Simulation of wave propagation in remote bonded FBG sensors using the spectral element method

Ultrasonic guided waves (GW) due to their ability to monitor large areas with few sensors, are commonly employed for structural health monitoring (SHM) in aerospace, civil, and mechanical industries. The FBG sensors in the edge filtering setup are re-emerging as a favored technique for GW sensing. The FBG sensors offer embeddability, ability to be multiplexed, small size, and immunity to electric and magnetic fields. To enhance the sensitivity of these sensors, these sensors are deployed in the so-called remote bonding configuration where the optical fiber is bonded to the structure while the FBG sensor is free. This configuration not only enhances the sensitivity but also opens up possibility of self-referencing. In this setup the GW in the structure is coupled to the fiber and converted into fiber modes. These modes propagate along the fiber and then are sensed at the FBG. The conversion of the plate modes to fiber modes is a phenomenon which is still being studied. The effect of the adhesive layer and the material properties of the adhesive on the coupling are still not known. Furthermore the directional nature of this coupling and its marked difference from the directly bonded configuration needs to be studied in detail. For this detailed study a computationally efficient model which captures the physics of the coupling is necessary. Hence, in this research we develop a numerical model based on the spectral element method (SEM) for the modeling of the remote bonded configuration of the FBG. The model comprises four meters long optical fiber bonded to the center of the plate by the adhesive layer and the piezoelectric disc (PZT) used for wave excitation. The ability of the SEM model to capture the effect of the adhesive and the remote bonding as well as the directional nature of the coupling has been studied in this paper. The model is validated with analytical and experimental results. It has been shown that the SEM model captures the physics of the coupling and is computationally more efficient than other methods using conventional finite element software

Impact of Neuronal Membrane Damage on the Local Field Potential in a Large-Scale Simulation of Cerebral Cortex

Within multiscale brain dynamics, the structure–function relationship between cellular changes at a lower scale and coordinated oscillations at a higher scale is not well understood. This relationship may be particularly relevant for understanding functional impairments after a mild traumatic brain injury (mTBI) when current neuroimaging methods do not reveal morphological changes to the brain common in moderate to severe TBI such as diffuse axonal injury or gray matter lesions. Here, we created a physiology-based model of cerebral cortex using a publicly released modeling framework (GEneral NEural SImulation System) to explore the possibility that performance deficits characteristic of blast-induced mTBI may reflect dysfunctional, local network activity influenced by microscale neuronal damage at the cellular level. We operationalized microscale damage to neurons as the formation of pores on the neuronal membrane based on research using blast paradigms, and in our model, pores were simulated by a change in membrane conductance. We then tracked changes in simulated electrical activity. Our model contained 585 simulated neurons, comprised of 14 types of cortical and thalamic neurons each with its own compartmental morphology and electrophysiological properties. Comparing the functional activity of neurons before and after simulated damage, we found that simulated pores in the membrane reduced both action potential generation and local field potential (LFP) power in the 1–40 Hz range of the power spectrum. Furthermore, the location of damage modulated the strength of these effects: pore formation on simulated axons reduced LFP power more strongly than did pore formation on the soma and the dendrites. These results indicate that even small amounts of cellular damage can negatively impact functional activity of larger scale oscillations, and our findings suggest that multiscale modeling provides a promising avenue to elucidate these relationships

DEEP LEARNING BASED SURROGATE MODELLING OF WAVE PROPAGATION AND DAMAGE DETECTION IN CRACKED ROD

Guided wave-based Structural Health Monitoring (SHM) tools utilize the guided wave responses to interrogate damage in structures. This research demonstrates the use of various objective functions in single (mono) objective and multi-objective genetic algorithms for damage identification in isotropic 1D structures. The time domain spectral element method and a deep-learning-based surrogate is utilized for simulating wave propagation in an isotropic cracked rod. The genetic algorithms employ results ('numerical experiment') obtained from the spectral element model and the deep-learning-based surrogate to determine the optimized crack locations and crack depths as output parameters. The obtained optimized parameters from genetic algorithms are compared in terms of errors for various objective functions

