Pit Surveillance (Rev -1) [Slides]
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45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
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Characterization of Stress-Corrosion-Cracking in Plutonium-Bearing Storage Containers
Introduction • The Integrated Surveillance Program, under the Department of Energy, is responsible for the periodic surveillance of storage containers containing plutonium-bearing materials. • The container-packages are designed to isolate the materials for 50 to 100 years with minimal surveillance. • During monitoring, corrosion-pitting and stress-corrosion-cracking (SCC) has been identified in some of the inner-cans near the weld regions. • SCC through wall penetration would result in an undesired increased risk of leakage. • An investigation is in progress to identify and characterize corrosion events to determine the prevalence of corrosion features and the likelihood of a throughwall breach. • Here, we discuss the characterization efforts carried out by Scanning Electron Microscopy (SEM), including 3D-tomography by Focused Ion Beam (FIB) methods.
Perez, Emmanuel E.↗
Pit depth analysis using automated software for three containers with complete imaging
The 3013 surveillance program is tasked with ensuring the long term safety of plutonium storage in 3013-compliant containers. One component of this task is to understand corrosion features found on the interior sidewall of the inner container closure weld region (ICCWR). To this end, three containers were selected to have laser confocal microscope (LCM) 20X imaging of the entire ICCWR. This report describes the analysis of the LCM pit depth data using the automated corrosion analysis software. The analysis included determining maximum pit depths and distributions of pit depths, volumes, and diameters for all pit features identified on the three containers. Results provided insight into possible corrosion mechanisms and were consistent with previous pit growth model predictions.
36 MATERIALS SCIENCE↗
Laser Confocal Microscopy Uncertainty Quantification Study
At Los Alamos National Laboratory (LANL), the Storage Safety and Engineering (SSE) team completes annual surveillance on a subset of in-use interim nuclear material storage containers in fulfilment of requirements outlined in DOE Manual M 441.1-1. The containers are selected through several methods, such as subject matter expert judgement, random selection, and trending items. Following these selections, the SSE team has the capacity to complete surveillance on 15-20 containers each fiscal year, composed of a combination of SAVY-4000 and Hagan storage containers. Through previous work, the stainless-steel components of the containers have been identified as life limiting components, with an emphasis on the thin-walled bodies. The team is focused on understanding the extent of general and pitting corrosion, due to observations of extensive corrosion from stored contents and bag-out-bag degradation. Quantifying corrosion effects on the thin-walled stainless steel container bodies, and understanding potential impacts to the respective design release rates and design qualification release rates is paramount to the team. To date, destructive examination (DE) has proven to be the most insightful method for developing an understanding on the extent of corrosion on used containers. To standardize this process, the SSE team developed a destructive examination guide for analyzing stainless steel components of the containers. Corroded containers of interest are identified during surveillance activities and set aside for sectioning and characterization. Following sectioning, a major step in the DE workflow is the utilization of laser confocal microscopy for scanning corroded samples of interest and extracting data on pits, such as count, depth, and equivalent diameter. Adhering to the techniques outlined in the DE guide, analysis has been completed on two Hagans and one SAVY-4000 container, with the maximum pit depth recorded as 139.1 ± 22.82 μm on a 17.5 year old Hagan. The findings from the completed destructive examinations will be utilized to support lifetime extension efforts of the SAVY-4000 as the team can better estimate corrosion rates and effects over time based on stored contents and age. Due to the implications of observing extreme pit depths that approach the nominal container body thickness of .0299 inches (0.759 mm) or minimum container thickness of 0.236” (0.6 mm), high confidence in the LCM measurements is desired. Through testing outlined in, it was concluded that the total error ascribed to the 20x objective when conducting large image mapping on the Keyence VK-X3050 laser confocal microscope (LCM) relative to a 50x objective (reference) is 16.4% (± 8.73%). For shallow features on the order of pristine SAVY surface defects (i.e. 5 μm), this uncertainty is appropriate. However, this conservative estimate of total error poses a fundamental concern for pit depths that approach the thickness of the measured samples. That is, with the measurement uncertainty currently employed on all measurements, the LCM would be unable to resolve if a pit with a depth of 515 μm is through wall. Standard step height samples were procured and used in the present study to assess the resolution and repeatability of height measurements. Understanding the resolution and repeatability of height measurements was the first focus of the team as it relates directly to pit depth, which is of primary concern. Calibration gratings were procured to evaluate the resolution and repeatability of measurements in the X and Y axes of the LCM stage. The results of the depth uncertainty study were conducted first and presented in the subsequent sections. The planar uncertainty study is appended to the depth study with conclusions from both summarized at the end of the report.
