This PDF file contains the front matter associated with SPIE Proceedings Volume 11379 including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

A physics-based approach to structural health monitoring (SHM) has practical shortcomings which restrict its suitability to simple structures under well controlled environments. With the advances in information and sensing technology (sensors and sensor networks), it has become feasible to monitor large/diverse number of parameters in complex real-world structures either continuously or intermittently by employing large in-situ (wireless) sensor networks. The availability of this historical data has engendered a lot of interest in a data-driven approach as a natural and more viable option for realizing the goal of SHM in such structures. However, the lack of sensor data corresponding to different damage scenarios continues to remain a challenge. Most of the supervised machine-learning/deep-learning techniques, when trained using this inherently limited data, lack robustness and generalizability. Physics-informed learning, which involves the integration of domain knowledge into the learning process, is presented here as a potential remedy to this challenge. As a step towards the goal of automated damage detection (mathematically an inverse problem), preliminary results are presented from dynamic modelling of beam structures using physics-informed artificial neural networks. Forward and inverse problems involving partial differential equations are solved and comparisons reveal a clear superiority of physics-informed approach over one that is purely datadriven vis-à-vis overfitting/generalization. Other ways of incorporating domain knowledge into the machine learning pipeline are then presented through case-studies on various aspects of NDI/SHM (visual inspection, impact diagnosis). Lastly, as the final attribute of an optimal SHM approach, a sensing paradigm for non-contact full-field measurements for damage diagnosis is presented.

Our aging infrastructure has high safety concerns due to increasing extreme weather events. Over time small damages in these structures may develop into catastrophic failure and therefore need to be monitored closely. Non-destructive evaluation techniques have been developed and achieve acceptable reliability in such damage identification. However, the high implementation cost dissuades bridge owners leaving visual inspections as the default technique. In this study, field implementation of low-cost Radio Frequency Identification (RFID)-based crack sensors for reinforced concrete buildings is provided. Commercially available low cost RFID tags were used for passive sensors on different extents of cracks, received powers were monitored in the field using new measurement methods (MM) to simulate a more controlled environment for improving accuracy and consistency of crack detection.

Shape sensing of gives insight into the structural health and operating conditions of flexible engineered structures in challenging environments. This work describes the dynamic validation of a low-cost and robust tool for near-real-time shape sensing using strain measurements along a cantilevered rectangular spar paired with a kinematic reconstruction algorithm. The spar was loaded with several mass distributions and subjected to an impulsive hammer strike. Measurements from the shape sensor was compared to measurements from several conventional accelerometers in the time domain and frequency domain. Accelerations inferred from the shape sensing spar exhibited an average RMS error of 2.23% when compared to accelerations from accelerometers. Measured natural frequencies and damping ratios exhibited errors of .13% and 1.83% respectively. Operating deflection shapes were in good qualitative agreement with calculated and simulated mode shapes. These results show the shape sensor to be a useful tool for dynamic measurements of compliant structures.

This paper proposes online input, state, and response estimation based on Augmented Kalman filter for systems without direct feedthrough, such as earthquake-excited building structures with absolute floor acceleration measurements. Measurement noise, modelling error, and incomplete absolute acceleration measurement are considered. The system model in this case lacks direct feedthrough, resulting in weak observability of system input, for which a small uncertainty in the model and measurement data would lead to a drastic change in the estimation. The augmented state Kalman filter for system without direct feedthrough is proposed for earthquake-excited building structures, in which the input with known variance is augmented with states in order to estimate them together. Compared with unbiased minimum-variance input and state estimation methods that make no assumption of input, the proposed online approach is able to perform robust estimation of states, input, and responses at unmeasured locations successfully using only a limited number of absolute acceleration measurements.

22 bridges fail every year on average in the USA because of scour, whereas in the UK soil erosion was the cause of 138 collapses of bridges in the last century. These digits fully explain why flood-induced scour is the leading cause of bridge failures worldwide. Scour assessments are currently based on visual inspections, which are expensive, and provide qualitative and subjective information. SHM offers the possibility to measure scour depth at any location of a bridge network; yet monitoring an entire infrastructure network is not economically feasible. In this paper, we propose a Decision Support System (DSS) for bridge scour management that achieves a more confined estimate of the scour risk for a bridge network through a probabilistic approach. A Bayesian Network (BN) is used to estimate, and update, the scour depth using real-time information from limited number of scour monitoring systems (SMSs) and river flow characteristics. Data collected by SMSs and BN’s outcomes are then used to inform a decision model and thus support transport agencies’ decision frameworks. The idea is to use this information to update the scour threshold after which bridges are closed. An infrastructure network, consisting of three bridges in Scotland, is built to test the functioning of the DSS. They cross the same river and Only one bridge is instrumented with a SMS. The BN is found to estimate the scour depth at unmonitored bridges and the decision model provides higher values of scour threshold compared to the ones implicitly chosen by transport agencies.

The piezoresistive effect and electrical impedance tomography (EIT) have been thoroughly explored for structural health monitoring (SHM) and nondestructive evaluation (NDE) applications in civil, mechanical, and aerospace venues. Conveniently, EIT has been used for detecting damage by imaging conductivity changes in piezoresistive nanocomposites and sensing skins. Although EIT can spatially resolve damage better than interpolated resistance change methods, its imaging capabilities are somewhat limited and indistinct to such an extent that it is difficult to infer the actual damage size and shape. In light of this limitation, we present a new methodology for determining damage size and shape in self-sensing piezoresistive materials. Our technique makes use of a genetic algorithm (GA) to inversely compute damage size from boundary voltage measurements and EIT-imaged conductivity changes. In this preliminary study, this new technique is first explored computationally and then experimentally validated. Our initial results show that the proposed GAsupplemented EIT formulation can indeed precisely reconstruct damage shapes. These preliminary results consequently suggest a pathway from mere damage localization via EIT to much more complete damage characterization in self-sensing materials.

