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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Microstructural characterization of nuclear graphite: from the microscale to the nanoscale

Multiple reactor designs use graphite as a moderator of the nuclear reactions and as structural support. During the lifetime of the reactor, multiple aging factors such as neutron irradiation, oxidation, and temperature along with others induce changes in the microstructure and crystal lattice of graphite components. The pore morphology and crystal structure of some phases in graphite can be used to trace the evolution of irradiation defects and mechanical properties of graphite. We present a combination of results from several microscopy techniques to investigate the differences between nuclear graphite grades and the effects of neutron irradiation and oxidation at multiple length scales. This multiscale approach is needed to understand the microstructural variations caused by the raw materials and manufacturing processes as well as how the different phases of graphite are affected by the reactor environment. The results provide insight into the oxidation- and radiation-induced changes of graphite and create a robust baseline of microstructure information that can be used for the selection of materials for the next generation of nuclear power stations. Moreover, the experiments conducted in this work provide an overview of the advantages and limitations of the most common techniques used to characterize nuclear graphite and how these techniques might be applied to study other carbon-based materials used in the nuclear industry.

Arregui Mena, Jose'↗
DOE OSTI2023

Overlapping Domain Coupling of Multidimensional and System Codes in NEAMS - Pronghorn and SAM

This report describes the implementation and testing of domain-overlapping coupling of the Pronghorn and SAM codes. Both Pronghorn and SAM are codes developed by the DOE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program for analysis of advanced nuclear reactors. Pronghorn focuses on analyzing multi-dimensional core flow conditions, while SAM focuses on analysis of the entire system including, among other components, piping, pumps, and heat exchangers. It is desirable for many advanced reactor thermal-hydraulics simulations to couple Pronghorn and SAM to obtain self-consistent solutions in the core and the system. The domain-overlapping coupling approach provides a robust and numerically efficient candidate for this coupling. We describe the algorithm for obtaining self-consistent solutions between a SAM network of flow channels and an overlapped multi-dimensional Pronghorn domain. The algorithm is tested for a variety of simple canonical test problems and a model of the Molten Salt Reactor Experiment (MSRE). Results demonstrate that the domain overlapping coupling approach provides consistent pressure, mass flow rate, enthalpy, and passive scalar distributions between Pronghorn and SAM and converges reliably and efficiently (at most 15 iterations, usually much less than 10) for the considered test problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A Novel Hollow Fiber Membrane Reactor for High Purity H 2 Generation from Thermal Catalytic NH 3 Decomposition (Final Report)

Ammonia (NH 3 ), as a promising carbon neutral liquid fuel (CNLF) and an effective H 2 source, can be synthesized from air and water (N 2 extracted from air and H 2 from water) using renewable energy sources. To produce H 2 as an intermediate, it is essential to develop effective and economic NH 3 decomposition technologies. We have been developing an innovative hollow fiber membrane reactor for high purity H 2 generation from thermal catalytic NH 3 decomposition. The objective of the proposed research is to fabricate a compact and robust prototype with high energy efficiency (>80%) to deliver high purity (>99%) H2 at high rate per volume (>0.15 g H 2 /h/cm 3 ) from NH3 decomposition at low temperature (<450 °C) and high conversion (>99%). Our overall technical approach is to effectively combine an active catalyst for NH 3 decomposition with a selective H 2 membrane separation process in a novel, compact modular system to generate high purity H 2 and simultaneously achieve high NH 3 conversion and optimize the entire system energy efficiency. This novel integrated membrane reactor design, together with low-cost, highly active Ru-based catalyst, makes it most appropriate for the project objective. The proposed applied research and development work, therefore, represents an innovative and transformational solution to H 2 generation from thermal catalytic NH 3 decomposition. During the course of our research we i) synthesized and characterized ruthenium(Ru)-based catalysts, evaluated their catalytic performance for NH 3 decomposition and optimized the catalyst composition and performance based on machine learning, ii) prepared SAPO-34, MFI membranes on α-alumina hollow fibers and measured their performance for H 2 /N 2 , H 2 /N 2 /NH 3 separation and evaluated carbon molecular sieve (CMS) and Pd/Ag membranes for H 2 /N 2 , H 2 /N 2 /NH 3 separation at different conditions, iii) designed and evaluated several types of membrane reactors for H 2 production from NH 3 decomposition, and iv) evaluated the H 2 delivery cost base on catalysts development, membrane development and membrane reactor. For 3,1,12 RuYK catalyst, H 2 productivity of 6.35 mmol/min/gcat with NH 3 conversion of 95~98% was achieved at 5 bar and 400 °C; for SAPO-34 membrane, the best membrane showed H 2 permeance of 7.56×10 -7 mol/(m 2 ∙s∙Pa) and H 2 /N 2 selectivity of 23.1 at 20 °C; For CMS and Pd/Ag membranes, both showed superior stability for 71%H 2 /24%N 2 /5%NH 3 mixture separation at 450 °C and 7 bar for over 120 h; for the membrane reactor, high H 2 purity of >99.99% with NH 3 conversion of >99.4% were achieved in Pd/Ag membrane reactor at 450 °C, and the NH 3 concentration was always below 10 ppb in H 2 product. The H 2 generation rate was calculated to be as high as 0.47 g/h/cm 3 and the energy efficiency was calculated to be 88%. The membrane reactor showed superior stability for over 370 h and high H 2 purity, H 2 production rate and NH 3 conversion. Based on calculation, the H 2 delivery cost can be as low as $3.66/kg.

