Oscillation sources and wave propagation paths in complex networks consisting of excitable nodes

In a complex system, perturbations propagate by following paths on the network of interactions among the system’s units. In contrast to what happens with the spreading of epidemics, observations of general perturbations are often very sparse in time (there is a single observation of the perturbed system) and in “space” (only a few perturbed and unperturbed units are observed). A major challenge in many areas, from biology to the social sciences, is to infer the propagation paths from observations of the effects of perturbation under these sparsity conditions. We address this problem and show that it is possible to go beyond the usual approach of using the shortest paths connecting the known perturbed nodes. Specifically, we show that a simple and general probabilistic model, which we solved using belief propagation, provides fast and accurate estimates of the probabilities of nodes being perturbed.

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FEA-Based Ultrasonic Focusing Method in Anisotropic Media for Phased Array Systems

Applied Sciences ◽

10.3390/app11198888 ◽

2021 ◽

Vol 11 (19) ◽

pp. 8888

Author(s):

Seongin Moon ◽

To Kang ◽

Soonwoo Han ◽

Kyung-Mo Kim ◽

Hyung-Ha Jin ◽

...

Keyword(s):

Wave Propagation ◽

Ultrasonic Wave ◽

Elastic Constants ◽

Grain Orientation ◽

Phased Array ◽

Ultrasonic Imaging ◽

Anisotropic Media ◽

Imaging Method ◽

Ultrasonic Wave Propagation ◽

Propagation Paths

Traditional ultrasonic imaging methods have a low accuracy in the localization of defects in austenitic welds because the anisotropy and inhomogeneity of the welds cause distortion of the ultrasonic wave propagation paths in anisotropic media. The distribution of the grain orientation in the welds influences the ultrasonic wave velocity and ultrasonic wave propagation paths. To overcome this issue, a finite element analysis (FEA)-based ultrasonic imaging methodology for austenitic welds is proposed in this study. The proposed ultrasonic imaging method uses a wave propagation database to synthetically focus the inter-element signal recorded with a phased array system using a delay-and-sum strategy. The wave propagation database was constructed using FEA considering the grain orientation distribution and the anisotropic elastic constants in the welds. The grain orientation was extracted from a macrograph obtained from a dissimilar metal weld specimen, after which the elastic constants were optimized using FEA with grain orientation information. FEA was performed to calculate a full matrix of time-domain signals for all combinations of the transmitting and receiving elements in the phased array system. The proposed approach was assessed for an FEA-based simulated model embedded in a defect. The simulation results proved that the newly proposed ultrasonic imaging method can be used for defect localization in austenitic welds.

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The Use of Dijkstra’s Algorithm in Assessing the Correctness of Imaging Brittle Damage in Concrete Beams by Means of Ultrasonic Transmission Tomography

Materials ◽

10.3390/ma13030551 ◽

2020 ◽

Vol 13 (3) ◽

pp. 551 ◽

Cited By ~ 6

Author(s):

Zbigniew Perkowski ◽

Karolina Tatara

Keyword(s):

Wave Propagation ◽

Damage Mechanics ◽

Concrete Beams ◽

Tomographic Reconstruction ◽

Ultrasonic Tomography ◽

Dijkstra’S Algorithm ◽

Brittle Damage ◽

Dijkstra's Algorithm ◽

Propagation Paths ◽

Damage In Concrete

The accuracy of transmission ultrasonic tomography for the detection of brittle damage in concrete beams can be effectively supported by the graph theory and, in particular, by Dijkstra’s algorithm. It allows determining real paths of the fastest ultrasonic wave propagation in concrete containing localized elastically degraded zones at any stage of their evolution. This work confronts this type of approach with results that can be obtained from non-local isotropic damage mechanics. On this basis, the authors developed a method of reducing errors in tomographic reconstruction of longitudinal wave velocity maps which are caused by using the simplifying assumptions of straightness of the fastest wave propagation paths. The method is based on the appropriate elongation of measured propagation times of the wave transmitted between opposite sending-receiving transducers if the actual propagation paths deviate from straight lines. Thanks to this, the mathematical apparatus used typically in the tomography, in which the straightness of the fastest paths is assumed, can be still used. The work considers also the aspect of using fictitious wave sending-receiving points in ultrasonic tomography for which wave propagation times are calculated by interpolation of measured ones. The considerations are supported by experimental research conducted on laboratory reinforced concrete (RC) beams in the test of three-point bending and a prefabricated damaged RC beam.