Artificial intelligence has the capacity to open new opportunities and potentials for engineering education. Artificial intelligence in education has undergone several paradigmatic shifts in its brief history. This study investigates the advent, development and future trends of artificial intelligence-based smart engineering education (AIED-Eng). Particular focus is placed on major paradigms in AIED-Eng including leaner-receiver, learner-partner and learner-center. The artificial intelligence techniques applied to these paradigms are evaluated. Computer-based tools enabling the engineering education paradigms are summarized. Further discussion is presented about the key role of artificial intelligence in improving smart engineering education as a guidance for future learning, teaching and design processes

Ultrasonic Phased Array imaging is a key method for fast and reliable nondestructive testing of structures, especially when only one side of the part is accessible. Full matrix capturing (FMC) in combination with the total focusing method (TFM) provides a strong tool for ultrasonic imaging of structures with complex flaw patterns. However, still, operator needs to go through the generated images and manually check for the possible defects. One important task is to separate true and false indications, as some of them are noises or artifacts. Inspecting large structures with TFM Phased Array Imaging generates a huge amount of data which takes a significant time to go through them manually. In this work, we evaluate the possibility of using the neural network as an artificial intelligent toolbox to identify the defects. Using finite element method and an in-house developed TFM code, the phased array images are produced as the input to the neural network. The output of the neural network, target, is defined as the probability of defect existence. After generating TFM final images with different flaw patterns, the network was trained and evaluated based on the stochastic genetic algorithm learning method. This made the training feasible with limited provided data. Results indicate the great potential of machine learning for automatic or assisted defect recognition. The main challenge to pursuing a comprehensive and reliable machine learning toolbox, is to train the system with a satisfactory number of examples in different situations to ensure the final product is able to cover all possible conditions. It is concluded the proposed neural network model is capable of image pattern recognition with limited provided training data.

Bolted joints are key components in machines and infrastructure transferring loads and interconnecting parts. Loosening of bolted joints, particularly in terms of those utilized in aviation and railway engineering sector, can be vital and lead to catastrophic consequences. Monitoring of bolt loosening and furthermore quantitative determination of pre-loading force is therefore essential to provide alarming for in-time maintenance or replacement. This paper systematically describes an ultrasonic approach for real-time monitoring of bolt loosening based on electro-mechanical impedance (EMI) measured from a pre-designed PZT sensing network, which forms an electro-mechanical couple with the host structure. Finite element analysis of the tested couple is carried out to for both indication of signal features and validation purposes. Furthermore, a specifically well-tuned graph convolutional network (GCN) is utilized in bolt load determination, through learning the measured EMI signatures considering the sensor-sensor and sensor-bolt relationships in the PZT sensing network. The approach is expected to adaptively assess loading conditions of bolts on various types of host structures with a considerable confidence.

Surface-bonded piezoelectric wafer active transducers (PWaTs) are frequently used for ultrasound generation and sensing in Structural Health Monitoring (SHM) applications. In this paper, the scattering parameters of an ultrasound pitch-catch system are analyzed to show that the surface-bonded PWaTs act like a resonator and their resonance could cause the fundamental symmetric (S0) mode pitch-catch signal to deviate from the applied excitation signal. A simulation model was implemented to reproduce the measurement results and to gain physical insights of the experimental observations. This study suggests that the excitation frequency should not overlap the resonance frequency in order to avoid the deviation of the S0 pitch-catch signal from the excitation signal.

In the monitoring of structural systems, the use of multiple high end sensors may prove to be economically prohibitive. The alternative approach would be to use fewer devices that move across the span of the structural system. In the proposed approach, the dynamic of a one-dimensional system is evaluated using a velocity sensor that is able to move across the domain and obtains pointwise velocity measurements at the desired locations. Based on the measured velocities, a state estimator is developed. The gain of the filter depends on the motion of the sensor. The motion of the sensor is defined using Lyapunov redesign methods and depends only on the estimation error at the current sensor position. The guidance policy is performance-based and steers the sensor to spatial regions of the structure with larger estimation errors. The proposed approach is validated with a one dimensional flexible structure, described by an Euler-Bernoulli partial differential equation. The moving sensor is simulated with the use of a laser scanning vibrometer, that provides both the moving measurements and additional measurements against which the proposed approach will be validated. Once a large number of locations is measured, the experimental results are fed to the algorithm that selects the instantaneous sensor location. Experimental results for a linear cantilever are presented that show the benefit of using a state estimator with a moving sensor. Analysis on how the state observer gains are optimally chosen will also be presented. The approach is demonstrated to be feasible and robust.

According to the Federal Highway Administration (FHWA), in 2012, there were 1,357,430 miles of unpaved road in the United States, accounting for almost 35 percent of the more than 4 million miles of roadway in the Nation. Maintaining unpaved roads in good condition requires frequent evaluation of their ride quality. Common methods of ride quality evaluation such as the international roughness index (IRI) and profilograph index (PrI) are applicable for paved roads only. They require special types of equipment that are expensive and time-consuming to use. Hence, agencies cannot afford to extend the capabilities of existing equipment to monitor the ride quality of unpaved roads. This paper evaluates the use of smartphones on regular vehicles as an alternative. The method used a road roughness index called the road impact factor (RIF) to quantify the ride quality. Field experiments showed that the method provides consistent measurements and, therefore, is an attractive alternative for monitoring the ride quality of all unpaved roads in the Nation.

This study is based on the development of a smart biomimetic flapping wing using composite materials. Its configuration is inspired by hummingbirds and is constructed with load distribution reflected by the deflections of the wings during flapping. Wing deformations are obtained using DIC technique. The fluid-structure interaction of the wing and the atmosphere is also numerically simulated. Results provide the stress distributions in comparison with the experimental counterpart. Flow fields also suggest that the stresses are related to the vortical structures at the leading and trailing edges. The analysis shows how simulation results supports the experiment measurements.

The understandings of seismic mechanisms of tensegrity are highly demanded for perfecting the design principle. Due to the characteristics of periodicity, the effect of pre-stress on the frequencies of unit cell is investigated. And the stiffness of the basic tensegrity units was derived analytically. Then the vibration analysis of the entire assembly tensegrity structure is conducted numerically. Through parametric study, the influence of the pre-stress level on strength capacity of tensegrity is investigated. The cables yielding and bars buckle are considered. The influential factors on stiffness were evaluated, which can provide a guidance to the seismic mitigation optimization.

