A novel MRE adaptive seismic isolator using curvelet transform identification

Magnetorheological elastomeric (MRE) material is a novel type of material that can adap-tively change the rheological property rapidly, continuously, and reversibly when subjected to real-time external magnetic field. These new type of MRE materials can be developed by employing various schemes, for instance by mixing carbon nanotubes or acetone contents during the curing process which produces functionalized multiwall carbon nanotubes (MWCNTs). In order to study the mechanical and magnetic effects of this material, for potential application in seismic isolation, in this paper, different mathematical models of magnetorheological elastomers are analyzed and modified based on the reported studies on traditional magnetorheological elastomer. In this regard, a new feature identification method, via utilizing curvelet analysis, is proposed to make a multi-scale constituent analysis and subsequently a comparison between magnetorheological elastomer nanocomposite and traditional magnetorheological elastomers in a microscopic level. Furthermore, by using this “smart” material as the laminated core structure of an adaptive base isolation system, magnetic circuit analysis is numerically conducted for both complete and incomplete designs. Magnetic distribution of different laminated magnetorheological layers is discussed when the isolator is under compressive preloading and lateral shear loading. For a proof of concept study, a scaled building structure is established with the proposed isolation device. The dynamic performance of this isolated structure is analyzed by using a newly developed reaching law sliding mode control and Radial Basis Function (RBF) adaptive sliding mode control schemes. Transmissibility of the structural system is evaluated to assess its adaptability, controllability and nonlinearity. As the findings in this study show, it is promising that the structure can achieve its optimal and adaptive performance by designing an isolator with this adaptive material whose magnetic and mechanical properties are functionally enhanced as compared with traditional isolation devices. The adaptive control algorithm presented in this research can transiently suppress and protect the structure against non-stationary disturbances in the real time

Shock isolation using magnetorheologically responsive technology

The purpose of this thesis is to develop a shock isolation system using magnetorheologically (MR) responsive technology to isolate shock input to various components in the light weight military vehicles susceptible to ballistic shock effects; Two methods are chosen for isolation of the shock. One is the friction damper based on MR fluid and the other is an elastomer based on magnetically responsive elastomer (MRE). Both approaches can be utilized for semi-active control schemes that have been widely used because of its unique feature of using variable damping and stiffness characteristics of the isolator; In this thesis, both computer simulation and experimental verification are presented to show the effectiveness of the technologies in isolating the shock and the performance is evaluated by the comparison with the passive isolator as a baseline

Analysis of damage control of thin plate with piezoelectric actuators using finite element and machine learning approach

In recent studies, piezoelectric actuators have been recognized as a practical and effective material for repairing cracks in thin-walled structures, such as plates that are adhesively bonded with piezoelectric patches due to their electromechanical effects. In this study, we used the finite element method through the ANSYS commercial code to determine the stress intensity factor (SIF) at the crack tip of a cracked plate bonded with a piezoelectric actuator under a plane stress model. By running various simulations, we were able to examine the impact of different aspects that affect this component, such as the size and characteristics of the plate, actuator, and adhesive bond. To optimize performance, we utilized machine learning algorithms to examine how these characteristics affect the repair process. This study represents the first-time machine learning has been used to examine bonded PZT actuators in damaged structures, and we found that it had a significant impact on the current problem. As a result, we were able to determine which of these parameters were most helpful in achieving our goal and which ones should be adjusted to improve the actuator's quality and reduce significant time and costs

Experimental and Analytical Investigation into the Effect of Ballasted Track on the Dynamic Response of Railway Bridges under Moving Loads

Ballasted tracks are among the most widespread railway track typologies. The ballast possesses multiple functions. Among them, it significantly affects the dynamic interaction between a rail bridge and a moving load in terms of damping and load distribution. These effects entail accurate modeling of the track-ballast-bridge interaction. The paper presents a finite-difference formulation of the governing equations of the track and the bridge, modeled as Euler-Bernoulli (EB) beams, and coupled by a distributed layer of springs representing the ballast. The two equations are solved under a moving load excitation using a Runge-Kutta family algorithm and the finite-difference method for the temporal and spatial discretization, respectively. The authors validated the mathematical model against the displacement response of a rail bridge with a ballasted substructure. In a first step, the modal parameters of the bridge, obtained from ambient vibration measurements, are used to estimate the bending stiffness of an equivalent EB beam representative of the tested bridge. In a second step, the authors estimated the coupling effect of the ballast by assessing the model sensitivity to the modeling parameters and optimizing the agreement with the experimental data. Comparing the bridge's experimental displacement responses highlights the ballast's significant effect on the load distribution and damping. The considerable difference between the damping estimated from output-only identification and that determined from the displacement response under moving load proves the dominant role of the ballast in adsorbing the vibrations transmitted to the bridge under the train passage and the different damping sources under high-amplitude excitation. The authors discuss the tradeoff between model accuracy and computational effort for a reliable estimation of ballasted tracks response under moving loads

