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DOE OSTI2026

Tuning Anion Composition and Mobility to Balance Ionic Conductivity and Cation Selectivity in Solid Polymer Electrolytes

Solid polymer electrolytes (SPEs) offer a promising route toward safe and high-performance electrochemical energy storage, yet a fundamental challenge in SPEs involves improving ionic conductivity while maintaining selective cation transport. The hurdle exists because ion transport is typically coupled closely to polymer segmental dynamics. Herein, a glassy single-ion-conducting polymer, poly[lithium sulfonyl(trifluoromethane sulfonyl)imide methacrylate] (PLiMTFSI), in which the anions were tethered to the polymer, was blended with a flexible polymer, poly(oligo-oxyethylene methyl ether methacrylate) (POEM), and a series of small-molecule lithium salts, in which the anions were untethered [lithium bis(trifluoromethanesulfonyl)imide (LiTFSI), lithium bis(fluorosulfonyl)imide (LiFSI), lithium trifluoromethanesulfonate (LiTf), or lithium perchlorate (LiClO 4 )]. The impact of salt anion volume and tethered-to-untethered anion ratio on the ion conduction behavior and thermal properties of blend electrolytes was investigated. In some cases, conductivity could be enhanced through this ternary blend approach. For example, a POEM-based polymer blend containing a bulky salt anion (TFSI⁻) and an equimolar mixture of PLiMTFSI and LiTFSI exhibited a Li + conductivity (4.8×10 -4 S/cm) an order of magnitude higher than that of a comparable POEM / LiTFSI system (6.3×10 -5 S/cm) at 100 °C. This enhancement was attributed to a more than ninefold increase in lithium transference number (0.66 in the ternary blend vs. 0.07 in POEM / LiTFSI). Overall, this study highlights the potential for tuning anion composition and mobility to achieve relatively high ionic conductivities and maintain selective cation transport in SPEs, offering a pathway to enable batteries that tolerate elevated temperatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Maximizing ion accessibility in MXene-knotted carbon nanotube composite electrodes for high-rate electrochemical energy storage

Improving the accessibility of ions in the electrodes of electrochemical energy storage devices is vital for charge storage and rate performance. In particular, the kinetics of ion transport in organic electrolytes is slow, especially at low operating temperatures. Herein, we report a new type of MXene-carbon nanotube (CNT) composite electrode that maximizes ion accessibility resulting in exceptional rate performance at low temperatures. The improved ion transport at low temperatures is made possible by breaking the conventional horizontal alignment of the two-dimensional layers of the MXene Ti 3 C 2 by using specially designed knotted CNTs. The large, knot-like structures in the knotted CNTs prevent the usual restacking of the Ti 3 C 2 flakes and create fast ion transport pathways. The MXene-knotted CNT composite electrodes achieve high capacitance (up to 130 F g -1 (276 F cm -3 )) in organic electrolytes with high capacitance retention over a wide scan rate range of 10 mV s -1 to 10 V s -1 . This study is also the first report utilizing MXene-based supercapacitors at low temperatures (down to -60 °C).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

Tradeoffs in single stage drift step recovery diode generator design

A high-voltage generator of nanosecond pulses utilizing a drift step recovery diode (DSRD) as an opening switch in the compression section is designed to operate at peak voltages of 4 kV and repetition rates of 2 MHz in burst mode at reduced voltages. The generator is used to perform empirical studies on the output metrics of a DSRD generator with a focus on matching the circuit of the reverse current loop parameters and DSRD to the load. The dynamic impedance introduced by nonlinear elements in the DSRD was investigated with respect to the driving circuit by varying the number of DSRDs connected in series. Experimental results indicate that optimization of the driving circuit for the DSRD is key to minimizing losses and achieving optimal pulse compression. Furthermore, the results also indicate trends in DSRD performance as larger storage inductances and longer pumping times are used, limiting the pulse compression capabilities of a single stage DSRD generator.