In recent years, the periodic inspection of aging transport infrastructure has become a global problem incurring significant costs and requiring the extensive use of manpower. As a solution, we propose a magnetostrictive vibration power generator that makes use of vehicle-induced highway vibrations to power a battery-free low power wide area (LPWA) module incorporating a titanium wire sensor. This system makes use of an LPWA module with a transmission range of over 1 km, and a titanium wire sensor which was inexpensive, easy to install, and could be used to inspect aging infrastructure. Using this system, infrastructure inspection, can be conducted without the need for conventional maintenance on the system, such as battery replacement. Our main research objective was the magnetostrictive vibration power generator. The generator was suitable for supplying power to the system because it was simple, robust, and was capable of high power output. First, the generator was scaled up to increase the output power in order to generate practically useful electric power with highway vibrations. The large generator was 150 mm×60 mm×50 mm in size and 0.6 kg in weight, incorporating a 16 mm×2 mm×50 mm plate of iron-gallium (Fe-Ga) alloy. We then reproduced the highway vibrations experimentally in a laboratory to ascertain the generator characteristics. We confirmed that it generated a peak voltage of 7 V, instantaneous maximum power of 36 mW, and total energy output of 52 mJ from the simulated highway vibrations over a period of 5 min. Finally, we field tested the system and succeeded in activating the battery-free LPWA modules within 16.5 min using vehicleinduced highway vibrations.

This study aimed at fabricating the PVDF-based piezoelectric energy harvester with high flexibility and efficiency. PVDF Nano-fiber was synthesized by using the electrospinning process. This study utilized the ink-jet printer to make the silver contact for the PVDF energy harvester. Later, the energy harvester was coated with Polydimethylsiloxane (PDMS) with different thickness. The result shown that the PVDF energy harvester tend to harvest the vibration at the frequency lower than 100 Hz. Moreover, the coating layer can decrease the energy harvesting efficiency due to its vibration absorption. This result was further validated by the Comsol Multiphysics simulation.

In this study, a magnetostrictive vibrational power generator was proposed for high-frequency vibration occurring in a machine tool and high-efficiency power conversion circuit for a battery-free wireless sensor. A conventional device was miniaturized according to the scale effect, and a device composed of a Fe-Ga alloy plate, of 2 × 0.25 × 8 mm³ with a frame of 0.5 mm thickness, was fabricated and evaluated. As a result, a generated voltage of 2 V and effective power of 234 μW were confirmed at a vibration of 522 Hz and 10 m/s² . In addition, the operating principle was verified in the power conversion circuit composed of an LC circuit (resonant circuit), a diode and a storage capacitor. As a result, it was confirmed that by using a large inductance, the Q value could be increased, and a considerable amount of energy could be stored in a 330 μF capacitor.

The periodic inspection of a bridge requires visual observation and hammering, which is very costly. In addition, the inspection is sometimes difficult, depending on the place of installation. Consequently, there is a need for remote health monitoring of structures using wireless modules. However, nowadays, these monitoring systems use batteries for power supply and need periodic battery replacement. As a solution, we investigate vibration generators using magnetostrictive materials (Fe-Ga alloy) as a power source for remote health monitoring. Using this system, we can inspect infrastructures without maintenance such as changing batteries. The generator features a simple structure, robustness, and high output and is close to practical application. In this paper, we propose an extra-large magnetostrictive vibration power generator that can generate practical power using a low frequency (10~20 Hz), such as bridge vibration. It is 300 mm in length, 5 kg in weight, and uses a Fe-Ga alloy plate with dimensions of 40 × 4 × 125 mm³ . First, we improve the output of the generator by adding a reinforcing plate and adjusting the bias magnetic field. Next, we confirmed the generation of an instantaneous maximum power of 0.58 W and effective power of 0.22 W under sinusoidal vibration (18.5 Hz, 0.2 G). Furthermore, we reproduced bridge vibration and evaluated the characteristics of the extra-large generator. Finally, we confirmed that 88 mJ of energy was stored in the storage capacitor from reproduced vibration of the bridge in 5 s.

Prognostics and health management (PHM) is a crucial measure to minimize the financial loss caused by downtime and damage in smart factories. To prevent the failures of industrial machines, a low-noise three-axis piezoelectric accelerometer in small scale is proposed to measure the vibration parameters for PHM application. It consists of a middle tungsten proof mass and 4 composite beams, which includes PZT and stainless steel layers. Simulation validates the principle of 3-axis measurement and gives the sensitivities of 0.714 mV/g, 0.714 mV/g and 1.72 mV/g for x-, y- and zaxis sensing. Calculated mechanical-thermal noise is 0.4 μg/√Hz, much lower than the typical noise of a commercial capacitive MEMS accelerometer. The designed sensor is fabricated with a metal-MEMS process and aerosol deposition method with the resonant frequency around 4.11 kHz. The testing results of x- and y-axis sensitivities, 0.5541 mV/g and 0.5420 mV/g, show the effectiveness of dual-axis measurement. In the end, the study analyzes hypotheses for the coupling of 3-axis measurement and proposes the improvement direction in further study.

In this paper, an auxetic design is proposed for the flexible membrane of a piezoelectric pulse sensor and computationally analyzed for a high-sensitivity vibration sensing in micro electro-mechanical system (MEMS). Auxetics are metamaterial structures with negative Poisson’s ratio which enables sensor’s flexible diaphragm to be expanded in both longitudinal and transverse directions easily. The sensitivity of a pulse sensor with an auxetic membrane was studied and compared to an equivalent plain membrane when the substrate was under harmonic bending. The sensing response was determined for the both models using detailed Finite Element Model (FEM) simulations. The sensor with the auxetic membrane demonstrated excellent sensitivity output over a harmonic pressure input which shows its strong potential for high-sensitive MEMS sensing applications. A detailed fabrication process is also discussed.

We present an ultrasonic array imaging approach based on deep learning to characterize structural defects. The proposed deep learning-based approach takes a raw ultrasonic defect image as an input and gives an output of a quantitative defect image. The test results obtained using finite element (FE) simulation and experimental data demonstrate that the fine structural features defects are successfully restored and visualized by the proposed deep learning approach.

Surface acoustic wave (SAW) sensors offer overwhelming advantages over other competitive sensing technologies due to its small size, cost-effectiveness, fast response time, passive and wireless capabilities. Development of SAW sensors allows investigation of their potential not only for measuring less-time dependent parameters, such as pressure and temperature, but also dynamic parameters like mechanical strains. The concept behind this work is to develop a passive flexible SAW sensor with optimized materials selection that can be used in harsh environments to measure mechanical strains occurring in aerospace applications. A flat 0-3 composite thin substrate is fabricated using a hot-press, an interdigital transducer (IDT) finger deposition is made through additive manufacturing. The sensor substrate comprises polyvinylidene fluoride as a polymer matrix, lead zirconate titanate powders as well as carbon nanotubes as nanoparticle fillers, exhibiting favorable flexibility and piezoelectric properties. The electromechanical property is enhanced using a non-contact corona poling technique with high electric field. IDT fingers are printed using direct printing additive manufacturing technique of conductive paste. Design parameters of SAW IDTs are optimized using a second-order transmission matrix approach. Rayleigh waves, generated on the fabricated substrate by an RF excitation signal, travel through the substrate and can provide useful information for desired parameters. In this work the sensing mechanism is based on the radio frequency scattering parameters response of the device. Results show a correlation between the amplitude and phase frequency response of the scattering parameters, and the mechanical strain. Experimental study on SAW substrate fabrication and analysis of sensed results with phase shift in wave speed due to strains are discussed.

