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To qualify the laser powder bed fusion (LPBF) 316H stainless steel (SS) for structural use in advanced nuclear reactors, a thorough understanding of the process-microstructure-property correlation needs to be developed. This report provides an overview of ANL's research effort towards this goal in Fiscal Year (FY) 24.
This work reports on the investigations done during FY 24 in collaboration with two 2024 Summer undergraduate interns through the DOE SULI and BNL SURP programs. These efforts were partially funded by the Nuclear Criticality Safety Program through the Technical Support Succession Plan task. The first project, developed by Ian Snider, focused on testing the impact of uncertainties in thermal cross sections for multiple materials in nuclear reactor simulations. The second project aimed implement concurrent into BNL’s machine-learning code to correct spin mis-assignments in neutron resonances, the Bayesian Resonance Reclassifier (BRR).
A test loop using NaK as the working fluid is presently in use to study material compatibility effects on various components that comprise a possible nuclear reactor design for use on the lunar surface. A DC electromagnetic (EM) pump has been designed and implemented as a means of actively controlling the NaK flow rate through the system and an EM flow sensor is employed to monitor the developed flow rate. These components allow for the matching of the flow rate conditions in test loops with those that would be found in a full-scale surface-power reactor. The design and operating characteristics of the EM pump and flow sensor are presented. In the EM pump, current is applied to a set of electrodes to produce a Lorentz body force in the fluid. A measurement of the induced voltage (back-EMF) in the flow sensor provides the means of monitoring flow rate. Both components are compact, employing high magnetic field strength neodymium magnets thermally coupled to a water-cooled housing. A vacuum gap limits the heat transferred from the high temperature NaK tube to the magnets and a magnetically-permeable material completes the magnetic circuit. The pump is designed to produce a pressure rise of 5 psi, and the flow sensor's predicted output is roughly 20 mV at the loop's nominal flow rate of 0.5 GPM.
Multiple liquid metal pump options are reviewed for the purpose of determining the technologies that are best suited for inclusion in a nuclear reactor thermal simulator intended to test prototypical space nuclear system components. Conduction, induction, and thermoelectric electromagnetic pumps are evaluated based on their performance characteristics and the technical issues associated incorporation into a reactor system. The thermoelectric electromagnetic pump is recommended for inclusion in the present system based on favorable quantitative and qualitative measures relative to the other options under consideration.
Multiple liquid-metal pump options are reviewed for the purpose of determining the technologies that are best suited for inclusion in a nuclear reactor thermal simulator intended to test prototypical space nuclear system components. Conduction, induction, and thermoelectric electromagnetic pumps are evaluated based on their performance characteristics and the technical issues associated with incorporation into a reactor system. The thermoelectric pump is recommended for inclusion in the planned system at NASA MSFC based on its relative simplicity, low power supply mass penalty, flight heritage, and the promise of increased pump efficiency over earlier flight pump designs through the use of skutterudite thermoelectric elements.
Multiple liquid metal pump options are reviewed for the purpose of determining the technologies that are best suited for inclusion in a nuclear reactor thermal simulator intended to rest prototypical space nuclear surface power system components. Conduction, induction and thermoelectric electromagnetic pumps are evaluated based on their performance characteristics and the technical issues associated with incorporation into a reactor system. A thermoelectric electromagnetic pump is selected as the best option for use in NASA-MSFC's Fission Surface Power-Primary Test Circuit reactor simulator based on its relative simplicity, low power supply mass penalty, flight heritage, and the promise of increased pump efficiency over those earlier pump designs through the use of skutterudite thermoelectric elements.
Microreactors have great potential to decrease capital costs and construction timelines, reducing the barriers to implementing advanced nuclear reactor technologies. However, the lower power output of these microreactors introduces economic challenges if the operation and maintenance costs cannot be reduced sufficiently. Many microreactor concepts use graphite materials for in-core moderator and structural purposes, which will require periodic inspection or, ideally, in situ structural health monitoring. This work describes an initial evaluation of the feasibility of leveraging graphite’s semiconducting properties to perform electrical impedance tomography (EIT) for defect localization. First, a study was performed to identify the best methods for bonding electrical contacts to minimize the effects of contact resistance that interfere with impedance measurements of the graphite. After determining the best bonding approach, electrodes were attached to multiple graphite components with varying geometries (e.g., simple block and more representative microreactor hexagonal block). In parallel, finite element analysis approaches were developed and implemented to analyze the impact of defects and contact resistances and to inform an iterative inverse model for reconstructing the conductivity distribution. The results of this study show that defect localization in graphite components is possible using EIT if a sufficient number of electrodes (to improve spatial resolution) can be bonded using a technique with very low contact resistance and those contact resistances remain stable during reactor operation. If the reactor core and vessel design do not allow electrodes to be bonded during reactor operation, then it may be possible to detach/reattach electrodes between reactor operation and outages if a suitable mechanical connection that offers repeatable contact resistance can be identified. Alternatively, future work could focus on identifying contacts that can survive the harsh reactor operating environment so that contacts do not need to be removed.
