Hydrogen isotopic analysis of nuclear reactor materials using ultrafast laser-induced breakdown spectroscopy
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This report discusses the current development and testing of additively manufactured temperature sensors and resistive/ capacitive strain gauges. This has potential to improve the sensor design and manufacturing techniques to meet the requirements of in-pile monitoring of mechanical properties and structural health of materials and specimens in current and advanced nuclear test reactors (i.e., in terms of environment conditions, sample geometry, and materials compatibility). The developmental additively manufactured strain gauges are exposed to separate effects testing (i.e., mechanical strain (up to 1000 µe), high temperature (up to 700 °C)) to determine environmental factors that affect the performance of the strain gauge. The robustness and integrity of a printed strain gauge layer exposed to a molten salt environment for up to 500 hours was evaluated, with the goal of assessing material compatibility of printed strain sensors for instrumented surveillance test articles in molten salt reactors. In addition, sensor qualification methodologies are further developed for determining the reliability and robustness at the interface of the additively manufactured strain gauge materials.
This report is a Milestone 2 deliverable in FY2025, under work package MT-25AN090211 to support research and qualification activities supported by the Advanced Materials and Manufacturing Technologies (AMMT) program. It provides an update on the work done on the laser based additive manufacturing of steels for nuclear applications, conducted collaboratively by Argonne National Laboratory and Pacific Northwestern National Laboratory. Building upon FY23 and FY24 efforts, the focus of FY25 includes continued optimization of process parameters for the alloys: A709 & G92. The work package also includes fabricating test samples to conduct a thorough microstructural analysis and perform preliminary mechanical testing. The major accomplishments of this work package are summarized below, applicable to both Laser Powder Bed Fusion (LPBF) processes conducted at Argonne National Laboratory and Laser Powder Directed Energy Deposition (LP-DED) processes conducted at Pacific Northwest National Laboratory.
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.
Heat pipes and two-phase thermosyphons are highly efficient heat transfer devices utilizing continuous evaporation and condensation of working fluid for two-phase heat transport in closed systems. Because of the nearly isothermal and fully passive phase-change heat transfer mechanism, heat pipes and thermosyphons have found many applications in nuclear engineering, space technologies, and other energy systems. High-temperature heat pipes are used in nuclear microreactors to remove fission power from the primary system and are coupled with power conversion systems or process heat applications. Modeling of the two-phase flow phenomena inside a heat pipe is essential to its design and safety analysis. In this study, a comprehensive one-dimensional two-phase three-field flow model has been developed for the analysis of heat pipes in normal operation conditions and transients. The conservation or field equations of mass, momentum, and energy were developed for the liquid film, vapor, and droplet. In addition, constitutive models or correlations were reviewed thoroughly and provided for the closure of the three-field equations. Specific constitutive equations regarding interfacial mass and heat transfer at two interfaces, namely film-gas interface and gas-droplet interface, were reviewed for droplet entrainment and deposition rates as well as film and droplet evaporation rates. Furthermore, mechanistic correlations of annular flow film thickness were recommended for the modeling of the thermosyphons without a wick as a critical constitutive correlation. Furthermore, experimental data needs from new experiments using a prototype working fluid or surrogate fluids for the model validation of high-temperature heat pipes in microreactors were recommended for future research.
Due to growing interest in non-light water reactor (NLWR) concepts, the predecessor to the American Nuclear Society (ANS)/American Society of Mechanical Engineers (ASME) Joint Committee on Nuclear Risk Management (JCNRM) initiated development of a NLWR probabilistic risk assessment (PRA) standard in 2006. The developed standard, released for trial use in 2013, was the first integral PRA standard created by the JCNRM, containing requirements ranging from the development of initiating events to the analysis of offsite radiological consequence. Based on feedback from pilot applications of the trial standard, a revised version of the standard was developed and balloted in 2020, with unanimous passage by the JCNRM and formal publication as an American National Standards Institute (ANSI) standard in 2021. The paper provides an overview of the standard and its development process. In addition, the paper reviews the latest status of U.S. Nuclear Regulatory Commission endorsement efforts through Regulatory Guide 1.247.
This paper describes conceptual design of a 112 MW thermal (50 MW electric) Fast Modular Reactor (FMR) system operating at 7 MPa with inlet/outlet temperatures of 509/800 ºC. The reactor system includes the reactor core, fuel assemblies, fuel rods, reactor internals, reflector, neutron control system, flow control, and structural components. The nuclear design and analysis were conducted to search for a baseline core with a cycle length greater than 8-year and the power peaking factor less than 1.5 by adjusting the fuel assembly, reactor core, and reflector configurations. The neutronics calculations of the baseline core showed that a refueling interval of 9-year is achieved with a total peaking factor of 1.47 and a fuel rod average linear power of 3.6 kW/m.
