Prediction Of Vibration Response Research Articles

Multiscale simulation and experimental studies on modal damping of ultra‐thick CFRP laminates

AbstractThe vibration characterization of ultra‐thick laminates widely used in aerospace and marine industries was different from that of thin laminates. However, modal testing and simulation methods were generally limited to relatively thin laminates. To explore the vibration behavior of ultra‐thick laminates, a novel multiscale simulation technology based on the representative volume modal strain energy (RVMSE) method is presented for predicting modal damping in this paper. The adaptive Greedy‐Based Generation (GBG) algorithm is used to create a 3D random distribution of fibers for composites. Modal shape information of the structure is obtained according to the Lanczos algorithm, which is used as an input parameter for the microscopic model. This is followed by homogenization based on the volumetric analysis performed on representative volume elements (RVEs) to determine the strain energy in six directions. The calculation of modal damping is performed by using the modal strain energy (MSE) method and the damping properties of the six directions. The fast rate of convergence and high accuracy of the method are demonstrated through different examples. The vibration response predictions produced by novel RVMSE methodology are shown to closely match experimental measurements, providing scope to expand the application of this approach to more complex, ultra‐thick laminate components.Highlights The RVMSE method uses a higher level of homogenization to overcome the limitations of the traditional MSE method for calculating the damping of ultra‐thick CFRP laminates. The modal parameter error is controlled to less than 10%, and the experimental results fill the gap of the damping data of ultra‐thick CFRP structure. The proposed RVMSE model improves the accuracy of dynamic response prediction for ultra‐thick laminates.

A method for predicting vibration characteristics of track structure based on rail acceleration and deep learning

ABSTRACT The dynamic test on track structure plays a crucial role in evaluating the operational state. Nonetheless, existing testing approaches involve numerous sensors, resulting in high costs and time consumption. This study proposes an economical, high-precision, and swift solution to predict vibration responses and displacement variations in track structures using rail acceleration. Taking the ordinary track structure and the damping track structure as examples, based on the high correlation between the vibration responses of the rail, track slab and tunnel wall, combined with the proposed Bayesian optimized temporal convolutional neural network model (BOA-TCN), and taking the rail acceleration envelope and vibration level as input, the vibration of tunnel wall and the displacement of rail and track slab are estimated. The results demonstrate that the BOA-TCN model effectively solves the limitation of traditional neural network in accurately capturing vibration characteristics in the frequency range of 1 ~ 100 Hz. The absolute error of the total vibration level prediction is significantly reduced, only 0.74 dB. The single point prediction accuracy of track structure displacement is increased by 80%, the absolute error is 50% of the traditional model, and R2 can be increased to more than 90%.

Vibration characteristics of subway turnout area during vehicle deceleration and assessment of the model accuracy

Frog gaps and rail joints produces intricate wheel-rail contact conditions within subway turnouts, which results in intense vibrations. This study describes vibration tests conducted on several typical cross-sections of a turnout, which aimed at understanding the vibration transfer characteristics of typical section in turnout area. To separation the vibration signals from each carriage, wavelet analysis and peak detection algorithms were utilized. Using a 2D kernel density algorithm, representative data from the carriages were clustered, effectively isolating the influence of interference data. A coupled vehicle-turnout-tunnel-soil vibration analysis model was established, combining multiple spatial and temporal scales and the accuracy of its prediction was evaluated by comparing the calculated results to statistical test results.The test results indicate that when the train decelerated through the turnout, there was a significant variation in the vibrations of different train carriages, with a 13 dB difference in the total rail vibration levels. The rail joints and frog gap present in the turnout resulted in high vibration level in the tunnel walls. The main frequency of tunnel wall vibration in the frog gap section was 63 Hz, and the total vibration level was 84.9 dB. The accuracy of vibration response prediction was evaluated by comparing the calculated results to statistical test results using probability density functions, and the model errors were maintained within 10 % of the test data. The presented vibration testing and modelling results of vibration propagation characteristics in various directions for the subway turnout area will provide a foundation for selecting vibration mitigation measures.