Critical heat flux (CHF) marks the transition from nucleate to film boiling, where heat transfer to the working fluid can rapidly deteriorate. Accurate CHF prediction is essential for efficiency, safety, and preventing equipment damage, particularly in nuclear reactors. Although widely used, empirical correlations frequently exhibit discrepancies when compared to experimental data, limiting their reliability in diverse operational conditions. Traditional machine learning (ML) approaches have demonstrated potential for CHF prediction but often suffer from limited interpretability, data scarcity, and insufficient knowledge of physical principles. Hybrid model approaches, which combine data-driven ML with base models, mitigate these concerns by incorporating prior knowledge of the domain. This study integrates an externally trained purely data-driven ML model and two hybrid models (using the Biasi and Bowring CHF correlations) within the CTF subchannel code via a custom Fortran framework. Performance was evaluated using two validation cases: a subset of the Nuclear Regulatory Commission (NRC) CHF database and the Bennett dryout experiments. In both cases, the hybrid models demonstrated significantly lower error metrics compared to conventional empirical correlations, with the best models often reducing relative error by about 5 percentage points. The pure ML model achieved comparable accuracy, outperforming the hybrid Biasi model in the NRC test case (3.3% versus 5.5% relative error) but exhibiting slightly higher error against the hybrid Bowring model in the Bennett test case (7.7% versus 6.1%). Trend analysis of error parity indicated that ML-based models reduced the tendency for CHF overprediction, improving overall accuracy. These results demonstrate that ML-based CHF models can be effectively integrated into subchannel codes and could potentially increase performance compared to conventional methods.
Developments related to generative artificial intelligence (AI) have brought a major breakthrough in AI. These developments are rapidly accelerating developments in different science and engineering applications. Nearly Autonomous Management and Control (NAMAC) system provides recommendations to the operator for maintaining the safety and performance of the reactor. The discrepancy checker (DC) is an important component of the NAMAC) system, whose goal is to determine if the plant is moving towards the expected system state after the control actions are injected. In this work, we explore generative AI methods, particularly, a generative pretrained transformer (GPT) for implementing the DC function in NAMAC. The GPT-based DC aims to alert the operator in situations outside NAMAC’s scope and act as a chatbot the operator can use to retrieve relevant information. This study involves two versions of GPT developed by OpenAI: GPT-3.5 and GPT-4. These GPTs are trained on huge amounts of undisclosed general domain datasets. We explored two methods to adapt GPTs for DC implementation in NAMAC: fine-tuning and retrieval augmented generation. A small knowledge base (information file) that encompasses rules for DC implementation and some general information related to NAMAC has been created to support DC implementation using GPT. In this work, the GPT-based DC implementations have been tested for their reasoning abilities, comprehension, information retrieval, and extraction abilities. It should be noted that this paper only presents a preliminary study to test the feasibility of DC implementation using generative AI technology. Given the potential risks and severe consequences associated with nuclear reactor applications, combined with the black-box nature of AI, extensive offline and online testing and reliability analyses of GPT-based DCs are needed for further developing such capabilities.
The rising interest in molten salts, both as thermal energy storage media and as high-temperature solvents for fissile materials in advanced nuclear reactors, has spurred significant growth in research on structural and functional salt-facing materials. Impurities, such as water, play a significant role in the degradation of materials in molten salt environments. Other impurities, such as fission products, are expected to accumulate in molten salts during normal reactor operation and can affect the properties of the salt and salt-facing materials. During normal reactor operation, rare-earth element species are expected to form as byproducts of fission reactions, potentially degrading or altering the performance of both structural and functional materials exposed to salts. Here, in this work, fission product surrogates, europium metal and EuCl 3 , were added to the KCl-MgCl 2 salt while exposing pure Cr specimens in the melt at 600 and 700 °C for 1000 h. Low reactivity was observed with the baseline salt, while both the Eu and EuCl 3 additions to the salt resulted in increased mass loss of Cr specimens. A capillary electrophoresis technique was developed to enable the detection of low parts-per-million (ppm) concentrations of europium (Eu) in chloride salt both before and after exposure of Cr metal. This technique provides a sensitive and reliable method for monitoring trace levels of Eu, which is critical for understanding the interactions and potential contamination effects in salt-facing materials during reactor operation. This quantification technique can be applied to other cationic and anionic contaminants in halide salts.