This paper describes conceptual design of a 112 MW thermal (50 MW electric) Fast Modular Reactor (FMR) system operating at 7 MPa with inlet/outlet temperatures of 509/800 Celsius degrees. The reactor system includes the reactor core, fuel assemblies, fuel rods, reactor internals, reflector, neutron control system, flow control, and structural components. The nuclear design and analysis were conducted to search for a baseline core with a cycle length greater than 8-year and the power peaking factor less than 1.5 by adjusting the fuel assembly, reactor core, and reflector configurations. The neutron transport calculations of the baseline core showed that a refueling interval of 9-year is achieved with a total peaking factor of 1.47 and a fuel rod average linear power of 3.6 kW/m. (authors)
NEUP Final Technical Report outlines experimental and modeling efforts to characterize performance of thermal energy storage systems with molten salt as the phase change material.
Predicting creep crack growth (CCG) of flaws found during operation in high-temperature alloy components is essential for assessing the remaining lifetime of those components. While defect assessment procedures are available for this purpose in design codes, these are limited in their range of applicability. This study assesses the application of a local damage-based finite-element methodology as a more general technique for the prediction of CCG at high temperatures on a variety of structural configurations. Numerical results for stainless steel 316H, which are validated against experimental data, show the promise of this approach. This integration of continuum damage mechanics (CDM) based methodologies, together with adequate inelastic models, into assessment procedures can therefore inform the characterization of CCG under complex operating conditions, while avoiding excessive conservatism. This article shows that such modeling frameworks can be calibrated to experimental data and used to demonstrate that the degree of tri-axiality ahead of a growing creep crack affects its rate of growth. The framework is also successfully employed in characterizing CCG in a realistic reactor pressure vessel geometry under an arbitrary loading condition. These results are particularly relevant to the nuclear power industry for defect assessment and inspections as part of codified practices of structural components with flaws in high-temperature reactors.
There is a growing sociopolitical desire to develop cleaner energy sources in the United States and maintain energy security. Regardless of politics, many coal-fired electric plants have already been shut down and many utilities are vowing to retire their current coal-fired assets within the next two decades. Replacement power assets require consideration of appropriate siting. A geographic information system (GIS)-based multicriteria decision analysis approach is useful to assist utility and energy companies, as well as policymakers, to evaluate potential areas for siting new plants in the contiguous United States. A GIS-based framework is simply a database of location information that allows for mapping, querying, modeling, and analyzing data based on location. The spatial output can be structured to be visual, allowing for easier analysis of location data. The need to site additional power assets, including renewable resources and clean power sources, such as nuclear, led to the development of the Oak Ridge Siting Analysis for power Generation Expansion (OR-SAGE) tool discussed in this paper. The tool takes inputs such as population growth, water availability, environmental indicators, and tectonic and geological hazards to provide an in-depth visual analysis for siting options. Energy companies and other stakeholders can use OR-SAGE to procure feedback quickly and effectively on land suitability based on technology specific inputs. Policymakers can use OR-SAGE to analyze the impacts of future energy technology decisions, while balancing competing resource use. Overall, this paper discusses the recent use of OR-SAGE for these purposes and plans for future development.
This report discusses the different options for coupling thermal energy storage (TES) systems to advanced nuclear power plants (A-NPPs) in order to enable flexible and hybrid plant operation. An advanced light-water reactor (A LWR), a high-temperature gas-cooled reactor (HTGR) and a liquid-metal fast reactor (LMFR) were selected as the initial use cases for demonstrating a thermally balanced energy storage coupling design for thermal power extraction. The models presented herein showcase several design considerations, focusing on optimal deployment methodologies for achieving steady-state and transient-state operation with minimum disruption to the nuclear power cycle. This first part of the study presents steady-state models developed using Aspen HYSYS®, with the thermal energy bypass for NPP-TES coupling being varied at up to 50%. The various components were sized using the Aspen Process Economic Analyzer (APEA) and Aspen Exchanger Design and Rating (EDR), when applicable. Cost functions from these models were developed using the latest publicly available data obtained from APEA V11. The TES-coupled A-NPP steady-state models and cost functions then provided a baseline for dynamic operation and process optimization by using Idaho National Laboratory (INL)’s Framework for Optimization of Resources and Economics (FORCE) tools. A stochastic optimization of the various energy storage systems coupled to the A-NPPs was then performed using the Risk Analysis Virtual Environment (RAVEN) and its dispatch optimization plugin, the Holistic Energy Resource Optimization Network (HERON). The signal processing and synthetic history capabilities of RAVEN were used to account for the unpredictable behavior of electricity markets. An autoregressive moving average (ARMA) model was used to analyze price signals from the Pennsylvania-New Jersey-Maryland (PJM) market and were applied to the HERON analysis in order to optimize a system with the best economics. Transient modeling evaluation was then performed using Modelica models within the HYBRID repository, which was developed at INL for the Department of Energy Integrated Energy Systems program for the characterization of dynamic integrated system behavior and feedback. This includes evaluation of the TES-coupled A-LWR systems’ impact on physical and thermal system response during imposed system demands. Additional TES-coupled reactor types, coupling approaches, markets, and TES technologies will be evaluated in future work.
