Search Results (67)

This study proposes a new approach to identify structural parameters under seismic excitation using classical and quantum-inspired algorithms. Traditional methods often struggle with complex effects, noise, and computing limits. A five-story building model with mass–spring–damper system was tested to find properties during earthquakes. The study used optimization methods including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and five quantum-inspired versions: Quantum Genetic Algorithm (QGA), Quantum Particle Swarm Optimization (QPSO), Quantum Non-Dominated Sorting Genetic Algorithm II (QNSGA-II), Quantum Differential Evolution (QDE), and Quantum Simulated Annealing (QSA). Additionally, statistical analysis used Shapiro–Wilk for normality, Levene and Bartlett for variance, ANOVA with Tukey–Bonferroni comparisons, Bootstrap model ranking, and Borda count. The results show that the quantum-inspired methods perform better than classical ones. QSA reduced mean squared error (MSE) by 15.3% compared to GA, and QNSGA-II reduced MSE by 8.6% and root mean squared error (RMSE) by 3.5%, with less variation and tighter rankings. The framework addresses computing cost and response time; quantum methods need significant computing power and their accuracy suits offline earthquake assessments and model updates. This balance helps monitor building health when real-time speed is less critical but accuracy matters. The method provides a scalable tool for checking civil structures and could enable digital twins. Full article

With the rapid development of transportation infrastructure, bridges increasingly face prominent issues of dynamic response and fatigue damage induced by vehicle–bridge interaction (VBI). To effectively suppress the coupled vibrations and enhance both vehicle ride comfort and bridge service life, this paper proposes an active inerter-spring-damper (ISD) suspension system based on Particle Swarm Optimization (PSO) algorithm and Linear Quadratic Regulator (LQR) control. By establishing a VBI model considering general boundary conditions and employing the modal superposition method to solve the system response, an LQR controller is designed for multi-objective optimization targeting the vehicle body acceleration, suspension dynamic travel, and tire dynamic load. To further improve control performance, the PSO algorithm is utilized to globally optimize the LQR weighting matrices. Numerical simulation results demonstrate that, compared to passive suspension and unoptimized LQR active suspension, the PSO-LQR control strategy significantly reduces vertical body acceleration and tire dynamic load, while also improving the convergence and stability of the suspension dynamic travel. This research provides a new insight into the control method for VBI systems, possessing both theoretical and practical engineering application value. Full article

The behavior of a cantilever beam equipped with a particle damper, subjected to impact loads at various locations, was investigated using the discrete element method (DEM). The flexible cantilever steel beam and the particle damper attached to the beam’s tip were modeled with bonded particles through DEM. Computational simulations were conducted to explore the influence of different particle damper porosities and positions along the beam’s length. It was observed that reducing the particle damper’s porosity decreases the beam’s displacement. The impact force was significantly influenced by the porosity, where having lower porosities resulted in higher impact forces. In addition, the time intervals between sub-impacts were also affected by the damper’s porosity, showing a reduction as the porosity of the damper decreases. The unique type of particle damper used in this study contained sand grains as fillers and was capable of pressurizing the sand within its housing. This feature was utilized to investigate the effect of different initial pressures on the beam’s response. It was revealed that an increase in initial pressure reduces the beam’s displacement. Based on the results obtained, the optimal location for the particle damper was determined to be at the point where displacement reduction is required. Full article

In the practical physical structure of the electromagnetic inerter–spring–damper (EM–ISD) suspension, parasitic damping inevitably coexists with the mechanical inerter effect. To investigate the intrinsic influence of this parasitic effect on the suspension system’s performance, this study first establishes a quarter-vehicle dynamic model that incorporates parasitic damping, based on the actual configuration of the EM–ISD suspension. Subsequently, the particle swarm optimization (PSO) algorithm is employed to optimize the key suspension parameters, with the objective of enhancing its comprehensive performance. The optimized parameters are then utilized to systematically analyze the dynamic characteristics of the suspension under the influence of parasitic damping. The results indicate that, compared to an ideal model that neglects parasitic damping, an increase in the parasitic damping coefficient leads to a deterioration in the root mean square (RMS) value of body acceleration, while concurrently reducing the RMS values of the suspension working space and dynamic tire load. However, by incorporating parasitic damping into the design considerations during the optimization phase, its adverse impact on ride comfort can be effectively mitigated. Compared with a traditional passive suspension, the optimized EM–ISD suspension, which accounts for parasitic damping, demonstrates superior performance. Specifically, the RMS values of body acceleration and suspension working space are significantly reduced by 11.1% and 17.6%, respectively, thereby effectively improving the vehicle’s ride comfort and handling stability. Full article

