The purpose of the Materials Aging and Degradation (MAaD) Pathway is to develop the scientific basis for understanding and predicting long-term environmental degradation behavior of materials in nuclear power plants and to provide data and methods to assess the performance of systems, structures, and components (SSCs) essential to safe and sustained nuclear power plant operations. The understanding of aging-related phenomena and their impacts on SSCs is expected to be a significant issue for any nuclear power plant planning for long-term operations (i.e., service beyond the initial license renewal period). Management of those phenomena and their impacts during long-term operations can be better enabled by improved methods and techniques for detection, monitoring, and prediction of SSC degradation. Elements of research necessary to produce the improved methods and techniques include the following: Integration of materials science understanding of degradation accumulation, with nondestructive measurement science for early detection of materials degradation. Development of robust sensors and instrumentation, as well as deployment tools, to enable extensive condition assessment of passive nuclear power plant components. Analysis systems for condition assessment and remaining life estimation from measurement data. It is likely that pursuing research for each of these elements in parallel will be necessary to address anticipated near-term deadlines for decision-making by plant owners and regulators (i.e., the first of the "second-round" license renewal announcements will likely start to be made in the 2015 to 2020 timeframe by the nuclear power plant owners). To address these research needs, the MAaD Pathway supported a series of workshops in the summer of 2012 for the purpose of developing R&D roadmaps for enhancing the use of Nondestructive Evaluation (NDE) technologies and methodologies for detecting aging and degradation of materials and predicting the remaining useful life. The workshops were conducted to assess requirements and technical gaps related to applications of NDE for cables, concrete, reactor pressure vessels (RPV), and piping fatigue for extended reactor life. An overview of the outcomes of the workshops is presented here. Details of the workshop outcomes and proposed R&D also are available in the R&D roadmap documents cited in the bibliography and are available on the LWRS Program website (http://www.inl.gov/lwrs).
Proactive aging management of nuclear power plant passive components requires technologies to enable monitoring and
accurate quantification of material condition at early stages of degradation (i.e., pre-macrocrack). Acoustic emission
(AE) is well-suited to continuous monitoring of component degradation and is proposed as a method to monitor
degradation during accelerated thermal fatigue tests. A key consideration is the ability to separate degradation responses
from external sources such as water spray induced during thermal fatigue testing. Water spray provides a significant
background of acoustic signals, which can overwhelm AE signals caused by degradation. Analysis of AE signal
frequency and energy is proposed in this work as a means for separating degradation signals from background sources.
Encouraging results were obtained by applying both frequency and energy filters to preliminary data. The analysis of
signals filtered using frequency and energy provides signatures exhibiting several characteristics that are consistent with
degradation accumulation in materials. Future work is planned to enable verification of the efficacy of AE for thermal
fatigue crack initiation detection. While the emphasis has been placed on the use of AE for crack initiation detection
during accelerated aging tests, this work also has implications with respect to the use of AE as a primary tool for early
degradation monitoring in nuclear power plant materials. The development of NDE tools for characterization of aging in
materials can also benefit from the use of a technology such as AE which can continuously monitor and detect crack
initiation during accelerated aging tests.
Cast austenitic stainless steel (CASS) that was commonly used in U.S. nuclear power plants is a coarse-grained,
elastically anisotropic material. In recent years, low-frequency phased-array ultrasound has emerged as a leading
candidate for the inspection of welds in CASS piping, due to the relatively lower interference in the measured signal
from ultrasonic backscatter. However, adverse phenomena (such as scattering from the coarse-grained microstructure,
and beam redirection and partitioning due to the elastically anisotropic nature of the material) result in measurements
with a low signal-to-noise ratio (SNR), and increased difficulty in discriminating between signals from flaws and signals
from benign geometric factors. There is therefore a need for advanced signal processing tools to improve the SNR and
enable rapid analysis and classification of measurements. This paper discusses recent efforts at PNNL towards the
development and evaluation of a number of signal processing algorithms for this purpose. Among the algorithms being
evaluated for improving the SNR (and, consequently, the ability to discriminate between flaw signals and non-flaw
signals) are wavelets and other time-frequency distributions, empirical mode decompositions, and split-spectrum
processing techniques. A range of pattern-recognition algorithms, including neural networks, are also being evaluated for
their ability to successfully classify measurements into two or more classes. Experimental data obtained from the
inspection of a number of welds in CASS components are being used in this evaluation.
Continuous on-line monitoring of active and passive systems, structures and components in nuclear power plants will be
critical to extending the lifetimes of nuclear power plants in the US beyond 60 years. Acoustic emission and guided
ultrasonic waves are two tools for continuously monitoring passive systems, structures and components within nuclear
power plants and are the focus of this study. These tools are used to monitor fatigue damage induced in a SA 312 TP304
stainless steel pipe specimen. The results of acoustic emission monitoring indicate that crack propagation signals were
not directly detected. However, acoustic emission monitoring revealed crack formation prior to visual confirmation
through the detection of signals caused by crack closure friction. The results of guided ultrasonic wave monitoring
indicate that this technology is sensitive to the presence and size of cracks. The sensitivity and complexity of guided
ultrasonic wave (GUW) signals is observed to vary with respect to signal frequency and path traveled by the GUW
relative to the crack orientation.
This paper presents an integrated sensor network and distributed event processing architecture for managed in-building traffic evacuation during natural and human-caused disasters, including earthquakes, fire and biological/chemical terrorist attacks. The proposed wireless sensor network protocols and distributed event processing mechanisms offer a new distributed paradigm for improving reliability in building evacuation and disaster management. The networking component of the system is constructed using distributed wireless sensors for measuring environmental parameters such as temperature, humidity, and detecting unusual events such as smoke, structural failures, vibration, biological/chemical or nuclear agents. Distributed event processing algorithms will be executed by these sensor nodes to detect the propagation pattern of the disaster and to measure the concentration and activity of human traffic in different parts of the building. Based on this information, dynamic evacuation decisions are taken for maximizing the evacuation speed and minimizing unwanted incidents such as human exposure to harmful agents and stampedes near exits. A set of audio-visual indicators and actuators are used for aiding the automated evacuation process. In this paper we develop integrated protocols, algorithms and their simulation models for the proposed sensor networking and the distributed event processing framework. Also, efficient harnessing of the individually low, but collectively massive, processing abilities of the sensor nodes is a powerful concept behind our proposed distributed event processing algorithms. Results obtained through simulation in this paper are used for a detailed characterization of the proposed evacuation management system and its associated algorithmic components.
Magneto-optic imaging (MOI) is a relatively new technology that produces analog images of magnetic flux leakage from surface and subsurface defects. An alternating current carrying foil serves as the excitation source and induces eddy currents in a conducting test specimen. Under normal conditions, the associated magnetic flux is tangential to specimen surface. Anomalies in the specimen result in generating a normal component of the magnetic flux density. The magneto-optic sensor produces a binary valued image of this anomalous magnetic field. The current system has two shortcomings. First, the presence of a textured background due to the domain structures in the sensor makes detection of third layer cracks and corrosion difficult. Second, the qualitative nature of the MO images does not provide a basis for making quantitative improvements to the MOI system. The availability of a theoretical model that can simulate the MOI system performance is extremely important for the optimization of the MOI sensor and hardware system. This paper presents a finite element model and its use in understanding the capabilities of the MOI system. In addition the paper also presents signal-processing methods for eliminating the background noise.