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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Hydrogen transport in yttrium hydride under asymmetric heat

Metal hydrides are a promising moderator material for high temperature fission reactors. Yttrium hydride can be loaded to a high hydrogen density with relatively high hydrogen stability at temperatures up to 800°C. This makes yttrium hydride a potential moderator material for microreactors as foreseen in the fission surface power program. However, during the operation of such advanced reactors temperature gradients are expected which can change the local hydrogen density in the moderator. Hydrogen diffusion in metals is driven by a concentration gradient (Fick’s law) and thermal diffusion (Soret diffusion). Thermal diffusion is the transport of hydrogen, or other species, due to a temperature gradient. For example, hydrogen might migrate from the hot side of a sample to the cold side of a sample. Measuring Fickian diffusion is achieved through various permeation or absorption experiments, however measuring thermal diffusion is challenging and has rarely been performed. The Hydrogen Experimental Apparatus for Thermal Diffusion (HEATD) experiment is designed to induce thermal diffusion in samples and quench those samples so that the hydrogen distribution can be analyzed using hot vacuum extraction (HVE). One side of the sample was heated to a high temperature e.g., 800°C, while the other side of the sample is at a lower temperature. The sample was held under the applied temperature gradient for a given time until the anticipated hydrogen diffusion has occurred. The actual time depends depend on the sample composition and hydrogen concentration. The results from HVE showed that thermal diffusion took place in the specimen and the Soret coefficient was calculated.

36 MATERIALS SCIENCE↗
DOE OSTI2023

The Deimos Experiment: Advanced Reactor Testbed

Advanced reactor initiatives are growing significantly through programs nationwide. This research area includes small modular reactors, microreactors, and space reactors. Many of the reactors being designed are untested concepts. They include unique moderators, varying fuel types, high temperatures, and compact configurations. The shift in fuel type, from highly enriched uranium (HEU) to high assay, low enriched uranium (HALEU), is particularly important as it has driven many of the other changes. For example, lower enrichment requires advanced moderators, which in turn require different reflectors to make the systems compact. The change in materials including the transition from HEU to HALEU affects the temperature feedback of the systems. Additionally, these advanced reactor concepts generally have a thermal neutron spectrum in contrast to earlier fast spectrum advanced reactor. With the extensive changes from previous reactor designs, validation experiments are needed. The National Criticality Experiments Research Center (NCERC) is uniquely equipped to perform such experiments. The Deimos experiment, designed for execution at NCERC, will serve as a testbed for advanced reactor concepts. It will use HALEU fuel in a graphite matrix, provide the ability to use advanced moderators, and allow measurements of temperature reactivity coefficients (TRCs).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Sockeye Heat Pipe Analysis Code Verification and Validation

Some of the most promising microreactor designs currently under development utilize heat pipe technology to transfer heat from the reactor core to the secondary side heat exchanger, due to the technology’s compactness, efficiency, passivity, and reliability. Sockeye is an engineering-scale heat pipe tool developed under the Nuclear Energy Advanced Modeling and Simulation Program to be used for the design and safety analysis of microreactors. Sockeye’s core capability lies in a 1D, two-phase, compressible flow model, used to simulate the working fluid inside the heat pipe. Sockeye is built on the Multiphysics Object-Oriented Simulation Environment framework, which allows for seamless multiphysics coupling with other Nuclear Energy Advanced Modeling and Simulation tools and can thus be used in a full-scale simulation of a microreactor assembly, which can include hundreds of heat pipes. This paper presents some initial verification and validation assessments performed for Sockeye. We demonstrate good agreement between Sockeye’s numerical results and steady-state analytic solutions for velocity and pressure drop, with differences attributable to the underlying assumptions made by the analytic solutions. We also show that Sockeye reproduces analytic predictions of key operational limits, such as the capillary limit and the sonic limit. Finally, we compare Sockeye results to experimental data for the SAFE-30 heat pipe module test.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Survey of Uranium Nitride and Mixed Oxide Fuels for Microreactor Applications

