Currently, spent nuclear fuel (SNF) from commercial nuclear power plants is stored in stainless-steel canisters for interim dry storage. To provide an inert environment, these canisters are backfilled with helium after vacuum drying. However, the helium environment may be contaminated during extended storage because of the material degradation. For example, the heavier fission gas xenon may be released from the fuel rods into the canister cavity should the fuel cladding be breached. Other gases such as air and water vapor may also be present as a result of leakage caused by chloride-induced stress corrosion cracking on the canister walls or by insufficient vacuum drying. Therefore, monitoring the gas composition can provide critical information about the health of SNF canisters. In this study, noninvasive testing was conducted on a 2/3-scaled SNF canister mock-up using acoustic sensing. Ultrasonic transducers were placed on the exterior surface of the canister to probe the gas composition. A dataset was collected by sealing the canister mock-up and introducing up to 1.53% argon or 1.29% air into the helium background gas. Three methods were used to detect changes in the gas composition: the time-of-flight (TOF) method, the differential method, and the autoencoder method. Results showed that the TOF method had sufficient resolution to detect abnormal gas concentrations of less than 1.0%. The differential method demonstrated a periodic in-phase and out-of-phase behavior between the benchmark (i.e., pure helium) and abnormal (i.e., with argon or air) state signals. The variational autoencoder (VAE) and the Wasserstein autoencoder (WAE) were trained on the benchmark data and were applied directly to the abnormal state data. It was found that both the unsupervised VAE and the WAE were able to distinguish the benchmark and abnormal states of the canister mock-up based on the reconstruction error.

Monitoring impurity gases in spent nuclear fuel (SNF) canisters is a novel structural health monitoring approach for SNF in dry storage. The SNF canisters are sealed containers that do not facilitate visual access to the inside. Acoustic sensing can be deployed by taking advantage of the pathways unobstructed by internal hardware. Although the ultrasonic time-of-flight measurement can provide valuable information, it is limited in its ability to discern the concentration of only one impurity gas. As such, deep learning algorithms, particularly convolutional neural networks (CNNs), offer a promising solution. In this study, CNN-based probabilistic deep learning models were implemented to detect and quantify multiple impurity gases in helium. An experimental platform was established to simulate canister conditions, and ultrasonic test data were collected. The presence of argon and air in helium at concentrations ranging from 0% to 1.2% at increments of 0.05% was considered. The multi-layer perceptron, decision tree, and logistic regression classifiers achieved high accuracies when distinguishing pure helium from helium with impurities. CNN with dropout layers and CNN using maximum likelihood estimation showed a similar performance, indicating their ability to capture uncertainties. The ensemble CNN model exhibited improved predictions and the ability to balance individual gas concentration by integrating 1D- and 2D-CNN models. These findings contribute probabilistic deep learning solutions for impurity gas detection and analysis within SNF canisters, thus ensuring safe storage and management of SNFs.

Safe storage of spent nuclear fuel (SNF) is critical to the nuclear fuel cycle and the future of nuclear energy. In the United States, SNF is primarily stored via two methods regulated by the U.S. Nuclear Regulatory Commission (U.S. NRC): wet storage in SNF pools, and dry storage in dry cask storage systems (DCSSs). After about five years of cooling in spent fuel pools, the fuel assemblies are transferred into DCSSs, and the systems are filled with helium and sealed by welding. Deterioration of conditions inside of a DCSS will be reflected by changes in the internal gas properties which motivates the development of acoustic techniques to monitor internal gas properties, over extended storage periods, using sensors mounted on the exterior of the storage packages. However, a major challenge in collecting acoustic signals is the impedance mismatch between the steel canister shell and the gas. Only a small fraction of the ultrasonic signal can be transmitted through the gas medium. In this paper, experimental studies on a full-scale canister mock-up were conducted to capture the gas-borne signals. Damping materials were pasted on the outside and blocking and unblocking tests were conducted to identify the gas-borne signal. The results showed that the excitation frequency plays an important role in maximizing the gas-borne signal. The gas-borne signal was successfully detected at around the theoretical time-of-flight (TOF). A high signal-to-noise ratio (SNR) was achieved in the measurements. Next, the acoustic impedance matching (AIM) layers were introduced, and the gas signal was drastically improved compared with no AIM layers.