Vibration response prediction and vibration safety assessment method for building structures around viaducts under the action of high-speed railway trains

Concerning the cumulative damage of building structures caused by the long-term effects from high-speed railway train loads and various environmental impacts, and to ensure the service safety of building structures around viaducts, this paper establishes a vibration safety assessment method for the entire life cycle of building structures with less workload and good applicability, while ensuring the accuracy and effectiveness of the prediction of vibration response of building structures. Firstly, the vibration source function is formed based on the mechanical model of the mobile vibration source of high-speed railway trains, and the vibration wave of high-speed railway trains is numerically simulated. Then the time and frequency characteristics of the simulated high-speed railway train vibration waves are verified through on-site monitoring tests. In addition, the peak particle vibration velocity attenuation formula for the foundation site of the viaduct is derived by the dimension analysis method, and it is used to correct the attenuation characteristics of the simulated high-speed railway train vibration waves. Finally, based on the response spectrum method to realize the prediction and assessment of the overall vibration response of the building structures, and using the cumulative damage coefficient to reflect the cumulative degree of the service damage of the building structure, a vibration safety assessment method for the whole lifecycle of the building structure is proposed. The method overcomes the shortcoming that vibration velocity does not reflect the impact of service damage on the vibration safety of building structures, and avoids neglecting localized vibration damage, and is therefore useful for the rapid, overall vibration safety assessment of any long-term service building structure in a viaduct site.

Forced vibration response prediction of Miura-ori core sandwich panel with impact defects

Miura-ori core sandwich panels have great application potential in aviation aircraft. Subject to the impact and vibration loads, the small impact defect will increase the dynamic stress, resulting in fatigue failure. Moreover, the Miura creases complicate the impact deformation, which is extremely time-consuming to analysis the dynamic stress only through simulation. Therefore, a rapid dynamic stress prediction method for Miura-ori core sandwich panels is first proposed based on the predicted impact deformation by arbitrary defects in this paper. The dynamic stress of sandwich panels could be directly calculated with few numerical results by establishing the relationship with impact deformation in advance. The vibration mechanism of sandwich panels with defects is revealed. Although the deformation area is below 32.1 % of Miura cell projection area, the dynamic stress will increase by 1.984–2.635 times with different defects. The impact deformation is predicted by the geometric relationship of Miura creases and material properties, and the prediction errors of hemispherical and drilling defects with arbitrary defect positions are below 12.5 % and 10.6 %, respectively. Taking two types of defects (hemispherical and drilling defects), different defect depths and positions into consideration, the prediction errors of maximum dynamic stress are from -7.9 % to 16.8 % compared to simulation.

Dynamic analysis of a plate system coupled through several nonlinear spring-mass couplers

Nowadays, plates have been widely used in various engineering fields, where nonlinear additional equipment is designed to control the vibration of plates. However, the current studies mainly concentrate on the single-plate system with nonlinear additional equipment. Seldom studies use nonlinear couplers to connect the plate system, making the working mechanism of nonlinear couplers on the vibration responses of the plate system unclear, limiting the application of nonlinear couplers on vibration control of the plate system. To explore the potential application of nonlinear couplers, this work introduces nonlinear spring-mass couplers to connect the plate system, where the transverse vibration analysis model of the plate system coupled through nonlinear couplers is established. The Galerkin truncation method (GTM) is used to predict vibration responses of the plate system coupled through nonlinear couplers. After ensuring the validation of numerical results, forced vibration responses of the plate system coupled by nonlinear couplers are investigated. According to numerical results, responses in the vibration of the plate system are influenced by nonlinear couplers greatly. Choosing suitable parameters for nonlinear couplers plays an important role in the vibration control of the plate system. Under a certain parameter range of nonlinear couplers, responses in vibration present complex characters. Under complex responses, the target energy transfer is present in the vibration responses of the plate system.

