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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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At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

What is the Role of Nb in Nickel-Rich Layered Oxide Cathodes for Lithium-Ion Batteries?

Nickel-rich layered metal oxide LiNi 1–y–z Mn y Co z O 2 (1 – y – z ≥ 0.8) materials are the most promising cathodes for next-generation lithium-ion batteries in electric vehicles. However, they lose more than 10% of their capacity on the first cycle, and interfacial/structural instability causes capacity fading. Coating and substitution are possible direct and effective solutions to solve these challenges. In this Letter, Nb coating and Nb substitution on LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811) is easily produced through a scalable wet chemistry method followed by sintering from 400 to 800 °C. A Li-free Nb oxide treatment is found to remove surface impurities forming a LiNbO 3 /Li 3 NbO 4 surface coating, to reduce the first capacity loss and to improve the rate performance. Furthermore, Nb substitution stabilizes the structure, as evidenced by less heat evolution on heating, thus providing better long cycling stability with a 93.2% capacity retention after 250 cycles.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Insights from high-fidelity modeling of industrial rotary bell atomization

The global automotive industry sprayed over 2.6 billion liters of paint in 2018, much of which through electrostatic rotary bell atomization, a highly complex process involving the fluid mechanics of rapidly rotating thin films tearing apart into micrometer-thin filaments and droplets. Coating operations account for 65% of the energy usage in a typical automotive assembly plant, representing 10,000s of gigawatt-hours each year in the United States alone. Optimization of these processes would allow for improved robustness, reduced material waste, increased throughput, and significantly reduced energy usage. Here, we introduce a high-fidelity mathematical and algorithmic framework to analyze rotary bell atomization dynamics at industrially relevant conditions. Our approach couples laboratory experiment with the development of robust non-Newtonian fluid models; devises high-order accurate numerical methods to compute the coupled bell, paint, and gas dynamics; and efficiently exploits high-performance supercomputing architectures. These advances have yielded insight into key dynamics, including i) parametric trends in film, sheeting, and filament characteristics as a function of fluid rheology, delivery rates, and bell speed; ii) the impact of nonuniform film thicknesses on atomization performance; and iii) an understanding of spray composition via primary and secondary atomization. These findings result in coating design principles that are poised to improve energy- and cost-efficiency in a wide array of industrial and manufacturing settings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

PMU-data-driven Event Classification in Power Transmission Grids

This paper presents an event classification in transmission grids. The convolutional neural network (CNN)-based classifier is proposed to capture the temporal similarity of time-synchronized data stream from phasor measurement units (PMUs). The proposed CNN is trained using Bayesian optimization to search for the best hyperparameters. The effectiveness of the proposed event classification is validated through the real-world dataset from the U.S. transmission grids. This dataset includes line outage, transformer outage, frequency, and oscillation events. The validation process also includes different PMU outputs, such as voltage magnitude, phase angle, current magnitude, frequency, and rate of change of frequency (ROCOF). The results show that ROCOF gives the best classification performance compared to other PMU outputs. In addition, it is shown that the classifier trained with a larger dataset has higher accuracy. Moreover, the superiority of the proposed method is validated through comparison with other state-of-the-art classification methods.

Niazazari, Iman↗
DOE OSTI2018

Optimizing DER Participation in Inertial and Primary-Frequency Response

This paper develops an approach to enable the optimal participation of distributed energy resources (DERs) in inertial and primary-frequency response alongside conventional synchronous generators. Leveraging a reduced-order model description of frequency dynamics, DERs' synthetic inertias and droop coefficients are designed to meet time-domain performance objectives of frequency overshoot and steady-state regulation. Furthermore, an optimization-based method centered around classical economic dispatch is developed to ensure that DERs share the power injections for inertial- and primary-frequency response in proportion to their power ratings. Simulations for a modified New England test-case system composed of ten synchronous generators and six instances of the IEEE 37-node test feeder with frequency-responsive DERs validate the design strategy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Machine learning and TDDFT software for stopping power computation

(SF-24-012) Stopping power describes the rate that a material slows radiation particles passing through it and is useful in designing many technologies. Few organizations can perform new measurements, which require significant resources and rare equipment, and all others rely on coarse approximations rendered from pre-existing data. Methods for computing stopping power in new materials, such as time-dependent density functional theory (TD-DFT), have only recently (circa-2015) become available but are too computationally costly to use frequently enough to have a pronounced impact. We have created a method that opens a pathway to computing stopping power without any need for experimental data by combining electronic structure computations and machine learning.

