Special Topics in Structural Dynamics & Experimental Techniques, Volume 5

Damage in a beam-like structure due to decreases in its stiffness and/or mass can cause local anomalies in its flexural guided wavefield at locations of damage. Usually, these local anomalies can be intensified by data-driven techniques such as the continuous wavelet transform, the gapped smoothing method, etc. However, the physics of beam-like structures are not considered in these data-driven techniques, leading to a lack of physical consistency in the damage identification results. In this paper, a baseline-free damage identification method is proposed to extract these local anomalies under the assumption that the pristine beam-like structures are homogeneous and isotropic. Flexural guided wavefield of a damaged beam-like structure is used to build a pseudo-pristine model of the beam-like structure by using physics-informed neural networks. When the pseudo-pristine model is built, the prediction of flexural guided wavefield can be generated, and those local anomalies can be approximated by the difference between the prediction of flexural guided wavefield and the corresponding measured flexural guided wavefield. The difference is used to yield an accumulative two-dimensional damage index for further damage identification. Effectiveness and noise-robustness of the proposed method are investigated in a numerical example. Results show that the proposed method is effective and noise-robust in identifying the location and extent of the damage.

Thanks to the unique properties of high elongation, reversibility, incompressibility, and energy dissipation, rubber materials are employed in numerous engineering applications in industrial practice. The rheological behavior of rubbers depends on chemical composition, presence of additives, and curing process. As a consequence, a large variety of materials may be encountered with a rich phenomenological description of their static and dynamic characteristics. Rate, amplitude, and temperature, among others, are operational parameters that affect rubber’s stiffness and damping properties. On top of that, to ensure the long-term performance and reliability of a rubber component, aging effects and fatigue life assessments are paramount. Existing laboratory vibrational techniques, based on uni-axial displacement-controlled cyclic tests, are capable of extracting storage and loss properties of the rubber under study as a function of frequency and amplitude of the oscillation. This paper investigates the robustness of a transmissibility-based shaker setup for the dynamic characterization of a rubber component extended to long-run operational loading. A preliminary analysis shows the influence of the adhesive substance used to hold the rubber in place in a variety of testing scenarios.

Applications of structural health monitoring (SHM) often use vibration data to detect when damage occurs in structures, by detecting changes in the modal properties. Damage causes changes in material/structural properties which affect the modal characteristics; however, inconsequential changes in environment can reduce the capability of the damage detector, as they also affect the modal characteristics. Furthermore, small differences between nominally identical structures may also manifest as changes in the modal characteristics, thus making fully enveloping baselines difficult to obtain for an entire population of structures. This challenge provides the motivation for the field of population-based SHM (PBSHM), where the population is considered as a whole, in order to generate a more robust damage detection strategy. An experimental campaign was conducted to collect vibration data on four nominally identical Grob G102 Astir glider wings, three of which were “healthy” and one of which was damaged; this was done to generate a dataset which can be used to develop and test PBSHM methods. The experimental campaign included testing over a range of temperatures, and included a variety “pseudo-damage” states, where masses were added to the structures. This paper highlights the challenges faced, which motivate PBSHM by showing some examples of the data collected, including frequency response data and estimated modal characteristics. In addition, the paper shows some preliminary work which aims to separate out data of individual structures or states, from data of the full population.

Using nature-inspired solutions for propulsion, this work investigates the use of traveling waves to generate thrust in water. A design based on a slender cantilever beam similar to flagella in bacteria is submerged in water and excited with a sinusoidal motion to study the impact of frequency and amplitude of the oscillation on the thrust generation. Structural measurements combined with advanced flow diagnostic techniques are used to characterize the behavior of the fluid–structure system.

The structural response and the induced traveling waves are first studied in air and characterized through laser vibrometry and high-speed digital image correlation. This demonstrated the possibility of inducing traveling waves in the structure and permitted to identify the conditions that maximize the traveling versus the standing wave contribution.

The characterization of the fluid–structure interaction has been done using Laser Doppler Anemometry (LDA). LDA measurement was carried out downstream from the beam at a fixed distance to measure the velocity of the induced flow at different excitation conditions (amplitude and frequency).

The results showed that the coupling between the structural motion and the thrust generated is nonlinear in nature and depends on the tip displacement of the beam. Empirical laws that relate the amplitude and frequency of excitation to the generated thrust are here proposed.