Electrical properties and parameters↗

DOE OSTI2023

Modeling Intercalation Chemistry with Multiredox Reactions by Sparse Lattice Models in Disordered Rocksalt Cathodes

Modern battery materials can contain many elements with substantial site disorder, and their configurational state has been shown to be critical for their performance. The intercalation voltage profile is a critical parameter to evaluate the performance of energy storage. The application of commonly used cluster expansion techniques to model the intercalation thermodynamics of such systems ab initio is challenged by the combinatorial increase in configurational degrees of freedom as the number of species grows. Such challenges necessitate the efficient generation of lattice models without overfitting and proper sampling of the configurational space under the requirement of charge balance in ionic systems. In this work, we introduce a combined approach that addresses these challenges by (1) constructing a robust cluster expansion Hamiltonian using the sparse regression technique, including -norm regularization and structural hierarchy; and (2) implementing semigrand-canonical Monte Carlo to sample charge-balanced ionic configurations using the table-exchange method and an ensemble average approach. These techniques are applied to a disordered rocksalt oxyfluoride (LMNOF) that is part of a family of promising earth-abundant cathode materials. The simulated voltage profile is found to be in good agreement with experimental data and particularly provides a clear demonstration of the and oxygen contributions to the redox potential as a function of content.

25 ENERGY STORAGE↗

DOE OSTI2023

Techno-Economic Analysis of a Microgrid System for Rural Communities in the United States

Access to running water has been a pressing issue in many developing countries across the globe, and also continues to be a problem in some rural areas in the United States. Today, more than two million Americans living in rural areas lack access to basic water supply and infrastructure. Rural communities tend to be located in environmentally fragile areas with poor economic conditions, making access to appropriate, low-cost technology for clean water supply and sanitation more challenging. In addition, many of the water resources located in these areas are being jeopardized by climate change in recent years. This research proposes a photovoltaic (PV)-battery microgrid system for powering water pumps in off-grid areas in an effort to identify economically and technologically feasible solution to the problems. Utilizing the HOMER Pro software, developed by the U.S. National Renewable Energy Laboratory (NREL), the case of the Navajo Nation is examined by analyzing the effects of this region's air temperature on the life and performance of battery storage and the economics of the proposed microgrid system.

battery↗

DOE OSTI2024

Towards Cross-Facility Workflows Orchestration through Distributed Automation

Modern science relies on end-to-end workflows that incorporate experimental instruments and utilize edge, cloud, or high-performance computing and storage resources. These components are geographically dispersed across various user facilities and interconnected through high-speed networks. In this paper, we present Zambeze, an automated distributed framework designed to facilitate this new class of cross-facility workflows. Utilizing swarm intelligence principles, Zambeze orchestrates science campaigns by managing distributed autonomous agents. These agents can offer a suite of services, including computing, storage, and data management. We demonstrate the feasibility of Zambeze through a real-world application involving electron microscopy, enhanced with Artificial Intelligence capabilities.

Skluzacek, Tyler↗

DOE OSTI2025

SlimIO: Lightweight I/O Path Design for Write Isolation in FDP-backed In-Memory Databases

In-Memory Databases (IMDBs) are widely used with HPC applications to manage transient data, often using snapshot-based persistence for backups. Redis, a representative IMDB, employs both snapshot and Write-Ahead Log (WAL) mechanisms, storing data on persistent devices via the traditional kernel I/O path. This method incurs syscall overhead, I/O contention between processes, and SSD garbage collection (GC) delays. To address these issues, we propose SlimIO, which adopts I/O passthru to minimize syscall overhead and inter-process I/O interference. Additionally, it leverages Flexible Data Placement (FDP) SSDs as backup storage to avoid performance degradation from SSD GC. Experimental results show that SlimIO reduces snapshot time by up to 25%, increases query throughput by up to 30% during non-snapshot periods, and lowers 99.9%-ile latency by up to 50%. Furthermore, it achieves a write amplification factor (WAF) of 1.00, indicating no redundant internal writes, thus extending SSD lifespan.

Lee, Sangyun [Sogang University]↗

DOE OSTI2021

SMC 2021 : Analyzing Resource Utilization and User Behavior on Titan Supercomputer

