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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Extending in situ X-ray Temperature Diagnostics to Internal Components

Time-resolved X-ray thermometry is an enabling technology for measuring temperature and phase change of components. However, current diagnostic methods are limited in their ability due to the invasive nature of probes or the requirement of coatings and optical access to the component. Our proposed developments overcome these challenges by utilizing X-rays to directly measure the objects temperature. Variable-Temperature X-ray Diffraction (VT-XRD) was performed over a wide range of temperatures and diffraction angles and was performed on several materials to analyze the patterns of the bulk materials for sensitivity. "High-speed" VT-XRD was then performed for a single material over a small range of diffraction angles to see how fast the experiments could be performed, whilst still maintaining peaks sufficiently large enough for analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Viability of Data Analytics to Ascertain Component Performance for Additive Manufacturing

The Transformational Challenge Reactor (TCR) program is leveraging additive manufacturing (AM) technologies to produce multiple nuclear components to be assembled into a fully functional microreactor core. AM was selected as the main manufacturing technology for TCR because it has the potential to disrupt the nuclear industry on two fronts: (1) it enables the manufacturing of very complex geometries with optimized and tailored material properties for the intended use of the component, opening up new options for reactor designs, and (2) it allows for a better understanding of the manufacturing process through real-time in situ monitoring, data analytics, and artificial intelligence, which can lead to a streamlined qualification and certification process. Within TCR, the development and deployment of a digital platform aims at addressing the latter opportunity. As part of this effort, the collection of pedigree datasets is vital, should these data be generated before, during or after the manufacturing process. This report focuses on the development of data analytics tools enabling qualitative assessment of the manufacturing processes to automate the identification of flaws in in situ monitoring imagery and to correlate those flaws to the resulting mechanical testing data. Through examples, the report gives an overview of the data collected and presents a path forward for the digital platform.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

High-Performance Coal-Based Commercial Facade Panels and Architectural Components (Phase I Final Report)

Semplastics has developed, tested, and documented the viability for commercialization of a new class of composite architectural panel materials that use coal as the primary constituent. This project produced sample panels using these novel materials that comprise 55% coal by mass (71% carbon by mass) and are comparable in dimensions to commercially available materials, while displaying superior mechanical strength, significant weight savings, and better insulating ability at a competitive cost. Phase I moved this coal-based composite materials technology from a Technology Readiness Level (TRL) of 3 to TRL 5. Semplastics’ new coal-based composite materials display an array of high-performance characteristics, including light weight, mechanical durability, temperature stability, and water resistance. These new materials require less energy to produce than comparable commercial products and could be manufactured on existing conventional plastic resin processing equipment in commercial quantities. The coal particles are completely encapsulated in ceramic from a polymer-derived ceramic (PDC) precursor, then bonded together by another inorganic resin. The material can be molded and cured to produce fireproof components such as ceiling panels, facades, and extruded underlayment, blocking, and backer boards, as well as other architectural design components such as moldings. The project included the production of sample panels for proof of concept, testing of the panels to show their improved characteristics, and development of a scale-up strategy to demonstrate the viability of the coal-based panels as a commercial alternative to existing building materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Damage Accumulations Predictions for Boiler Components Via Microstructurally Informed Material Models

The goal of the project was to model material behavior and degradation during cyclic plasticity— with and without hold time—for nickel-based superalloys used in USC (ultra-super-critical) and A-USC (advanced-ultra-super-critical) boiler components. The study provided physically informed models, capturing the microstructural changes taking place in the industrial components under cyclic loading and long duration stress (up to 300,000 hours) and high temperature exposure (1100°F/593°C to 1400°F/760°C). The major developments were: 1) Qualitative and quantitative understanding of microstructure evolution (gamma prime precipitates), deformation (dislocation density), and damage mechanisms of Haynes ® 282 alloy. 2) Qualitative understanding of microstructural features generating local strain variations. 3) A continuum damage mechanics model (CDM) for Haynes ® 282 alloy at 1100°F to 1400°F capturing cyclic behavior with and without hold time. 4) Structural analysis for creep and LCF life predictions of an USC thick-wall Grade 91 superheater steel header and understanding life sensitivity to wall thickness of an AUSC Haynes ® 282 header.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2020