Design, Modelling and Control of an Adaptive Vibration Isolator Featuring Magnetorheological Elastomer

Magnetorheological elastomers (MREs) are smart materials whose viscoelastic properties can be varied upon the application of an external magnetic field. They are solid analogue of well-known MR fluids (MRFs) in which magnetic particles are embedded in a non-magnetic elastomer matrix instead of carrier fluids. Compared with their fluid counterparts, MREs do not have problems associated with particles' sedimentation, stability and leakage often encountered in MRFs. Besides in contrast to MRF-based adaptive devices, MRE-based systems can provide field dependent variable stiffness and damping simultaneously due to viscoelastic properties of MREs. This unique behaviour of MREs enable them to be effectively utilized in the development of adaptive isolators or absorbers to supress vibrations in wide range of frequencies. The present research study aims to provide a comprehensive investigation of the material characterization and phenomenological modelling of MREs under varying dynamic loading conditions, design and development of a novel vibration and shock isolator featuring magnetorheological elastomers, design optimization of the proposed isolator to enhance its dynamic range and finally design and implementation of semi-active control strategies to mitigate vibration and shock under different external disturbances. MREs with a 25% volume fraction of soft magnetic particles (carbonyl iron) were used to investigate variation of storage and loss moduli of MRE under varied frequencies, strain amplitudes, and magnetic field densities. Considering operation of MREs in the linear range, field dependent linear viscoelastic models based on the Kelvin–Voigt, Maxwell, Standard Linear Solid, and Generalized Maxwell models, were formulated to predict the variation of storage and loss moduli under varying driving frequency and applied magnetic flux densities. The performance of these models to capture the response behaviour of MREs under different applied frequencies and magnetic field were subsequently compared. A semi-active MRE-based vibration isolator operating under shear mode with embedded electromagnet was then proposed. Analytical magneto-static model of the magnetic circuit of the proposed adaptive isolator was first formulated using Ampere’s law to estimate the induced magnetic flux density in the MRE region gaps versus applied current to the electromagnet. The validity of the analytical results was verified using the finite element magneto-static analysis. A multidisciplinary design optimization problem was subsequently formulated to optimize the isolator geometrical parameters as design variables to maximize its frequency bandwidth under weight, material magnetic saturation, and total volume constraints. A hybrid approach based on combination of Genetic Algorithm (GA) and gradient based Sequential Quadratic Programming (SQP) was used to accurately capture the global optimal solution for the optimization problem. Finally, closed-loop control strategies, based on on-off sky-hook and PID, were implemented and compared to assess the capability of the proposed adaptive isolator to mitigate vibration and shock under different disturbances

A Magnetorheological Damper with Embedded Piezoelectric Force Sensor: Experiment and Modeling

This chapter describes configuration, fabrication, calibration and performance tests of the devised self-sensing MR damper firstly. Then, a black-box identification approach for modeling the forward and inverse dynamics of the self-sensing MR damper is presented, which is developed with the synthesis of NARX model and neural network within a Bayesian inference framework to have the ability of enhancing generalization.Department of Civil and Environmental Engineerin

Modeling the nonlinear rheological behavior of magnetorheological gel using a computationally efficient model

Magnetorheological (MR) gel is a novel generation of smart MR material, which has the inherent hysteretic properties and strain stiffening behaviors that are dependent on applied excitation, i.e. magnetic field. The main challenge for the application of the MR gel is the accurate reproduction of the above characteristics by a computationally efficient model that can predict the dynamic stress-strain/rate responses. In this work, parametric modeling on the non-linear rheological behavior of MR gel is conducted. Firstly, a composite MR gel sample was developed by dispersing carbon iron particles into the polyurethane matrix. The dynamic stress-strain/rate responses of the MR gel are obtained using a commercial rheometer with strain-controlled mode under harmonic excitation with frequencies of 0.1 Hz, 5 Hz and 15 Hz and current levels of 1 A and 2 A at a fixed amplitude of 10%. Following a mini-review on the available mathematical models, the experimental data is utilized to fit into the models to find the best candidate utilizing a genetic algorithm. Then, a statistical analysis is conducted to evaluate the model's performance. The non-symmetrical Bouc-Wen model outperforms all other models in reproducing the non-linear behavior of MR gel. Finally, the parameter sensitivity analysis is employed to simplify the non-symmetrical Bouc-Wen model and then the parameter generalization is conducted and verified for the modified non-symmetrical Bouc-Wen model

Optimization of structural parameters using artificial neural network for vibration reduction in beams

Recently optimization of structural parameters like stiffness coefficient and damping coefficient, using artificial neural network for vibration reduction in beam has become a major application in aerodynamics and many other fields. By placing the PVC dampers of different cross sectional area at different locations along a beam, we have to minimize the response and the response time of the beam due to vibrations. Measuring factor of damping (reduction in vibration) is the LOSS FACTOR (Ω) for the system. Along the beam, where the loss factor is more, it gives us an optimal position for placing the damper. It also gives stability to the structure. The stability of the beam enhances due to increase in core loss factor also