Innovative thin film multilayer metal/ceramic barrier coatings have been developed by Argonne National Laboratory to protect fuel cladding against different phenomena that can be encountered under the harsh operating conditions of advanced nuclear reactors. One phenomenon is Fuel-Cladding Chemical Interaction (FCCI), which can represent a challenge to the performance of steel-clad metallic fuels in sodium-cooled fast reactors (SFRs), especially when operating to high burnups at elevated cladding temperatures, and long fuel residence time. Another phenomenon is the corrosion of zirconium-based cladding under prototypic boiling water reactor (BWR) conditions and associated cladding during off normal conditions, including loss of coolant accidents (LOCA). This work provides details of the development and testing of a thin film (few microns) multilayer metal/ceramic (CrY/Y 2 O 3 ) coatings on steel cladding and its performance against FCCI. The coating architecture evolved through systematic mechanical testing, radiation tolerance assessments, and high-temperature diffusion studies from a preliminary multilayer concept to an optimized high-performance design. The optimized structure leverages stoichiometric Y 2 O 3 for its exceptional radiation stability, chemical inertness, and ultra-low fission product diffusivity (e.g., Ce diffusivity ~10 -20 m 2 /s in irradiated Y 2 O 3 at 650 °C). This is paired with ductile chromium-rich interlayers (Cr 95 Y 5 ), which significantly enhance fracture toughness and mechanical resilience. Design refinements, including increased ceramic layer thickness and optimized Cr–Y interlayer composition, eliminated fracture propagation and preserved coating integrity under extreme mechanical stress (>10× typical reactor conditions) and prolonged radiation exposure (~300 dpa). Those conditions are well beyond expected conditions during normal operations in SFR, and cover some of the transient conditions as well. In addition, the coating deposition was demonstrated to be applied uniformly to the inner surface of steel tubes representing prototypic SFR cladding. Another phenomenon under consideration in this work is the corrosion of zirconium-based cladding under prototypic boiling water reactor (BWR) conditions and associated off normal conditions including loss of coolant accident (LOCA). Another multilayer thin film coating is considered for mitigation of this phenomenon, where a coating of yttrium aluminum and yttrium aluminum ceramic/metal thin film coating. Preliminary results on the coating performance during high temperature thermal cycling (up to 1000 C) have shown promising performance of the coating. On-going optimization of the coating will continue in FY26.
Although nuclear reactors have been the sole source for producing plutonium in all nuclear weapons states, accelerator-driven subcritical reactors are a tantalizing alternative. This report provides a review of accelerator driven technologies and components with a focus on export control.
SAS4A/SASSYS-1 is a software simulation tool used to perform deterministic analysis of anticipated events as well as design basis and beyond design basis accidents for advanced nuclear reactors. Detailed, mechanistic models of steady-state and transient thermal, hydraulic, kinetic, and mechanical phenomena are employed to describe the response of the reactor core, the reactor primary and secondary coolant loops, the reactor control and protection systems, and the balance-of-plant to accidents caused by changes in coolant flow, loss of heat rejection, or reactivity insertion. The consequences of single and double-fault accidents can be modeled, including fuel and coolant heating, fuel and cladding mechanical behavior, core reactivity feedbacks, coolant loop performance including natural circulation, and decay heat removal. Analyses are typically terminated upon demonstration of reactor and plant shutdown to permanently coolable conditions, or upon violation of design basis margins. The objective of the analysis is to quantify accident consequences as measured by the transient behavior of system performance parameters, such as fuel and cladding temperatures, reactivity, and cladding strain. Originally developed for analysis of sodium cooled reactors with oxide fuel clad by stainless steel, the models were subsequently extended and specialized to metallic fuel clad with advanced alloys and to several other coolant options, including lead, LBE, and water.