Different fuel systems are being proposed for use in microreactors including TRISO fuel and metallic U-Zr based fuel. TRISO fuel is considered the main fuel option for most of the industry teams. Given the need for compact size core and potential for long fuel life of a microreactor, high density fuels such as metallic and nitride fuels are potential options. Of interest here is the uranium nitride (UN) option, which can allow for higher fissile material content and higher thermal conductivity compared to conventional uranium oxide fuel, and also has higher melting temperature compared to metallic fuel. Meanwhile, the current availability is limited for high assay low enriched uranium (HALEU) that is needed for high density fuels, which motivates the consideration of using Pu as a potential replacement for HALEU until adequate production capacity is in place. Current Pu availability is mainly attributed to the inventory of excess weapons Pu rather than through reprocessing of spent nuclear fuel. This inventory of excess Pu can be used in both metallic and oxide fuel systems to replace HALEU. Of interest here, the oxide form, that is the mixed oxide form of PuO 2 and UO 2 (MOX). In this report, the options of using UN or MOX, in both pellet and TRISO fuel forms, in microreactors are evaluated in relation to their properties, fuel performance and irradiation data, as well as fabrication. Gaps related to those areas are identified for both fuel systems, to guide future activities by DOE programs such as the advanced fuels campaign (AFC), to enable their use in microreactors.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Securing Future Energy Supplies: From Renewables to Microreactors

This session will provide insight into how future energy deployments, critical to national-level programs focused on reducing carbon emissions, can be secured-by-design using lessons learned from current energy infrastructure. It will begin with an overview of current threats and risks associated with renewable energy assets and systems, primarily wind and solar, focusing on their control architecture and key system functions for both efficient and safe operations. This talk will then translate the key takeaways from current renewable infrastructure into applications for securing future energy systems, including microreactors and small modular reactors (SMRs), based on planned concepts of operations and control. Microreactors and SMRs are intended to be factory-assembled with commercially available components and deployed in more remote or distributed environments, necessitating centralized control centers, remote monitoring, and offsite maintenance and technical support. All of these factors lead these assets to a security posture and controls more similar to today's renewable energy assets than today's nuclear reactors, which represents a significant shift in mindset for the nuclear industry. This talk will provide justification for this shift as well as a path forward to motivate securing these groundbreaking technologies from the outset of their design and deployment.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Non-destructive structural characterization of graphite components using mechanical resonance and deep learning

As compared to conventional nuclear reactors, microreactors have the potential to significantly reduce construction timelines and capital costs, decreasing the barriers for advanced nuclear reactor technologies. However, the lower power output of these microreactors (typically < 20 MWe) creates challenging economics if operation and maintenance costs cannot be sufficiently reduced. The compact size of these designs presents an opportunity for comprehensive in-situ structural health monitoring to provide real-time feedback in order to reduce operational costs associated with maintenance and downtime. Many microreactor concepts use graphite for both in-core neutron moderation and as a structural material, which has typically required some form of periodic and laborious inspection. This report provides a description and assessment of recent work with graphite to couple acoustic-based experimental measurements and characterization with machine learning models to mature structural health monitoring capabilities and generate benefits for the nuclear microreactor industry. With resilient embedded sensors in development in other programs funded by the US Department of Energy’s Office of Nuclear Energy and elsewhere, the work described herein builds upon previously funded efforts to mature non-destructive testing technology that relates measured vibrational signatures to structural changes, using a combination of new experimental measurements and machine learning processing. Building on past successful demonstrations of predictive workflows to identify structural changes in a hexagonal stainless steel test article with excellent acoustic propagation, we first performed baseline characterization on graphite samples with canonical geometries to ensure compatibility and confidence in the applied techniques for a material with distinctly different mechanical properties. In contrast to efforts in previous years, we worked exclusively with unidirectional vibration data that is more comparable to those expected from the existing embedded sensor technologies which are suitable for deployment in a reactor setting. Established acoustic and modern machine-learning-based characterization approaches were applied to the resulting datasets from these simple geometries. Both approaches were found to be highly capable of detecting even small geometric irregularities amongst nominally identical samples. As such, we then moved to testing these approaches for detection of artificial local stress perturbations introduced into a more complex geometry: a hexagonal block with drilled holes. A main outcome of this work is that a generalizable ML workflow can be used to detect and predict the characteristics of small artificial anomalies in a graphite component with a relevant geometry. While this work was performed using surficial vibration data, we expect the approach to be flexible and viable for other monitoring scenarios, such as those with different arrangements or types of sensor arrays. As compared to previously funded efforts, an existing ML workflow based on neural networks was enhanced through the addition of recently developed Fourier neural operators. As applied to previously collected and new vibration datasets, prediction accuracies of anomaly characterizations were greatly improved with minimal added computational cost. As trained on small durations of vibration data (tens of seconds) collected over a realistic number of locations, the model was able to reliably determine the presence of a subtle stress anomaly and begin to provide location estimates. Such an approach is likely to be viable for more relevant reactor damage scenarios for graphite components, such as progressive crack growth or creep.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Multiphysics Analysis of Load Following and Safety Transients for MicroReactors