In power inspection fields, snake-like robots are often used for transmission line inspection tasks, requiring accurate identification of various power fittings. However, traditional visual sensors perform poorly under varying light intensity and complex background conditions. This paper proposes a non-visual perception method for the high-precision classification of different power fittings (e.g., vibration dampers, suspension clamps, and tension clamps) in snake-like robot transmission line inspection for high-voltage lines. This method, unaffected by light intensity changes, uses machine learning to classify the magnetic induction electromotive force signals around the fittings. First, the Dodd–Deeds eddy current model is used to analyse the magnetic field changes around the transmission line fittings and determine the induction coil distribution. Then, the concept of condition number and singular value decomposition (SVD) are introduced to analyse the impact of detection position on classification accuracy, with optimal detection positions found using the particle swarm optimization algorithm. Finally, a BP neural network optimised by a genetic algorithm is used for power fitting identification. Experiments show that this method successfully identifies vibration dampers, tension clamps, suspension clamps, and transmission lines at detection distances of 5 cm, 10 cm, 15 cm, and 20 cm, with accuracies of 99.8%, 97.5%, 95.1%, and 92.5%, respectively. Full article

In industrial particle mixing processes, vibrations can intensify the Brazil nut effect (BNE), resulting in larger particles ascending, which contributes to vertical segregation based on particle size differences. These vibrations arise due to mass imbalance, motor functioning, and external disturbances, which negatively influence the efficiency of mixing. This research employs magnetorheological (MR) damping technology to reduce vibration-induced particle separation and consequently diminish the BNE. A drum mixer with an integrated MR damper was designed and constructed for experimental purposes, and its effectiveness in reducing vibrations was evaluated. Furthermore, simulations using the discrete element method (DEM) were performed to study the decrease in the BNE in different vibration settings. The results demonstrated that MR damping effectively decreased vibration levels, which significantly diminished the BNE and maintained the consistency of particle mixing. This study demonstrates the potential of MR damping as a practical solution to enhance mixing performance in industrial applications. Full article

Conventional magnetorheological fluids (MRFs) exhibit a constrained shear strength that restricts their deployment in high-performance damping systems. This study introduces a dual-axis innovation strategy combining material science and device physics to fundamentally redefine MRF capabilities. We develop a hierarchical particle architecture through the controlled integration of micro/nano-sized carbonyl iron particles (CIPs), enhanced by polyethylene glycol/oleic acid surface engineering to optimize magnetic chain formation and interfacial bonding. The engineered MRF demonstrates a shear yield strength of 99.6 kPa at 0.757 T, surpassing conventional single-component MRFs by a significant margin. Integrated with a self-decoupling damper that isolates magnetic flux from mechanical motion, this synergistic design achieves exceptional force modulation: damping forces scale from 281.5 kN (5 mm stroke) to 300 kN (60 mm stroke), with current-regulated adjustability factors reaching 3.34. The system exhibits substantial improvements in both maximum damping force (93.9 kN enhancement) and energy dissipation efficiency compared to standard MRF dampers. Through co-optimization of the particle architecture and magnetic circuit design, this work establishes new performance benchmarks for smart fluid technology. The achieved force capacity and dynamic response characteristics directly address critical challenges in seismic engineering and industrial vibration control, where extreme load-bearing requirements demand simultaneous high strength and tunable damping capabilities. Full article

The wheel–rail contact is an intrinsic characteristic of rail transport. This contact is one of the main reasons why rails are so efficient for transportation, mainly due to the very low friction coefficient between them and the wheels. However, this strong argument also leads to a disadvantage: the wheel contact is also associated with excessive vibration and noise, which have a strong impact on the passengers’ comfort and especially the surrounding community. These noises and vibrations impact the public in several ways, like disturbing sleep, increasing stress and heart-associated diseases. The main objective of the present work is to investigate the rail vibration attenuation by applying particle dampers. Four different particles will be studied, and their effectiveness in reducing the rail vibrations will be analysed. Promising results were found, where under certain conditions, the particle dampers, such as lead and magnetite particles, were able to reduce peak vibration levels by more than an order of magnitude. The application of this system may have a strong impact on the communities using and in the vicinity of rail systems by reducing the noise and vibration, consequently improving people’s health and well-being. Full article