Ward, Logan↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

Towards Resolving the Atomization Process of an Idealized Fire Sprinkler with VOF Modeling

Fire suppression modeling depends on accurate characterization of the atomization processes of fire sprinklers.Numerical modeling, particularly the volume of fluid (VOF) method, has been applied to understanding the atomization behavior of an idealized sprinkler geometry consisting of a 9.5 mm inner-diameter cylindrical nozzle and a flat, 25.4 mm diameter disk with a liquid flow rate of 0.87 L/s. The simulations have been performed with an OpenFOAM based VOF solver, using the isoAdvector scheme for interfacial reconstruction. The sheet breakup distance and film thickness were calculated and compared with measurements from a previous study. A mesh refinement study identified the sensitivities in the predicted quantities to mesh resolution. This study enables furtherapplication of the model to simulation of the fully atomized spray.

Engineering↗
DOE OSTI2017

Fault diagnosis and severity estimation in nuclear power plants - 196

Signed Directed Graph (SDG) is a type of fault diagnosis method with a capability to display the complex relationship between plant parameters and reveal the potential danger and the propagation path when a fault occurs in a nuclear plant. However, the fault diagnosis method based on qualitative SDG model inevitably has a number of disadvantages, and some of these disadvantages include its inability to identify reoccurring faults; ambiguous reasoning that can result in low resolution; performance that relies on the accuracy of the model; slow diagnosis rate for large systems and poor real-time diagnosis, among others. In this paper, we propose an SDG fault diagnosis methodology based on Granular Computing Theory, referred to as Granular Computing Signed Directed Graph (GCSDG). This paper shows how knowledge reduction nature of granular computing based fault diagnosis can simplify the decision table, reduce the allocation of resources, scale-down the complexity of the problem and improve the efficiency of data processing. Then, we estimate the severity of the fault using Back Propagation (BP) neural network approach. The proposed methodology was verified by considering certain faults in the primary loop of a PWR, using PCTRAN. The result shows a considerable improvement in fault diagnosis and the severity estimated serves as a useful information for the operator, providing a firm foundation for further intervention. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Automated Waterbox Inspection for Nuclear Power Plants Using Computer Vision - Based Change Detection

Nuclear power plant waterboxes require regular inspection for leaks, missing components, and structural damage during maintenance outages. Traditional manual inspection is time-consuming and poses safety risks from confined space entry. We developed an automated computer vision system for drone-based waterbox inspection in partnership with Florida Light and Power. Our approach uses feature detection and matching to identify critical changes between baseline and current inspection images, automatically flagging additions (leaks/debris), removals (missing plugs), and translations (displaced components) while compensating for drone movement and environmental variations. We systematically evaluated six feature matching methods, from classical approaches (SIFT+BF) to state-of-the-art neural networks (SuperPoint+SuperGlue), using both standard benchmarks (HPatches) and waterbox-specific validation with real-world augmentations. SuperPoint+SuperGlue achieved superior performance with 7.82 pixels RMSE and 100% success rate—2.8x better accuracy than our baseline. While the pre-trained model has commercial licensing restrictions for nuclear deployment, our findings validate this architecture for custom training. We implemented a real-time GUI demonstrating the SIFT+BF approach for immediate deployment, processing drone feeds at 30 FPS with color-coded change visualization. Future work includes training a custom SuperPoint+SuperGlue model on waterbox data and integrating Vision-Language Models for automated reporting and maintenance guidance.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2022

The encasulation effect on thermal performance of micro-encapsulated phase change materials during energy absorption

Understanding the performance of the thermal storage materials is required to successfully meet the needs of next generation concentrating solar power projects. This study focuses on the thermal characteristics of the micro-encapsulated phase change material (MEPCM) with metallic core under high temperature. A numerical model was developed and verified to study the transient heat absorption process of MEPCMs made of tin, aluminum and copper core. Convective heat transfer rate, total energy absorption and volumetric energy density were presented. The effect of encapsulation on theses variables was analyzed under various boundary and initial conditions. Its impact on characteristic time variables was discussed. The method presented can be adopted to analyzing both manufacturing and application processes for MEPCMs of various compositions, during which melting and solidification happen. Given certain operating conditions, the results from the model can be referenced to obtain optimal thermal performance of these materials.

