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Current generation light water reactors (LWRs), sodium cooled fast reactors (SFRs), small modular reactors (SMRs), and next generation nuclear plants (NGNPs) produce harsh environments in and near the reactor core that can severely tax material performance and limit component operational life. To address this issue, several Department of Energy Office of Nuclear Energy (DOE-NE) research programs are evaluating the long duration irradiation performance of fuel and structural materials used in existing and new reactors. In order to maximize the amount of information obtained from Material Testing Reactor (MTR) irradiations, DOE is also funding development of enhanced instrumentation that will be able to obtain in-situ, real-time data on key material characteristics and properties, with unprecedented accuracy and resolution. Such data are required to validate new multi-scale, multi-physics modeling tools under development as part of a science-based, engineering driven approach to reactor development. It is not feasible to obtain high resolution/microscale data with the current state of instrumentation technology. However, ultrasound-based sensors offer the ability to obtain such data if it is demonstrated that these sensors and their associated transducers are resistant to high neutron flux, high gamma radiation, and high temperature. To address this need, the Advanced Test Reactor National Scientific User Facility (ATRNSUF) is funding an irradiation, led by PSU, at the Massachusetts Institute of Technology Research Reactor to test the survivability of ultrasound transducers. As part of this effort, PSU and collaborators have designed, fabricated, and provided piezoelectric and magnetostrictive transducers that are optimized to perform in harsh, high flux, environments. Four piezoelectric transducers were fabricated with either aluminum nitride, zinc oxide, or bismuth titanate as the active element that were coupled to either Kovar or aluminum waveguides and two magnetostrictive transducers were fabricated with Remendur or Galfenol as the active elements. Pulse-echo ultrasonic measurements of these transducers are made insitu. This paper will present an overview of the test design including selection criteria for candidate materials and optimization of test assembly parameters, data obtained from both out-of-pile and in-pile testing at elevated temperatures, and an assessment based on initial data of the expected performance of ultrasonic devices in irradiation conditions. [ABSTRACT FROM AUTHOR]

Advanced small modular reactors (AdvSMRs), which are based on modularization of advanced reactor concepts, may provide a longer-term alternative to traditional light-water reactors and near-term small modular reactors (SMRs), which are based on integral pressurized water reactor (iPWR) concepts. SMRs are challenged economically because of losses in economy of scale; thus, there is increased motivation to reduce the controllable operations and maintenance costs through automation technologies including prognostics health management (PHM) systems. In this regard, PHM systems have the potential to play a vital role in supporting the deployment of AdvSMRs and face several unique challenges with respect to implementation for passive AdvSMR components. This paper presents a summary of a research gaps and technical needs assessment performed for implementation of PHM for passive AdvSMR components. [ABSTRACT FROM AUTHOR]

A prototypical ferritic/martensitic alloy, HT-9, of interest to the nuclear materials community was investigated for microstructure effects on Barkhausen noise emission and first-order reversal curve (FORC) analysis for three different heat-treated samples. It was observed that Barkhausen noise emission and reversible component of magnetization, computed from the FORC data, decreased with increasing measured mechanical hardness. The results are discussed in terms of the use of magnetic signatures for use in nondestructive interrogation of radiation damage and other microstructural changes in ferritic/martensitic alloys. FORC analysis is shown to be particularly useful for detailed characterization of defect density and pinning, which can be correlated to bulk non-destructive evaluation field measurements such as Barkhausen noise emission. [ABSTRACT FROM AUTHOR]

Effective reconstitution approaches for cyber systems are needed to keep critical infrastructure operational in the face of an intelligent adversary. The reconstitution response, including recovery and adaptation, may require significant reconfiguration of the system at all levels to render the cyber-system resilient to ongoing and future attacks or faults while maintaining continuity of operations. A theoretical basis for optimal dynamic reconstitution is needed to address the challenge of ensuring that dynamic reconstitution is optimal with respect to resilience metrics, and is being developed and evaluated in this project. Such a framework provides the technical basis for evaluating cyber-defense and reconstitution approaches. This paper describes a preliminary framework that may be used to develop and evaluate concepts for effective autonomous reconstitution of compromised cyber systems. [ABSTRACT FROM PUBLISHER]