Vibration response of viscoelastic nanobeams including cutouts under moving load

This manuscript presents a mathematical model and numerical solution to predict vibration responses of nonlocal strain gradient (NLS) perforated viscoelastic nanobeam under dynamic moving loads. The microstructure and size length scales are considered in the frame of NLS theory. The viscoelastic damping of the structure is considered by linear Kelvin Voigt viscoelastic model. Timoshenko and Euler Bernoulli beam theories are developed to consider both thin and thick structure response. The moving load is portrayed by point load and harmonic type. The dynamic equations of motion incorporating viscoelasticity and size dependency effects is derived by using Hamilton principle. A differential quadrature method (DQM) with Chebyshev–Gauss–Lobatto formula is used to discretize the space domain and solve the model numerically. The obtained results are validated and compared with available literature. Numerical studies are conducted to show the influence of the material viscosity parameter, perforation parameters, shear deformation effect, nonlocal strain gradient, moving load velocity, and beams slenderness ratio on the dynamic behavior of viscoelastic perforated nanobeams under moving load. The material viscosity parameter can effectively control the magnitude and stability of the magnification factor profiles. Higher filling ratios result in larger values of the dynamic magnification factor, whereas larger numbers of hole rows lead to smaller values. The proposed procedure is supportive in the analysis and design of perforated viscoelastic NEMS structures under moving load.

Theoretical and experimental analysis of electric vehicle motor dynamics under the coupling of harmonic current and temperature excitation

The NVH performance of electric vehicles is crucial for human comfort, and the electric drive system is key to NVH performance. Permanent magnet synchronous motors experience high-temperature increases under high-speed operating conditions, resulting in motor structure deformation at high-temperatures. The radial deformation of the stator alters the airgap dimensions of the motor’s electromagnetic field. The airgap of the motor’s magnetic field has high-sensitivity to these changes aggravating the vibration and noise problems. Harmonic currents in high-speed vehicle motors add to these problems. Therefore, motor dynamics must be studied under the joint influences of airgap distortion and harmonic currents. We proposed a method of modeling permanent magnet synchronous motors under the joint effect of harmonic currents and temperature fields. And established an accurate multi-field coupled vibration response prediction model for permanent magnet synchronous motors. The accuracy of the vibration prediction model was subjected to a bench test. The vibration characteristics of the motor were studied using order and spectrum analyses. We found that the airgap magnetic-field distortion, due to the motor and stator thermal deformation, significantly affected the motor vibration characteristics. The main motor vibration noise was caused by the 24th and 48th order electromagnetic force waves. This provides a reference for establishing an accurate vibration prediction model for permanent magnet synchronous motors.

Coupling effect of shaft torsional vibration and advanced injection angle on medium-speed diesel engine block vibration

Engine body vibration is an important safety concern for diesel engines as it can lead to severe engine damages and even catastrophic failures. Traditional methods for excitation computation assume that the rotational speed and the cylinder pressure remain stable under each working cycle, which makes it impossible to precisely predict vibration peaks at certain frequencies. But in practice, the existence of shafting torsional vibration will lead to the fluctuation of rotational speed and cylinder pressure. To predict engine block vibration more accurately, this paper proposes a novel method considering the instantaneous variations in rotational speed and cylinder pressure. It is implemented by two steps. Firstly, an innovative model considering the coupling effect between shaft torsional vibration and advanced injection angle is proposed, it particularly explains how the cylinder pressure and instantaneous rotational speed vary due to the coupling effects. Secondly, the excitation forces that cause block vibration are analyzed and their computational formulas are improved. This method also considers the offset of excitation loading position caused by torsional vibration. The coupling model is experimentally verified and the excitations are loaded into a finite element model of the diesel engine for vibration response prediction. The results reveal that the coupled model can predict vibration peaks at sideband frequencies and thus makes the frequency spectrum much richer than that of the traditional method. Furthermore, it shows that the acceleration results obtained by the coupled model have better prediction accuracy than those obtained from traditional methods, with the deviation percentage reducing from 6.31% and 8.41% to 0.64% and 0.32% in the X and Z direction. It indicates that the coupled model is more reliable in predicting the vibration response of the engine block. Finally, the effect of advanced injection angle on engine block vibration is investigated and the optimal value of the advanced injection angle is determined to be 29 CA, which makes the engine block vibration reduced by 3.60%, 2.72%, and 3.16% in the X, Y, and Z direction comparing to the original advanced injection angle. Overall, this study presents an improved approach for predicting engine block vibrations and offers valuable insights for the design of shafting structures and fuel injection parameters in marine diesel engines.