Benner, Jingru↗
DOE OSTI2016

Uncertainty Quantification and Sensitivity Analysis Applications to Fuel Performance Modeling

Best-estimate fuel performance codes such as BISON currently under development at the Idaho National Laboratory, utilize empirical and mechanistic lower-length-scale informed correlations to predict fuel behavior under normal operating and accident reactor conditions. Traditionally, best-estimate results are presented using the correlations with no quantification of the uncertainty in the output metrics of interest. However, there are associated uncertainties in the input parameters and correlations used to determine the behavior of the fuel and cladding under irradiation. Therefore, it is important to perform uncertainty quantification and include confidence bounds on the output metrics that take into account the uncertainties in the inputs. In addition, sensitivity analyses can be performed to determine which input parameters have the greatest influence on the outputs. In this paper we couple the BISON fuel performance code to the DAKOTA uncertainty analysis software to analyze a representative fuel performance problem. The case studied in this paper is based upon rod 1 from the IFA-432 integral experiment performed at the Halden Reactor in Norway. The rodlet is representative of a BWR fuel rod. The input parameters uncertainties are broken into three separate categories including boundary condition uncertainties (e.g., power, coolant flow rate), manufacturing uncertainties (e.g., pellet diameter, cladding thickness), and model uncertainties (e.g., fuel thermal conductivity, fuel swelling). Utilizing DAKOTA, a variety of statistical analysis techniques are applied to quantify the uncertainty and sensitivity of the output metrics of interest. Specifically, we demonstrate the use of sampling methods, polynomial chaos expansions, surrogate models, and variance-based decomposition. The output metrics investigated in this study are the fuel centerline temperature, cladding surface temperature, fission gas released, and fuel rod diameter. The results highlight the importance of quantifying the uncertainty and sensitivity in fuel performance modeling predictions and the need for additional research into improving the material models that are currently available.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Development of a Performance Portable Non-Equilibrium Plasma Fluid Solver on Adaptive Grids

This presentation will describe the numerical techniques, programming paradigms, verification, and performance of a non-equilibrium plasma fluid solver that can effectively utilize current and upcoming central processing and graphics processing unit (CPU+GPU) architectures. Our plasma fluid model solves the conservation equations for self-consistent electrostatic Poisson, electron and heavy species transport, and electron temperature on adaptive Cartesian grids. Our solver is written using performance portable adaptive mesh management library, AMReX (Zhang et al., JOSS, 4 (37) 1370, 2019), and can be built and run on widely available vendor specific GPU architectures (NVIDIA/AMD/Intel). We utilize a non-subcycled second order semi-implicit time-stepping method where all adaptive mesh refinement (AMR) levels are advanced with the same time step. The composite multi-level multigrid solver from within AMReX is used for each of the governing equations that are cast into a Helmholtz equation form. We have also developed a python based chemical mechanism parser framework that uses a similar format as CANTERA (Goodwin et al., Zenodo, 2018) yaml files as input. Our custom parser reads the yaml file and provides C++ files with transport and production rate functions that can be executed on both host (CPU) and device (GPU). We present verification of our solver using method of manufactured solutions that indicate formal second order accuracy with central diffusion and fifth order weighted-essentially-non-oscillatory (WENO) advection scheme. We also verify our solver with published literature on low-pressure capacitive and high-pressure streamer discharges. Our initial performance studies indicate 10X speed-up using 20 NVIDIA GPUs versus 200 CPUs for an atmospheric streamer discharge problem solved on a 512 x 1024 x 512 grid.

graphics processing units↗
DOE OSTI2022

Robust Carbon Dioxide Plume Imaging Using Joint Tomographic Inversion of Seismic Onset Time and Distributed Pressure and Temperature Measurements (Final Report)