The aim of this study is to develop a simple sensor node with a sensor and actuator system which interacts with its host structure in an autonomous and self-sufficient manner. Such a sensor and actuator system embedded in a structure may perform like a biological system surrounded by an environment that eventually earns a recognition of its internal and external state through the embodied interaction with the host as well as other nodes in the neighborhood. To this end, this paper presents a structurally embedded piezoelectric ultrasonic active sensor that intermittently oscillates using energy harvested by itself from the operational vibration of the host structure. The presented sensor node consists of three parts: a piezoelectric element (PE) that performs as an energy harvester, sensor, and actuator; an oscillation circuit which induces an ultrasonic burst of the PE at one of the natural frequencies of the host structure, and a charge controller that switches the PE between an energy storage and the oscillation circuit. Since the oscillation frequency of the ultrasonic burst and the burstto-burst period respectively reflect the structural dynamics in high-frequencies and the magnitude of the low-frequency vibration at the sensor location, both of which can be used to understand the structural integrity and operating conditions. In this paper, an energy harvesting performance measure for the PEs directly attached on the structural surface is first presented. A design of the oscillation circuit with negative resistance is then presented, and experimental verifications are made to show the basic validation and future possibility of the proposed concept.

The infrared thermal imaging detection technology is often utilized for the debonding detection of concrete reinforced by fiber reinforced plastics (FRP) sheets. However, the traditional heat source excitation infrared thermal imaging method is affected by factors such as short heating distance, low thermal sensitivity and high power consumption. In order to solve these problems, the debonding detection of FRP-reinforced concrete structures based on optical excitation line laser heat source infrared thermal imaging method was proposed to detect the debonding of FRP reinforced concrete. The surface local heat distribution anomalies caused by debonding in the structure could be measured by an infrared camera. Based on the numerical and experimental results, it is proved that the method has the advantages of debonding detection in FRP reinforcement structure: (1) the feasibility of using laser scanning thermal imaging technology to detect the debonding damage in FRP modified concrete structure; (2) FRP reinforcement long-distance, high thermal sensitivity and low power consumption damage detection of concrete structures.

The most significant shortfall in countering the super-fast weapons threat is gaps in sensor coverage for persistent birthto-death target tracking. The Department of Defense Space Development Agency’s is a notional architecture proposed to consist of massive numbers of Low Earth Orbit (LEO) satellites to detect and track multiple hypersonic weapons and ballistic missiles [1]. This paper presents a trade study analysis of a rectangular flexible foldout phased array antenna structure to support the detection and tracking of these weapons from multiple LEO constellations. Potential integration of the array into a solar panel structure, for improved cost and launch mass properties, will introduce several positional error sources, both correlated (sinusoidal) and random in nature. These deformations can have a major impact on performance including antenna gain, sidelobes (peak, RMS), angle accuracy, and pointing error. These degradations can result in reduced detections and increased false targets, which stresses overall system performance.

The Automated Fiber Placement process is established in the aerospace industry for the production of composite components. This technique places several narrow material strips in parallel. Within current industrial Automated Fiber Placement processes the visual inspection takes typically up to 50% of overall production time. Furthermore, inspection quality highly depends on the inspector. Therefore, automation of visual inspection offers a great improvement potential. To ensure reliable defect detection the segmentation of individual defects must be investigated. For this reason, this paper focusses on an assessment of defect segmentation algorithms. Therefore, 29 structural, statistical and spectral algorithms from related work were assessed, theoretically, using the 12 most relevant criteria as assessed from literature and process requirements. Then, seven most auspicious algorithms were analysed in detail. For reasons of determinism, Neural Network approaches are not part of this paper. Manually labelled prepreg defect images from a laser line scan sensor were used for tests. The test samples contain five defect types with 50 samples of each. Additionally, layups without defects were analysed. It was concluded that Adaptive Thresholding works best for global defect segmentation. The Cell Wise Standard Deviation Thresholding performs also quite well, but is very sensitive to grid size. Feasible algorithms perform reliable defect segmentation for layed up material.

The infrared thermography method has been used as a non-contact and quick diagnostic technique for the measurement of deformation inside concrete structures. Measurement from the in-vehicle camera is indispensable for quick diagnosis, yet motion blur while running is the essential problem. In this study, we developed a system using the galvanometer mirror and the thermography camera for compensating this motion blur. In the indoor and outdoor experiment assuming the measurement at 40 km/h, it was confirmed that our system compensated the motion blur effectively in the infrared region and detected the delamination of concrete structures.

Microfluidic cell cultures are of research interest to the biomedical engineering community. These types of cultures are able to expedite pathogen detection and pharmaceutical development using very small sample volumes ranging from nanoliters to microliters. These tiny cell culture volumes reduce the analytical process times and allow for the rapid development of medical treatments customized to the individual patient. The low permeability of the biocompatible-glass microfluidic cultures limits evaporation of culture serums, unlike conventional polydimethylsiloxane (PDMS). Microfluidic optical fiber tools may also compliment flow-cytometry tools. Challenges exist, however, in delivering cell samples to the air channels of the optical fiber. The technical challenges of delivering 3T3 cells to air-channels in photonic crystal fibers (PCF) are discussed in this proceeding. The difficulty of cells entering the microstructured fiber for optical detection of the cells is linked to cell deformation and rupturing due to pumping techniques. Experimental results include microscopy images of shredded cells clumping inside the fiber. To investigate cell transport in PCFs, 3T3 cells were employed. These cells are an embryonic mouse cell line and are commonly used in cell biology. The benefit of this cell line is that the cells grow in flat monolayers making them ideal candidates for cell culture studies. The individual cell-size ranges from 5 μm to 15 μm. The air channels in the PCF are 22 μm in diameter, allowing 3T3 cell transport. The findings of this study will be of interest to microfluidic cell culture and flow-cytometry technical communities.