The tools developed within the U.S. Department of Energy (DOE) Office of Nuclear Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aim at providing high fidelity multiphysics modeling capabilities to support design and licensing of various types of advanced nuclear reactors, including the technologies being developed by U.S. microreactor vendors relying on heat pipe and gas-cooled technologies. In FY-2022, the NEAMS Multiphysics Applications team made significant progress both in demonstrating capabilities applied to microreactor problems, and in supporting NEAMS developers.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Acoustic-based monitoring and machine learning of component status for microreactor applications

This report provides a description and assessment of recent efforts to couple acoustic-based experimental measurements and characterization with machine learning models in order to enhance structural health monitoring capabilities for nuclear microreactors. With resilient embedded sensors in development by others supported by programs funded by the US Department of Energy’s Office of Nuclear Energy, the work described herein builds upon ongoing efforts to improve non-destructive testing technology that relates measured acoustic signatures to component stresses and/or structural defects, using a combination of new experimental measurements and machine learning architectures. The experimental procedure remained similar to that developed for the previous year’s demonstration of damage detection by the authors, with the same damaged sample tested under similar applied stress conditions. Notably, a new mounting fixture was designed and implemented to improve measurement consistency and a more sophisticated laser Doppler vibrometer was employed to make high-fidelity vibration measurements. Two nominally identical sets of training data were collected for each experimental setup to better understand the repeatability of the experiment and to better test the generality of trained neural network models. Additionally, we obtained new high-quality 3D mode shapes of the damaged test article at various stress and excitation levels, providing greater insights into the physical response of the sample during testing. Previously, we demonstrated that a machine learning model based on a convolutional neural network can predict structural details of an artificially introduced interface (intact, rough cut, smooth cut), and the applied torque level. In this study, we have transitioned to graph-based neural network architectures to better develop and test a flexible framework that is more suitable to being transferred away from controlled benchtop experiments and into more applied settings where less-structured data inputs may be expected. In general, performance testing of a graph neural network on frequency-domain representations of the data indicates strong and consistent identification of test conditions for datasets recorded on damaged components. With goals of predicting damage location and other changing experimental conditions using limited datasets, predictive models using a graph neural network architecture correctly predicted the applied torque level with an accuracy of 85% using only a single measurement point and predicted within one torque level in 95% of test windows. Predictions of damage location had limited success due to the symmetry and minimal number of the damage scenarios presented during model training. Results were ambiguous as to whether the model could detect the location of the artificial damage, or if it was instead learning the location of a given measurement point on the part and subsequently detecting which points were closest to the location of the damage. This finding will be factored into upcoming planned work on damaged graphite components, where new experimental tests with a larger number and variety of damage scenarios are expected to provide improved validation of recent developments in monitoring methodology.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Comparative investigation of Ga- and In-CHA in the non-oxidative ethane dehydrogenation reaction