36 MATERIALS SCIENCE↗
FY21 Progress Report: SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking
The development of algorithms for machine learning and data analysis for the 3013 Surveillance Program is a collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). For corrosion detection, Laser Confocal Microscope (LCM) or Wide Area 3D Measurement System (WAMS) data is extracted from large binary files, with software written to convert the data to physical attributes (e.g., height, color and grayscale values; all as functions of a location in a plane projection). A user-friendly Matlab Graphical User Interface (GUI) that reads data from either LCM or WAMS files was developed to integrate input data with software developed for processing and evaluation. The GUI can selectively download binary data, interrogate data attributes, label data, flag significant features, execute Machine Learning (ML) algorithms, output parameters for trained ML algorithms, report ML model accuracy with respect to labeled data, and generate graphical representations for various analyses. Features can be called out by user-specified thresholds, manual labeling or machine learning algorithms when they have been completed. The ability to rapidly label data is important because of the volume of data required for training machine learning algorithms. The GUI has the flexibility to allow addition of improved ML algorithms, methods for data visualization, and statistical computations. Statistical analyses via the GUI include areas of pits within a defined range of pit depths, correlations between Red-Green-Blue (RGB) or grayscale intensity and relative surface height, covariances between values associated with features, and feature histograms. The development of supervised machine learning algorithms, however, has been hindered by a lack of training data. The machine learning algorithms for crack identification are being refined but require improvements to the true positive rate for crack detection. This shortcoming is an artifact of the limited training data currently available, perhaps more so than the structure of the neural networks. At present, the best results are had from a consensus over an ensemble of randomly generated Deep Neural Network (DNN) or Convolutional Neural Network (CNN) algorithms. Although the consensus accuracy method has yielded optimum true positive and true negative rates in excess of 80%, additional validation testing is necessary. In addition to the suite of LCM data that was initially used, and which represents the majority of the work presented in this report, WAMS image data was also reviewed at a preliminary level. The review included a comparison between image resolution and dynamic range for each method. WAMS (ZON file) image data was found to have a pixel pitch of 3.69μm compared to 1 μm for the LCM (vk4 file) data, which implies a lower resolution for the WAMS images. Conversely, the ratio of dynamic range of the WAMS data to the LCM data was approximately 41:20 for height data, suggesting that information from WAMS should more accurately determine the depth of pits. At present, the significance of the greater dynamic range of the WAMS data relative to the LCM data has not yet been evaluated.
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FY25 Progress Report: SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking
Algorithms for Machine Learning (ML) and image analysis for the 3013 Surveillance Program have been developed in an ongoing collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). The objective of the algorithms is to automate the identification of corrosion and cracks in the Inner Container Closure Weld Region (ICCWR) of the canister system used to store Pu-bearing material. Data for corrosion and cracking is collected from large binary files generated by a Laser Confocal Microscope (LCM), the Wide Area 3D Measurement System (WAMS), or, in a recent proposal, by a Scanning Electron Microscope (SEM). The ML software uses the physical attributes in the data files (e.g., one or more of: height, color, and 16-bit grayscale values as functions of position in a plane projection) to detect signs of surface corrosion and cracking after being trained on similar data with the features to be detected. Although the initial scope included screening for broader indicators of corrosion, e.g., pitting, the identification of potential cracks was prioritized for the past several years at the request of program leadership.