Various types of ultrasound transducers have been developed for use in structural health monitoring, nondestructive testing, medical imaging and diagnostics applications. Recently, research efforts have been focused on development of optical ultrasonic transducers due to certain advantages that they possess over the traditional transducers, e.g. compact size, ability to be permanently integrated into structural components, safety in 1 volatile environments, and immunity to electromagnetic interference. Optical transducers generate ultrasound using a photoacoustic effect, where the absorbed light is turned into heat, which causes rapid expansion of the absorber material creating an elastic wave. In this work we develop a numerical model for analysis of a fiber-optic based transducer with the absorbing composite material made of polydimethylsiloxane (PDMS) with gold nanoparticles. Previous numerical and experimental studies have demonstrated effective generation of ultrasound by such transducers in mediums that have acoustic properties that are very well matched to those of PDMS (e.g. water and biological tissues). Here, we investigate the effects of laser pulse parameters and the absorbing layer thickness on the generation of acoustic pressure waves when coupled into carbon steel. We attempt to get insight into the formation of the temporal function of the pressure wave transferred from the absorber layer into steel. This study also aids in evaluating the feasibility of developing optoacoustic transducers for structural health monitoring applications. The obtained results are useful for development of designs for experimental transducers.

Guided waves (GW) allow fast inspection of a large area and hence have received great interest from the structural health monitoring (SHM) community. Fiber Bragg grating (FBG) sensors offer several advantage but their use has been limited for the GW sensing due to their limited sensitivity. But the use of the edge-filtering approach in the remote bonded configuration has enhanced their sensitivity and allows use of these sensors for GW measurement. Although these sensors are light in weight and need no additional wiring there is still significant motivation to reduce the number of sensors while maintaining the quality and reliability of the assessment due to their high cost. In addition, for the safety and reliability of the structures 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. The problem posed for the optimization of the FBG sensors for GW is different from any other work in this area due to the directional sensitivity shown by these sensors. Hence a novel optimization problem is developed for the application of FBG sensors for the GW based SHM. This paper develops a genetic algorithm based optimization methodology to incorporate the directional sensitivity. The methodology is shown analytically, using inputs from experimental investigations.

Ultrasound measurement for damage detection in practical structural health monitoring (SHM) applications is often affected by varying environmental condition. In this case, a baseline reference signal measured under initial conditions may not be valid for comparison with the distorted signal (due to structural damage) that is measured under different conditions. In this study, we investigate a self-referencing ultrasound detection of fiber Bragg grating (FBG) by bonding an optical fiber at two different locations away from the FBG. We first investigate the extraction of ultrasonic waves from two different adhesive bond locations, which are measured with a single FBG sensor located between the two bonds. Based on understanding the ultrasound coupling mechanism through two adhesive bonds, we test the self-referencing ultrasound with the presence of a damage in a structure, examining the output FBG response that contains a combined signal of distorted and reference signals extracted through each bond.

Inspection of railway tracks using ultrasonic techniques has been growing in importance since the last few years. Most of the existing technologies, however, operate at low speeds (~30 mph) using specialized test vehicles. This paper is based on a new technology utilizing non-contact air-coupled ultrasonic transducers for high-speed (up to 80 mph) rail inspection through the extraction of the acoustic Green’s function of a rail segment between a pair of sensors. The Green’s function is extracted passively using an output-only approach with the wheels of the locomotive acting as the source of excitation. The paper will focus on the results of various field tests conducted at the Transportation Technology Center in Pueblo, CO. Specifically, the detection performance of the “passive” prototype will be determined based on Receiver Operating Characteristic (ROC) curves that are obtained for various rail discontinuities (joints, welds, defects) and with varying operational parameters (speed, length of baseline distribution, number of runs, etc.). The optimum selection of these parameters will be determined based on these curves.

Boiler tubes in power plants develop defects including creep and thermal fatigue damage that can lead to fluid leakage over the operation period. Such leakage is the main cause of outages and power generation losses in thermal power plants. Therefore, early detection of leaks in boiler tubes is necessary to avoid more than 60% of boiler outages. To monitor and detect tube leaks in real-time, Acoustic Emission (AE) technique is widely used in power plants. A boiler tube leak could be detected using Average Signal Level (ASL) of the acquired AE signal using a network of sensors attached to the body of the boiler. Changes in ASL are proportional to the tube leakage; however, background signals generated by operating soot blowers bury the features which represent the tube leaks in the boiler and makes it nearly impossible to detect them automatically with established threshold methods. Soot blowers are used to remove the soot that is deposited on the tubes to maintain the efficacy and continuous operation of boilers. In this study, a bidirectional long short-term memory (LSTM) recurrent neural network (RNN) is developed to automatically detect tube leaks in power plant boilers. This detection method aims at identifying abnormal acoustic signals which differ from the reference/normal data that the system was trained with. The neural networks are trained on a sample boiler and the evaluation was done on the same boiler on the intervals with leak presence. Once the developed machine learning algorithm was tested with AE signals acquired from boiler tubes, the results show that this novel approach can detect anomalies in the signal levels as an indication of tube defects with an acceptable accuracy.

Acoustic emission (AE) is a well-developed structural health monitoring method that relies on capturing the elastic waves generated by the energy released during crack formation in a solid medium A novel modeling approach based on moment tensor concept is presented in this paper to simulate AE due to fatigue crack. The AE source due to fatigue crack growth and rubbing/clapping was assumed as components of moment tensor source, and the simulation was performed. The simulation results were compared with experimental observations, and a good agreement of simulation and experiment was observed.

This paper proposes a new brake-by-wire system for intelligent/unmanned vehicles. Magnetorheological fluid clutch (MRC) with the characteristics of simple structure, fast response and large range of controllable transmission torque is employed for the brake-by-wire system to realize continuous and fast braking force output of the electro-mechanical brakes (EMBs). The proposed brake-by-wire actuator is mainly composed of an AC servo motor, a planetary gear set, a MRC and a screw set. The planetary gear set amplifies the output torque of the AC servo motor, through controlling the current in the excitation coil of the MRC to realize the real-time control of the braking force at the end of the screw set. Specifically, the structure of the MRC is designed and verified by magnetic simulation. The optimization for structural design of the MRC is carried out via Mechanical APDL and the prototype is manufactured. The mechanical properties of the MRC are calibrated, and the real-time braking force tracking experiment based on PID control is conducted and analysed.