We develop and demonstrate rapid and cost-effective methodologies for spatiotemporal tracking of CO2 plumes during geologic sequestration using joint inversion of seismic data and distributed pressure and temperature measurements. Key elements of our methodology are: (a) a computationally efficient approach to pressure and temperature propagation, (b) analysis of time lapse seismic data using a novel ‘seismic onset time’ approach to detect fluid front propagation, and (c) data assimilation and uncertainty assessment via joint inversion of pressure, temperature and time lapse seismic data, and (d) validating the numerical tomographic inversion using a CO2 injection demonstration projects, specifically data collected from the from the Petra Nova Parish Holdings CCUS project in the West Ranch Field, Texas and the Chester-16 reef CO2 injection site in Northern Michigan which is part of the DOE Midwestern Carbon Sequestration Project. The research team is led by Texas A&M University and includes Battelle as a subcontractor with support from Shell, Anadarko, Chevron and JX Nippon. A carbon dioxide (CO2) water-alternating-gas (WAG) pilot was conducted to gain insights into tertiary oil recovery potential via CO2 flood in the West Ranch Field as part of the Petra Nova project, the world’s largest post-combustion CO2 capture and utilization initiative. With a fluvial formation geology and large contrasts in permeability, this is a challenging and novel application of CO2 enhanced oil recovery (EOR). We build a predictive dynamic model of the subsurface that incorporates the multiphase and compositional data acquired during the pilot operation. The calibrated model is used for the carbon dioxide plume imaging. The study began with an initialization of the pilot sector model extracted from a calibrated full-field model. The pilot model calibration follows a two-step hierarchical workflow. First, we performed a large-scale update of the permeability distribution by integrating available bottomhole pressure and multiphase production data. In the second step, local permeability field is fine-tuned using a streamline-based method to match CO2 breakthrough times at the producers. The predictive capability of the calibrated model was verified through two blind validation tests: (1) the model showed good agreement with saturation logs acquired at two observation wells; and (2) the model reproduced the CO2 recovery as a fraction of the injected CO2. The use of seismic onset times has shown great promise for integrating near-continuous seismic surveys for updating geologic models. In this study, we analyze the impact of seismic survey frequency on the onset time approach aiming to extend the application of onset time to infrequent seismic surveys. In addition, we quantitatively examine the nonlinearity of the onset time method and compare it to the commonly used amplitude inversion method. We carry out a sensitivity analysis of seismic survey frequency based on the complete seismic survey data (over 175 surveys) of steam injection in a heavy oil reservoir (Peace River Unit) in Canada. Our results show that an adequate onset time map can be obtained from the infrequent seismic surveys by interpolation between seismic surveys as long as there is no change in the dominant underlying physics between the successive surveys. The study also shows that nonlinearity of the onset time method can be -smaller than that of the amplitude inversion method by several orders of magnitude. Application to the Brugge benchmark case shows that the onset time method obtains comparable permeability update as the traditional seismic amplitude inversion method with faster computation and improved convergence characteristics. We extend the streamline-based data integration approach to incorporate distributed temperature sensor (DTS) data using the concept of thermal tracer travel time. Then, a hierarchical workflow composed of evolutionary and streamline methods is employed to jointly history match the DTS and pressure data. Finally, CO2 saturation and streamline maps are used to visualize the CO2 plume movement during the sequestration process. The hierarchical workflow is applied to a carbon sequestration project in a carbonate reef reservoir within the Northern Niagaran Pinnacle Reef Trend in Michigan, USA. The monitoring data set consists of distributed temperature sensing (DTS) data acquired at the injection well and a monitoring well, flowing bottom-hole pressure data at the injection well, and time-lapse pressure measurements at several locations along the monitoring well. The history matching results indicate that the CO2 movement is mostly restricted to the intended zones of injection which is consistent with an independent warm-back analysis of the temperature data. In addition to employing simulation models and inverse methods for CO2 plume imaging, we also initialized a data-driven technology for detecting inter-well connectivity based on production and pressure data. Our machine-learning framework is built on the statistical recurrent unit (SRU) model and interprets well-based injection/production data into inter-well connectivity without relying on a geologic model. We test it on synthetic and field-scale CO2 EOR projects utilizing the water-alternating-gas (WAG) process. The validation of the proposed data-driven inter-well connectivity assessment is performed using synthetic data from simulation models where inter-well connectivity can be easily measured using the streamline-based flux allocation. The SRU model is shown to offer excellent prediction performance on the synthetic case. Despite significant measurement noise and frequent well shut-ins imposed in the field-scale case, the SRU model offers good prediction accuracy, the overall relative error of the phase production rates at most producers ranges from 10% to 30%. It is shown that the dominant connections identified by the data-driven method and streamline method are in close agreement. Texas A&M University, the lead organization in the project, was primarily responsible for the development of tomographic approaches for CO2 plume mapping in conjunction with distributed pressure, temperature and seismic onset time data. Battelle, as a subcontractor, was primarily responsible for the development of analytical and empirical methods for analyzing transient injection rate and pressure data from point/line sources such as injection and monitoring wells. An additional area of emphasis for Battelle was the use of machine learning for such tasks as inferring reservoir connectivity information from injection-production data, and identifying variable importance for machine learning-based proxy models developed from full-physics simulations. The two organizations also collaborated on the application of the tomographic inversion methodology for a field data set.