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FY24 Progress Report: SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking
Algorithms for Machine Learning (ML) and data analysis for the 3013 Surveillance Program have been developed in an ongoing collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). The objective of the algorithms is to automate the identification of corrosion and crack formation in the Inner Container Closure Weld Region (ICCWR) of the canister system used to store Pu-bearing material. Data for corrosion and cracking is collected from large binary files generated by a Laser Confocal Microscope (LCM), the Wide Area 3D Measurement System (WAMS), or,in a recent proposal, by a Scanning Electron Microscope (SEM). The ML software uses the physical attributes in the data files (e.g., one or more of: height, color, and 16-bit grayscale values as functions of position in a plane projection) to detect signs of surface corrosion and cracking after being trained on similar data, with the features to be detected. Although the initial scope included screening for broader indicators of corrosion, e.g., pitting, identification of potential cracks was prioritized for the past several years at the request of program leadership. Labeled training data is essential to developing the ML algorithm, and enhancements to data labeling capability have been developed to address this essential precursor to application of ML routines. Efficient labeling is particularly important in view of the large volume of data required to train ML algorithms and the relative rarity of cracks in the ICCWR data set. The updated program will read binary data from either LCM, WAMS or SEM files, interrogate data attributes, facilitate user labeling of data for training ML algorithms, execute ML algorithms, output parameters from trained ML algorithms, report ML model accuracy with respect to labeled data, and generate graphical representations for various analyses. In FY24, hourglass neural networks (HNNs) that were initiated in FY22 were further developed and tested using available LCM data, and their performance was tested against that of the alternative U-Net Neural Network algorithm structure. HNNs along with previously developed Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) comprise a suite of ML tools for identification of cracks in the ICCWR
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Flying SATS Higher Volume Operations: Training, Lessons Learned, and Pilots' Experiences
Developments in aviation, including new surveillance technologies and quicker, more economical small aircraft, have been identified as driving factors in a potential expansion of the use of non-towered, non-radar airports. The Small Aircraft Transportation System (SATS) project has developed the Higher Volume Operations (HVO) concept that enables pilots to safely arrive and depart these airports in instrument conditions at an increased rate as compared to today's procedures. This is achieved by transferring some traffic management tasks to centralized, ground-based automation, while assigning others to participating pilots aided by on-board tools. This paper describes strategies and lessons learned while training pilots to fly these innovative operations. Pilot approaches to using the experimental displays and dynamic altering systems during training are discussed. Potential operational benefits as well as pit-falls and frustrations expressed by subjects while learning to fly these new procedures are presented. Generally, pilots were comfortable with the procedures and the training process, and expressed interest in its near-term implementation.
Conway, Sheila↗
MIS Shelf-Life Final Report for Plutonium Oxide Item CAN92 (SSR147) from Rocky Flats Analytical Laboratory Operations
A plutonium/uranium oxide material from the Material Identification and Surveillance (MIS) Program inventory has been studied to determine the gas generation and corrosion behavior in a storage environment. Sample CAN92 represents plutonium/uranium oxides stored in 3013 containers. The material originated in the analytical laboratory at Rocky Flats. This study followed over time the gas pressure and composition of a sample with nominally 0.5 wt% water in a sealed container with an internal volume scaled to 1/400th of the volume of a 3013 container. Gas compositions had been measured periodically over 5 ½ years. The maximum observed gas pressure from gas generation was 117 kPa. The increase over the initial pressure of 77.5 kPa was largely due to the generation of nitrogen and carbon dioxide with minor amounts of hydrogen and nitrous oxide. The internal components of SSR147 were inspected 6.5 years after it was removed from the array. The inside surfaces of the inner bucket exposed to the original sample for 1,937 days had an etched appearance and general corrosion. No pitting was observed on the inner bucket that held the sample.