This article presents a novel infrared thermographic approach for damage detection by utilizing the heat generated around damage sites during vibrations below 1000 Hz induced by lightweight piezoelectric actuators. In this research, the optimal location of excitation was first investigated through finite element analyses, where two generalized equations were obtained to describe the relationship between the excitation and the resulting displacement response. These observations were then verified experimentally on an aerospace-grade composite plate, followed by vibrothermographic tests conducted on the same structure to demonstrate the effectiveness of the proposed damage detection process employing only a single lightweight piezoelectric disk as the actuator.

The growing use of 3D-printed (additively manufactured) structural components implies the need to develop effective methods of damage assessment. This study focuses on guided wave propagation and its interaction with structural damage. The waves were excited using a laser scanning system which allows for easy excitation of the waves at various points at the surface. Also, the excitation is broadband, giving the ability to excite more guided wave modes at once. The combined laser scanning with a single piezoelectric measurement transducer takes advantage of reciprocity to reconstruct the full propagating wavefield. The investigated sample was printed from an aluminum alloy. The first set of measurements was realized for an intact (healthy) sample. Next, an artificial damage was introduced in order to study the wave interaction with it. Machine learning-based signal process algorithms were developed to analyze the wave interaction with the damaged plate. The obtained results show a good potential of guided wave-based techniques for the structural health monitoring of 3D-printed structures.

The need to measure complex mechanical structures has grown in importance for efficiency in the design process and for structural monitoring. Despite the convenience of using a 3D scanning laser Doppler vibrometer (LDV), fixed acceleration sensors are still widely used for industrial applications. However, the effects of sensor attachments cannot be disregarded for lightweight structures. This paper provides a thorough description of how various sensor attachment techniques affect vibration measurements over a wide frequency range (up to 16 kHz). Frequency response function (FRF) measurements were conducted on an aluminum plate using both the LDV and an accelerometer simultaneously, excited by a scalable automatic modal hammer (SAM). For particular frequency ranges of interest, recommendations for fixation methods are proposed. Recommendations for fixation techniques are proposed for certain frequency ranges. The study’s findings offer practical advice for industrial structure measurement evaluations.

An important outcome of this study is how the fixation method influences the experimental results of the modal properties. The research reveals that the fixation, the contact area, the sensor, and the test specimen built a dynamic system that influences the results especially at higher frequencies and should be considered for precise measurements.

Automatic impulse hammers have proven their advantages in experimental testing. Up to now, adjusting the resulting peak force is a cumbersome hand-tuning of the parameters of the hammer electronic. As the hammer is equipped with a high-quality force sensor anyway, the idea is close to utilize that force sensor signal to control the hammer. Usually these sensors are ICP-Sensors that are directly connected to the data acquisition system that is used to perform the desired measurements. This contribution shows a way to interpose the controller of the hammer and so allow the usage of the measured force. This is used in combination with an iterative learning control scheme to find the best parameters to achieve the desired impact force on the structure in a repeatable and convenient way.

An experimental study into the fluid–structure interaction of gas–liquid flows as they pass through a pipeline riser, like those utilized in the oil and gas industry, was carried out. In this study, a 10-m-long pipe with inner diameter of 50 mm at 10 bar (gauge) pressure was used with operating fluids of water and nitrogen. A two-phase flow regime diagram is used to determine liquid and gas volume flow rates that fall within the slug flow regime, and this was further refined using PETEX GAP and finally CFD software, STAR-CCM+, to determine the experimental test matrix. Axial strain gauges were positioned close to the flanges at the ends of the riser to measure strain at the cardinal points of the pipe at both the top and bottom of the riser. At approximately 2 m and 3 m horizontal distance from the entrance to the riser, accelerometers were attached to monitor the motion of the pipe at these positions. The motion of the pipe was observed to be almost exclusively in-plane, with only small out-of-plane motion registered on the accelerometers. Strain gauge measurements demonstrated that the increase in mass flow rate of the liquid phase/decrease in mass flow rate of the gas phase served increases the mean values of stain at both the entrance and exit of the riser. A monotonic increase in the magnitude of the rms of the fluctuations of the strain was observed for an increase in the flow rate of either the liquid or the gas phase. The highest flow rates tested introduce an irregular cyclic whipping motion in the flexible pipe in addition to the smooth oscillation motion.