Complex radius in the R-matrix algorithm for inclusion of direct capture component [Slides]

In the low-energy range there are two possible physical mechanisms for γ-ray emissions. While both mechanisms emit γ-rays, the compound radiative capture (CRC) process is generated by the formation of a compound nucleus state before γ-emission. CRC has a characteristic resonance structure in the cross section, The direct radiative capture (DRC) process goes to the final state by the electromagnetic radiation of the incident neutron. DRC has a smooth behavior in the cross section. For light nuclei, a non-negligible component of the neutron capture process can be often associated to DRC process. Although these two mechanisms should be described by a unified theoretical formalism, in the nuclear data libraries, the CRC and DRC components are evaluated and reported separately. The updated R-matrix algorithm will allow the ability to implicitly include correlations between CRC and DRC mechanisms, to improve the quality of nuclear data evaluations, and to facilitate simple large-scale applicability to current nuclear data evaluations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components (Final Technical Report)

Metal Additive Manufacturing (AM) is a promising method for cost-efficient fabrication of complex shape structures for applications in harsh environment, such as in a nuclear reactor. However, internal defects (pores) occur in high-strength AM alloys, which are manufactured with Laser Powder Bed Fusion (LPBF) AM method. Pulsed Infrared Thermography (PIT) is an efficient nondestructive evaluation (NDE) method to examine actual structures, because this method offers one-sided non-contact measurements, and fast processing of large sample areas. However, imaging of material defects, particularly defects with sizes at microscopic level, is challenging. In this report, we benchmark the performance of several Unsupervised Learning (UL) algorithms designed to enhance imaging of microscopic defects in metals with PIT. UL aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Performance of Independent Component Analysis (ICA), Sparse Coding (SC), Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) was compared using F-score, UL model training time and defects reconstruction time. We obtained the average F-score of 0.75, and a highest F-score of 0.89 for the EFA algorithm. Overall, EFA outperforms other UL algorithms considered in this study. In another approach, we investigate Thermal Tomography (TT), which is a computational method for reconstruction of depth profile of internal material defects from PIT nondestructive evaluation (NDE). TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of subsurface internals defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. Performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shape imported from scanning electron microscopy (SEM) images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on TT images of elliptical defects is capable of classifying shape and orientation of irregular defects. Training the CNN on irregular defect shapes instead of on elliptical shapes would make the resulting classifications more descriptive of actual defect shapes. However, this requires a much higher volume of SEM images of material defects, which are difficult to obtain because of random occurrence of defects in LPBF. To address this challenge, we developed a generative adversarial network (GAN) to augment the existing dataset of SEM defect images. The GAN model is demonstrated to create novel yet realistic defect shapes that can be used as input for simulated PTT images to train CNN. We also investigate several approaches based on Gaussian Random Circle and Bezier Curves for constructing parametric models of irregular-shape defects.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Hanford Low-Activity Waste Glass Minor Component Concentration Boundary Expansion

This study investigated the effects of increasing concentrations of minor components on simulated Hanford low-activity waste (LAW) glass properties to allow for LAW glass composition envelope expansion. A test matrix of ten glasses was generated by spiking three previously studied simulated Hanford LAW glass compositions with increasing concentrations of minor component mixes up to a total concentration of 2 wt%. In this study, the ten glasses were fabricated, and key properties were measured including crystal formation after canister centerline cooling (CCC), crystallinity as a function of temperature, density, viscosity, electrical conductivity, glass durability using the Vapor Hydration Test (VHT), product consistency using the Product Consistency Test (PCT), and sulfur solubility. The data from these tests are presented in this report.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Graph-theoretical KMC Framework for Calculating Effective Diffusivity in TPBAR Components: Effective Diffusivity of Tritium in α-Zr Grain Boundaries