In the current work, two novel tandem-based tuned mass damper configurations are introduced. These configurations extend the recently proposed tuned tandem mass damper inerter (TTMDI) by replacing the linking dashpot with an inerter (i.e., the inerter-connected TTMDI (ICTTMDI)), and an integrated tuned tandem mass damper inerter (I-TTMDI) by integrating recently proposed tuned tandem mass damper (TTMD) configurations. The control efficiency of the optimally designed dampers for a single-degree-of-freedom (SDOF) system was evaluated in a uniform framework to reveal and compare the performances of the ICTTMDI and I-TTMDI with those of other recently proposed tandem-based configurations. The optimum design of all the studied configurations was determined by the particle swarm optimization (PSO) algorithm. The evaluation of the performances included the effectiveness in the frequency domain and that of the norm and maximum reduction in the displacement and absolute acceleration in the time domain under 21 earthquake records with different characteristics. Additionally, the strokes of the dampers, the structure energies, and the power spectral densities (PSDs) of the responses were investigated. The optimum design of the I-TTMDI revealed the best configuration by determining the optimum distributions of the mass and inertance between the tandem mass and inerter links, respectively. The proposed configuration not only demonstrated improved response reduction across the displacement and acceleration measures but also maintained remarkable robustness under 21 earthquake records (far-fault, near-fault forward-directivity, and fling-step records). Furthermore, the advantages of the side inerter distribution were particularly effective at widening the operating frequency band, breaking through the traditional limitations of TMD-based devices. The consistent performances of the newly proposed configurations prove that they can be used to advance the development of more reliable structural control systems. Full article

Nowadays, it is very important to reduce structural vibrations and control seismic reactions against earthquakes. Nonlinear viscous dampers are known as one of the effective tools for absorbing and dissipating earthquake energy to reduce structural responses. The characteristics of nonlinear viscous dampers, including the damping coefficient, axial stiffness, and velocity exponent, play a crucial role in their performance. In this research, the optimization of nonlinear viscous damper characteristics to minimize the peak absolute displacement of the roof in three- and five-story reinforced concrete flexural frames under the El Centro earthquake record has been investigated. Structural modeling and dynamic analyses are performed using OpenSees 3.5.0 software, and damper parameter optimization is performed through a new combination of two marine predator algorithms (MPA) and particle swarm optimization (PSO). Furthermore, the performance of the new algorithm is compared with each of these methods separately to evaluate the efficiency improvement for displacement reduction. The results show that the hybrid algorithm has demonstrated significant performance improvement compared to the independent methods in identifying optimal values. Specifically, in the three-story frame, the roof displacement using the MPA-PSO method was 0.77026, which is lower than 0.77140 with the PSO method. Additionally, the damping coefficient in this method decreased to 14.22824 kN·s/mm, which is a significant reduction compared to 19.32417 kN·s/mm in the PSO method. Furthermore, in the more complex five-story frame, the two comparison methods were unable to reach the optimal solution, while the proposed method successfully found an optimal solution. These results validate the performance and advantages of the proposed hybrid algorithm. Full article

Magnetic fluids (MF), composed of ferromagnetic nanoparticles, surfactants, and a carrier liquid, exhibit tunable physical properties under external magnetic fields due to the formation of chain-like nanoparticle structures. Using dissipative particle dynamics (DPD), we simulate the structural evolution of these fluids and establish a computational model incorporating magnetic nanoparticles and solvent particles. Our simulations confirm qualitative agreement with the literature results, validating the chosen time integration methods. Through radial distribution function analysis, we further demonstrate how the mass of solvent molecules and magnetic interaction strength govern the fluid’s microstructure. This work provides insights into the design of magnetic fluids for applications such as targeted drug delivery, adaptive dampers, and advanced magneto-rheological devices. Full article

