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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Simultaneous reaction and separation of chemicals

The reaction rate of hydrocarbon pyrolysis can be increased to produce solid carbon and hydrogen by the use of molten materials which have catalytic functionality to increase the rate of reaction and physical properties that facilitate the formation and contamination-free separation of the solid carbon. Processes, materials, reactor configurations, and conditions are disclosed whereby methane and other hydrocarbons can be decomposed at high reaction rates into hydrogen gas and carbon products without any carbon oxides in a single reaction step. The process also makes use of specific properties of selected materials with unique solubilities and/or wettability of products into (and/or by) the molten phase to facilitate generation of purified products and increased conversion in more general reactions.

McFarland, Eric W.↗
DOE OSTI2021

Cladding Profilometry Analysis of Experimental Breeder Reactor-II Metallic Fuel Pins with HT9, D9, and SS316 Cladding

BISON finite element method fuel performance simulations were conducted using an existing automated process that couples the Fuels Irradiation & Physics Database (FIPD) and the Integral Fast Reactor Materials Information System database by writing input files and comparing the BISON output to post-irradiation fuel pin profilometry measurements contained within the databases. The importance of this work is to demonstrate the ability to benchmark fuel performance metallic fuel models within BISON using Experimental Breeder Reactor-II fuel pin data for a number of similar pins, while building off previous modeling efforts. Changes to the generic BISON input file include implementing pin specific axial power and flux profiles, pin specific fluences, frictional contact, and irradiation-induced volumetric swelling models for cladding. A statistical analysis of irradiation-induced volumetric swelling models for HT9, D9, and SS316 was performed for experiments X421/X421A, X441/X441A, and X486. Between these three experiments, there were 174 post-irradiation examination (PIE) profilometries used for validating the swelling models presented using a standard error of the estimate (SEE) method. Implementation of the volumetric swelling models for D9 and SS316 claddings was found to have a significant impact on the BISON profilometry simulated, where HT9 clad pins had an insignificant change due to low fluence values. BISON profilometry simulated for HT9, D9, and SS316 fuel pins agreed with PIE profilometry measurements, with assembly SEE values being 4.4 × 10−3 for X421A, 2.0 × 10−3 for X441A, and 2.8 × 10−3 for X486. D9 clad pins in X421/X421A had the highest SEE values, which is due to the BISON simulated profilometry being shifted axially. While this work accomplished its purpose to demonstrate the modeling of multiple fuel pins from the databases to help validate models, the results suggest that the continued development of metallic fuel models is necessary for qualifying new metallic fuel systems to better capture some physical performance phenomena, such as the hot pressing of U-Pu-Zr and the fuel cladding chemical interaction.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Corrosion testing needs and considerations for additively manufactured materials in nuclear reactors

Metal additive manufacturing holds significant promise as an enabling technology for the 21st century nuclear energy industry. Metal additive manufacturing (MAM) can allow the fabrication of novel materials and innovative component designs that are not achievable through conventional manufacturing. Due to its very different fabrication methods, as-fabricated MAM components are characteristically different from conventionally manufactured components. MAM materials exhibit very different microstructures from conventional cast or wrought materials. For example, austenitic stainless steels fabricated by laser powder bed fusion exhibit columnar grain structures, dislocation cell structures, and melt pool fingerprints. In addition, MAM fabrication may result in defects such as porosity, incomplete processing of the feedstock (e.g., lack of fusion in melt-based methods), and oxide inclusions. Heat treatments may further evolve the microstructure, microsegregation, and stresses within the component. Furthermore, MAM components have a rough surface with feature sizes on the order of the feedstock material, as opposed to smooth surfaces resulting from conventional machining and forming operations. As a result, the corrosion behavior of MAM components will likely be significantly different from that of conventionally formed components. Corrosive environments for structural materials within advanced reactor environments include molten fluoride and chloride salts, liquid sodium and lead-bismuth, and high-temperature helium. The Advanced Materials and Manufacturing Technologies program within the Department of Nuclear Energy in the United States Department of Energy is assessing the unique concerns of MAM component corrosion and testing methodologies in advanced nuclear reactor environments. We discuss these concerns and testing strategies in this paper.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Dynamic data-driven multiscale modeling for predicting the degradation of a 316L stainless steel nuclear cladding material