We report the development of a computer simulation tool based on the Graph-theoretical kinetic Monte Carlo (GT-KMC) approach that can be used to simulate diffusion and derive effective diffusivity of species in complex structures of TPBAR components. The GT-KMC framework was implemented in AKSOME, an on-lattice self-learning kinetic Monte Carlo tool developed at PNNL and was previously used to study solute diffusion in metal alloys. This newly developed tool will be helpful in simulating atom diffusion and extracting their effective diffusivities in grain boundaries and interfaces in various TPBAR components. Once benchmarked, the tool will be used to simulate the diffusion of tritium (hydrogen) along grain boundaries in α-Zr using the activation energy barrier data previously obtained from Density Functional Theory calculations.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Component-Level Bottom-Up Cost Model for Pumped Storage Hydropower

Pumped Storage Hydropower (PSH) is currently the largest source of utility-scale electricity storage in the U.S. and worldwide. As the accelerating deployment of variable renewable technologies creates opportunity and value for energy storage, it has become increasingly important to characterize PSH costs to understand how it competes. Site-specific considerations and limited cost data in the public domain make it difficult to estimate capital costs for potential new PSH sites. This report documents a spreadsheet-based tool that addresses this challenge and creates a component-level bottom-up cost model for PSH that can be made publicly available for widespread use. It uses detailed site-level physical characteristics and design specifications to calculate key performance and cost parameters for individual components and the project as a whole. The model was developed in consultation with HDR, Inc. and Small Hydro Consulting to ensure it aligns with industry expectations. It enables PSH cost exploration across a wide range of system assumptions and could be customized or extended for the needs of a variety of users.

cost model↗
DOE OSTI2022

Assessment of Acoustic Sensor Application to Structural Health Monitoring of Reactor Components

The objectives of SHM of advanced fission reactors include the following: (1) Maintain safe, reliable, and efficient operation of structures, systems, and components in accordance with design intent; (3) Reduce cost; (4) Improve the comprehensive life management of structures, systems, and components; and (5) Extend the operational life of a power system through retirement for cause. Advanced reactor designs currently being considered are expected to operate at higher temperatures than light water-cooled reactors and to support missions beyond baseload electricity generation. Methods to monitor and detect for these mechanisms will require structural health monitoring (SHM) techniques, with ultrasonic methods an ideal candidate given their widespread use for NDE. This document summarized the state of technology for ultrasonic technologies, as part of an assessment of technology gaps and needed research. The great majority of sensor development to date addresses elevated temperatures and radiation tolerance, and more work is needed in both areas. Little data exists regarding corrosion effects of advanced coolants on sensor materials and couplants. Of high importance is the need for ARDs and regulatory bodies to engage and define what level of SHM is needed for autonomous or semi-autonomous control of reactors.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Quantification of Commercially Planned Battery Component Supply in North America through 2035

There is the potential for rapid growth in battery manufacturing in North America in the coming years, as indicated by investment plans which companies have announced. Argonne National Laboratory is tracking these investment announcements to understand the availability of domestically produced battery cells and battery components over the next decade. We find that companies have made announcements for over 1,300 GWh/year of lithium-ion battery cell production by 2030, enough to conservatively supply ten million electric vehicles and expected growth in stationary grid storage. From 2021 to 2032, battery cell production is modeled to grow 28-fold. For each of the core battery components of lithium-ion batteries (cathodes, anodes, separators, electrolytes, and foils), we describe the current announced capacities for materials with respect to cell production and end-use demand, finding similar rapid growth for each.