Shear Thickening Fluid (STF) is a specialized high-concentration particle suspension capable of rapidly and reversibly altering its viscosity when exposed to sudden impacts. Consequently, STF-based dampers deliver a self-adaptive damping force and demonstrate significant potential for applications in structural vibration control. This study presents both a modeling and experimental investigation of a novel double-rod structured STF damper. Initially, a compound STF is formulated using silica particles as the dispersed phase and polyethylene glycol solution as the dispersing medium. The rheological properties of the STF are then experimentally evaluated. The STF’s constitutive rheological behavior is described using the G-R model. Following this, the flow behavior of the STF within the damper’s annular gap is explored, leading to the development of a two-dimensional axisymmetric fluid simulation model for the damper. Based on this model, the dynamic mechanism of the proposed STF damper is analyzed. Subsequently, the STF damper is optimally designed and subjected to experimental investigation using a dynamic testing platform under different working conditions. The experimental results reveal that the proposed STF damper, whose equivalent stiffness can achieve a nearly threefold change with excitation frequency and amplitude, exhibits good self-adaptive capabilities. By dividing the damper force into two parts: the frictional damping pressure drop, and the osmotic pressure drop generated by the “Jamming effect”. A fitting model is proposed, and it aligns closely with the nonlinear performance of the STF damper. Full article

Pneumatic artificial muscles (PAMs) possess compliant properties desirable for certain applications such as prosthetics and robotic structures. However, this compliance along with their inherent nonlinear dynamics make them difficult to accurately model and as such accurately control under certain control architectures. Common approaches to this problem include measuring the actuator’s physical properties and approximating a model based on these parameters or using deep learning methods to train a model with the actuator’s behaviours. This paper introduces an optimisation-based modelling approach based on a particle swarm optimisation (PSO) algorithm using a mass–spring–damper approximation for the PAM, as well as a piecewise modelling method that accounts for nonlinear dynamics. The use of optimisation to estimate model parameters removes the need to measure physical properties, and the three-element approximation allows for fast model generation with low computational complexity and training data requirements. Through multiple tests comparing model behaviour with real PAM motion, the accuracy of these models is confirmed to be promising for future work. Dynamic nonlinearities are properly accounted for using the piecewise modelling method, including both hysteresis and disproportionate input/output relationship across the stroke length of the actuator. Compared with other PAM modelling techniques, this method has improved generation time, lower computational load requirements, and complexity and can be applied to actuators for which the phenomenological mass–spring–damper model is a good approximation. Full article

As smart materials, electrorheological elastomers (EREs) formed by pre-treating active electrorheological particles are attracting more and more attention. In this work, four Mg-doped strontium titanate (Mg-STO) particles with spherical, dendritic, flake-like, and pinecone-like morphologies were obtained via hydrothermal and low-temperature co-precipitation. XRD, SEM, Raman, and FT-IR were used to characterize these products. The results showed that Mg-STOs are about 1.5–2.0 μm in size, and their phase structures are dominated by cubic crystals. These Mg-STOs were dispersed in a hydrogel composite elastic medium. Then, Mg-STO/glycerol/gelatin electrorheological composite hydrophilic elastomers were obtained with or without an electric field. The electric field response properties of Mg-doped strontium titanate composite elastomers were investigated. We concluded that dendritic Mg-STO composite elastomers are high-performance EREs, and the maximum value of their energy storage was 8.70 MPa. The significant electrorheological performance of these products is helpful for their applications in vibration control, force transducers, smart structures, dampers, and other fields. Full article

Due to their advantages—longer internal force delay compared to bulk materials, resistance to harsh conditions, damping of a wide frequency spectrum, insensitivity to ambient temperature, high reliability and low cost—granular materials are seen as an opportunity for the development of high-performance, lightweight vibration-damping elements (particle dampers). The performance of particle dampers is affected by numerous parameters, such as the base material, the size of the granules, the flowability, the initial prestress, etc. In this work, a series of experiments were performed on specimens with different combinations of influencing parameters. Energy-based design parameters were used to describe the overall vibration-damping performance. The results provided information for a deeper understanding of the dissipation mechanisms and their mutual correlation, as well as the influence of different parameters (base material, granule size and flowability) on the overall damping performance. A comparison of the performance of particle dampers with carbon steel and polyoxymethylene granules and conventional rubber dampers is given. The results show that the damping performance of particle dampers can be up to 4 times higher compared to conventional bulk material-based rubber dampers, even though rubber as a material has better vibration-damping properties than the two granular materials in particle dampers. However, when additional design features such as mass and stiffness are introduced, the results show that the overall performance of particle dampers with polyoxymethylene granules can be up to 3 times higher compared to particle dampers with carbon steel granules and conventional bulk material-based rubber dampers. Full article