The safe storage of spent nuclear fuel (SNF) in dry cask storage systems (DCSSs) is critical to the nuclear fuel cycle and the future of nuclear energy. A crucial component of DCSSs is the SNF canister, which is a sealed stainless structure first vacuum dried and then backfilled with helium. The structural deterioration within a canister can be monitored through its internal gas properties. This monitoring serves as the driving force behind the non-invasive ultrasonic sensing approach in this paper. A major challenge in collecting gas-borne signals is the impedance mismatch between the stainless-steel canister and the helium gas inside. Only a small fraction of the ultrasonic signal makes its way from the transmitter to the receiver through the gas medium. In this paper, experimental studies on a partial full-scale canister mock-up were carried out to capture the gas-borne signals. Damping materials were applied on the outside, and blocking and unblocking tests were conducted to identify the gas-borne signal. The research results showed that the excitation frequency played an important role in maximizing the gas-borne signals. The gas-borne signal was successfully detected at around the theoretical time-of-flight (TOF) at 225 kHz. A high signal-to-noise ratio (SNR) was achieved in the measurements. Next, acoustic impedance matching (AIM) layers were added, and it was found that the gas signal energy was improved by 160.4% compared with that of no AIM layers. Subsequently, the relative humidity (RH) level and temperature of the gas were varied to simulate abnormal internal conditions of the canister. The non-invasive testing system demonstrated reliability and sensitivity in detecting gas temperature and RH variations. Theoretical calculations demonstrated the potential for detecting low-level xenon and air within an actual SNF canister filled with helium. Last, an active noise cancellation (ANC) method was verified on the canister mock-up for the first time. The results showed that the SNR of the gas signal was improved by 213.6% compared with that of no ANC.

The fiber-optic distributed acoustic sensing (DAS) technique has increasingly become more attractive for structural health monitoring (SHM) and non-destructive evaluation (NDE) purposes. When it comes to traditional acoustic NDE methods, the presence of weldings can present a significant challenge as it can heavily scatter waves resulting in complex data analysis and interpretation. The present work aims to develop an improved understanding and interpretation framework in cases where welds play an important role in the signal with an emphasis on the steel shell of a canister, typically used for Dry Cask Storage Systems (DCSSs) that house spent nuclear waste fuel rods. We also introduce a promising approach in the use of guided ultrasonic waves along with fiber optic sensors that seeks to overcome the challenges that emerge when using traditional acoustic sensing based NDE techniques in welded structures. The study is conducted in a simulation theoretical manner, using a canister model constructed from a representative stainless-steel plate, with different configurations of weldings typically present for DCSS structure. Progressively increasing complexity of the weld physical representation is considered to fully incorporate in physics-based analysis. Furthermore, the acoustic response of these models is obtained from the simulations as a response of an assumed DAS or quasi-distributed acoustic sensing Q-DAS system network. The features originated from the welds are extracted and analyzed, and additional features associated with structure integrity associated with corrosion defects, etc. will also be explored for NDE inspection as in a traditional acoustic NDE approach.