Vibration Control of Fgm Thick Shells Using Higher Order Shear Deformation Theory

Analytical solutions of Functionally Graded Material (FGM) shells with embedded magnetostrictive layers are presented in this study. These magnetostrictive layers are used for the vibration suppression of the functionally graded shells. The higher order shear deformation theory (HSDT) is employed to study the vibration suppression characteristics. The exact solution for the FGM shell with simply supported boundary conditions is based on the Navier solution procedure. Negative velocity feedback control is used. The parametric effect of the location of the magnetostrictive layers, material properties, and control parameters on the suppression effect are investigated in detail. Higher order shear deformation theory has significant influence on prediction of vibration response of thick shells. Further, it is found that (i) the shortest vibration suppression time is achieved by placing the actuating layers farthest from the neutral plane (ii) the use of thinner smart material layers leads to better vibration attenuation characteristics, and (iii) the vibration suppression time is longer for a smaller value of the feedback control coefficient.

Vibration Prediction and Evaluation System of the Pumping Station Based on ARIMA–ANFIS–WOA Hybrid Model and D-S Evidence Theory

Research on the vibration response prediction and safety early warning is of great significance to the operation and management of pumping station engineering. In the current research, a hybrid prediction method was proposed to predict vibration responses of the pumping station based on a single model of autoregressive integrated moving average (ARIMA), a combined model of the adaptive network-based fuzzy inference system (ANFIS) and whale optimization algorithm (WOA). The performance of the developed models was studied based on the effective stress vibration data of the blades in a shaft tubular pumping station. Then, the D-S evidence theory was adopted to perform safety early warning of the operation state by integrating the displacement, velocity, acceleration and stress indicators of the vibration responses of the pumping station. The research results show that the proposed prediction model ARIMA–ANFIS–WOA exhibited better accuracy in obtaining both linear and nonlinear characteristics of vibration data than the single prediction model and hybrid model with different optimization algorithms. The D-S evidence fusion results quantitatively demonstrate the safe operation state of the pumping station. This research could provide a scientific basis for the real-time analysis and processing of data in pumping station operation and maintenance systems.

Prediction of Vortex-Induced Vibration Response of Open Bridge Girder Based on Machine Learning Method

The [Formula: see text]-shaped open section girder is a very common section, which has been widely used in the construction of modern bridges. However, compared with a streamlined box girder, it is easy to cause vortex-induced vibration (VIV) in bridges due to its bluff section shape and poor aerodynamic stability. In this paper, a typical open section with a height–width ratio of 10:1 is taken as the research object. Through wind tunnel tests and numerical simulation technology, the time-history data of VIV of the [Formula: see text]-shaped section are obtained. By selecting three different neural network models to establish the relationship between external parameters (wind attack angle, wind speed, turbulence) and structural response parameters (the onset wind speed of VIV, the length of the lock-in interval). After comparing different machine learning methods, the most suitable machine learning model is selected to predict the VIV response characteristics of the main girder. The research results show that the typical open section has a serious VIV phenomenon. The BP neural network model has the best performance in predicting the VIV performance of the open section in terms of calculation speed, prediction accuracy and solving nonlinear adaptability. At the same time, it is found that different characteristic parameters of the incoming wind field have different influences on the prediction results of the VIV response. Among them, the wind attack angle is the key parameter that affects the onset wind speed of VIV, but the turbulence intensity has a greater impact on the maximum amplitude of VIV. At the same time, the optimized BP model can predict the characteristic parameters of VIV response under unknown wind fields. This paper provides a new idea to study the effect of different incoming flow parameters on the performance of section VIV and further improves people’s understanding of VIV system.

Analytical and experimental studies for the vibration transmission of the double-plated structure containing the inner fluid and elastic connections

The vibration transmission of the double-plated structure containing the inner fluid and elastic connections are researched by the analytical and experimental techniques. An analytical method is firstly proposed to predict vibration responses of the double-plated structure. The improved Fourier series are used to describe the transverse displacements of the plates and the sound pressure of the acoustic cavity. The elastic connections are regarded as springs. Motion equations are derived by the energy method and vibration results are gained via the Rayleigh–Ritz technique. Meanwhile, a test sample of the double-plated structure is manufactured. Four vibration isolators are utilized as the elastic connections. The inner fluid is air or water. Forced vibration tests are conducted with a random white-noise force. The results from both experiments and the finite element method (FEM) are well consistent with ones calculated by the analytical method. Then the effects of important parameters on the vibration transmission are studied by the analytical and experimental analyses, including the stiffness of elastic connections, the height of the inner fluid, the boundary condition of plates, the spring position, the inner fluid loading and the coating damping layer. It is found that the inner water acts as a short circuit in the vibration transmission, and the inner air leads to the less vibration transmission from the upper plate to the lower one. Some other new phenomena are also observed and analyzed in this paper.