Over the operational lifespan of a nuclear reactor, reactor pressure vessel (RPV) steels are subjected to significant neutron irradiation, resulting in complex microstructural changes and the consequent degradation of mechanical properties. Various physically motivated correlation models have been developed to predict neutron irradiation-induced embrittlement of RPVs under different irradiation conditions. However, the efficient and accurate characterizations and quantification of radiation-induced clusters in RPVs are still challenging, which will affect the precision of the predictive models for embrittlement of RPV components. In the DOE Visiting Faculty Program (VFP) research work at Oak Ridge National Lab (ORNL), I integrate machine learning to aid Scanning Transmission Electron Microscopy – Energy Dispersive X-ray Spectroscopy (STEM-EDS) analyses, which improve the characterization and quantification of radiation-induced clusters in RPV steels, thereby enabling more accurate predictions of material behavior under irradiation. The surveillance base- and welded- RPV steels were annealed at various temperatures of 340 °C, 450 °C and 500 °C for up to 168 hours, respectively. Afterwards, I have characterized radiation-induced clusters using advanced STEM-EDS techniques and subsequently applying machine learning algorithms to analyze and refine STEM-EDS datasets, enhancing the quantification of clusters compositions and distributions. In the end, an efficient workflow for integrating STEM-EDS data analysis with machine learning to address challenges including noise reduction has been developed. The completion of this VFP work will support bridge critical gaps in the accurate quantification of radiation-induced clusters in RPV steels using STEM-EDS and support the development of more precise models for predicting RPV embrittlement in the Light Water Reactor Sustainability program supported by Department of Energy and enhancing the collaboration between ORNL and Alred University. The outcome of the VFP project will leverage a few research papers submission to peer-reviewed journals in the relevant scientific field and a few oral presentations at national and international conferences.
The development of MOOSE has kept accelerating since the last release, with over 2,100 pull requests merged over the last 30 months that involved nearly fifty contributors across close to a dozen institutions internationally. The growth in MOOSE's capabilities and downstream applications is reflected in the growth of the community. User support provided on the GitHub discussions forum has steadily increased to nearly 50 daily interactions. New simulation projects, notably to model advanced nuclear reactor and fusion devices, are driving a significant expansion of the capabilities. This paper reports on these developments, with several major released features, new physics modules, and key improvements to the user experience and simulation workflow.
An arc-heater driven hyperthermal convective environments simulator was recently developed and commissioned for long duration hot hydrogen exposure of nuclear thermal rocket materials. This newly established non-nuclear testing capability uses a high-power, multi-gas, wall-stabilized constricted arc-heater to produce hightemperature pressurized hydrogen flows representative of nuclear reactor core environments, excepting radiation effects, and is intended to serve as a low-cost facility for supporting non-nuclear developmental testing of hightemperature fissile fuels and structural materials. The resulting reactor environments simulator represents a valuable addition to the available inventory of non-nuclear test facilities and is uniquely capable of investigating and characterizing candidate fuel/structural materials, improving associated processing/fabrication techniques, and simulating reactor thermal hydraulics. This paper summarizes facility design and engineering development efforts and reports baseline operational characteristics as determined from a series of performance mapping and long duration capability demonstration tests. Potential follow-on developmental strategies are also suggested in view of the technical and policy challenges ahead. Keywords: Nuclear Rocket Engine, Reactor Environments, Non-Nuclear Testing, Fissile Fuel Development.
The DIF3D-VARIANT solver is part of the DIF3D code which was developed at ANL for engineering level predictive analysis of nuclear reactor cores. Over the course of the last 20 years, the DIF3D-VARIANT option of DIF3D has become increasingly more used because of improvements in computer memory capabilities and performance. This is beneficial to the users of DIF3D as DIF3D-VARIANT is able to apply both p- and h-spatial mesh refinement strategies in diffusion and transport theory. Given its greater use, utility programs were created to assist in post-processing the DIF3D output. This document discusses the basic execution process of DIF3D and the output files it creates but is primarily focused on being the manual for the utility programs EvaluateFlux, VARPOW, VARPEAK, and DIF3DtoVTK.
The subchannel module of moose (SCM), previously referred to as Pronghorn-SC, is a subchannel code designed to resolve single-phase flow fields and calculate the relevant flow variables, in nuclear reactor fuel-pin assemblies. The assemblies it models are: water-cooled, with bare fuel-pins in a square ducted, quadrilateral lattice and liquid-metal/water-cooled, with bare/wire-wrapped fuel pins in a hexagonal ducted, triangular lattice. Previous publications have presented the development, validation and verification of SCM. This work presents an comparative overview of the different solvers implemented within SCM. This includes the solver algorithm description, and a performance comparison. The two test cases chosen to demonstrate the solver performance were taken from the PSBT enthalpy mixing benchmark and the ORNL-19 pin benchmark.