08 HYDROGEN↗
DOE OSTI2022

Statistical significance of the sterile-neutrino hypothesis in the context of reactor and gallium data

We evaluate the statistical significance of the 3+1 sterile-neutrino hypothesis using $v_e$ and $\bar{v}_e$ disappearance data from reactor, solar and gallium radioactive source experiments. Concerning the latter, we investigate the implications of the recent BEST results. For reactor data we focus on relative measurements independent of flux predictions. For the problem at hand, the usual χ 2 -approximation to hypothesis testing based on Wilks’ theorem has been shown in the literature to be inaccurate. We therefore present results based on Monte Carlo simulations, and find that this typically reduces the significance by roughly 1σ with respect to the naïve expectation. We find no significant indication in favor of sterile-neutrino oscillations from reactor data. On the other hand, gallium data (dominated by the BEST result) show more than 5σ of evidence supporting the sterile-neutrino hypothesis, favoring oscillation parameters in agreement with constraints from reactor data. This explanation is, however, in significant tension (~3 σ) with solar neutrino experiments. In order to assess the robustness of the signal for gallium experiments we present a discussion of the impact of cross-section uncertainties on the results.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Microstructure and Temperature Dependent Indentation Response of Additively Manufactured Precipitation-Strengthened Al 0.3 Ti 0.2 Co 0.7 CrFeNi 1.7 High Entropy Alloy

The temperature-dependent [from room temperature (RT) to 500°C] nanoindentation behavior of a precipitation-strengthened Al 0.3 Ti 0.2 Co 0.7 CrFeNi 1.7 high-entropy alloy (HEA) processed via two different additive manufacturing (AM) techniques was investigated in the as-deposited and annealed conditions. The hierarchically heterogeneous microstructures were achieved via simple one-step annealing treatments, exploiting the residual stresses in the AM-processed HEA to partially recrystallize the microstructure, performed remarkably better than the nearly homogeneous microstructures in the as-deposited state. Further, the one-step annealed conditions revealed < 6.6% reduction in hardness values at 500°C compared to RT, while the as-deposited conditions showed a > 18% reduction in the hardness. The one-step annealed conditions also exhibited significantly higher hardness than the as-deposited conditions owing to their L1 2 -strengthened FCC microstructures. Furthermore, serrated yielding or the Portevin-Le Chatlier effect indicative of microstructural instability was observed during nanoindentation deformation (at 500°C) for the as-deposited conditions but not for the one-step annealed conditions. This, therefore, signifies the robustness of the hierarchically heterogeneous microstructures at elevated temperatures presenting a strong avenue for tuning the HEAs for future nuclear reactor applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A dual dynamic shutter system for accelerating ion irradiation sample throughput via lateral gas implantation gradients