25 ENERGY STORAGE↗
DOE OSTI2024

Machine learning assisted prediction of tungsten heavy alloy plasma facing component performance for fusion energy applications

Tungsten and tungsten heavy alloys (WHAs), known for their remarkably high hardness, durability, and corrosion resistance, play a critical role in the thriving development of nuclear fusion reactors in recent years. However, the exploration in tungsten alloys for the nuclear-related applications has been limited by the difficulty of manufacturing and the complexity of experiments to reproduce the environment of nuclear reaction. Therefore, this project aims to utilize nanoscale simulation methods such as density functional theory (DFT) and molecular dynamics (MD) with the help of machine learning techniques to not only understand the mechanisms of tungsten alloys but also allow us to computationally predict their mechanical behaviors under extreme environments. One critical problem of the application of WHAs in nuclear reactors is the surface melting. In the current design of the SPARC reactor, the WHA, W97Ni2.1Fe0.9 or W97NiFe, is chosen to be the first wall components to confine the plasma where the particles are fiercely moving and colliding into each other to create nuclear fusion reaction. This process will generate extremely high heat flux onto these WHA tiles, leaving high surface temperature that could possibly melt the surface of the WHA tiles, As illustrated in Fig. 1(a). a laser experiment previously done illustrates that a rough surface damage would be made after the surface melting where the matrix area mainly composed of nickel and iron as shown in Fig. 1(b), will first melt and then leave vacancies between these tungsten grains. Unfortunately, these kinds of roughness on the first-wall components could be deadly to the plasma inside a Tokmak reactor because the heat that is supposed to dissipate at a designed ratio through the tiles may in turn be excessively absorbed and accumulated on any uneven area of the surface, which will eventually make the whole nuclear reaction fail. In this project, we will introduce a machine learning potential, Allegro, based on DFT calculation and then build a MD model for W-Ni-Fe alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Uncertainty Quantification for Component Modeling Using the Discrete-Direct Approach

Threaded fastener behavior can be an important aspect of complex component and system behavior, but there is no one-size-fits-all finite element analysis technique. Proper modeling of threaded fastener joints requires careful consideration of many details, from test setup and data acquisition to constitutive modeling and uncertainty quantification approaches. This report details analysis of a “mini-radax” bolted-joint exemplar where a Discrete-Direct uncertainty quantification approach is employed to evaluate margin of the component. The mini-radax geometry is tested to failure on a drop table, and single-coupon tests of individual fasteners serve as foundational data for the analysis. Analysis predictions complement the test data well and provide additional context for engineering decision-making.

42 ENGINEERING↗
DOE OSTI2015

Effect of Minor Components on Salt Formation

In work supported by ORP over the past several years, VSL has collected extensive data on LAW glass formulations to increase sulfate and sodium loadings, which are the major waste loading limiting components in LAW. However, there are insufficient data to define acceptable sulfate loadings in LAW glasses in the presence of variable amounts of components such as Cl, F, P and Cr. The present work focused: a) on collecting data on acceptable sulfate loading in the presence of varying amounts of halides (Cl, F) and a fixed amount of Cr; and b) studying the effect of phosphorous on salt formation.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Data-Driven Optimization of the Processing Window for 316H Components Fabricated Using Laser Powder Bed Fusion