The objective of this work is to develop processing algorithms to detect and localize flaws using ultrasonic phased-array data. Data was collected on cast austenitic stainless stell (CASS) weld specimens onloan from the U.S. nuclear power industry' Pressurized Walter Reactor Owners Group (PWROG) traveling specimen set. Each specimen consists of a centrifugally cast stainless stell (CCSS) pipe section welded to a statically cst(SCSS) or wrought (WRSS) section. The paper presents a novel automated flaw detection and localization scheme using low frequency ultrasonic phased array inspection singals from the weld and heat affected zone of the based materials. The major steps of the overall scheme are preprocessing and region of interest (ROI) detection followed by the Hilbert-Huang transform (HHT) of A-scans in the detected ROIs. HHT offers time-frequency-energy distribution for each ROI. The Accumulation of energy in a particular frequency band is used as a classification feature for the particular ROI. [ABSTRACT FROM AUTHOR]

There is growing interest in longer-term operation of the current US nuclear power plant (NPP) fleet. This paper presents an overview of prognostic health management (PHM) technologies that could play a role in the safe and effective operation of nuclear power plants during extended life. A case study in prognostics for materials degradation assessment, using laboratory-scale measurements, is briefly discussed, and technical gaps that need to be addressed prior to PHM system deployment for nuclear power life extension are presented. [ABSTRACT FROM PUBLISHER]

Currently in the United States, periodic sensor recalibration is required for all safety-related sensors, typically occurring at every refueling outage; and it has emerged as a critical path item for shortening outage duration in some plants. International application of calibration monitoring has shown that sensors may operate for longer periods within calibration tolerances. This issue is expected to also be important as the United States looks to the next generation of reactor designs (such as small modular reactors and advanced reactor concepts), given the anticipated longer refueling cycles, proposed advanced sensors, and digital instrumentation and control systems. Online monitoring (OLM) can be employed to identify those sensors that require calibration, allowing for calibration of only those sensors that need it. The U.S. Nuclear Regulatory Commission (NRC) accepted the general concept of OLM for sensor calibration monitoring in 2000, but no U.S. plants have been granted the necessary license amendment to apply it. This paper summarizes a recent state-of-the-art assessment of online calibration monitoring in the nuclear power industry, including sensors, calibration practice, and OLM algorithms. This assessment identifies key research needs and gaps that prohibit integration of the NRC-approved online calibration monitoring system in the U.S. nuclear power industry. Several technical needs were identified, including an understanding of the impacts of sensor degradation on measurements for both conventional and emerging sensors; the quantification of uncertainty in online calibration assessment; determination of calibration acceptance criteria and quantification of the effect of acceptance criteria variability on system performance; and assessment of the feasibility of using virtual sensor estimates to replace identified faulty sensors in order to extend operation to the next convenient maintenance opportunity. [ABSTRACT FROM PUBLISHER]

Flaw profile estimation from measurements is a typical inverse problem in electromagnetic nondestructive evaluation (NDE). The increasing availability of multiple measurement modes in NDE requires the development of multisensor data fusion algorithms to solve the NDE inverse problem. This paper proposes a multisensor data fusion algorithm for flaw profiling, based on a recursive state space approach. The problem of flaw profile estimation from given multisensor data is formulated using multiple measurement process models and a state transition model. This formulation enables the application of Bayesian non-linear filters based on sequential Monte Carlo methods. The new approach is computationally efficient if computationally simple measurement models are employed. Moreover, the technique is robust to noisy measurement data. The initial results indicate significant improvement in the accuracy of inversion results when more than one type of measurement data is used for flaw profile estimation. [ABSTRACT FROM AUTHOR]

The ability to accurately detect flawed regions and estimate flaw sizes and shapes is important for practical NDE techniques. Most probes used in practice are not point probes, and the resulting measurement of a flaw tends to be “blurred”, causing the footprint (or extent) of the flaw to appear to be larger and resulting in an overestimate of the flaw size. Image deconvolution methods, that model the measurement process as the convolution of the true flaw footprint with a probe blur kernel, have been used to determine the “true” flaw footprint by eliminating the effect of the blur kernel. In this paper, we examine a deconvolution technique based on Mumford-Shah regularization that assumes partial knowledge of the blur kernel, and has the ability to segment out the flaw region while simultaneously deconvolving the image. Results of applying the technique for detection and flaw size estimation from eddy current measurements are presented. [ABSTRACT FROM AUTHOR]