Here, we have developed a long short-term memory stacked ensemble (LSTM-SE) surrogate modeling approach that can provide rapid predictions of microstructural evolution and the resultant mechanical properties of American Iron and Steel Institute (AISI) 316L series stainless steel (316LSS) fuel cladding under conditions of varying temperature and radiation dose rate. To acquire training data, we developed and implemented a kinetic Monte Carlo (KMC) model to simulate precipitation kinetics of M 23 C 6 , γ', and G phases within SS316L cladding. Experimentally reported precipitation kinetics of SS316L in literature were linked to the kinetic parameters of the simulated precipitation in our KMC model. The model was then used to simulate microstructure evolution under synthetically generated treatments of varying temperature and radiation dose rate, for periods of up to 3000 hours. Changes in volume fraction, number density, and particle size of precipitates were recorded, and particle area fractions were correlated using statistical methods to develop the surrogate model. Simultaneously, the mechanical properties of the simulated microstructures were evaluated using microstructure-based finite element method (FEM) analysis to determine the elastic modulus, yield stress, ultimate tensile strength, and elongation to failure of the aged microstructures. Using this approach, our surrogate model can predict precipitation behavior within 0.25% volume fraction and mechanical properties within 6% relative error from the values predicted by the KMC and FEM models using 50 training simulations as input. The trained recurrent neural network-based model can return estimations of precipitation kinetics and mechanical properties ~1000 times faster than the physics-based codes. This work demonstrates, as a proof of concept, that reactor material service lifetimes under variable service conditions can be predicted for a statistics-based model from a practicably obtainable dataset.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Microreactor Optimization Using Simulation And Economics (mouse)

Microreactor Optimization Using Simulation and Economics (MOUSE) is a tool that integrates both nuclear microreactor design and reactor economics to provide comprehensive evaluations and optimizations. This tool enables stakeholders to explore the interplay between technical and economic variables, guiding them towards effective and competitive microreactor solutions. For the reactor core simulations, MOUSE leverages the OpenMC Monte Carlo Particle Transport Code to perform detailed core simulations for various microreactor designs. The included OpenMC models are 2D core designs of a Liquid Metal Thermal Microreactor (LMTR), a Gas-Cooled TRISO-Fueled Microreactor (GCMR), and a Heat Pipe Microreactor. Beyond core design, MOUSE includes simplified calculations for: - Calculating the masses of heat exchangers within the system. - Mechanical power of pumps. - Estimating the area occupied by various buildings within the nuclear plant. For the economic analysis, MOUSE provides detailed bottom-up cost estimates, encompassing a wide range of costs including preconstruction costs, direct costs, indirect costs, training costs, financial costs, operation & maintenance (O&M) costs, and fuel costs. These cost estimations are developed using data from the MARVEL project and additional literature sources, enabling the calculation of total capital costs and levelized cost of energy for both first-of-a-kind and nth-of-a-kind microreactors. MOUSE also enables analysis of the cost drivers and competitiveness in the electricity market. MOUSE allows users to modify a wide array of technical and economic parameters to evaluate different scenarios and their impacts. Examples of these parameters include: Fuels, coolants, or reflector materials Enrichment levels Control drum materials and geometry Fuel pin geometry and materials Moderator pin geometry and materials Reactor core and reflector dimensions Packing factor for the TRISO particles Nuclear reactor power and reactor burnup Number of sensors Shielding thickness Reactor vessel and guard vessel dimensions Operational staff requirements Number of emergency shutdowns Levelization period Interest rate Construction duration Since MOUSE is powered by the WATTS toolkit, it supports optimization studies, parametric analyses, and uncertainty calculations/propagation. The optimization techniques enable users to identify optimal design and economic configurations. The parametric analysis tools allow users to explore the sensitivity of various parameters, while uncertainty propagation helps quantify the impact of uncertainties on overall performance and cost. User Interface and Workflow: Currently, MOUSE is a command-line-based tool. Users can input various reactor design or economic parameters, modify the designs, run simulations, and visualize results through comprehensive data visualization and reporting capabilities. The typical workflow involves setting up the reactor model, defining economic parameters, running simulations, and analyzing the results to make informed decisions. By combining advanced design calculations with detailed economic modeling, MOUSE provides a robust framework for optimizing nuclear microreactor technologies, enhancing their competitiveness, and guiding stakeholders towards innovative and cost-effective solutions.