Autonomous operation is essential for the deployment of microreactors and fission batteries, both in terrestrial and space applications. However, prototypes of microreactors and fission batteries do not exist yet, and even the design space has not been narrowed down conclusively, making the instrumentation and control system design difficult. For this reason, there is a need for flexible computational capabilities to create a numerical stand-in of potential microreactor and fission battery designs. The latter can be used to design and test control strategies to support autonomous operations. In this paper, we describe the initial implementation of a pluggable sensor system for the easy implementation of realistic sensor models in the multiphysics object-oriented simulation environment (MOOSE) framework. This new capability will enable MOOSE users to create a numerical stand-in of microreactors and fission batteries, ultimately allowing them to easily test new control algorithms, and instrumentation strategies for advanced systems in the design phase
The aim of this study is to develop model of a small High Temperature Gas cooled Reactor (HTGR) including the balance of plant. This microreactor produces electricity using the thermal power of the nuclear reaction. This work utilizes the MOOSE Multiphysics simulation tools, which are mainly developed at Idaho National Laboratory (INL, Idaho, United States of America). Its thermal-hydraulics and Heat conduction Modules are used to study the fluids behavior in the primary loop and power conversion system and their interactions with the heating structures. More specifically, one verifies that the temperatures, pressures and mass flow rates of the fluids in both loops are consistent and that, at the same time, all the power transfers occur as expected. Moreover, the various mechanical components characteristics (turbine, compressor, pump) are adapted to the reactor operating conditions. This first analysis is conducted using simplified and one-dimensional model for the core. Ultimately, the obtained results are used to build a higher fidelity core model. This step aims to verify that the initial simplified simulation is consistent with the three-dimensional core modeling. In addition, the calculated material temperatures are checked. This study provides a fairly complete model of the thermal hydraulic phenomena for a particular design of High Temperature Gas cooled Reactor.
Autonomous operation is essential for the deployment of microreactors and fission batteries, both in terrestrial and space applications. For this reason, recent studies have investigated autonomous control by using adaptive model predictive control and multi-objective optimization for heat pipe–cooled microreactors under normal and heat pipe failure conditions. However, prototypes of microreactors and fission batteries do not exist yet, and even the design space has not been narrowed down conclusively, making the instrumentation and control system design difficult. For this reason, there is a need for flexible computational capabilities to create a numerical stand-in of potential microreactor and fission battery designs. The latter can be used to design and test control strategies to support autonomous operations. In this poster, we describe the initial implementation of a pluggable sensor system for the easy implementation of realistic sensor models in the multiphysics object-oriented simulation environment (MOOSE) framework. This new capability will enable MOOSE users to create a numerical stand-in of microreactors and fission batteries, ultimately allowing them to easily test new control algorithms, and instrumentation strategies for advanced systems in the design phase.
Monte Carlo codes are essential components of many reactor physics simulation workflows as high-fidelity continuous-energy neutron transport solvers. Among Monte Carlo radiation transport codes, MCNP is particularly notable due to its diverse simulation capabilities, large user base, and long validation history. Despite being a powerful simulation tool, MCNP provides limited capabilities to allow automated execution, model transformation, or support for user-defined logic and abstractions that limit its compatibility with modern workflows. Here, to better integrate MCNP into a modern scientific workflow, we have developed an intuitive yet full-featured MCNP Application Program Interface (API) in Python, named MCNPy, which provides a specialized set of classes for MCNP input development. Moreover, to guarantee that our reading, writing, and modeling capabilities remain self-consistent (and to render the huge scope of the MCNP API manageable), we have adopted a strategy of model-driven software development in which a generalized model of the MCNP input format has been created. From this generalized model, or “metamodel,” problem-specific implementations such as an engine for input validation or a codebase for programmatic operations may be automatically generated. Since MCNPy primarily acts as a Python front-end to the underlying Java API that directly interfaces with the metamodel, it is intrinsically linked to the metamodel and thus remains maintainable. With MCNPy, users can programmatically read, write, and modify any syntactically valid MCNP input file regardless of its origin. These capabilities allow users to automate complicated tasks like design optimization and model translation for nuclear systems. As examples, this work demonstrates the use of MCNPy to find the critical radius of a plutonium sphere and to translate a 9000+ line MCNP input file into a corresponding OpenMC model.