Colorectal cancer (CRC) is a common form of cancer that affects the large intestine. CRC is one of the most severe and aggressive forms of cancer, and thus, early treatment and detection are essential. Early detection of CRC is primarily available through the detection of polyps through endoscopic imaging procedures. This method is labor-intensive and subject to human error. To circumvent these issues associated with human error and improve upon limitations associated with human detection, deep learning-based procedures have been developed and convolutional neural networks (CNNs) have been introduced for the automated detection and segmentation of polyps. Current problems associated with polyp segmentation with CNNs are overfitting, boundary pixel definitions, an inability to account for the different range of textures, sizes, and shapes with polyps, among other issues. With the ultimate goal of addressing these issues, we developed a multiscale segmentation network (MSSNet) designed specifically for polyps (Lewis and Cha, Sci Rep, 2023). In this paper, we conducted some additional case studies to investigate the performance of MSSNet. This dual model network surpasses state-of-the-art results (SOTA) and is evaluated using the CVC-ClinicDB dataset. The mean intersection-over-union (mIoU) and dice (mDice) score were 0.889 and 0.935, respectively.

This study presents the framework and initial characterization of a hydroelastic solver for the dynamic model of an underwater marine hydrokinetic (MHK) kite. MHK kites are systems designed to optimally harvest current and tidal energy from bodies of water by executing cross-current flight patterns to augment velocity and therefore power generation. Due to the significant fluid-dynamic loading experienced by MHK kites, robust structural analysis of the kites is integral to successful system design. Hydroelastic analysis allows the coupled hydrodynamic loading and structural deformation of a MHK kite wing to be predicted. The hydroelastic solver outlined here uses a finite-element method to discretize a given wing geometry into a user-selected number of nodes. At each node, hydrodynamic and structural parameters are defined. The hydroelastic solver contains two sub-solvers: (i) a hydrodynamic solver and (ii) an elastic solver. The hydrodynamic solver uses airfoil coefficient lookup tables and Prandtl lifting line theory to estimate lift, drag, and pitching moment at each node across the wing. The elastic solver receives the point loads and moments from the hydrodynamic solver and deforms a defined structural representation of the wing. This hydroelastic solver process is iterated until wing deformation has converged. The integrated model is used to predict the cyclic variation in wing deflection and twist during closed-loop-controlled cross-current flight. Such predictions could prove useful for evaluating hydroelastic effects on optimal kite flight controller tuning, or appropriate design of the kite wing for structural fatigue life considerations.

Finite element modeling (FEM) and analysis (FEA) are commonly employed for structural design evaluation and iteration. With current technology, full-fidelity modeling of larger assemblies is often computationally prohibitive, requiring simplification of the model’s complex features. One such feature that is frequently simplified is the bolted joint, which is ubiquitous in engineering structures. However, approximate methods for modeling joints introduce inaccuracies. To understand this model-induced error, this paper explores a novel, computationally efficient method for the modeling of bolted connections. This method introduces several parameters local to the joints that can be adjusted to improve the accuracy of the model; these parameters define the size of a tied contact area and the properties of a virtual material region. To maintain computational efficiency on complex structures, this method is compatible with a linear, eigenvalue modal analysis. To test this novel method, it was applied to the finite element modeling of bolted connections in a four-story structure. Experimental modal analysis was conducted to validate the FE model. Additionally, Bayesian model calibration was used to quantify the model parameter uncertainties and update geometric and material properties. Overall, this paper presents a viable, experimentally validated method to efficiently model bolted connections in multi-connection structures.

With the advent of additive manufacturing comes the opportunity to design novel components and systems, especially when one considers hierarchical structures. Our recent work Bielecki et al. (Struct Multidiscip Optim 64(6):3473–3487, 2021); Bielecki et al. (Multiscale compliant topology optimization for twistable wing design. In: AIAA AVIATION 2021 FORUM (2021), pp. 2429) has demonstrated a highly effective framework of multi-scale topology optimization for designing such structures. In this work, we focus on enhancing the efficiency of the analysis at the meso-scale by introducing a new complementary energy formulation. This enables the computationally attractive formulation of parametrically defined filament-based meso-structures of arbitrary path within each unit cell. We have implemented this framework for dynamical problems, specifically, for modal analysis. We considered mass lumping and static condensation within the unit meso-scale cells. Computational experiments are provided to test the robustness, accuracy, and computational efficiency of the approach.

Recently introduced class of fiber-reinforced thermoplastic polymer composites (FTPC) in aerospace structures offer a high stiffness-to-weight ratio, cost-saving, and recyclability advantages compared to their thermoset composites. However, their structural performance under high-frequency vibratory loads during their service life is not well understood. Such loads instigate internal material friction inside the microcracks. In this paper, we propose a combined dynamic characterization and fracture mechanics modeling for evaluating the evolution of damage precursors (microcracks) in FTPC due to the presence of internal frictional forces. In order to run simulations to calculate self-heating caused by vibration, one must run transient analysis, because of the contact elements which calculate the amount of friction generated. This work proposes for the first time to apply the structure’s Operating Deflection Shape as a dynamic load to include that friction. An innovation of the proposed method is the inclusion of microscopic self-heating in the microcracks as a consequence of high-frequency frictional forces. The Finite Element Method (FEM) is utilized to perform the dynamic analysis for a thermoplastic composite beam with a microcrack exposed to high-frequency vibration fatigue.