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Reliability of crack quantification via acousto-ultrasound active-sensing structural health monitoring using surface-mounted PZT actuators/sensors

Structural Health Monitoring ◽

10.1177/1475921720921536 ◽

2020 ◽

pp. 147592172092153

Author(s):

Susheel Kumar Yadav ◽

Spandan Mishra ◽

Fotis Kopsaftopoulos ◽

Fu-Kuo Chang

Keyword(s):

Wave Propagation ◽

Structural Health Monitoring ◽

Sensor Network ◽

Health Monitoring ◽

Active Sensing ◽

Structural Components ◽

Network Configuration ◽

Structural Health ◽

Damage Quantification ◽

Propagation Paths

This work presents the introduction and experimental investigation of an active-sensing acousto-ultrasound structural health monitoring approach for damage size quantification based on piezoelectric sensors/actuators mounted on multiple seemingly identical structural components. The objective of this work is to determine how reliable the damage diagnostics can be from one component to another similar (nominally identical) component using surface-mounted PZT (lead zirconate titanate) sensors/actuators, and also to evaluate how sensitive a sensor network configuration in terms of the number of sensors/actuators is with respect to its detection reliability. Extensive crack growth experiments on multiple identical coupons outfitted with the same sensor network configuration under cyclic loads were conducted to assess the damage quantification reliability from one coupon to another using the same diagnostic algorithm. The results of the study indicate that the crack size estimates obtained from the active-sensing structural health monitoring system can vary within the population of identical structural components (coupons), but the difference in quantifying damage among coupons decreases with the increase in the number of sensors and actuators used, that is, wave propagation paths. Furthermore, it is shown that the diagnostic results in terms of damage quantification converge with the increase in the number of sensors. The results of the study indicate that the diagnostic approach using a multi-path sensor network can reduce the damage quantification error from one component to another within a “hotspot” configuration (damage location is known or suspected a priori). Finally, the results of this study indicate that the more wave propagation paths used in the diagnostic active-sensing algorithm, the more reliable the damage quantification results are, provided that the same sensor network is used and installed at nominally identical locations for all coupons.

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Acoustic Emission-Based Diagnosis Using AlexNet: How Wave Propagation Effects Classification Performance

Structural Health Monitoring ◽

10.21741/9781644901311-22 ◽

2021 ◽

Keyword(s):

Acoustic Emission ◽

Wave Propagation ◽

Composite Materials ◽

Mechanical Damage ◽

Classification Performance ◽

Performance Estimation ◽

Damage Mechanisms ◽

Source Mechanisms ◽

Propagation Paths ◽

The Impact

Abstract. Composite materials are frequently used due to light weight and high stiffness. However, the use of composite materials is limited due to several micro-mechanical damage mechanisms, which are currently not well understood. Therefore, Acoustic Emission (AE) is frequently suggested for in-situ diagnosis of composite materials in Structural Health Monitoring. Elastic stress waves in the ultrasound regime are recorded using highly sensitive measurement equipment. Based on suitable analysis and interpretation of the waveform data, different micro-mechanical damage mechanisms such as delamination or fiber breakage can be distinguished. Frequently, data-driven approaches are suggested for classification of AE data. In literature, attenuation of AE due to wave propagation is currently the main limiting factor in AE-based diagnosis. In particular, AE is strongly attenuated in composite materials due to dispersion as dominant attenuation mechanism. Furthermore, depending on the source location, which is usually not known a-priori, different propagation paths are obtained in practice. Therefore, the effect of wave propagation on AE is important and can not be neglected to achieve reliable classification. However, the effect of different propagation paths on the classification performance is often not considered explicitly. Due to dependence of wave propagation behavior on waveform characteristics (e.g. frequency), it can be expected that the impact of wave propagation on AE classification performance depends also on the related source mechanism. Therefore, it is worth to study how classification performance of different source mechanisms is effected by wave propagation. In this paper, the dependence of the classification performance on different propagation distances is experimentally investigated in detail. To achieve highly reproducible AE measurements, different artificial AE sources are induced using surface mounted piezo elements. The corresponding waveforms are measured at two different locations. For classification, a convolutional neural network-based classification scheme is established. The pre-trained AlexNet architecture is fine-tuned using measurements obtained using different excitation signals. The classification performance is evaluated with particular focus on the impact of wave propagation. The variations in propagation distance have a strong impact on the classification performance. As main conclusion for AE-based SHM it can be stated that variations in the propagation path should be considered. Furthermore, the underlying source mechanisms should be taken into consideration for reliable performance estimation.

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