Active control strategies are more powerful and attractive than passive and semi-active control strategies. An active control device, namely active mass damper (AMD), consists of mass, a guideway, and an AC-driven motor and can provide a widely applicable range of control forces with a very limited power requirement. This device can be more effective to suppress earthquake- or wind-induced vibrations to civil structures. Various innovative control algorithms have been developed to drive AMD against seismic loads, and these new or improved algorithms are also found as a key element in smart structure technology. Some research on active control strategies for the reduction of the seismic responses uses either full-state or, at least, velocity feedback; however, the accurate measurement of the displacement and velocity is typically unavailable from real-world structures. Instead, the control algorithms based on acceleration feedback are more feasible for the practical implementation of control system. In this study, an active control system is developed by integrating an AC-driven AMD and accelerometers. This system is comprised of a real-time embedded system with a control algorithm which executes the sliding mode control (SMC) with acceleration feedback. The acceleration feedback SMC controller is designed in accordance with an identified model of a model building. Subsequently, this control system is experimentally implemented and verified using shake table testing. Performance of the integrated system as well as the responses of the frame are evaluated and discussed to demonstrate the effectiveness of the acceleration feedback SMC algorithm in mitigating vibrations of seismically-excited structures.

This paper presents a framework to transform bar-joint linkages into continuum/compliant mechanisms (CCMs) whose motion is enabled by smart material actuators. The rigid bars and pin-joints of the precursor mechanisms are thickened into continuum/compliant regions defined through a set of shape and size parameters. These parameters are optimized such that the CCMs exhibit the same target motion as the precursor bar-joint linkages. The motion of the non-self-intersecting, planar CCMs is thermally-driven by axial wire actuators that connect two points in the CCMs or by bending actuators placed at thickened bar members. Various CCMs with different target motions are provided as implementation examples. Applications of this framework include micro-scale manipulation devices, biomedical devices, and metamaterials and metastructures.

Multi-functional additive manufacturing is a promising route to achieving exciting new rapid prototyping and in-the-field manufacturing capabilities. Ideally, multi-functionality could be imparted to materials that can be used with existing additive manufacturing hardware with little-to-no modification of the hardware. However, because much of the additive manufacturing hardware currently available is highly sensitive to the properties of the input material, it is important to understand the relationship among the processing/development of the input material, its physical properties, and quality and properties of the additively manufactured part. To that end, this project explores the effects of processing conditions on the electrical properties and printability of nanofiller-modified fused deposition modeling (FDM) filament. Specifically, pulverized polylactic acid (PLA) is dry mixed with carbon nanofibers (CNFs) and extruded through a commercially available single-screw filament extruder. Filament resistivity and diameter are then statistically characterized as a function of extrusion temperature and number of extrusions. Printability is also quantitatively and qualitatively characterized using a commercial FDM printer. Insights developed through this work could be of considerable significance to next-generation additively manufactured piezoresistive-based sensors, actuators, and electrical components.

Advanced sensor network in aircraft can enable in-situ structural health monitoring, reduce unscheduled groundings, and consequently decrease maintenance expenses. This research aims to fabricate lightweight, flexible, wireless, and passive thin film sensors consisting of magnetostrictive thin films, a sensing coil, and yttria-stabilized zirconia substrates. External stress variation changes the magnetic permeability of the magnetostrictive thin films, resulting in an electrical impedance shift on the sensing coil, and thus can be detected wirelessly using a secondary excitation coil. Specifics for the additive manufacturing procedures, experimental data characterized, and theoretical numerical simulations for the magnetostrictive thin film sensors are presented.

Aerospace-grade textiles have decades of flight heritage for protection against harsh elements of the space environment. However, these substrates have remained electrically passive despite occupying useful large-area real-estate on the exterior walls of persistent spacecraft. By leveraging electronic textiles in an aerospace context, hybrid fabrics can be developed that simultaneously protect spacecraft while also detecting debris or micrometeoroid hypervelocity impactors. Specifically, this paper describes prototype development and preflight testing of piezoelectric Beta cloth ahead of a scheduled late 2020 material resiliency test on the International Space Station. Two accessible manufacturing methods for piezoelectric fiber are introduced based on modifications to piezoelectric cable that reduce diameter, increase mechanical flexibility of the fiber, and improve compatibility with textile weft insertion techniques. A Beta cloth simulant with piezoelectric fiber is introduced and custom ultra low power readout electronics are specified, which allow for a first-order power consumption estimate for scaling of this material across large-area spacecraft walls. Finally, high-velocity impact sensor data measured using the Laser Induced Particle Impact Test (LIPIT) facility is presented, building towards an accurate prediction of impactor velocity.

Sensing of dispersion and adhesion of PU type aircraft topcoat layer for LSP (Lightning Strike Protection) was evaluated by 2 dimensional (2D) electrical resistance (ER) mapping with different treatment times and multi-wall carbon nanotube (MWCNT) weight fractions. Conductive MWCNT was treated using hydrogen peroxide to improve dispersion in polyurethane (PU) type paint for several days. After treatment processing, MWCNT was dispersed in PU type coating solution using sonication dispersion method. CNT/PU coating solution was applied on the aircraft surface of carbon fiber reinforced epoxy composite (CFRC) using spray method. Static contact angle was performed using 4 types of solvents to calculate the work of adhesion between CNT/PU coating layer and CFRC surface. Surface ER of MWCNT added PU coating layer was measured to determine MWCNT dispersion. Visualization of MWCNT dispersion exhibited using 2-D ER mapping, whereas adhesion between MWCNT/PU coating layer and CFRC was evaluated via cross hatched cut test. The optimized condition of MWCNT treatment time and MWCNT weight fraction was found intensively.

Magnetorheological brakes (MRBs) have the characteristics of simple structure, fast response and large controllable range, which would effectively improve the vehicular braking efficiency and stability. They have been utilized in the intellectualization and electrification of vehicles. A brake-by-wire actuator namely MRB, is designed, optimized, manufactured in this paper. It is composed of an input shaft, brake discs, fixed discs, an excitation coil, and shells. A hardware-in-the-loop test bench is established and the anti-lock braking system (ABS) experiment is conducted. Specifically, the braking torque of MRB is matched to a minicar and a multi-disc MRB is designed. In order to improve the mechanical properties and lightweight design of MRB, the magnetic circuit design and multi-objective structural optimization are carried out by ANSYS/ADPL programming. The prototype development and calibration of MRB mechanical properties are conducted according to the optimized parameters. The test bench of the braking system is established, and the single-wheel ABS tests for the MRB is implemented and evaluated.