Ga- and In-exchanged chabazite (CHA) zeolites with same Si/Al and metal/Al ratios were prepared via the incipient wetness impregnation method, were characterized using N 2 adsorption, electron microscopy, temperature-programed reactions and were evaluated for the ethane dehydrogenation reaction using flow microreactors. Ga-CHA has higher reaction rates and a lower activation energy of 107 kJ/mol than In-CHA (E a = 175 kJ/mol). Rietveld refinement of the X-ray powder diffraction pattern shows that the In + cation is predominantly located above the 6-ring of the CHA cage. It is proposed that the reaction proceeds through the alkyl mechanism based on stability of alkyl hydride intermediates as determined using DFT calculations. The oxidative addition of ethane to the metal shows much lower Gibbs free energy for Ga-CHA (+27.95 kJ/mol) vs In-CHA (+124.85 kJ/mol). Finally, these results indicate that oxidative addition may be the rate-limiting step of ethane dehydrogenation in these materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

High-Fidelity Multiphysics Modeling of a Heat Pipe Microreactor Using BlueCrab

Researchers who are actively developing nuclear microreactors are planning to employ innovative designs and features using traditional commercial modeling tools that may be inadequate for their design and licensing activities. The codes developed under the U.S. Department of Energy Office of Nuclear Energy Advanced Modeling and Simulation (NEAMS) program provide flexibility in terms of geometry modeling and multiphysics coupling and are particularly well suited for modeling novel microreactor concepts. To test the maturity of these codes, this paper introduces a conceptual heat pipe microreactor (HP-MR) designed to gather various technologies of interest to microreactor developers such as control drums, heat pipes, and hydride moderators. Here, the objective of this effort is to demonstrate NEAMS tools capability to perform high-fidelity multiphysics simulations, using coupled neutronics (via the Griffin code), heat conduction (via the BISON code), heat pipe modeling (via the Sockeye code), and hydrogen redistribution in hydride metal moderator (via the SWIFT code). Codes are coupled in-memory through the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, which permits flexible multiphysics data transfer schemes. The analysis confirmed two key aspects of the HP-MR concept: (1) its ability to follow the power load requested from the heat pipe and (2) its ability to avoid heat pipe cascading failure unless designed with high power close to operating failure limits of its heat pipes. The developed computational model was distributed publicly on the Virtual Test Bed for training purposes to accelerate adoption by industry and to provide a high-fidelity multiphysics solution for benchmarking against other tools. Additional multiphysics analyses including other transients and coupled physics were identified as necessary future work, together with a focus on validating multiphysics behavior against experiments.

Microreactor↗
DOE OSTI2026

BISON analyses of TRISO fuel performance, its dependence on time-at-temperature, and possible implications for fuel design and qualification

The Advanced Gas Reactor Fuel Development and Qualification (AGR) program has established a substantial technical foundation to support private entry into the U.S. high-temperature gas-cooled reactor market. However, emerging tristructural isotropic (TRISO)-fueled reactor applications include small modular reactors and microreactors with longer fuel residence times, which may expose fuels to higher time-at-temperature (TAT) values than were explored by the AGR program. Increased TAT could affect diffusive and thermomechanical behaviors such as Pd penetration, fission gas release, creep, and fission product transport. In this work, we applied multiscale best-estimate BISON fuel performance modeling to assess these effects within a representative design space based on the AGR-5/6/7 experiment and analyzed trends in predicted particle and compact fuel performance metrics with possible implications for near-term fuel design and qualification. BISON unambiguously predicted that TRISO fuel performance is sensitive to TAT. Increasing TAT was not predicted to increase the magnitude of failure-inducing tangential stresses in particle coating layers. Predictions obtained using a mechanistic model for Pd penetration indicated that penetration depth does not depend strongly on TAT. While these observations suggest that AGR testing provides a conservative upper bound for the steady-state operation of TRISO particles at lower powers and higher residence times, BISON also predicted that the release of poorly retained Ag would increase with TAT. Because these analyses applied models to extrapolate beyond the available experimental data, the authors recommend performing targeted experiments to confirm these predictions. Nevertheless, these predictions may provide reactor developers with enough confidence to make near-term design decisions associated with the potential fuel performance trade-offs of increasing TAT.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Fuel Fabrication Capability Assessment in Support of Advanced Reactor Deployments