Resource utilization statistics of submitted jobs on a supercomputer can help us understand how users from various scientific domains use HPC platforms and better design a job scheduler. We explore to generate insight regarding workload distribution and usage pattern domains from job scheduler trace, GPU failure information, and project-specific information collected from Titan supercomputer. Furthermore, we want to know how the scheduler performance varies over time and how the users' scheduling behavior changes following a system failure. These observations have the potential to provide valuable insight, which is helpful to prepare for system failures. These practices will help us develop and apply novel machine learning algorithms in understanding system behavior, requirement, and better scheduling of HPC systems. There are two datasets, RUR and GPU. RUR: This dataset is the job scheduler traces collected from the Titan supercomputerfrom 01/01/2015 to 07/31/2019 (2015.csv - 2019.csv). These were collected usingResource Utilization Report (RUR), a Cray-developed resource-usage data collectionand reporting system. It contains the usage information of its critical resources (CPU,Memory, GPU, and I/O) of each running job on Titan during that period [2]. ProjectAreas: Every job is associated with a project ID. TheProjectAreas.csvdatasetprovides a mapping of the project ID to its domain science. GPU: There have been some hardware-related issues in the GPUs in Titan that caused some GPUs to fail, sometimes irrecoverably during some job runs. This dataset provides information regarding these failures during the execution of the submitted jobs. GPUs on Titan are uniquely identified by a serial number (SN), and they are installed in a location. A GPU can be installed in a location, then removed from that location following a failure, and then re-installed in a different location after fixing the problem. If the failure can't be recovered, the GPU might be removed entirely from Titan. There are two prominent types of failures that resulted in the removal of GPUs from Titan: Double Bit Error (DBE) and Out of the Bus (OTB). The dataset (gc_full.csv) has the following fields: 1. SN : Serial number of a GPU 2. location : The location where it is installed 3. insert : The time when it was inserted into that location 4. remove : The time when it was removed from that location 5. duration : Amount of time the GPU spent in this location 6. out : If the device was taken out entirely w/o a re-installment into a new location. 7. event : If the GPU was taken out entirely, the reason for its removal. To learn more about this dataset, please refer to the git repositoryhttps://github.com/olcf/TitanGPULifeand the related publication [1]. References [1] George Ostrouchov, Don Maxwell, Rizwan A Ashraf, Christian Engelmann, MallikarjunShankar, and James H Rogers. Gpu lifetimes on titan supercomputer: Survival analysisand reliability. InSC20: International Conference for High Performance Computing,Networking, Storage and Analysis, pages 1-14. IEEE, 2020. [2] Feiyi Wang, Sarp Oral, Satyabrata Sen, and Neena Imam. Learning from five-yearresource-utilization data of titan system. In2019 IEEE International Conference onCluster Computing (CLUSTER), pages 1-6. IEEE, 2019.

42 ENGINEERING↗

DOE OSTI2020

GPU Lifetimes on Titan Supercomputer: Survival Analysis and Reliability

George Ostrouchov, Don Maxwell, Rizwan Ashraf, Mallikarjun Shankar, and James Rogers. 2020. GPU Lifetimes on Titan Supercomputer: Survival Analysis and Reliability. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '20). Association for Computing Machinery, New York, NY, USA. Data and code for SC20 paper about Titan GPU reliability analysis: https://github.com/olcf/TitanGPULife. Includes R code to generate graphics for paper and additional analyses. See code/README for instructions. Includes original Titan GPU reliability data on over 100,000 collective hours of operation: data/titan.gpu.history.txt - history data, data/titan.service.txt - service nodes for exclusion. Includes output data files produced by code/TitanGPUmodel.Rmd: data/gc_full.csv - cleaned up data (see paper and R code); data/gc_summary_loc.csv - one record per GPU (variables: SN, time, nlife, nloc, last, col, row, cage, slot, node, max_loc_events, time_max_loc, dbe, dbe_loc, otb, otb_loc, out, batch, days, years, dead, dead_otb, dead_dbe) (see paper and R code). Includes .Rmd analysis document as TitanGPUmode.html. Includes Python code to process data/gc_full.csv into graphics from time-between-failure analyses: See code/tbf-analyses/README for instructions.

42 ENGINEERING↗

DOE OSTI2024

Crystallization and assembly at interfaces: Celebrating the achievements of a vibrant research community

Crystallization is one of the cornerstones of modern materials science and engineering and plays a critical role in industries ranging from petroleum derivative manufacturing to microstructural engineering of structural materials and the defect-free growth of silicon single crystals for integrated chip technology. Also, in the realm of environmental and biological processes, the mineralization of diverse compounds has shaped the vast array of ecosystems we observe today. Conversely, understanding the crystallization and assembly of building blocks of various sizes at interfaces has broader impacts on materials synthesis, performance of energy storage devices, optimized processing conditions, and more.