Performance of Artificial Neural Network and Modified Gravitational Search Algorithm Models to Predict Vibration Response of Geocell Reinforced Based Weak Sand

The use of a fast evolving artificial intelligence technology (AIT) to forecast the vibration response of maritime soil based on geocells is explored in this research. The vibration response is represented by an indicator called peak particle velocity (PPV). For the purpose of predicting PPV, the artificial intelligence techniques Artificial Neural Network (ANN) and Modified Gravitational Search Algorithm (MGSA) are employed. To create the dataset for the model, a number of field vibration tests were first performed over the geocell-reinforced beds. PPV variation was investigated by varying the test variables—footing embedment, dynamic load, infill material modulus, width, and depth of geocell mattress placement—during the test performed. The various statistical indicators were determined in order to evaluate the prediction performance of a constructed model. Plate load results on geocell-reinforced foundation beds have been used to validate the proposed hybrid ANN-MGSA model. High accuracy and consistency were found when the findings of the ANN-EHO, JSA, MOA, and RNN method were compared, particularly at predicted and actual resolution levels. A parametric sensitivity has also been examined in order to better understand the behaviour of geocell-reinforced structures.

Comparison of vibration response prediction on the main deck of a catamaran using experimental, numerical and correlated mode shapes

This work analyses the influence of three types of modal matrices on the prediction of vibration response (virtual sensing), at unmeasured degree of freedoms (DOFs), on a catamaran’s main deck: (1) uncorrelated finite element (FE), (2) correlated FE and (3) experimental modal matrices.A multi-objective genetic algorithm (MOGA) framework was developed to handle the optimization and prediction processes. This framework introduces a new metric called Time–Frequency-Error Response Assurance Criteria (TFERAC) to assess the prediction quality. This metric also allows estimating the best set of modal acceleration vectors, which is a critical step in the virtual sensing process.As a case study, only 06 accelerometers and 13 vibration modes (within each modal matrix) were used in the virtual sensing. The MOGA framework’s performance was evaluated using a variance analysis test (ANOVA) between measured and predicted response signals.Results showed that: (1) any one of the modal matrices could be used successfully for virtual sensing on the main deck, that is, there is no need to use correlated FE or experimental modal matrices; (2) the newly proposed metric TFERAC leads to smaller errors in the prediction of both vibration time series and vibration spectra;(3) it is possible to perform a virtual sensing on a ship’s main deck using a limited number of sensors and a numerical modal matrix without being correlated with experimental data.

Nonlinear dynamic modeling and vibration analysis of whole machine tool

The machine tool structure vibration during machining operation is directly linked to machining quality, production efficiency as well as machine reliability. Hence, this research focuses on the dynamic modeling of the whole machine tool considering the milling process to predict vibration response and dynamic characteristics during machining. Applying the lumped parameter and finite element hybrid method, in order to improve virtual simulation efficiency, the analytical models of the workpiece clamping worktable system and the tool clamping column spindle system are theoretically developed considering the nonlinearity of moving joints and the position-dependent dynamics. The integrated model of the whole machine tool dynamics is established by coupling the models of two subsystems with the generated nonlinear dynamic load in the ball end milling process including the effects of vibrations and tool system errors. Subsequently, the frequency response functions and the instantaneous cutting forces and vibration responses under different cutting conditions are measured and compared with simulation results to evaluate the prediction ability of the proposed model. Finally, the experimentally confirmed model is used to carry out the parameter analysis of machine tool. The investigations reveal that the worktable system dynamic characteristics vary with the joint assembly preload, but the change becomes no longer significant when the preload increases to a certain value. Besides, cutting conditions have a significant influence on the vibration characteristics of machine tool during machining.