Components manufactured by laser powder bed fusion (LPBF) additive manufacturing can have particle dampers designed into the part by leaving unfused powder inside a defined pocket of the part during manufacturing. These pockets of unfused material have inherent damping capabilities that suppress the vibrations and potentially reduce component wear. Particle dampers have been shown to be a simple and effective way to increase the damping of a structural component manufactured by LPBF; however, the compressing effects of the particle damper pocket inside the beam have not been studied. The amount of unfused powder inside the pocket is difficult to control (or even measure) during manufacturing; therefore, this study reports preliminary investigations on the effects of energy absorption provided by changing the volume of the pocket. In this study, beams are printed with 316L stainless steel powder with single particle dampers. Thereafter, the cover of the pocket is deformed, decreasing the volume of the pocket. The energy absorption characteristics of the particle damper are quantified. The unfused powder pocket’s damping characteristics are assessed by observing the structure’s response to various excitation methods. An input-output relationship can be deduced using a reference accelerometer and an accelerometer mounted passed the damper pocket with respect to the fixity. With this relationship established, the magnitude of damping and the phase attributed to it is determined. Studying the damping characteristics of various compressed pocket sizes and powder quantities will provide helpful information to enhance particle dampers’ efficiency using LPBF techniques.

The lack of reliable, nondestructive part qualification for additively manufactured (AM) parts hinders their adoption in key industries of national interest such as aerospace and defense. Resonant ultrasound spectroscopy (RUS) is a relatively low-cost and nondestructive method for accurately determining material properties. In this work, we explored potential applications for using machine-learning techniques to computationally speed up RUS in deriving the material properties of AM parts, as well as identifying print quality of parts post-build. We performed mode identification on 226 cylinders manufactured via laser powder bed fusion (LPBF) from an A20X alloy. A lack of visual separation in the data lead to the use of statistical and dimensionality reduction techniques with the resonance peaks as well as examining the overlaid resonance spectra. We then performed classic RUS using a genetic algorithm to find the density, Young’s modulus, and Poisson’s ratio. By constraining these three parameters according to porosity relations relating all three material properties and training a random forest regression from finite element analysis simulations within a range of representative values, we could predict the material parameters with a lower mean RMS than compared to those values resulting from the less-constrained genetic algorithm.

Additive manufacturing methods have advanced a lot in the past year. Methods like Fused Deposition Modeling (FDM) have gained popularity due to their versatility in material and minimal limitation on the geometry. Structural modeling of additively manufactured parts can, however, be complicated. This is primarily due to the dependence of material stiffness on print settings such as layer height, infill density, and infill pattern. In addition, the material can exhibit anisotropic characteristics due to parameters like the adhesiveness of layers, the orientation of the raft, and the layer deposition speed. This study conducts an experimental modal analysis on a 40% scaled model whose geometry is based on the aircraft Initial Concept 3.X (IC3X). The test article was manufactured using FDM, and ABS was chosen as the material. Fiber-optical strain sensors were attached to the test article and were used to record the response to structural excitation. Static tests were performed in addition to dynamic testing to further evaluate the test article’s stiffness. The static test cases were used to update a finite element model of the test article and to obtain precise values of the Young’s modulus, which is dependent on printer settings. The natural frequencies obtained from both the numerical model and dynamic testing showed good agreement.

Additive manufacturing is becoming more popular in the rapid prototyping of sensors, even for force transducers like load cells. These transducers are largely used in static or quasi-static measuring but their dynamic performances are of a certain interest too. Therefore, the enhancement of their dynamic properties is getting higher and higher attention. In this paper, an improvement in the dynamic characteristics of a strain gauge load cell is proposed with the redesign of the force transducer relying on additive manufacturing possibilities. The response characteristics of the force transducers built with both additive and conventional manufacturing methods are evaluated by analyzing the frequency response function estimated with an impulsive method. Dynamic parameters from both transducers have been evaluated and compared. The possibilities offered by the additive manufacturing technology led to an increase in the measurable frequency band of the considered transducer.