Ion irradiation for material performance testing is limited due to its serial nature, which allows for only one value of the implantation (appm) versus dose (dpa) parameter space to be explored for each ion and experiment at a time. While ion irradiation can accelerate the process by up to three orders of magnitude compared to neutron irradiation experiments, the sample throughput for ion irradiation remains relatively low. To address these limitations, a novel capability has been developed at the Michigan Ion Beam Laboratory (MIBL), enabling for the creation of single- and two-dimensional lateral ion implantation gradients using recently installed motorized-controlled ion-beam shutters. This advancement can generate a wide scope of the two-dimensional (H+, He2+) implantation parameter space within a single sample. Integration of this new capability now allows for dual- and triple-ion beam experiments to be performed with full user control over not only the ion implantation depth, but also laterally across the sample by imposing ion implantation concentration gradients, thus providing researchers with a high-throughput means for material testing under various irradiation conditions. Furthermore, recent improvements in MIBL's microbeam ion-beam analysis (IBA) target station now allow for probing these concentration gradients in irradiated alloys with exceptional spatial resolution, down to 10 µm. These two approaches promise to significantly improve ion irradiation capabilities and increase the sample throughput by several orders of magnitude. The application of the shutter technique plus the subsequent microbeam characterization of the imposed implantation gradients are showcased by two proof-of-principle ion-irradiated experiments, one performed on single-crystal Si and the other on the fusion-candidate alloy F82H-IEA. These advancements mark a substantial leap in ion-beam technology, offering researchers a robust, high-throughput method to efficiently investigate candidate alloys with high technological readiness for both advanced fission and fusion reactor applications, in a time- and cost-effective manner.

36 - MATERIALS SCIENCE↗
DOE OSTI2024

Architecture Design for Remote Operation of Microreactors: Poster

The nuclear sector is pursuing a number of advanced reactor concepts, one of which is the microreactor, an advanced reactor characterized by a power output of less than 20 MWth. These reactors are intended for use in applications where traditional power solutions are economically or logistically impractical. One key feature required for the successful deployment of microreactors is a remote operation capability, which can dramatically cut staffing expenses by eliminating the need for licensed operators to be present at the site of each reactor and instead concentrate in a centrally located operations center. In moving to a remote operations framework for nuclear reactors, the number of potential attack surfaces for a cyber adversary looking to cause harm or disruption increases. Therefore, a robust cybersecurity architecture is required to mitigate these potential vulnerabilities. This paper explores the functional infrastructure, security, and communication requirements to adapt remote operations to nuclear applications. It then presents a reference architecture for remote operations of microreactors that applies best practices in cybersecurity and remote communications in the context of a digital twin remote operation system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Computational Modeling of Molten Salt Infiltration and Oxidation in Nuclear Graphite

Graphite is utilized as a moderator and reflector in advanced nuclear reactor designs due to its high thermal conductivity, neutron moderation properties, and resistance to radiation damage. However, its longterm performance and reliability are challenged by degradation mechanisms such as molten salt infiltration in molten salt reactors (MSRs) and oxidation in gas-cooled reactors (GCRs). These mechanisms can compromise the structural integrity and operational lifetime of graphite components, necessitating a more detailed assessment of their physical behavior. This report focuses on the development of computational models for molten salt infiltration and oxidation of graphite to aid the design and performance analysis of graphite components. For molten salt infiltration, a computational framework is developed that couples incompressible Navier-Stokes and phase-field model to simulate the penetration of molten salt into graphite?s interconnected pore structure. Initial model verification is performed using two-phase flows in two dimensions, demonstrating the models ability to capture fundamental physical behavior and agree with analytical solution. This framework is then applied to a realistic IG110 nuclear graphite , where a computed tomography extracted pore geometry is used to analyse the infiltration behavior of FLiNaK molten salt. This model provides insights into how the microstructure and other relevant parameters influence the transport pathways of molten salt into graphite, potentially offering a means to rapidly evaluate a graphite grade?s resistance to infiltration. For oxidation, the report details pore-scale mass and heat transport models, describing the diffusion of gases, reaction kinetics, and thermal effects. Additionally, this report highlights inconsistencies in the existing volume-averaged macroscopic model, particularly in upscaling of reaction kinetics and flux terms, and surface to volume transformations. These inconsistencies suggest that current formulations may not accurately capture the experimentally observed graphite oxidation process, highlighting the need for improved model development. This work advances the development of physics-based computational models for graphite degradation, contributing to improved predictive models for next-generation nuclear reactor designs. Future efforts will focus on refining the infiltration model to address non-physical behaviors and enhance its robustness. Additionally, for oxidation, further studies will employ the principles of volume averaging to rigorously derive the upscaled equations, potentially in collaboration with subject matter experts.