02 PETROLEUM↗
DOE OSTI2023

Criticality Experiments to Reduce Compensating Errors in Plutonium Nuclear Data

Compensating errors between nuclear data observables in a library can adversely impact application simulations. The primary goal of the EUCLID project (Experiments Underpinned by Computational Learning for Improvements in Nuclear Data) is to reduce compensating errors in nuclear data. A new criticality experiment, described in this work, was designed with the specific target nuclear data of 239 Pu fission, inelastic scattering, elastic scattering, capture, nu-bar, and prompt fission neutron spectrum (PFNS). This work will focus on the design and execution of the EUCLID experiment, performed on the Planet vertical lift critical assembly machine at the National Criticality Experiments Research Center (NCERC). The criticality experiment includes two different configurations with very different geometries: one is cube-like to minimize neutron leakage while the other is slab-like to maximize leakage. Having these two widely varying configurations allows the scattering sensitivities of 239 Pu to the neutron multiplication factor to be greatly changed while minimally impacting the other cross section sensitivities. Both configurations utilize the Pu ZPPR (Zero Power Physics Reactor) plates as fuel. The experiments were designed using a D-Optimality criteria, which is an optimization method minimizing the log-determinant of the adjusted nuclear data covariance for the target reactions. These experiments include not only inference of k eff , as done in all critical benchmark experiments, but several other responses as well, such as neutron multiplication measurements and reaction rate ratios. After analysis of the measured data is complete, adjustment of nuclear data will be performed to assess whether the new experimental data successfully reduced compensating errors.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2019

Lithiophilic V 2 O 5 nanobelt arrays decorated 3D framework hosts for highly stable composite lithium metal anodes

The lithium metal anode has been considered to be an optimal anode material for high-energy-density battery system due to its ultrahigh specific capacity. However, the sharp dendrite growth and infinite volume expansion have significantly impeded its commercial application. In this paper, we propose that V 2 O 5 as the lithiophilic substance can be decorated onto 3D stable frameworks to fabricate dendrite-suppressed composite Li metal anodes via a molten Li infusion method. It is demonstrated that the energetic chemical reaction between V 2 O 5 and molten Li together with the capillary effect based on the nanostructure of interconnected V 2 O 5 nanobelt arrays contributes synergistically to the great Li affinity of the V 2 O 5 -Ni foam (V 2 O 5 -NF) host and facilitate the efficient and uniform intake of molten Li into the 3D framework. Benefitting from the homogeneous Li distribution in the framework, the decreased local current density of the electrode and the stable property of the host, dendrite-free Li stripping/plating behavior and alleviated volume fluctuation have been achieved for the Li-V 2 O 5 -NF composite anode. Compared with the bare Li anode, the as-obtained Li-V 2 O 5 -NF composite anode exhibits much stable stripping/plating profiles with low overpotential (~18 mV) for ultralong lifespan (1600 h) at a current density of 1 mA cm -2 in symmetric Li/Li cell. Furthermore, outstanding rate capability and long-term cycling performance (78.8% capacity retention after 500 cycles under 2 C) are obtained in full cells when coupled with Li 4 Ti 5 O 12 (LTO) cathodes, indicating a promising potential for practical application. Moreover, this work demonstrates that using a new lithiophilic material, V 2 O 5 , should be an effective method to construct high stable 3D Li metal anode for Li metal battery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