Flaw profile estimation from measurements is a typical inverse problem in electromagnetic nondestructive evaluation (NDE). This paper proposes a novel state-space approach to combat the ill-posedness in the solution to the inverse problem, particularly in the presence of measurement noise. The inversion approach formulates the inverse problem as a tracking problem with state and measurement equations. This state-space model resembles the classical discrete-time tracking problem, and enables the application of Bayesian non-linear filters based on sequential Monte Carlo methods in conjunction with numerical models that represent the measurement process (i.e. solution of forward problem). The proposed approach is applied to simulated NDE measurements from known flaw shapes. Functional models of the NDE inspection process are used as the measurement model and a probabilistic state transition model is defined for the proposed technique. Initial results of applying the proposed approach to eddy current NDE inverse problems indicate the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Iterative algorithms that incorporate a numerical forward model have been typically used to solve the problem of flaw profile estimation from measured magnetic flux leakage (MFL) data. These approaches use the forward model to determine the measurement signal for a given defect profile, and obtain the desired profile by iteratively minimizing a cost function. The use of numerical models is computationally expensive, and alternative forward models are needed. This paper presents a finite element neural network (FEN) obtained by embedding a finite element model in a parallel neural network architecture that enables fast and accurate solution of the forward problem. Previous results have indicated that the FEN performance as a forward model is comparable to that of the conventional finite element method. In this paper, we investigate the applicability of the FEN to determining flaw profiles from MFL data in pipeline inspection and present results on synthetic MFL data. [ABSTRACT FROM AUTHOR]

In typical nondestructive evaluation (NDE) of materials, the material under test is inspected using one or more NDE techniques to evaluate its condition. However, measurement data from different inspection techniques are often complementary in nature and higher accuracy may be achieved by fusing information from these different inspection modes. This paper proposes a classifier-fusion based approach to combine multifrequency eddy current and ultrasound data for material characterization. The proposed algorithm uses a hierarchy of classifiers to determine the material state (e.g. stress, heat treatment etc.) and level of exposure to this condition, with classifier fusion achieved through a majority-voting rule. Preliminary results on applying the proposed algorithm to data from Inconel 600 samples are presented. © 2007 American Institute of Physics [ABSTRACT FROM AUTHOR]

C-scan data obtained from multifrequency eddy current inspection are smeared representations of true flaws due to finite size of inspection probes. Deconvolution algorithms are commonly used to remove the effect of finite probe size and estimate the true flaw footprint. Single frequency blind deconvolution techniques are insufficient to correctly determine the true flaw footprint since single frequency eddy current inspection does not provide sufficient information about a flaw. This paper proposes a blind deconvolution algorithm that estimates the flaw footprint by using complementary information present across multifrequency eddy current inspection data. Initial results of applying the algorithm to steam generator tubing inspection data indicate the feasibility and robustness of the proposed algorithm. © 2005 American Institute of Physics [ABSTRACT FROM AUTHOR]

Neural network based data fusion techniques are becoming increasingly popular for quantifying corrosion in aircraft lap joints because of their accuracy and ability to learn the mapping between measurements and the corresponding wall thickness. However, this approach to data fusion is slow and less robust because of high input dimensionality. This paper proposes a multiresolution based data fusion algorithm to improve the performance of corrosion quantification in aircraft lap joints. The proposed algorithm reduces the dimensionality of the input and improves the robustness of the network by separating fusion and corrosion characterization operations. Initial results validate these claims and indicate the feasibility of the algorithm. © 2005 American Institute of Physics [ABSTRACT FROM AUTHOR]

Corrosion in ageing aircraft lap joints is a serious problem for the aviation industry. While there are many methods to detect the presence of hidden corrosion, there is a lack of approaches that provide accurate quantification of the amount of corrosion (or equivalently, the thickness loss of the specimen). This is particularly so when the corrosion is in the second layer. This paper proposes the use of wavelet neural network based data fusion approaches for combining the information provided by multiple inspection techniques. The proposed algorithm is applied to fusing data obtained from multifrequency eddy current testing and pulsed eddy current testing of ageing aircraft lap joints. Initial results indicate that the proposed data fusion approach provides better thickness estimates than traditional approaches. © 2004 American Institute of Physics [ABSTRACT FROM AUTHOR]

A signal processing technique known as independent component analysis (ICA) is proposed for enhancing flaw information in eddy current testing. Although the observed signal itself includes relevant information about defects, it is often corrupted by noise and signals from external supports that makes the analysis challenging. ICA, along with an affine transformation as a preprocessing stage, is shown to extract the defect signal from a combination of defect, support and noise signals, while improving the SNR. © 2004 American Institute of Physics [ABSTRACT FROM AUTHOR]