36 MATERIALS SCIENCE↗

DOE OSTI2020

EBS Task Force: Task 9/FEBEX Modeling Final Report: Thermo-Hydrological Modeling with PFLOTRAN

This report outlines Sandia National Laboratories modeling studies applied to Stage 1 and Stage 2 of the Full-scale Engineered Barriers Experiment in Crystalline Host Rock (FEBEX) in situ test for the SKB EBS Task Force Task 9. The FEBEX test was a full-scale test conducted over ~18 years at the Grimsel, Switzerland Underground Research Laboratory (URL) managed by NAGRA. It involved emplacing simulated waste packages, in the form of welded cylindrical heaters, inside a tunnel in crystalline granitic rock and surrounded by a bentonite barrier and cement plug. Sensors emplaced within the bentonite monitored the wetting-up, heating, and drying out of the bentonite barrier, and the large resulting data set provides an excellent opportunity for validation of multiphysics Thermal-Hydrological (TH), Thermal-Hydrologic-Chemical (THC), and Thermal-Hydrological-Mechanical (THM) modeling approaches for underground nuclear waste storage and the performance of engineered bentonite barriers. The present status of the EBS Task Force is finalizing Task 9, which follows years of modeling studies of the FEBEX test, by many notable modeling teams (Gens et al., 2009; Sanchez et al. 2010; 2012; Samper et al., 2018). These modeling studies generally use two-dimensional axisymmetric meshes, ignoring threedimensional effects, gravity and asymmetric wetting and dry out of the bentonite engineered barrier. This study investigates these effects with use of the PFLOTRAN THC code with massively parallel computational methods in modeling FEBEX Stage 1 and Stage 2 results. The PFLOTRAN numerical code is an open source, state-of-the-art, massively parallel subsurface flow and reactive transport code operating in a high-performance computing environment (Hammond et al., 2014). Section 2 describes the applied partial differential equations describing mass, momentum and energy balance used in this study, considerations derived by assuming phase equilibrium between gas and liquid phases, constitutive equations for granite, cement plug, and bentonite domains, and specific approaches for use inthe PFLOTRAN code. Section 3 describes the geometry, meshing, and model set-up. Section 4 describes modeling results, Section 5 compares modeling results to field testing data, and Section 6 gives conclusions. The Appendix provides detailed information required by the EBSTask Force for final reporting.

42 ENGINEERING↗

DOE OSTI2020

TWF Waste Container Receipt, Inspection and Transfer to Storage

This procedure provides instructions for the receipt, inspection, and transfer to storage of Transuranic (TRU) Waste Containers and Pipe Overpack Containers (POC) within the Transuranic Waste Facility (TWF) (Waste Storage Buildings [WSBs] and Characterization and Waste Storage Building [CWSB]). Performance of this procedure meets the requirements of AC 5.8.9, AC 5.8.13 2nd Bullet, SAC 5.9.1, SAC 5.9.3, SR 4.2.1.1, 4.2.1.2, 4.2.1.5, 4.2.1.6, 4.2.2.1, 4.2.2.2, 4.2.2.3, 4.2.2.4, 4.2.3.1, 4.2.3.2, 4.2.3.3, 4.3.4.1, 4.4.1.1, 4.4.1.2 and 4.4.1.3.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

TBAA20: Task-Based Algorithms and Applications

The new challenges posed by Exascale system architectures have resulted in difficulty achieving a desired scalability using traditional distributed memory runtimes. Task-based programming models show promise in addressing these challenges, providing application developers with a productive and performant approach to programming on next generation systems. Empirical studies show that task-based models can overcome load balancing issues that are inherent to traditional distributed memory runtimes, and that task-based runtimes perform comparably to those systems when balanced. This panel is designed to explore the advantages of task-based programming models on modern and future HPC systems from an industry, university, and national lab perspective. It aims at gathering application experts and proponents of these models to present concrete and practical examples of using task-based runtimes to overcome the challenges posed by Exascale system architectures. This report describes the objectives, activities, and outcomes of the panel TBAA: Task-Based Algorithms and Applications which was held at the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC 20) on November 18, 2020.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Considerations for Defining G-Values for Aluminum-Clad Spent Nuclear Fuel