Computational Modeling of Molten Salt Infiltration↗
DOE OSTI2025

Capability Building Progression of an Insider Threat Mitigation Program at an International Research Reactor

The nuclear industry recognizes the difficulties involved in developing effective managerial and leadership skills in a highly technical and proficient workforce such as that found in nuclear facilities. Implementing an insider threat mitigation program (ITMP) within the nuclear industry is a complex and ongoing process that demands a comprehensive understanding of human behavior, an organization’s security culture, and rigorous regulatory requirements yet also accounts for facility characteristics, physical security, material flow, and activities involving nuclear material. Given the high-consequence nature of research reactor operations, even minor lapses can lead to safety, security, and reputational risks. An effective ITMP requires a defense-in-depth approach that incorporates behavioral analysis, robust vetting procedures, continuous monitoring, and cross-disciplinary coordination. It must also promote a culture of vigilance and accountability at all levels up to and including executive leadership but be flexible enough to adapt to evolving global threats and technological advances. Insider threat mitigation is not a one-time effort but rather a sustained commitment to excellence in safety and security. Establishing a culture in which personnel proactively report incidents and issues that could affect nuclear safety and security is vital to maintaining a safe and secure operational environment. This document was developed to guide senior management and research reactor organizations in creating comprehensive programs to effectively manage and mitigate insider threat behaviors and actions. It focuses on the key pillars of an effective ITMP, including the national legal framework, security culture, preventive and protective measures, cyber security, and performance evaluation. By using a systematic approach during implementation, facilities can foster environments conducive to insider threat detection and support long-term program sustainability. The document also provides strategies for improving communication across all levels of an organization, helping to eliminate barriers that hinder the development of robust ITMPs and enhance overall security culture. In today’s organizations, the concept of leveraging safety and security culture lessons to facilitate knowledge transfer is rapidly evolving to expedite insider threat management and security culture improvements. This document outlines the rationale for evaluating an ITMP based on national customs, culture, and stakeholders. The elements are all germane to reliability and trustworthiness and relate to security concerns that states may encounter. The document focuses not only on individual perceptions regarding security issues and capability building but also on team building and how to resolve concerns. The implementers of a facility’s ITMP may zero in on indicators of insider threats within their enterprise. This material will benefit organizations when it is applied using a systematic and structured approach as demonstrated throughout the document.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

MELCOR 2.2 Benchmarks of Peach Bottom NUREG/CR 7155 Uncertainty Analysis

The U.S. Nuclear Regulatory Commission (NRC) performed a first-of-a-kind uncertainty analysis (UA) of the accident progression, radiological releases, and offsite consequences for the State-of- the-Art Reactor Consequence Analyses (SOARCA) of an unmitigated long-term station blackout (LTSBO) severe accident scenario at the Peach Bottom Atomic Power Station. The objective of the UA was to evaluate the robustness of the SOARCA deterministic "best estimate results and conclusions documented in NUREG-1935, and to develop insight into the overall sensitivity of the SOARCA results to uncertainty in key modeling inputs. The study was completed in 2015 and documented in NUREG/CR-7155. Since 2015, two other SOARCA UAs were completed for two pressurized water reactor (PWR) plants. The PWR UAs incrementally updated the approach and methodology, including using the latest release of the MELCOR 2.2 computer code. There were also advances made in the state-of-the-art modeling related to NRC efforts using the Peach Bottom model in NUREG-2206, which provided the technical basis for the containment protection and release reduction rulemaking for boiling water reactors with Mark I and Mark II containments. This report documents the input model changes from the NUREG/CR-7155 study and performs a small number of reference calculations to assess the changes of the new computer code and the model input updates. The objective of the work is to verify whether the updated Peach Bottom MELCOR model and updated version of MELCOR support the conclusions formed in the Peach Bottom SOARCA UA by performing these representative calculations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Advanced Cross Section Library Generation using Reduced Order Models

Deterministic neutronics calculations rely on multigroup neutron cross section libraries, which consist of databases of tabulated values, used to calculate the neutron cross sections through multivariate linear interpolation. However, interpolation of the multidimensional cross section data becomes memory inefficient and time consuming as the number of tabulations increases, significantly slowing down the neutronics calculation, especially in the case of microscopic cross section libraries where every isotope (on the order of hundreds) has its own set of specific reactions and cross sections. In order to address this challenge, this work constructs efficient and robust reduced-order models (ROMs) of the multi-group cross sections to support the Griffin simulation of high-temperature gas-cooled reactors (HTGRs). The first part of the study investigates the linearity of the multigroup cross section data across isotopes, reaction types, and energy groups on pre-generated datasets for the purpose of dimensionality reduction. Secondly, a down-selection of ROM techniques is presented on representative classical machine learning (ML) techniques, including variants of linear regression, kernel-based methods, tree-based algorithms, and artificial neural networks. The selection criteria jointly consider the memory efficiency, predictive accuracy, prediction speed, and scalability in comparison to the multidimensional interpolation. Among all the ML techniques, deep neural networks (DNNs) have proven to be the best selection with sufficient accuracy, high robustness, good memory efficiency, great scalability, and superior flexibility. DNNs have been trained for all isotopes in this work and systematic Griffin testing is ongoing to ensure the feasibility of this ROM technique for predicting cross section and reducing memory requirements without a significant sacrifice in computational performance.