Performance of Artificial Neural Network and Modified Gravitational Search Algorithm Models to Predict Vibration Response of Geocell Reinforced Based Weak Sand

The use of a fast evolving artificial intelligence technology (AIT) to forecast the vibration response of maritime soil based on geocells is explored in this research. The vibration response is represented by an indicator called peak particle velocity (PPV). For the purpose of predicting PPV, the artificial intelligence techniques Artificial Neural Network (ANN) and Modified Gravitational Search Algorithm (MGSA) are employed. To create the dataset for the model, a number of field vibration tests were first performed over the geocell-reinforced beds. PPV variation was investigated by varying the test variables—footing embedment, dynamic load, infill material modulus, width, and depth of geocell mattress placement—during the test performed. The various statistical indicators were determined in order to evaluate the prediction performance of a constructed model. Plate load results on geocell-reinforced foundation beds have been used to validate the proposed hybrid ANN-MGSA model. High accuracy and consistency were found when the findings of the ANN-EHO, JSA, MOA, and RNN method were compared, particularly at predicted and actual resolution levels. A parametric sensitivity has also been examined in order to better understand the behaviour of geocell-reinforced structures

Numerical Analysis on Spanwise Correlation of Vortex-Induced Force of Split Double-Box Beam

The vortex-induced force of bridges is not synchronized along the span direction. The spatial correlation of vortex-induced force becomes obvious when the span of the bridge structure gradually increases, and the mass gradually decreases. Therefore, it is important to study the spanwise correlation of vortex-induced forces of long-span bridges to ensure the accuracy of the prediction of vortex-induced vibration response of bridges. The study established and improved the theory and experimental research methods for vortex-induced vibration analysis of large-span bridges by discussing the spanwise correlation during vortex-induced vibration of the split double-box beam. Taking Xihoumen Bridge as the object, two-dimensional (2D) and three-dimensional (3D) numerical models of the scaled-down sections were designed and established based on the ANSYS Fluent platform and the RANS SST turbulence model. Based on the Newmark-β algorithm, a User Defined Function (UDF) was written for vortex vibration calculation, the three-dimensional bypass of the split double-box beam in static conditions and vortex-induced vibration of the split double-box beam of which were calculated, and the spanwise correlations of the aerodynamic coefficients, the surface pressure coefficients, and the wake wind velocity of the main girder section were analyzed for the static and vibration states of the bridge. The results show that the self-excited force of the split double-box beam in vortex-induced vibration improved its spanwise correlation. Compared with those in a static state, the spanwise correlation of the lift coefficient and torque coefficient increased by 55% and 87%, respectively, and the resistance coefficient increased by more than 10 times. The correlation of the pressure coefficients increased by 153%. The correlation of wake wind velocity increased by 37% in the along-wind direction and that in the across-wind and vertical-wind direction increased by more than 10 times. The accuracy of the numerical simulation results was verified by comparing the pressure distribution and pressure spanwise correlation of the main beam with field-measured data.

Research on acoustic reconstruction methods of the hull vibration based on the limited vibration monitor data

The prediction of hull vibration response is the basis for the quantitative control of cabin noise and underwater radiated noise, which has an important engineering value to obtain vibration characteristics accurately. In this paper, a new method is proposed to reconstruct hull vibration by some monitoring data in low frequencies. The hull vibration response is reconstructed by the monitoring data and the transfer function, and the ill-posed problem of inversion for the transfer function matrix is solved by the total least-squares regularization. A P-GRU (Peak-based Gated Recurrent Unit) neural network correction model is proposed to improve the prediction accuracy of vibration response. Taking a certain cabin structure as an example, some research such as the hull vibration data monitoring, the vibration transfer function calculation, vibration response reconstruction, and correction based on experimental data were carried out, and the simulation calculations and verification of experiment models were obtained. The multi-parameter comprehensive method is adopted to quantitatively evaluate the results between the simulation and the experiment. The results show that the acoustic reconstruction results are in good agreement with the experimental data. The overall level error is within 3 dB, the main peak value correlation coefficient is above 0.9, and the main peak difference is within 4 dB. The mechanism of the rapid and accurate prediction of the low-frequency vibration for the hull based on limited monitoring data is discussed by using a semi-physical simulation method combining the simulation calculation, vibration monitoring, and machine learning. The problem of phase asynchrony was solved, and strong support was provided for the construction of the ship's acoustic digital twin.

Multi-point Vibration Response Prediction Based on Deep Transfer Learning

Multi-point Vibration Response Prediction Based on Deep Transfer Learning