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3013 Inner Container Closure Weld Region (ICCWR) Characterization by Wide Area 3D Measurement System (WAMS) Analysis (FY21 Progress Report)
As part of the 3013 Surveillance Program, through-wall penetration from stress corrosion cracking (SCC) of the 3013 inner containers has been identified as the most credible condition for failure withing the 50- years lifetime. Chlorides contained in Pu-bearing material, together with intra-canister humidity levels, metallurgical conditions, and internal stresses have been found to produce corrosion in the Inner Container Closure Weld Region (ICCWR) of the 3013 canister system. A Laser Confocal Microscope (LCM) is used as part of the 3013 Surveillance Program protocol to identify the prevalence of corrosion and corrosionrelated cracking in the ICCWR2. With the LCM, a close visual examination is made of the ICCWR surface along with measurements of corrosion-related features. LCM inspections produce immense amounts of image data that is time intensive to analyze. There is also a 9-year backlog of images, with approximately 49 canisters that must be evaluated. To expedite data analysis and reduce the amount of generated data, a Wide Area 3D Measurement System (WAMS) microscope has been added to the examination protocol. Although WAMS is a lower resolution microscope, small features of interest can be still identified. The advantage of collecting data for the full circumference using the WAMS is that it can take about 1/16 of the time needed with the LCM. Both systems offer capabilities that combined can be utilized to expedite the examination of the ICCWR. The WAMS is an efficient system for screening and identification of corrosion features while the LCM can be utilized to obtain higher resolution images areas identified by the WAMS. This report explains and justifies the data collection methods used with the WAMS. It includes a summary of the data generated by WAMS in FY21. The goals set for data collection in FY21 were met. A total of twenty-two DE’s were imaged, and analysis was carried out on eleven samples. The analyzed samples showed large numbers of potential cracks and pits distributed throughout the surfaces. Lastly, to explain the advantages of the WAMS, its capabilities were compared to those of a simpler microscope. Micrographs of the samples imaged in FY21 and those analyzed are included in the appendices.
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MIS Shelf-Life Final Report for Plutonium Oxide Item 1000089 (SSR136 and SSR136A) from Rocky Flats Peroxide Precipitation and Calcination Process
A plutonium dioxide material from the Material Identification and Surveillance (MIS) Program inventory has been studied to determine the gas generation and corrosion behavior in a storage environment. Sample 1000089 represents product-quality plutonium oxides produced in the Rocky Flats peroxide precipitation process currently stored in 3013 containers. This study followed over time the gas pressure and composition of a sample with nominally 0.5 wt% water in a sealed container with an internal volume scaled to 1/500th of the volume of a 3013 container. Gas compositions had been measured periodically over almost 13 years. The maximum observed gas pressure was 204 kPa and was related to a temperature excursion. The maximum observed gas pressure from gas generation was 126 kPa. The increase over the initial pressure of 86.7 kPa was due to the generation of hydrogen, oxygen, and nitrogen. Carbon dioxide, methane and carbon monoxide were minor components of the headspace gas. The material exhibited unusual behavior in that the atmosphere reached flammable levels of hydrogen and oxygen within the first 30 days and remained flammable for the duration of the experiment. It has been determined that the sample inside the reactor was a mixture of the AR material, material calcined at 800 °C, and material calcined at 950 °C. The experiment was terminated in 2012 and unloaded in 2016. A new reactor, SSR136A, was loaded with the original sample freshly calcined at 950 °C. The total pressure inside SSR136A remained close to the initial value of 93 kPa. The total pressure is composed of He (steady levels after the reloading) and hydrogen which is 10% of the reactor’s total pressure. Nitrogen and oxygen compose trace amounts of the reactor headspace. The internal components of SSR136 were inspected in 2021, five years after it was removed from the array. The inside surfaces of the inner bucket exposed to the original sample for 4,576 days had an etched appearance, and pit-like features were observed in the microscopic analysis. No corrosion was observed on the SSR136A inner bucket that held the freshly calcined sample that was exposed for 1,303 days.
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