Hanna, Botros [Idaho National Laboratory (INL), Id↗
DOE OSTI2025

Directed energy deposition of functionally graded V-4Cr-4Ti to Fe-9Cr transition for fusion power systems

This study proposes a graded structure via additive manufacturing for divertor and first wall blanket applications in fusion reactors. Materials were selected based on thermodynamic calculations to operate from 1100 °C at the plasma-facing level to 550 °C at the structural steel level. Conventional joining methods often lead to failures due to discrete reaction layers with significant mechanical property differences. Using laser beam-directed energy deposition (LB-DED), this study demonstrates the fabrication of a VCrTi-Gr91 steel functionally graded component through a novel process parameter optimization framework. A systematic approach included powder characterization, single-track depositions, and construction of printability maps. Near full-density specimens of each interlayer were additively manufactured, and a transition from V-based alloys to reduced activation ferritic martensitic steels was achieved. Computational material selection of interlayer alloys and thermodynamic/diffusion kinetics simulations prevented most interface incompatibilities. A brittle intermetallic formed at one interface, causing cracking, which was not predicted by current thermodynamic models. Transition alloy design approach was updated with a more recent database and a mitigation strategy has been proposed to eliminate the formation of deleterious intermetallic phases. Ultimately, LB-DED has proven effective for producing multi-material graded systems for fusion applications, with the demonstrated process parameter optimization framework applicable to various materials.

Additive manufacturing↗
DOE OSTI2024

Chemical composition based machine learning model to predict defect formation in additive manufacturing

With a goal of exploiting additive manufacturing to improve the manufacturing of existing reactor materials, we developed a chemical composition-based machine learning model to predict the printability of any given alloy in laser powder bed fusion (L-PBF) using experimental data from peer-reviewed literature. We defined printability as the ability to avoid defects like cracking, balling, porosity, and lack of fusion, that are caused by thermal stresses (during solidification or liquation), molten pool disintegration into disconnected small beads or lack of heat input respectively. Our models predict the tendency of balling defect formation and porosity percentage for a given composition, under a given set of processing conditions. To predict the likelihood of balling defect, three models: a random forest classifier, a gradient boost regressor and a neural network were trained on a dataset containing both traditional alloys and high entropy alloys. The neural network model showed the highest accuracy of 92.3 % in predicting the balling defect formation. A random forest regressor, gradient boost regressor and neural network were trained and tested on a dataset of various alloys to predict porosity. The random forest regressor showed the best predictions with an R 2 score of 0.97. The models also revealed the relative importance of the input descriptors on defect-formation tendency. Of particular significance was the identification of carbon as an important element in determining the occurrence of balling and percent porosity in alloys like steel, as well as being moderately important to the percentage porosity in other alloys as well as steel. Manganese was also identified as a key descriptor for the percentage of porosity in steel and other alloys. Manganese’s low thermal conductivity and consistent presence in the dataset is the likely cause for its contribution. Carbon’s role is attributable to its relatively high specific heat and high melting temperature. In conclusion, our model serves as a swift, chemistry-based tool to design experiments and find modified compositions better suited for additive manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Dynamic Data-Driven Multiscale Modeling for Predicting Microstructure and Mechanical Properties of 316L Stainless Steel