Beryllium carbide (Be 2 C) is an attractive alternative to graphite as a moderator material because of its high melting point, moderating efficiency, and theoretical environmental compatibility in MSRs, FHRs and HTGRs. However, its behavior under neutron irradiation is not yet known. Research on otherwise promising beryllium compounds are restricted because of the toxic nature of the material. For this work, a novel experiment was designed to safely irradiate beryllium-containing samples at The Michigan Ion Beam Laboratory (MIBL). Using this new capability, Be 2 C samples were irradiated with 9 MeV C 3+ ions from 2 dpa to 30 dpa, at temperatures up to 500 °C. Samples were characterized to investigate radiation tolerance. No evidence of phase precipitation, dislocation loops, or amorphization was observed up to 30 dpa, suggesting good radiation tolerance. One sample left in air for ~3 months oxidized slowly and lost structural integrity, while a sample kept in argon did not, suggesting that Be 2 C is not suitable for service in moist environments, but maintains its integrity in dry environments.
As presented in this report, a weighting function–based inferencing method is being applied to synthesize the power distribution in next-generation and university research reactors based on simulated self power neutron detector (SPND) responses. The overall goal is to assess the impacts of sensor uncertainty and true power distribution perturbations on the error in the synthesized power distribution. Regarding sensor uncertainty, the NuScale Small Modular Reactor (SMR) and the Westinghouse AP1000 serve as testbeds for analyzing the impact of varying the sensor uncertainty, as well as varying the number of sensors per sensor string in the reactor core. The reactor models are informed by Monte Carlo N-Particle (MCNP) neutron flux tallies. For the NuScale SMR and Westinghouse AP1000, the SPND response functions (i.e., the response of the SPNDs to individual segments of fuel) were determined homogeneously. Regarding an analysis of power distribution perturbation detection, the Texas A&M Testing, Research, Isotopes, General Atomics Reactor (TAMU TRIGA) reactor was used as a demonstration case with one particular arrangement of SPNDs; the response functions for this reactor model were determined heterogeneously, making this a uniquely high-fidelity demonstration of perturbation detection. Finally, SPND models generated in the Geometry and Tracking 4 (Geant4) code have been generated and tested for comparison with traditionally implemented analytical SPND models, with the intent for Geant4 integration with the full methodological framework. SPND current outputs as a function of distance from some fuel assembly segment in the NuScale SMR are compared with the analytically determined currents. Results from the sensor uncertainty simulations for the NuScale SMR and AP1000 indicate that the average error in the inferred power distribution on the fuel assembly segment level is reasonably low, being slightly less than the random uncertainty applied to all respective SPNDs in both cores. For example, if all SPNDs in the core have a random uncertainty of 5%, then the corresponding fuel assembly segment level error (i.e. difference between the true and inferred local power) is ~2–3%. However, the maximum error in the inferred power distribution on the fuel assembly segment level can be considerably high (>15%) when SPND random uncertainties start to exceed ~3%. In general, the average and maximum errors in the inferred power distribution were slightly higher in the AP1000 as opposed to the NuScale SMR for the sensor string configurations considered herein. Another result determined from analysis of the sensor uncertainty simulations was that increasing the number of SPNDs per string does not clearly reduce inferred power distribution error and can in fact make the error large in some cases; however, this assessment may skewed due to imposed iteration limits. Results from the perturbation detection demonstration using the high-fidelity TAMU TRIGA model indicate that, given the arrangement of 17 SPND strings and 4 SPNDs per string considered herein, there is a clear, provable ability to infer a localized Gaussian-type peak perturbation in the 3D power distribution. Such a perturbation was detected with an average fuel assembly segment level error of 0.19%, and the general visualization of the detected perturbation clearly indicates that the magnitude and shape were appropriately resolved. Finally, the electrical current output generated by the Geant4 modeled SPND indicates significant magnitude differences than the analytically modeled SPND, demonstrating the need for accurate SPND models which account for finite sensor geometry effects to inform the power synthesis work described herein.