Dataset on full ultrasonic guided wavefield measurements of a CFRP plate with fully bonded and partially debonded omega stringer

The fourth dataset dedicated to the Open Guided Waves platform [1] presented in this work aims at a carbon fiber composite plate with an additional omega stringer at constant temperature conditions. The dataset provides full ultrasonic guided wavefields. A chirp signal in the frequency range 20-500 kHz and Hann windowed tone-burst signal with 5 cycles and carrier frequencies of 16:5 kHz, 50 kHz, 100 kHz, 200 kHz and 300 kHz are used to excite guided waves. The piezoceramic actuator used for this purpose is attached to the center of the stringer side surface of the core plate. Three scenarios are provided with this setup: (1) wavefield measurements without damage, (2) wavefield measurements with a local stringer debond and (3) wavefield measurements with a large stringer debond. The defects were caused by impacts performed from the backside of the plate. As result, the stringer feet debonds locally which was verified with conventional ultrasound measurements

Application of a Laser-Based Time Reversal Algorithm for Impact Localization in a Stiffened Aluminum Plate

Non-destructive testing and structural health monitoring (SHM) techniques using elastic guided waves are often limited by material inhomogeneity or geometrical irregularities of the tested parts. This is a severe restriction in many fields of engineering such as aerospace or aeronautics, where typically one needs to monitor composite structures with varying mechanical properties and complex geometries. This is particularly true in the case of multiscale composite materials, where anisotropy and material gradients may be present. Here, we provide an impact localization algorithm based on time reversal and laser vibrometry to cope with this type of complexity. The proposed approach is shown to be insensitive to local elastic wave velocity or geometrical features. The technique is based on the correlation of the measured impact response and a set of measured test data acquired at various grid points along the specimen surface, allowing high resolution in the determination of the impact point. We present both numerical finite element simulations and experimental measurements to support the proposed procedure, showing successful implementation on an eccentrically stiffened aluminum plate. The technique holds promise for advanced SHM, potentially in real time, of geometrically complex composite structures

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

A Study of Sensor Placement Optimization Problem for Guided Wave-Based Damage Detection

Guided waves (GW) allow fast inspection of a large area and hence have attracted research interest from the structural health monitoring (SHM) community. Thus, GW-based SHM is ideal for thin structures such as plates, pipes, etc., and is finding applications in several fields like aerospace, automotive, wind energy, etc. The GW propagate along the surface of the sample and get reflected from discontinuities in the structure in the form of boundaries and damage. Through proper signal processing of the reflected waves based on their time of arrival, the damage can be detected and isolated. For complex structures, a higher number of sensors may be required, which increases the cost of the equipment, as well as the mass. Thus, there is an effort to reduce the number of sensors without compromising the quality of the monitoring achieved. It is of utmost importance that the entire structure can be investigated. Hence, it is necessary to optimize the locations of the sensors in order to maximize the coverage while limiting the number of sensors used. A genetic algorithm (GA)-based optimization strategy was proposed by the authors for use in a simple aluminum plate. This paper extends the optimization methodology for other shape plates and presents experimental, analytical, and numerical studies. The sensitivity studies have been carried out by changing the relative weights of the application demands and presented in the form of a Pareto front. The Pareto front allows comparison of the relative importance of the different application demands, and an appropriate choice can be made based on the information provided

Model of the Propagation of Synchronous Firing in a Reduced Neuron Network

We studied the spread of synchronous repetitive firing in an array of purely excitatory neurons. The network consisted of an array of up to 250 by 250 neurons connected locally. We used a modified Rinzel's model for single neurons. Each neuron was connected with two neurons randomly chosen from eight neighbors. We determined the parameters of a network model needed to reproduce synchronized activity in locally connected neurons. The results of simulations in the full array of neurons suggest that the spread of activity and the velocity of spread is dependent on the strength of the connections. We found a range of synaptic weights for which the velocity of propagation is in agreement with measurements of the propagation of epileptiform activity in neocortex