Verification and Validation of Computational Models for Ultrasonic Testing

The Pacific Northwest National Laboratory (PNNL) is conducting confirmatory research for the U.S. Nuclear Regulatory Commission (NRC) for the verification and validation of computational models used in ultrasonic testing (UT). This paper discusses some of the findings obtained from simple geometrical reflectors in isotropic, fine-grained, homogeneous materials with conventional ultrasonic transducers. A total of 348 ultrasonic raster scans were acquired using pulse-echo conventional probes with different diameters, beam angles, wave modalities, and frequencies. The raster scans contained reflections from machined notches (flaws) of different sizes and orientations. High frequency pencil beam models, and flaw scattering models employing the Kirchhoff and geometric theory of diffraction approximations, were then used to simulate the corresponding experimental measurements. CIVA, a semi-analytical nondestructive evaluation (NDE) simulation platform developed by the French Commissariat à l’Energie Atomique (CEA), was used to run simulations based on the approximation models. The experimental measurements were compared to their modeled counterparts using quantitative metrics obtained from the C-scans, B-scans and A-scans. To address uncertainties in the input parameters to the computational model, parametric studies and sensitivity analyses were conducted on all the relevant inputs. Sources of discrepancies between the experiment and simulated results are discussed in terms of numerical model errors and uncertainties in the experiments and model input parameters

In-situ fatigue monitoring procedure using nonlinear ultrasonic surface waves considering the nonlinear effects in the measurement system

Second harmonic generation using nonlinear ultrasonic waves have been shown to be an early indicator of possible fatigue damage in nuclear power plant components. This technique relies on measuring amplitudes, making it highly susceptible to variations in transducer coupling and instrumentation. This paper proposes an experimental procedure for in-situ surface wave nonlinear ultrasound measurements on specimen with permanently mounted transducers under high cycle fatigue loading without interrupting the experiment. It allows continuous monitoring and minimizes variation due to transducer coupling. Moreover, relations describing the effects of the measurement system nonlinearity including the effects of the material transfer function on the measured nonlinearity parameter are derived. An in-situ high cycle fatigue test was conducted using two 304 stainless steel specimens with two different excitation frequencies. A comprehensive analysis of the nonlinear sources, which result in variations in the measured nonlinearity parameters, was performed and the effects of the system nonlinearities are explained and identified. In both specimens, monotonic trend was observed in nonlinear parameter when the value of fundamental amplitude was not changing. Keywords: Nonlinear ultrasound, Surface wave, In-situ monitoring, High-cycle fatigu

Verification and Validation of Computational Models for Ultrasonic Testing

The Pacific Northwest National Laboratory (PNNL) is conducting confirmatory research for the U.S. Nuclear Regulatory Commission (NRC) for the verification and validation of computational models used in ultrasonic testing (UT). This paper discusses some of the findings obtained from simple geometrical reflectors in isotropic, fine-grained, homogeneous materials with conventional ultrasonic transducers. A total of 348 ultrasonic raster scans were acquired using pulse-echo conventional probes with different diameters, beam angles, wave modalities, and frequencies. The raster scans contained reflections from machined notches (flaws) of different sizes and orientations. High frequency pencil beam models, and flaw scattering models employing the Kirchhoff and geometric theory of diffraction approximations, were then used to simulate the corresponding experimental measurements. CIVA, a semi-analytical nondestructive evaluation (NDE) simulation platform developed by the French Commissariat à l’Energie Atomique (CEA), was used to run simulations based on the approximation models. The experimental measurements were compared to their modeled counterparts using quantitative metrics obtained from the C-scans, B-scans and A-scans. To address uncertainties in the input parameters to the computational model, parametric studies and sensitivity analyses were conducted on all the relevant inputs. Sources of discrepancies between the experiment and simulated results are discussed in terms of numerical model errors and uncertainties in the experiments and model input parameters.</p

Ensembles of Novelty Detection Classifiers for Structural Health Monitoring Using Guided Waves

Guided wave structural health monitoring uses sparse sensor networks embedded in sophisticated structures for defect detection and characterization. The biggest challenge of those sensor networks is developing robust techniques for reliable damage detection under changing environmental and operating conditions (EOC). To address this challenge, we develop a novelty classifier for damage detection based on one class support vector machines. We identify appropriate features for damage detection and introduce a feature aggregation method which quadratically increases the number of available training observations. We adopt a two-level voting scheme by using an ensemble of classifiers and predictions. Each classifier is trained on a different segment of the guided wave signal, and each classifier makes an ensemble of predictions based on a single observation. Using this approach, the classifier can be trained using a small number of baseline signals. We study the performance using Monte-Carlo simulations of an analytical model and data from impact damage experiments on a glass fiber composite plate. We also demonstrate the classifier performance using two types of baseline signals: fixed and rolling baseline training set. The former requires prior knowledge of baseline signals from all EOC, while the latter does not and leverages the fact that EOC vary slowly over time and can be modeled as a Gaussian process

A deep learning approach to training a brain activity-based trial-by-trial classifier for rapid serial visual presentation imagery

Image classification aided by brain activity measured during rapid serial visual presentation (RSVP) shows promise to aidhuman viewers to quickly triage large volumes of images with support of an EEG technology. Fast perceptual responsesare parsed with a brain-activity classifier operating on EEG signals to select an image subset containing visual informationsimilar to the viewers target. However, current processes for training brain activity classifiers are experimentally andcomputationally expensive. We propose a deep learning model that classifies images based off of brain-activity. Usingthe satellite visual images and EEG data provided from Bigdely-Shamlo et al. (2007), we compare different machinelearning (Support Vector Machines) and deep learning (Convolutional Neural Networks and Recurrent Neural Networks)approaches along with different data manipulation styles for classifying the satellite images. This initial report summarizesthe efforts to establish benchmarks for deep learning, exploring the potential to streamline and improve brain-activity basedclassification

Informal Cairo: Between Islamist Insurgency & the Neglectful State?

From the late 1980s, Islamist militants established a ‘state within the state’ in the Egyptian capital Cairo, situated in ‘informal’ neighbourhoods developed without official authorization, planning or public services. After government security forces in late 1992 crushed these efforts in the neighbourhood of Munira Gharbiyya, informal Cairo became pathologized in public discourse as ashwa’iyyat (‘random’ or ‘haphazard’ areas), a zone of socio-spatial disorder threatening Egypt as a whole and demanding state intervention. However, this securitizing move did not lead to heavy-handed intervention against informal Cairo more generally. Following the suppression of the militants, the Mubarak government instead returned to long-term patterns of indifference and neglect that had allowed informal neighbourhoods to flourish since the 1960s. In part, the absence of intervention can be explained in terms of resource constraints and risk avoidance. More profoundly, however, it reflects numerous linkages between informal urbanization and the Egyptian state. The ashwa’iyyat are, to a significant degree, both a consequence of an authoritarian political order and embedded in the informal control stratagems used by Egyptian governments to bolster their rule. Informal Cairo should thus not be understood as a disorderly zone of subaltern dissidence. Rather, the Egyptian state is best seen as facing its own oblique reflection