Ultrasonic sensing is a non-invasive technique for monitoring impurity gas composition in various industrial applications where safety and regulatory compliance are crucial. In this study, ultrasonic sensing and neural networks were used to analyze impurity gases (i.e., air and argon) in helium. An experimental platform was established to acquire ultrasonic data. In the forward problem, an artificial neural network (ANN) model was used to forecast the response and time-of-flight (TOF) based on the excitation, and argon and air concentrations. The inverse problem was solved using a convolutional neural network (CNN) to predict the argon and air concentrations given the ultrasonic response and excitation. The results showed that the ANN accurately predicted the ultrasonic response and the change in TOF with concentration. As the air concentration was increased from 0 to 9.8%, the TOF sensitivity to detect argon decreased by 39.8% and 16.1% from ANN and sound speed theory, respectively. The CNN demonstrated high accuracy in predicting concentrations for inputs in the testing dataset. The application of the trained CNN indicated that it over-predicts air concentration while under-predicting the argon concentration. To improve accuracy, the predicted air and argon concentrations should be corrected by -0.992% and 1.027% bias, respectively.

Ultrasonic wave propagation may be used in non-invasive sensing to identify the properties of a fluid in a container. An ultrasonic excitation can be coupled into the liquid or the gas through the walls of the container and detected by a receiver on the other side. However, in addition to this fluid path, another signal will also propagate through the structure and is referred as noise in this study. For most materials, this structural noise will be of higher amplitude compared to the fluid signal due to the large acoustic impedance difference between the solid container and the fluid. In this paper, an active noise cancellation (ANC) method is developed and demonstrated on an experimental setup. It was shown that this method cancels more than 90% of the noise signal in a given time window of interest. A sequential version of this cancellation method is also proposed, and it was shown that its performance improves as the number of canceling excitations is increased. The performance was found to be independent of the location of canceling transducers and position of the silence window but dependent on the width of the silence window. As an example, the ANC method was applied to noise cancellation of a signal travelling through water in a stainless-steel container. The signal-to-noise ratio (SNR) of the water signal in the window of interest was increased by 148% from 13.98 dB to 34.68 dB by using the proposed ANC method.

data analysis for NDE and identify any gaps or shortcomings in current ML technologies as applied to the automated analysis of NDE data. In particular, there is a need to determine the impact of ML on the NDE reliability. This paper discusses the findings from a literature survey on the current state of ML for the automated analysis of data from ultrasonic NDE of weld flaws. It discusses an overview of ultrasonic NDE as used for weld inspections in nuclear power and other industries. Herein, data sets and ML models used in the literature are summarized, along with a generally applicable workflow for ML. Findings on the capabilities, limitations and potential gaps in feature selection, data selection, and ML model optimization are discussed. The paper identified several needs for quantifying and validating the performance of ML methods for ultrasonic NDE, including the need for common data sets.

This paper describes development and demonstration of nondestructive examination (NDE) technologies to support periodic examinations of interim dry storage system (DSS) canisters for spent nuclear fuel in the USA to verify continued safe operation and that the canister confinement is intact and performing its intended safety function. Specifically, this work relates to NDE technology development for "canister" based DSS systems, which form the majority population of DSSs in the USA for interim storage of spent nuclear fuel. Consideration of potential degradation of the welded stainless-steel canister in these systems is required for continued usage in the period of extended operation (PEO) beyond the initial license or certified term. Physical access to the canister surface is constrained due to narrow annulus spaces between the canister and the overpack, tortuous entry pathways, and high temperatures and radiation doses that can be damaging to materials and electronics related to inspections. Several activities to demonstrate NDE technologies for the inspections of different DSS systems are summarized.

This paper presents efforts to overcome challenges with empirical probability of detection (POD) estimations in the nuclear power industry through the utilization of a novel virtual flaw method. A virtual round robin (VRR) study was conducted under the Program for Investigation Of NDE by International Collaboration (PIONIC), organized by the United States Nuclear Regulatory Commission (NRC) utilizing data generated by the virtual flaw method. Analysis of results from the VRR was performed by teams from Pacific Northwest National Laboratory (PNNL), Electric Power Research Institute (EPRI), and Aalto University. Empirically derived POD estimations are presented, and challenges associated with obtaining these estimations are discussed. The virtual flaw method is introduced and some details of its implementation for the VRR activity are described. Results from POD analysis of the VRR data by PNNL, EPRI, and Aalto University are presented and a discussion regarding differences in analysis results is provided. Finally, potential future efforts to improve the application of the virtual flaw method and its estimation of POD are discussed.