We have developed a long short-term memory stacked ensemble (LSTM-SE) surrogate modeling approach that can provide rapid predictions of microstructural evolution and the resultant mechanical properties of American Iron and Steel Institute (AISI) 316L series stainless steel (316LSS) fuel cladding under conditions of varying temperature and radiation dose rate. To acquire training data, we developed and implemented a kinetic Monte Carlo (KMC) model to simulate precipitation kinetics of M 23 C 6 , γ', and G phases within SS316L cladding. Experimentally reported precipitation kinetics of SS316L in literature were linked to the kinetic parameters of the simulated precipitation in our KMC model. The model was then used to simulate microstructure evolution under synthetically generated treatments of varying temperature and radiation dose rate, for periods of up to 3000 hours. Changes in volume fraction, number density, and particle size of precipitates were recorded, and particle area fractions were correlated using statistical methods to develop the surrogate model. Simultaneously, the mechanical properties of the simulated microstructures were evaluated using microstructure-based finite element method (FEM) analysis to determine the elastic modulus, yield stress, ultimate tensile strength, and elongation to failure of the aged microstructures. Using this approach, our surrogate model can predict precipitation behavior within 0.25% volume fraction and mechanical properties within 6% relative error from the values predicted by the KMC and FEM models using 50 training simulations as input. The trained recurrent neural network-based model can return estimations of precipitation kinetics and mechanical properties ~1000 times faster than the physics-based codes. This work demonstrates, as a proof of concept, that reactor material service lifetimes under variable service conditions can be predicted for a statistics-based model from a practicably obtainable dataset.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Irradiation Behavior of Piezoelectric Materials for Nuclear Reactor Sensors

In-situ measurement of surface acoustic wave (SAW) resonators were used to characterize piezoelectric materials behavior in a nuclear reactor environment. Lithium niobate and aluminum nitride devices were tested up to 500°C temperature and 450 kW reactor power (1.9x10 12 n/cm 2 s). The resonant frequency responds to step changes in temperature and power. Materials’ response are inferred from the observed resonant frequency changes, in particular the kinetic behavior is used to determine mechanisms responsible for observed changes. The observed response is attributed to two mechanisms: temperature increase from gamma heating and accumulation of radiation-induced defects. Both of these mechanisms alter the physical properties of the piezoelectric materials, particularly the elastic constants. The demonstrated repeatability and reliability of SAW devices are attractive for sensor applications in extreme environments.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Demonstration of Advanced Experimental and Theoretical Characterization of Hydrogen Dynamics and Associated Behavior in Advanced Reactors

Advanced materials development, manufacturing, and modeling capabilities for innovative reactor designs support nuclear security and mission-focused science through enhanced technology for safer and more efficient and secure production of nuclear energy. The research in this project has established: 1) a state-of-the-art neutron-based hydrogen mapping and cross-section measurement capability as well as detailed crystallographic characterization of hydrogen atoms at LANSCE, and 2) a multi-physics framework for simulating behavior of moderator materials and other material performance in advanced nuclear reactors. Through the course of this project, we successfully developed and demonstrated measurement techniques for hydrogen distribution and atomistic-scale behavior of hydrogen atoms using pulsed neutron techniques. In parallel, advanced multi-physics simulation tools to predict the behavior of hydrogen atoms, e.g. in a moderator for a nuclear reactor, through materials performance, neutron transport, and thermal mechanical behavior were enhanced. Multi-discipline areas across the laboratory were involved in the project as the integration of improved experimental capabilities with enhanced modeling and simulation through MST, NEN, SIGMA, and XCP division subject matter experts.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Light Water Reactor Sustainability Program: Materials Research Pathway: FY 23 Technical Program Plan

Components in operating commercial nuclear power plants must withstand very harsh environments that include extended time at neutron and gamma irradiation, stress, and temperature, as well as possible exposure to corrosive media. The many modes of materials degradation are complex and often include synergies between multiple environmental variables and conditions that vary depending on locations and materials. Understanding and managing materials degradation is a requirement for the continued safe and reliable operation of nuclear power plants.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Distribution of Tritium in the Near Surface of Candidate Structural Materials for Fusion Reactors. Type 304L Stainless Steel, Inconel, Hastelloy, Eurofer-97, Oxide Dispersion-Strengthened Alloy 14YWT