The use of touch screens and displays is quickly increasing in the automotive industry, especially for supercars. Touchscreen commands are affected by the problem of feedback to drivers, i.e. the driver cannot look at the touch display; indeed, he needs to understand if the required command is received from the car. Haptic surfaces represent the solution to this problem; hence, touch screens with embedded actuators are suitable to switch from mechanical vibrations to human feeling. This paper focuses on an innovative electromechanical device called Niceclick. It is a compact and powerful actuator able to modulate the haptic surface vibrations. After an overview of the tactile perception, measurement systems, human sensibility to vibrations, and psychophysical compliance are analysed to define the parameters of an ideal actuator suitable to create some specific signals, the related frequency bandwidth, and the associate energy profile. The tailored tool, appropriate to get these goals, is described, modelled, and experimentally tested coupled to the fundamental co-system where it is applied. A comparison between rigid and deformative coupled structures is considered to define performance and aims.

Simultaneous passive vibration suppression and energy harvesting through piezoelectric materials have been raising growing interest in the general area of structural dynamics. In the vibration isolation context, metastructures composed of multiple unit cells have played a major role in the design of suitable structures for the generation of frequency bandgaps, capable of mitigating structural vibration signals in a given frequency range. Additionally, concurrent energy harvesting in a given megastructure is possible through adequate positioning of piezoelectric layers on strategic locations along the metastructure geometric configuration. This work presents numerically simulated and experimental results of a study carried out on a given metastructure composed of a host beam with multiple resonators attached to it. Each resonator is designed from the arrangement of multiple beams forming a fan-folded structure that will significantly affect both, the vibration suppression as well as the capability of harvesting energy. Numerically simulated results are obtained through finite element modeling and a lumped parameter modeling methodology. Experimental results are gathered on a test prototype to verify the numerical results. Interesting results are obtained from multiple configurations of the individual fan-folded resonators as well as the total number of resonators positioned on the host beam.

Diagnostics of an induction motor is a serious issue for improving plant reliability. Induction motors are workhorse of the world today. Motor current signature analysis (MCSA) and vibration data are commonly used for diagnosing induction motor problems. However, the fault signatures are complicated and lack consensus. The problem is further complicated by the loading effects of the defect signatures. This paper will present a comparison of vibration and motor current signals for induction motor subjected to different levels of torque, unbalance, and misalignment loadings. Experiments were performed on intentionally faulted motor with varying degrees of electro-mechanical defects. Different levels of mechanical torque, unbalance, and misalignment loadings were applied to the rotor side. The data were analyzed using both vibration and motor current sensors. Results indicate motor current signature provides better indication of certain electrical faults such as airgap eccentricity and broken rotos bars, and vibration signature is better indicator of mechanical defects. The results suggest that both motor current and vibration measurements are required for more complete diagnostics of induction motors.

Condition monitoring of gearboxes in industrial settings is often based on trending vibration levels at gearmesh frequencies and sideband frequencies associated with matting shaft rotational speeds. However, it is debated on the vibration levels for setting different alarms related to the gearbox health. Another issue of controversy is what an indicator of fault severity; is it energy in gearmesh frequencies or the energies in sidebands. In this work, we analyzed vibration signature caused by gear tooth seeded faults of different levels. The data are analyzed in both time and frequency domains. The experimental study is conducted on a Machinery Fault Simulator ™ (MFS). The pinion gear in the gearbox is intentionally faulted with increasing severities, and vibration signal was collected for each case using IEPE accelerometers. Data are also obtained using a high-resolution encoder. Signals are analyzed using time synchronous averaging and traditional spectrum analysis. The results indicate that the vibration signature of a faulted bevel gear tooth is a pulse in time domain. Because of this impulse signal, strong sidebands arise in the spectrum around the mesh frequency. And the energy inside bands are better indicator of tooth defect severity.

The transient response of a time-varying oscillator under a chirp loading is considered in this paper. It is assumed that the natural frequency of the system changes with time during sweeping. Numerical simulations indicate that the maximum response amplitude and the apparent damping ratio are both higher than in the case without time-varying natural frequency. These two specificities of the time-varying problem are caused by the prolongated locking of the excitation frequency to the natural frequency. An asymptotic solution of the problem is derived and demonstrates that, at leading order, the time variant system responds like a time invariant system subjected to another sine sweep, having a slightly lower frequency rate. This suggests a simple equivalence. Previous studies of the time invariant problem indicate that a decrease in the sweeping rate results in increased amplification and apparent damping. This shows that the proposed equivalence is able to capture the main feature of the transient response of the time variant problem.