This paper describes development and demonstration of remote crawling systems to support periodic examinations of interim dry storage system (DSS) canisters for spent nuclear fuel in the USA. Specifically, this work relates to robotic crawler developments for "canister" based DSS systems, which form the majority population of DSSs in the USA for interim storage of spent nuclear fuel. Consideration of potential degradation of the welded stainless-steel canister in these systems is required for continued usage in the period of extended operation (PEO) beyond their initial licensed or certified terms. Challenges with performing the periodic examinations are associated with physical access to the canister surface, which is constrained due to narrow annulus spaces between the canister and the overpack, tortuous entry pathways, and high temperatures and radiation doses that can be damaging to materials and electronics. Motivations for performing periodic examinations and developing robotic crawlers for performing those examinations remotely will be presented, and several activities to demonstrate robotic crawlers for different DSS systems are summarized.

The U.S. Nuclear Regulatory Commission (NRC) in cooperation with the nuclear industry has developed a probabilistic fracture mechanics code called xLPR (extremely Low Probability of Rupture). xLPR is a modular-based probabilistic assessment tool for determining probability of leakage and rupture for pressure boundary piping. One of the modules in xLPR is the In-Service Inspection (ISI) module which models the probability of detection (POD) and sizing performance of NDE performed during in-service inspection to account for and predict the influence of periodic inspections on the probability of component leakage and rupture. The accuracy of the ISI module in xLPR is dependent on the quality of the estimates of detection and sizing performance that are input to the module. Multiple efforts have been attempted to quantify the detection and sizing performance of NDE in the nuclear industry. These efforts include an analysis of inspection data collected as part of the industry’s Performance Demonstration Initiative (PDI) and data collected from NRC sponsored round robin studies such as PINC-Program for the Inspection of Nickel Alloy Components and PARENT-Program to Assess the Reliability of Emerging Nondestructive Techniques. Usage of a given data set in the xLPR ISI module requires understanding of which set of data is most representative for the specific scenario under consideration. A comparative analysis of detection performance data from PDI, PINC, and PARENT is provided in this paper in an effort to elucidate for users of xLPR types of applications the above mentioned data sets may be most appropriate for.

This report documents efforts to create actual chloride-induced stress corrosion cracks (CISCCs) in stainless-steel specimens for evaluating application of nondestructive examination (NDE) technologies for detection and sizing of CISCC in stainless-steel canisters in dry storage systems (DSSs) for spent nuclear fuel. In the United States, several DSSs for commercial spent nuclear fuel are approaching the end of their initial licensed or certified term. Many of these systems were originally licensed or certified for 20 years, after which the licensing or certification may be renewed for periods up to 40 years, according to Title 10 of the Code of Federal Regulations (10 CFR), Part 72, “Licensing Requirements for the Independent Storage of Spent Nuclear Fuel, High-Level Radioactive Waste, and Reactor-Related Greater than Class C Waste.” Guidance for review of renewal applications is provided in NUREG-1927, Standard Review Plan for Renewal of Spent Fuel Dry Cask Storage System Licenses and Certificates of Compliance. A review of aging effects that could affect important-to-safety structures, systems, and components is required in renewal applications and applicants are required to address aging effects using either a time-limited aging analysis or an aging management program. The U.S. Nuclear Regulatory Commission (NRC) issued NUREG-2214, Managing Aging Processes In Storage (MAPS) Report, which provides guidance on the evaluation of known aging degradation mechanisms to determine if they could affect the ability of dry storage system components to fulfill their safety functions in the 20- to 60-year period of extended operation.