One of the most common methods of surface treatment of the aluminum is anodizing, which improves several metal properties (mechanical, electrical, magnetic, optical, etc.) as well as corrosion resistance. The aim of this work was to investigate the ability of this oxide to protect the tested material from corrosion and its response under the mechanical cyclic loading. In the present study, specimens of Al 1050-H16 were surface anodized at constant voltage using sulfuric acid solution and a layer of aluminum oxide is produced on the surface of the specimen. The anodized specimens were subjected to a corrosive environment simulating the physical exposure to seawater and their subsequent mechanical stress by fatigue method. Two complementary nondestructive evaluation methods, infrared thermography and acoustic emission, were used for predicting the material's fatigue life. The results of the tests of the anodized specimens with and without corrosion were compared with each other, as well as with the corresponding data of the same materials without any treatment. In addition, white light interferometry was used for profiling observation of the aluminum samples in order to characterize the effect of the corrosion process on the specimens’ surface.

When performing various technological processes in energy, the velocity of air flows is one of the important parameters for metrological control. In addition, in the coal industry, with the deepening of mines, there is an urgent need to solve the problems of ventilation of mining, compliance with safe working conditions and, as a consequence, measuring air velocities of 0.1 m/s and higher. The national primary measurement standard of air flow in the range from 0.1 m/s to 1 m/s, created in NSC “Institute of Metrology”, provides traceability of measurements for working measuring instruments. The numerical simulation and evaluation of the aerodynamic configuration of the fairing for the national measurement standard of air flow velocity are performed in this paper. The calculations of the length of the measuring section of the inverse installation of the national measurement standard for the selected range from 0.1 m/s to 1 m/s are carried out. The purpose of the work is to investigate and improve the metrological characteristics and to provide recommendations for expanding the measurement range.

Morphing aircraft can sense load and attitude in real time and adaptively deformed according to different flight environments and tasks. They can achieve excellent performance in different environments and tasks. It is one of the main hotspots in recent years. However, the torsional stiffness of deformable wing structure with flexible skin will be greatly reduced, so the wing is prone to torsion during flight, which is not conducive to flight. In this paper, a stiffness compensate device is proposed. When the wing is subjected to torque, the rotating torque is transmitted to the stiffness compensation device, which is transformed and transmitted inside the device, and finally balanced by the spring inside the device, so as to compensate for the reduced torsional stiffness of the wing due to the use of flexible skin and increase the torsional resistance of the wing.The mechanical properties of the device are studied by theoretical analysis and a case is analyzed. The ability of the device to improve the torsional stiffness of the wing and its influencing factors are analyzed in this paper. The feasibility of the device is verified. The torsion resistance of the deformable wing can be greatly enhanced by this device.

The most important step of flutter analysis is to predict the flutter boundary of the aircraft to ensure that there will be no flutter in the flight envelope. However, due to the low signal-to-noise ratio and modal density of flutter signal, traditional modal identification methods cannot effectively extract the modal information of the data. Therefore, in order to solve this problem, this paper proposes a method which combines wavelet denoising and a masking signal. Wavelet denoising can effectively reduce the noise interference, and masking signal can effectively alleviate the problem of mode mixing which improves the accuracy of the signal modal identification.

The variable camber wing can significantly improve the aerodynamic characteristics of the aircraft and is an important form of morphing aircraft. Flexible skin technology is one of the key technologies. According to the skin deformation features of the variable camber wing, a flexible skin form is proposed in this paper. The fishbone-shaped reinforcing structure (FBRS) is applied as the main component of the flexible skin to bear aerodynamic loads. Rubber material with excellent deformation ability wraps the FBRS to obtain a smooth and flat skin surface. Thorn-shaped branches on adjacent FBRSs are arranged in a staggered manner. In order to increase the out-of-plane stiffness of the flexible skin, the flexible skin needs to be used in combination with the corrugated structure. Each wave crest of the corrugated structure is connected with the FBRS of the flexible skin. By setting the wave crest of the corrugated structure into a platform shape, a stable connection between the FBRS and the corrugated structure is maintained. In this paper, the stiffness expressions of FBRS and corrugated structure are derived. The chordwise deformation capacity and out-of-plane bearing capacity of the flexible skin are verified by the method of finite element simulation. The results show that the FBRS can transmit aerodynamic loads well and maintain the smoothness and flatness of the rubber surface. Supported by a corrugated structure, this type of flexible skin has good chordwise deformation ability and high out-of-plane bearing capacity.

Based on the time series model, the modal parameters of the continuous variable flutter test response signal are identified, and a set of flutter boundary prediction methods suitable for turbulent excitation is developed in this paper. In order to ensure the accuracy of the method, a modal parameter identification method is used to analyze the traditional autoregressive model (AR) and the time-varying autoregressive model (TVAR), and compare the accuracy of the two models. Finally, the method is applied to the flutter boundary prediction of turbulence signals. The prediction method combines the time series model with the stability criterion, constructs the stability parameters of the response signal, and the prediction results of flutter critical velocity are obtained by fitting and extrapolation. The numerical example shows the analysis results of the two models and proves the feasibility and effectiveness of the method. Finally, this method is used to predict the flutter boundary of low-speed wind tunnel test data, and the prediction error is less than 5%.

An essential issue in materials research, quality control, and in practical planning and implementation of construction projects, is the understanding of the curing process of fresh cement-based materials. Immediately after mixing, cementitious materials exhibit a significant damping effect on ultrasonic wave propagation together with low pulse velocity. During the curing process, ultrasonic waves, especially the nonlinear acoustic behavior of the material, are sensitive to the point at which the solid phase appears. After this point, the ultrasonic pulse velocities and signal amplitudes increase continuously. The point of solidification is of practical significance since the connectivity of the solid phase is responsible for the load-bearing capacity of the cement composite and its long-term behavior. The aim of this study is to monitor the early stages of fresh cement-paste composites during the hydration process using nonlinear elastic waves. The measurements in this work were performed using a combination of contact ultrasonic transducer and noncontact optical detection measurement device. The principle of operation of the detection device is based on the doppler effect. Using this technique, the amplitudes of harmonic vibrations of an elastic wave with a fundamental frequency propagating through the material can be assessed. This leads to the evaluation of important materials characteristics, such as the changes in internal microstructure of fresh concrete during curing, the evolution of higher order elastic contants of the material expressed in the form of nonlinear parameters, as well as the longitudinal wave velocity.

A Buck-Boost converter with four fully synchronous transistors is proposed in this paper which achieves the output voltage lower or higher than the input voltage, in the un-isolated application. The control strategy and the working principle of circuit are discussed in this paper. Then, the implement of Buck-Boost converter with four fully synchronous transistors is expatiated, which is based on the LM5116. Furthermore, the design of key circuit parameter and the selection of power components are introduced. At last, the simulation and experimental results show the correction of the parameter designing.