42 - ENGINEERING↗
DOE OSTI2022

Developing reactors for electrifying bio-methanation: a perspective from bio-electrochemistry

The integration of microbial synthesis with renewable electricity is an emerging route for both CO 2 utilization and seasonal energy storage in the form of stored bio-electrofuels. The major benefits of electrifying bioreactors include: using highly selective bio-catalysts for CO 2 conversion under mild reaction conditions; decoupling the production of more facile electrochemical intermediates, such as hydrogen, at the electrode from the production of bio-catalyzed multi-electron and/or carbon products, such as methane or acetate; using microbes as robust and self-regenerating catalysts enabling higher efficiency and durability in CO 2 conversion systems compared to inorganic catalysis. In this Perspective, we propose research aimed at developing electro-bioreactor components that will increase the productivity of the reactor while maintaining high energy efficiency and biocompatible reaction conditions to fully realize the benefits of electrified bioreactors. Furthermore, these developments include: flow reactors with tailored 3D electrodes to optimally use the reactor volume, electrocatalysts designed for peak performance in neutral pH electrolytes, high conductivity microbial media, and new membrane separator materials with high ion conductivity and low gas permeability. Production of methane via a hybrid electrical-biological approach is taken as a case study to motivate these developments. Finally, an iterative design–manufacture–test cycle, enabled by additive manufacturing and 3D printing technologies, is proposed to rapidly prototype components prior to large-scale manufacturing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Laser-spectroscopy testbed for impurity monitoring in liquid metal-cooled fast reactors

A significant challenge in sodium-cooled fast reactors is controlling impurities, in particular oxygen impurities, within the sodium coolant, as they can accelerate corrosion and indicate leaks. Optical methods offer the potential to rapidly detect small concentrations of both gaseous and metal impurities that accelerate corrosion, plug coolant channels, and lead to increased activation of isotopes in the coolant. We present the design and performance of an apparatus designed to enable the application of multiple optical analytical techniques, such as laser-induced breakdown spectroscopy, to detect elemental impurities in the sodium melt with high sensitivity. Here, we experimentally demonstrate the detection of characteristic sodium and oxygen spectral lines in liquid sodium, which sets the stage for the optimization of its analytical sensitivity. A robust sensor of this type integrated with the sodium cooling loop has the potential to significantly improve the safety and operational efficiency of generation IV nuclear reactors.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Adaptive Control for Load-Following of Boiling Water Reactors Part I: Linear Systems and Fully-Observable Dynamics

Automation control is a key strategy to improve the economic competitiveness of nuclear power plants. Not only does it help reduce operational costs, but it also extends the value proposition of these plants to nontraditional markets, including unattended operations in remote villages and space. However, the dynamics of the operating environments of nuclear reactors are subject to changes over time, and there are no widely adopted methods to ensure that the automation strategy will remain effective over the extended durations required for these applications. Adaptive control is a discipline that offers the possibility to accommodate such changes online. However, it relies on mathematical assumptions that must be respected to ensure robustness and reliability. In this work, we derive an adaptive control formulation for linear systems in which all states are observable and apply it to an instance of load-follow operation for Boiling Water Reactors. We assumed uncertainty in two factors: the temperature coefficient and the control rod worth, both of which are affected over time by the evolution of the nuclear reactor core environment. With an arbitrary penalty factor of 5, we found that the mean absolute and integral time absolute errors can be reduced by more than 90%, underscoring the strength of adaptive control. To extend the application to more challenges, different uncertainties and load-follow trajectories, as well as new formulations that include non-linearity and partial observability, are currently being developed.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

OpenSn: A massively parallel, open-source simulation environment for discrete ordinates radiation transport