The Advanced Materials and Manufacturing Technologies Program is focused on accelerating the development and deployment of advanced materials and components fabricated via additive manufacturing with a specific focus on laser powder bed fusion (LPBF). As an initial case study, the program has selected 316H stainless steel (SS) as an initial material around which to develop a code case development strategy. This strategy involves two parallel approaches: (1) an equivalency approach whereby round-robin testing across multiple collaborating laboratories demonstrates repeatability in processing and direct comparisons with conventional wrought 316H material and (2) a revolutionary approach to code qualification combining in situ data collection and high-fidelity modeling to capture, predict, and bound the performance of LPBF 316HSS components. As part of this campaign, this work package has initiated an extensive process optimization campaign across three laboratories, each printing variations of LPBF 316HSS using three different LPBF units (Concept Laser, EOS, and Renishaw). In FY23, ORNL has focused on unique experimental designs spanning wide ranges in energy inputs and turning knobs such as scan speed, laser power, hatch spacing, layer thickness, spot size, scan rotation, and more. On the Concept Laser M2, 72 different combinations of processing variables were investigated with duplicate samples and different powder compositions. In total, 252 samples were printed with combined in situ sensing data. A parallel design of experiments was conducted on the Renishaw AM400 with an additional 390 printed specimens for analysis. All 642 miniature specimens, each with unique features included in each print to capture geometry-related heterogeneity, were subjected to high-throughput x-ray computed tomography (XCT) analysis to enable the downselection of specific processing parameters of interest. Then, using electrical discharge machining (EDM), miniature tensile specimens were extracted for mechanical testing and microscopy investigations. From the analysis performed in FY23, it was found that powder composition drastically affects the resulting microstructure and mechanical performance of 316SS. Specifically, changing from 316L to 316HSS powder results in a wide range of grain sizes with varying degrees of preferred grain orientation, which increases as a function of energy density. It was also found that due to stored heat in thin fin–type features, large microstructural differences can be seen within one part printed with one set of processing parameters. These variations in microstructure features, including grain size, the nanoscale dislocation structure, and grain texture, will all affect the irradiation performance and high-temperature mechanical performance of LPBF 316HSS parts. Two sets of concept laser processing parameters, spanning both refined and columnar grain structures, were scaled to print larger 316H builds for campaign testing (high-temperature creep and irradiation). In addition, at least two optimized processing parameter sets were identified for the Renishaw AM400 for round-robin testing in FY24 with Argonne National Laboratory. Future work includes printing samples using identical parameters identified by partner institutions, providing material for corrosion and high-temperature mechanical testing, and continuing evaluations of heterogeneity in larger printed parts.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Robust Dissimilar Metal Friction Welded Spool for Enhanced Capability for Steam Power Components (Final Program Report)

This project successfully demonstrated the feasibility and advantages of producing durable, friction-welded dissimilar-metal spools, both with and without transition pieces, as well as the use of advanced oxidation-protective coatings for steam power applications. The work encompassed the optimization and full-scale production of NFA tubing, overcoming manufacturing and processing challenges to achieve enhanced strength, creep, and fatigue properties validated by extensive microstructural characterization. Optimized welding techniques were developed and refined for joining dissimilar materials, resulting in defect-free welds optimized for both low- and high-pressure boiler circuits. Systematic evaluation of welding parameters, coupled with heat treatments, ensured reliable performance and minimized weld defects such as banding and cracking. Comprehensive mechanical and non-destructive testing, including tensile, bend, fatigue, and CT scanning, confirmed the superior integrity and durability of the friction-welded joints under representative service conditions. To address oxidation-related degradation, a range of protective alloy coatings—such as IN625, APMT, Duplex 2507, and FeCrAlY—were selected and evaluated for their ability to counteract oxide notch formation at dissimilar metal interfaces. These coatings demonstrated improved oxidation resistance and long-term stability under thermal cycling, thereby extending weld service life. Thermal cyclic testing and long-term studies captured the phase stability and diffusion behavior of coated and uncoated configurations. Validated models for low-cycle fatigue (LCF), creep, and thermal mechanical fatigue (TMF) were developed to predict component life and failure modes. These models confirmed that friction-welded configurations exhibited significantly enhanced performance compared to conventional gas-tungsten arc welding (GTAW), with up to 7× improvement in creep resistance and at least 2× enhancement in fatigue properties. The introduction of a tailored transition piece further reduced thermally induced strains, enhancing fatigue life. The results demonstrate a robust pathway for retrofitting existing steam fleet components and upgrading future equipment to deliver substantially improved cold-start cycling behavior and reduction of unplanned outages due to premature weld failures.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

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

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

36 MATERIALS SCIENCE↗
DOE OSTI2025

Feasibility Assessment of Using Electrical Impedance Tomography for Damage Localization in Graphite Microreactor Components

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.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