More than 30 U.S. companies are designing a variety of advanced reactor concepts, and several companies are planning to demonstrate their reactor designs in the mid-2020s to late 2030s time frame. In 2020, the U.S. Department of Energy (DOE) announced a series of awards under the Advanced Reactor Demonstration Program (ARDP) to accelerate the successful deployment of 10 of these reactors under three pathways. TerraPower and X-energy were awarded grants under the Advanced Reactor Demonstration Program to deploy their respective Natrium reactor and Xe-100 reactor designs in the next 7–10 years. These demonstrations are in addition to several parallel programs, including the U.S. Department of Defense’s (DoD’s) interest in the development of microreactors, and interest of the National Aeronautics and Space Administration in space nuclear power and propulsion. The National Reactor Innovation Center’s (NRIC’s) mission is to accelerate the demonstration and deployment of advanced reactors; NRIC is partnering with several reactor developers and harnessing the world-class capabilities of the U.S. National Laboratory system to deliver on its mission. Several of these reactor designs will require advanced fuel forms that are not commercially available today, including metal fuel, molten salt fuel, TRi-structural ISOtropic (TRISO) particle fuel, and uranium nitride fuel. Recognizing that there may be potential gaps in the laboratory-scale process development and pilot-scale first-of-a-kind (FOAK) production of these fuel forms leading to delivery of the FOAK cores, NRIC commissioned this study to look at the challenges that need to be overcome for successful deliveries, including the evaluation of existing facilities and the potential need for a new fuel fabrication facility.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Road Map for the Development of Commercial Maritime Applications of Advanced Nuclear Technology

The U.S. Department of Energy (DOE) is making significant investments in advanced nuclear reactor development and demonstration programs such as the Advanced Reactor Demonstration Program (ARDP). The innovations in advanced nuclear reactor technologies (defined in Section 1.4.1), including concepts of microreactors and small modular reactors (SMRs), have opened the door to a wide range of applications beyond land-based power stations providing electricity to the grid. Applications for transportation and industrial operations could offer reliable carbon-free energy to achieve decarbonization goals by supporting energy-intensive activities such as alternative fuel production, water desalinization, and local power supply. The DOE recognizes the transformational power of these technologies and is investing in foundational research and development as well as reactor demonstration projects. However, unique and significant challenges exist in each application domain to bring projects into reality beyond just the underlying reactor technology maturation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Flattening the Radial Temperature Profile across the Transformational Challenge Reactor Core

The Transformational Challenge Reactor (TCR) program is demonstrating an agile development approach to advanced nuclear reactor design, which has traditionally utilized a linear design process. In leveraging artificial intelligence, additive manufacturing, advanced materials, and cutting-edge modeling and simulation, the TCR program aims to minimize the high cost and lengthy deployment timelines now standard in the nuclear industry. Within a relatively short period of time, a robust and mature advanced gas-cooled reactor was iteratively designed under the TCR program, using these cutting-edge technologies. The TCR is a 3 MWt gas-cooled microreactor fueled with uranium nitride (UN) tristructural isotropic (TRISO) fuel particles. Though manufactured via traditional means, these UN TRISO particles are loaded into additively manufactured silicon carbide (SiC) cans [4]. Once loaded with TRISO particles, the SiC cans are densified using a chemical vapor infiltration process. The additively manufactured SiC enables significantly more freedom in the design of the fuel form than could ever be achieved using traditionally manufactured SiC. The helium coolant, pressurized to 5 MPa, enters the core at 300°C and nominally exits it at 500°C. Typically, the most thermally limiting components in any reactor design are the fuel assemblies in the core center. To provide a wide thermal margin in these central fuel assemblies, the flow may be biased toward the center of the core to more effectively cool these fuel assemblies with more power deposition and flatten the core’s radial temperature distribution. An analytical fluid model of the TCR core was developed to explore methods for biasing the flow away from the cooler outer fuel assemblies and towards the hotter inner ones. Higher-fidelity models developed in STAR-CCM+ 2020.3.1, a computational fluid dynamics code, were then utilized to verify the analytical model’s findings.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