Five different candidate structural materials for fusion have undergone pure tritium gas soaking at room temperature and at 310-mbar(a) pressure. The tritium uptake on the surface and in the bulk of the alloys has been analyzed using surface leaching, chemical etching, and thermal desorption. The nickel-based alloys: Inconel-X-750 and Hastelloy-X, absorbed the least amount of total tritium compared with austenitic stainless steel AISI 304L, reduced activation ferritic-martensitic (RAFM) Eurofer-97, and advanced nanoferritic alloy 14YWT. Microstructural analyses using electron backscatter diffraction (EBSD) indicate that the number of grains and mean grain size is not a dominant factor in near surface tritium uptake. The quantity of iron dissolved in the surface oxide appears to be the major factor in encouraging tritium absorption.

O'Callaghan, James↗
DOE OSTI2024

Corrosion testing needs and considerations for additively manufactured materials in nuclear reactors

Additive manufacturing (AM) technologies have developed rapidly in recent years, creating new opportunities and challenges for the nuclear industry; however, adoption requires that their corrosion performance be evaluated. Here, we discuss known reactor-specific corrosion issues for multiple reactor types and engineering concerns such as regulations and standards. A review of corrosion studies conducted on select AM alloys informs a discussion on key bulk and surface factors likely to impact corrosion behaviors. Recommendations to assess corrosion performance for AM materials are provided, including management of the unique nature of as-built AM surfaces and the inherent process variability that occurs for AM components.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Effects of increasing the initial nitric acid concentration from a maximum of 7.5 to 8.5 M during the dissolution of aluminum spent nuclear fuel

H-Canyon is blending solutions from the dissolution of High Flux Isotope Reactor (HFIR) and Material Test Reactor (MTR) fuels with Target Residue Material (TRM) to prepare feed solution for the 1st Cycle of solvent extraction. The initial acid concentration for HFIR fuel dissolution is limited to 7.5 M by the flowsheet; however, an increase in the initial concentration is desirable to more easily achieve the target acidity for solvent extraction using the current blending strategy. To provide flexibility in batching the highly enriched uranium (HEU) solutions, the Savannah River National Laboratory (SRNL) was requested to evaluate the potential for increasing the maximum HNO3 concentration for HFIR fuel dissolution from 7.5 to 8.5 M. In response to this request, a literature review was performed to evaluate the impact of a higher starting HNO3 concentration on the dissolution of aluminum spent nuclear fuels (ASNF) including both HFIR and MTR fuels.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

An approach to combine neutron and ion irradiation data to accelerate material qualification for nuclear reactors

Next-generation nuclear power plants are generally characterized by higher operating temperatures, increased neutron fluences and energies, and distinct corrosive coolant environments versus the existing light water reactor fleet. Whether using existing materials in new environments, newly developed materials tailored for these environments, or new manufacturing methods, the traditional decades-long approach for materials qualification does not facilitate rapid deployment. Ion irradiation has demonstrated success in reproducing material microstructure and select property evolution resulting from neutron irradiation with three to four orders of magnitude reduction in time and cost, making it an ideal candidate for accelerated irradiation testing. Because microstructure has a large impact on bulk material properties, limited neutron irradiation data at lower damage levels can in principle be combined with accelerated ion testing results and modeling and simulation to form an accurate prediction of microstructure evolution and select properties under different neutron irradiation conditions and at higher damage levels. The objective of this work is to present a conceptual framework of specific steps to fulfill several technical challenges associated with qualifying materials for performance in radiation environments on an accelerated time frame. A brief review of the regulatory landscape for materials in nuclear environments is presented, followed by additional overviews to understand the current state of the art for correlation of materials properties across radiation environments using experimental and computational methodologies. Finally, the roles of academia, national laboratories, and industry in the advancement of this accelerated materials qualification framework are discussed as a path forward, with possible case studies presented.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