Kinematics analysis of multi-joint robot play an important part in robotics research. Because of the non-uniqueness of its inverse solution, robot kinematics with more than 6 freedom degrees is still the focuses of the research. For the inverse kinematics problem, some optimization methods ,like neural network , are mostly used.These methods have many problems such as heavy calculation and low accuracy. In this paper, a new inverse kinematics algorithm based on geometric and analytical methods is proposed and verified by simulation. In this paper, the D-H model is established according to the robot structure and the structure parameters of the robot are determined. The positive and negative solutions of the robot are analyzed and calculated. Finally, MATLAB is used as the simulation tool to verify the correctness and accuracy of the results. It provides a new idea for the improvement, design and application of the robot with more than 6 axises.

This paper presents a new estimation method of tire stiffness based in improved Kalman filter of vehicle suspension control system. In recent years, the need for systems monitoring the current pressure in pneumatic tires has grown dramatically. Incorrect pressured tire will affect the handling performance, tire life time and fuel economy. For these reasons, tire pressure monitoring system(TPMS) is required to ensure the vehicle safety and ride quality. However, traditional TPMS requires a battery in each tire in order to power the sensor and circuits inside the tire and it has temperature dependent capacity problem. To overcome this problem, indirect methods are proposed. One of the promising indirect methods is the sensor fusion method from automotive control systems. In this study, adaptive extended Kalman filter(AEKF) approach is proposed to identify structural parameter, such as tire stiffness. Simulation results demonstrate that proposed approach is capable of estimating tire pressure based on experiment of relation between tire pressure and tire stiffness.

In this work the use of many stand-alone devices is considered for active vibration control. A stand-alone device represents a control unit which is able to independently perform the vibration control task, since it is embedded with sensors, an inertial actuator and a microcontroller, in which the control algorithm is implemented. The developed active controller is also embedded with a wireless module, in order to share information with other devices within the network. The proposed solution aims to improve the performance a decentralized control architecture and it is based on the optimal control theory. The Linear Quadratic Regulator works with the fullstate of the system, which is provided through a state estimation. A state recovery algorithm is then adopted to improve the quality of the estimation without placing a hefty burden on the wireless channel. Numerical analysis is made in order to study the advantages of this method. Finally, the proposed solution is validated with experimental results from a clamped-clamped beam.

In this paper the application of an inerter to a control system based on inertial actuators is investigated. Since the dynamics of this kind of actuators affects the stability of the controlled system, the application of the inerter aims to limit instability problems by shifting down the resonance frequency of the actuator. The interaction of this passive element with the other vibration modes of the structure under control is not negligible. The inerter should be physically placed in parallel with the elastic suspension of the actuator and the transducer; in this paper, the inerter behavior is simulated by the inertial actuator through an acceleration feedback. First, the study is carried out on a two degrees-of-freedom model and stability considerations are made; then, the approach is implemented on the Finite Element model of a clamped-clamped beam which is also used as experimental test rig. Finally, the proposed solution is validated with experimental results.

Static structural health monitoring (SHM), aimed at the continuous measurement of slow-varying parameters over a long period, has been proved to be a powerful tool to support the diagnosis of masonry heritage structures. In such applications, the initial interpretation task involves the identification of evolutionary conditions from recorded data. However, this can be difficult since monitored features are influenced by environmental changes. In addition, many masonry heritage structures are characterised by a complex structural behaviour stemming from the interaction among different elements, making the task of interpreting SHM data for diagnosis very challenging. One such structure is the church of the monastery of Sant Cugat close to Barcelona, built mostly between the 12th and 15th centuries. Certain key structural parameters of the church have been monitored since 2017 with the aim of understanding the cause of visible pathologies and identifying any active deterioration mechanisms that could pose a threat to the structural integrity of the church in the future. This paper presents the application of an automated data analysis methodology to this problem. The method uses dynamic regression models to filter out components related to reversible seasonal fluctuations from measurements and automatically classifies monitored parameters into evolutionary states based on predicted evolution rates and dispersion metrics from the filtering procedure. A tool is presented which allows analysis results to be updated as new data is received. Finally, results from the proposed methodology are used for the diagnosis of the structure and their usefulness in a broader decision-making framework is discussed.

corrosion still responds for huge maintenance cost of nationwide civil structures. In this study, we explored a machine learning approach to extract information from sensory data for early-age corrosion-induced damage identification and classification. Lamb-wave guided signals of steel samples collected from simulated corrosion damage were used for model training and calibration. The results showed that the machine learning method allowed effective information fusion for early-age corrosion.

Design of civil structures is founded on principles of solid mechanics, structural analysis, and material science, and use stresses and deflections as limit criteria. Therefore, evaluation of stresses and deflections in real-life settings became one of the central aims of Structural Health Monitoring (SHM) and Non-Destructive Evaluation (NDE) of structures. However, direct measurement of stresses and deflections on real structures is, in general, practically impossible due to several limitations pertaining to sensing technologies, site conditions, and properties of structural materials. While cases exist, where stresses and deflections could be directly monitored, these cases are rather exceptions enabled by specific applications that cannot be generalized to many other cases.

Lamb wave devices were fabricated and tested to study parametric mixing through a nonlinear elastic modulus. A carrier wave was used to drive the time-variation of the elastic modulus in a traveling wave fashion. The signal wave is launched in the same direction as the carrier wave. The nonlinearity results in the signal to be mixed up and down with the carrier signal generating intermodulation terms. Through optimization, next generation RF components that are 5 orders in magnitude smaller than the current state of the art can be designed, freeing up real estate for larger batteries and additional sensors.

Sensing of physical parameters forms an important part of smart structures. The discovery of many nanomaterials in the past decades with unique physical properties has led to the development of nanomaterial-based sensors for smart structures and Structural Health Monitoring (SHM). Among the nanomaterials, MXenes (Titanium Carbide with surface termination, Ti₃C₂Tx; Tx: -F, -OH, -O) are two-dimensional (2D) nanomaterials with unique conductivity and hydrophilic behavior. These properties help in the fabrication of pure MXene films and MXene nanocomposites which can have multifunctional sensing behavior. The use of polymer as a matrix with MXene fillers helps in controlling the electrical and mechanical properties of the nanocomposites. Studying the dynamic response of these MXene nanomaterial-based structures become necessary for developing sensors. This paper focuses on the investigations of MXene based structures for SHM.