OpenSn is an open-source, massively parallel deterministic radiation transport code for solving the discrete-ordinates ( S N ) form of the Boltzmann transport equation on unstructured, arbitrary polyhedral meshes. It supports high-fidelity simulations involving steady-state, eigenvalue, and adjoint problems for neutral particles (e.g., neutrons, photons, multi-particles), using the multigroup approximation in energy. OpenSn combines angular discretization via discrete ordinates with a discontinuous Galerkin finite element method (DGFEM) in space, enabling accurate resolution of transport physics on arbitrary polyhedral cells, included locally refined spatial grids. It includes multiple angular quadrature types, including locally refined angular quadratures. Written in modern C++ with a Python API, OpenSn runs efficiently on platforms ranging from laptops to supercomputers. The transport sweep algorithm is implemented using a task-based, directed-acyclic-graph (DAG) approach for each angle and supports asynchronous parallelism across thousands of MPI ranks. Group-set aggregation improves compute intensity, and synthetic acceleration techniques (e.g., diffusion synthetic acceleration, second-moment method) enhance solver convergence. OpenSn has been verified on reactor physics problems and demonstrated excellent weak and strong scaling performance on more than 32,768 processes, making it a versatile and robust platform for large-scale transport simulations in complex geometries.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

High bias machine learning for antineutrino-based safeguards for small reactors

The statistical methods used for antineutrino detection will need to be improved to effectively monitor the inventory of next-generation nuclear reactors. In this sensitivity study, we evaluate machine learning models compared to previously used statistical approaches to identify diversion scenarios in a simulated Advanced Fast Reactor (AFR)-100. A chi-square goodness-of-fit technique, which individually compares the simulated antineutrino yields to the expected antineutrino yield, resulted in precise but low diversion detection probability. Various support vector machine (SVM) models were applied with diverse training datasets to evaluate the robustness of the method towards unexpected or “unseen” diversion scenarios. Furthermore, our results indicate that while the SVM models significantly improved the detection probability of near-field antineutrino-based safeguards, up to a probability of ~0.04, for the simulated small reactor, the detection system still needs improvements to reach the 0.2 detection limit established by the International Atomic Energy Agency.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

Terry Turbopump Expanded Operating Band Modeling and Simulation Efforts in Fiscal Year 2020 - Progress Report

The Terry Turbine Expanded Operating Band Project is currently conducting testing at Texas A&M University as part of a revised experimental program meant to supplant previous full-scale testing plans under the headings of Milestone 5 and Milestone 6. In consultation with Sandia National Laboratories technical staff and with modeling and simulation support from the same, the hybrid Milestone 5&6 plan is moving forward with experiments aimed at addressing knowledge gaps regarding scale, working fluid, and turbopump self-regulation. Modeling and simulation efforts at Sandia National Laboratories in FY20 fell under the broad umbrella of Milestone 7 and consisted exclusively of MELCOR-related tasks aimed at: 1) Constructing/improving input models of Texas A&M University experiments, 2) Constructing a generic boiling water reactor input model according to best practices with systems-level Teny turbine capabilities, and 3) Adding code capability in order to leverage experimental data/findings, address bugs, and improve general code robustness Project impacts of the Covid-19 pandemic have fortunately been minimal thus far but are mentioned as necessary when discussing the hybrid Milestone 5&6 progress as well as the corresponding Milestone 7 modeling and simulation progress.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

In a carbon-constrained world, future uses of nuclear power technologies can contribute to climate change mitigation as the installed electricity generating capacity and range of applications could be much greater and more diverse than with the current plants. To preserve the nuclear industry competitiveness in the global energy market, prognostics and health management (PHM) of plant assets is expected to be important for supporting and sustaining improvements in the economics associated with operating nuclear power plants (NPPs) while maintaining their high availability. Of interest are long-term operation of the legacy fleet to 80 years through subsequent license renewals and economic operation of new builds of either light water reactors or advanced reactor designs. Recent advances in data-driven analysis methods—largely represented by those in artificial intelligence and machine learning—have enhanced applications ranging from robust anomaly detection to automated control and autonomous operation of complex systems. The NPP equipment PHM is one area where the application of these algorithmic advances can significantly improve the ability to perform asset management. This paper provides an updated method-centric review of the full PHM suite in NPPs focusing on data-driven methods and advances since the last major survey article was published in 2015. The main approaches and the state of practice are described, including those for the tasks of data acquisition, condition monitoring, diagnostics, prognostics, and planning and decision-making. Research advances in non-nuclear power applications are also included to assess findings that may be applicable to the nuclear industry, along with the opportunities and challenges when adapting these developments to NPPs. Finally, this paper identifies key research needs in regard to data availability and quality, verification and validation, and uncertainty quantification.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