. The basic microstructural exams of kernels following irradiation were not capable of showing a correlation between kernels that exhibited excessive swelling and those that did not. Characterization of a subset of samples using the Advanced Photon Source and more detailed microstructural examination of unirradiated kernels revealed that a subset of kernels contained very high UC2 phase fractions. The anomalous swelling response is hypothesized to have been driven by this chemical variation. The results of this irradiation highlight the potential of accelerated fission rate irradiation testing to explore such behaviors and inform the development of fuel specifications.

Adorno Lopes, Denise [ORNL] (ORCID:000900023705987↗
DOE OSTI2024

Experimental Study for Validation of Resilient Response of Controls - Thrust 2 Controls Assessment

The focus of this project is to design, execute, and analyze experiments around resilience response. As part of the Resilience through Data-Driven, Intelligently Designed Control (RD2C) initiative, this project builds upon the previously designed control algorithms and experiments. It designs detailed uses cases to test RD2C-developed mitigation schemes in specific realistic scenarios, analyze and save the measurement data sets. Having a high-confidence and consistent data set is crucial for identifying relevant metrics to describe the system resilience in specific real-life-like scenarios of power grid operation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Developing an oxidation materials ontology for data-driven materials design

Materials data is complex, and managing and storing materials data for use and reuse is a common challenge. An ontology-based data management framework can address these challenges through encoding data attributes and relationships in a flexible way. This presentation discusses the creation of an ontology for alloy oxidation test data and reviews the logic, structure and interoperability of the ontology.

advanced alloy development↗