Sealed-canister dry storage of aluminum-clad spent nuclear fuel (ASNF) generated by research reactors is an alternative to current storage and disposition pathways as directed by the U.S. Department of Energy. The major challenge faced for this storage approach is radiolytic H 2 generation, including from the aluminum (oxy)hydroxide layers on the surface of ASNF. Experimental and modeling activities have been carried out to characterize the radiolytic yield as part of a DOE-sponsored research program to develop the technical basis for ASNF dry storage. The G-value is a commonly way to report results of radiolysis testing and is defined as the radiolytic yield of a species (e.g. molecular hydrogen) per unit radiation energy deposited into the material system. An independent technical review of the ASNF dry storage technical basis performed by Pacific Northwest National Laboratory raised questions about differences in G-value definitions used for experiments on ASNF surrogates consisting of aluminum samples with adherent (oxy)hydroxides compared to G-values reported in prior literature and how the magnitudes compared between different studies. Material systems resembling ASNF pose complications for measuring/defining G-values to predict the evolution of H 2 in a sealed canister, including i) accounting for radiolytic yields potentially arising from multiple sources, i.e., residual free (vapor), physisorbed, and chemisorbed/chemically bound waters; ii) deciding what portions of the multi-material system to include in the absorbed energy (radiation dose) calculation, considering possible energy exchange between materials as well as measurement limitations, and iii) capturing variations in G-value associated with non-linear yield vs. dose curves and/or dependence on the cover gas. This report summarizes previous literature information on radiolytic H 2 generation and associated G-values from mixed-material systems (generally oxides in contact with water or organic compounds) and from (oxy)hydroxides/hydrates to compare with the definitions and values for ASNF surrogate samples containing adherent aluminum (oxy)hydroxides.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

On Final Results From the Muon $g\mathrm{-}2$ Experiment at Fermilab

The Muon g-2 Experiment at Fermilab has measured the muon anomalous magnetic moment, a_mu, with unprecedented precision, leveraging a dataset from Runs 1 6 that is 21 times larger than its Brookhaven predecessor. This talk will present the experiment s final result, which serves as a benchmark for testing the Standard Model with high precision. The measurement was performed in a storage ring with a highly uniform magnetic field, where precise beam dynamics understanding is vital to determine the muon anomalous precession frequency. Key to this effort were advanced simulation tools including COSY INFINITY, precise fringe field modeling, and calculations of beam dynamics characteristics like tunes and chromaticity. We will conclude by exploring how the experiment's findings reshape our understanding of particle physics.

Valetov, Eremey [Michigan State U.]↗

DOE OSTI2026

Illuminating the Material World: Autonomous Microscopy to Understand Order, Disorder, and Everything In Between

Artificial intelligence (AI) holds immense promise for revolutionizing microscopy, yet its widespread adoption has been hindered by challenges ranging from user inexperience to limited model transferability and difficulties in operationalizing machine learning. This presentation showcases our approach to developing practical autonomy for materials discovery, aiming to accelerate the integration of AI into everyday microscopy workflows. As shown in Fig. 1, I will focus on three key areas: understanding order-disorder transitions, quantifying point defects, and achieving truly device-scale microscopy. First, I will demonstrate the power of multi-modal knowledge graphs for integrating diverse microscopy data. By combining imaging, spectroscopy, and diffraction data, these graphs provide a holistic view of material behavior, capturing the intricate relationships between different modalities [1,2]. I will present a case study on how these models illuminate the structural and chemical changes associated with irradiation in oxide thin films, revealing critical insights for designing materials for extreme environments like spaceflight and nuclear energy. Specifically, I will show how multi-modal analysis clarifies the evolution of order-disorder transitions under irradiation, a key factor influencing material performance in these applications. Next, I will address the challenge of quantifying point defects in 2D materials. We demonstrate the application of computer vision and transfer learning to accurately identify and classify various defect types, such as vacancies and substitutional atoms, and to quantify their concentrations. This information is crucial for understanding and tailoring the properties of 2D materials for applications in electronics, optoelectronics, and catalysis. For example, I will show how our models can characterize the topological distribution of point defects in MXene transition metal carbides, providing valuable insights for optimizing their performance in energy storage and separation science. Finally, I will discuss our progress toward autonomous device-scale microscopy [3,4]. We are fundamentally redesigning electron microscopes around the principles of machine reasoning, enabling automation beyond basic tasks like sample navigation and data acquisition to include sophisticated experimental design. This approach paves the way for truly reproducible and massively scaled analysis campaigns. I will emphasize the importance of autonomous microscopy platforms for high-throughput materials discovery and characterization, facilitating the rapid screening of materials for a broad range of applications and accelerating the development of next-generation technologies.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Production and characterization of extracellular cellulases from a native strain of Trichoderma harzianum using wheat straw from the Mexicali valley as the carbon source. Producción y caracterización de celulasas Extracelulares a partir de cepa nativa de Trichoderma harzianum usando como fuente de Carbono paja de trigo del valle de Mexicali