FY2017 ILAW Glass Corrosion Testing with the Single-Pass Flow-Through Method

The inventory of immobilized low-activity waste (ILAW) produced at the Hanford Tank Waste Treatment and Immobilization Plant (WTP) will be disposed of at the near-surface, on-site Integrated Disposal Facility (IDF). When groundwater comes into contact with the waste form, the glass will corrode and radionuclides will be released into the near-field environment. Because the release of the radionuclides is dependent on the dissolution rate of the glass, it is important that the performance assessment (PA) model accounts for the dissolution rate of the glass as a function of various conditions. To accomplish this, an IDF PA glass dissolution model based on Transition State Theory (TST) can be employed. The model is able to account for changes in temperature, exposed surface area, and pH of the contacting solution as well as the effect of silicon solution concentrations, specifically the activity of orthosilicic acid (H4SiO4), whose concentration is directly linked to the glass dissolution rate. In addition, the IDF PA model accounts for the ion exchange process. The effect of temperature, pH, H4SiO4 activity, and the rate of ion exchange can be parameterized and implemented directly into the PA rate model. The rate model parameters are derived from laboratory tests with the single-pass flow-through (SPFT) method. The provided data can be used by glass researchers to further the understanding of ILAW glass behavior, by IDF PA modelers to use the rate model parameters in PA modeling efforts, and by Department of Energy (DOE) contractors and decision makers as they assess the IDF PA program.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Further Development of the Tamped Richtmyer-Meshkov Instability Method and Application to Molybdenum Dynamic Strength Calibration and Tabulation

The high pressure and high strain rate dynamic strength of Mo is experimentally and computationally investigated in the 3–20 GPa stress, 50–600 C temperature, and 10 5 –10 6 /s strain rate regimes using a modified tamped Richtmyer-Meshkov instability (RMI) method. Modifications to the tamped RMI method include a method to determine loading states during strain, a new strength calibration function based on interface shape, and a robust uncertainty quantification method. These modifications improve fidelity of the tamped RMI method, allowing evaluation of the compensating effects of pressure hardening, strain rate hardening, strain hardening, and thermal softening. The new calibration function based on interface shape is not limited to sinusoidal corrugations and could be applied to additional interface shapes. Plate impact experiments are performed at Argonne National Laboratory’s Advanced Photon Source’s Dynamic Compression Sector operated by Washington State University (DCS), driving a planar shock front through a corrugated Mo-D 2 O or Mo-C 8 F 18 interface, forcing the corrugation to significantly deform. The extent of interfacial deformation, RMI growth, is experimentally observed using X-ray phase contrast imaging at the DCS. RMI jet lengths and jet shapes are extracted from the experimental radiographs, then used to calibrate numerical simulations performed with the Sandia National Laboratories (SNL) hydrocode CTH. Mo yield strength, Y, as a function of shock pressure, P, strain rate, $\dot{\varepsilon }$, accumulated strain, ϵ, relative volumetric compression, RD , and temperature, T , is determined for each impact experiment and presented. The calibrated Mo yield strength values range 1.2–1.8 GPa, with strength generally decreasing as the impact stress increases. This trend is likely caused by thermal softening or strain localization. The tabular yield strength versus loading condition data presented in this paper can be used to fit complex strength models.

Voorhees, T. J. [Sandia National Lab. (SNL-CA), Li↗
DOE OSTI2024

Thermal and hydraulic performance of volumetrically heated triply periodic minimal surface heaters

Advanced heat transfer surfaces, mainly Triply Periodic Minimal Surfaces (TPMSs) have great potential to increase heat transfer performance over traditional heat transfer surfaces due to their tortuous flow path and higher surface area-volume ratio. The emergence of additive manufacturing of nuclear fuel motivates experimental investigation of such advanced geometries in the context of a nuclear reactor core. Here, this study employed a novel method to explore the performance of gyroid and diamond TPMS geometries. Volumetrically heated heaters were additively manufactured from conductive polymer filament, and airflow was used to simulate the convective heat transfer from the core within a nuclear reactor. The mass flow and volumetric generation rates were varied, local Nusselt number correlations were obtained using embedded thermocouples to measure the solid and fluid temperature, and friction factor correlations were developed using differential pressure measurements. The gyroid TPMS presented a friction factor 1.5 times higher than the diamond TPMS and 90 times that of the rod bundle. TPMS geometries showed Nusselt numbers 8–10 times higher than that of the rod bundle, also maintaining colder internal temperatures, which suggested that they could sustain 250–275 % higher power density than the rod bundle for a given centerline temperature. Hence, having the potential to greatly increase the safety margins and power output of a nuclear reactor core with similar volume.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