Welded stainless steel canisters are used in dry storage systems of spent nuclear fuel from commercial power reactors in the U.S. These systems were initially licensed for 20 years and may receive license renewals for 40 more years from the U.S. Nuclear Regulatory Commission (NRC). The requirements for license renewal include aging management programs (AMPs) to ensure systems, structures, and components (SSCs) continue to perform their safety function. Nondestructive examination (NDE) plays an important role in AMPs that rely on inspection to detect service degradations such as chloride-induced stress corrosion cracking (CISCC) of austenitic stainless steel canisters so that mitigation actions can be taken to manage the aging effects. The American Society of Mechanical Engineer (ASME) is currently developing a code case for the in-service inspection (ISI) of dry storage canisters. The nuclear industry is also developing inspection systems for conducting examinations of canisters. This paper is intended to summarize the on-going efforts to develop inspection systems and to evaluate the effectiveness of inspection systems to access the canister and detect flaws. This paper also documents efforts by Pacific Northwest National Laboratory (PNNL) to develop CISCC flaws on stainless steel specimens in an attempt to compare NDE responses to simulated flaws to assess the impact of flaw characteristics on the NDE performance.

phased-array ultrasound techniques provided the highest performance for flaw detection and depth sizing in dissimilar metal piping welds. The effective detection of flaws in BMIs by eddy current and ultrasound shows that it may be possible to reliably inspect these components in the field. The goal of PARENT is to continue the work begun in PINC and apply the lessons learned to a series of open and blind international round-robin tests that will be conducted on a new set of piping components including large-bore (≈900-mm diameter) DMWs, small-bore DMWs, and BMIs. Open round-robin testing will engage universities and industry worldwide to investigate the reliability of emerging NDE techniques to detect and accurately size flaws having a wide range of lengths, depths, orientations, and locations. Blind round-robin testing will invite testing organizations worldwide, whose inspectors and procedures are certified by the standards for the nuclear industry in their respective countries, to investigate the ability of established NDE techniques to detect and size flaws whose characteristics range from easy to very difficult to detect and size. This paper presents highlights of PINC and reports on the plans and progress for PARENT round-robin tests.

....................................................................................................................................................... iii Summary ....................................................................................................................................................... v Acknowledgments ....................................................................................................................................... vii Acronyms and Abbreviations ...................................................................................................................... ix 1.0 Introduction .......................................................................................................................................... 1 2.0 Nuclear Plant Materials Harvesting ...................................................................................................... 1 3.0 Materials and Harvesting Prioritization ................................................................................................ 2 3.1 Literature Sources ........................................................................................................................ 2 3.2 Literature Assessment .................................................................................................................. 4 3.3 Criteria for Prioritizing Harvesting .............................................................................................. 5 3.3.1 Criteria ............................................................................................................................... 5 3.4 Examples ...................................................................................................................................... 7 3.4.1 Electrical Cables ................................................................................................................ 7 3.4.2 Cast Austenitic Stainless Steel ........................................................................................ 11 3.4.3 Dissimilar Metal Welds ................................................................................................... 14 3.4.4 Vessel Internals ............................................................................................................... 17 4.0 Harvesting Plans ................................................................................................................................. 19 4.1 Ex-plant Harvesting Experience ................................................................................................. 19 4.1.1 Harvesting Projects ......................................................................................................... 19 4.1.2 Cable Harvesting Experience .......................................................................................... 19 4.1.3 Harvesting of Internals .................................................................................................... 22 4.1.4 Harvesting of RPV Materials .......................................................................................... 23 4.1.5 General Lessons Learned from Harvesting Examples .................................................... 24 4.2 Harvesting Plans General Requirements .................................................................................... 24 5.0 Information Tools for Harvesting Planning ........................................................................................ 27 5.1 Reactor Reliability and Integrity Management Library ............................................................. 27 5.1.1 Overview ......................................................................................................................... 27 5.1.2 Work to Date ................................................................................................................... 29 6.0 Summary and Path Forward ............................................................................................................... 30 7.0 References .......................................................................................................................................... 31