The purpose of this study was to produce cellulase enzyme from a native strain Trichoderma harzianum, isolated from crop soil. Cellulase production was carried out by two different methods of fermentation, submerging and solid state fermentation. Mandels mineral medium was used as a source of nitrogen and ammonium sulfate. Dry and ground wheat straw was used in fermentation as a carbon source. Cellulase activity was determined using the 3,5-dinitrosalicylic acid (DNS) method. The results showed that wheat straw has the potential to be used as a substrate in the production of cellulases. Trichoderma harzianum showed enzymatic activity of 0.125 FPU / ml (filter paper units) and 1. 35 U / ml CMC (carboxymethyl cellulose units) by solid state fermentation (FSS) after 96 hours of fermentation. The optimum pH for the two substrates was determined by testing with citrate buffers (pH 4 to 6), phosphates (pH 6 to 7) and tris-HCL (pH 8 to 9). To determine the optimum temperature the range of 30 ° C to 80 ° C was investigated. Storage tests were performed every seven days over a period of 6 weeks. The optimum activity pH on paper and CMC was found to be 6. The optimum activity temperature is 70 ° C for PF and 80 ° C for CMC. El propósito de este estudio fue producir enzima celulasa a partir de una cepa nativa Trichoderma harzianum, aislada de suelo de cultivo. La producción de celulasa se realizó por dos métodos diferentes de fermentación, sumergida y fermentación en estado sólido. Se utilizó medio mineral Mandels y como fuentes de nitrógeno urea y sulfato de amonio. Se utilizó paja de trigo seca y molida en las fermentaciones como fuente de carbono. La actividad de la celulasa se determinó por medición de azúcares reductores al papel filtro y a la carboximetilcelulosa usando el método del ácido 3,5- dinitrosalicílico (DNS). Los resultados mostraron que la paja de trigo tiene el potencial para usarse como sustrato en la producción de celulasas. Trichoderma harzianum mostró actividad enzimática de 0.125 FPU/ml (unidades de papel filtro) y 1.35 U/ml a la CMC (unidades de carboximetilcelulosa) por fermentación en estado sólido (FSS) después de 96 horas de fermentación. El pH óptimo para los dos sustratos se determinó realizando pruebas con buffers de citratos (pH 4 a 6), de fosfatos (pH 6 a 7) y tris- HCL (pH 8 a 9). Para la determinación de la temperatura óptima se manejó el rango de 30°C a 80°C. Las pruebas de almacenamiento se realizaron cada siete días en un periodo de 6 semanas. Se encontró que el pH óptimo de actividad en papel y CMC es de 6. La temperatura óptima de actividad es de 70°C para PF y de 80°C para CMC.

09 BIOMASS FUELS↗

DOE OSTI2025

Hydride Reprecipitation in Prototypical 17x17 PWR Fuel Rods after Simulated Vacuum Drying Conditions

Light water reactor (LWR) fuel rod cladding picks up hydrogen as it oxidizes in reactor. Once the hydrogen concentration exceeds the solubility limit, zirconium hydride platelets precipitate, generally in the circumferential direction. In preparation for dry storage, elevated temperatures can dissolve some of the circumferential hydrides, which during cooling can then reprecipitate in a direction perpendicular to the hoop stress (radially). From 2019 to 2024, ORNL published a series of reports that included metallographic observations of cladding after a thermal transient to 400°C followed by slow cooling. The simulated dry storage process was performed on three full-length 17x17 PWR fuel rods at their as-discharged rod internal pressure. There were two important observations from the metallographic examinations of these heat-treated rods: (1) the propensity for hydride reorientation is strongly influenced by nearby as-discharged precipitated hydrides, (2) the radial hydrides observed near the cladding inner diameter were often associated with cracks in the pellet. Subsequent modeling using the BISON fuel performance code that considered the effects of pellet cracks and pellet-cladding bonding observed in high-burnup fuel support the conclusion that pellet cracks enhance the local hoop stress in the cladding creating preferential sites for hydride reorientation, and that the influence of the pellet cannot be ignored in accurate fuel rod performance predictions.

Montgomery, Rose [ORNL] (ORCID:0000000286038936)↗