In the present study, free vibration and aeroelastic stability (flutter) analyses are investigated for a three-directional functionally graded material (FGM) conical panel subjected to a yawed supersonic fluid flow in a thermal environment. It is assumed that the material is fabricated from a non- homogeneous combination of metal and ceramic. To describe the material gradation, it is assumed that the volume fractions of the metal and ceramic vary simultaneously in the meridional, circumferential, and transverse directions based on several distribution patterns. The panel is modeled based on the first-order shear deformation theory (FSDT), and the aerodynamic pressure is estimated according to the piston theory. The governing equations are formulated by using Hamilton’s principle and are solved for several boundary conditions by using the differential quadrature method (DQM). The effects of the mass fraction, material gradation, temperature, and boundary conditions on the natural frequencies and critical aerodynamic pressure (CAP) are examined through a parametric study. It is concluded that the flutter boundaries are affected more significantly by the material gradation in the transverse direction than by the material gradation in the meridional and circumferential directions. The present study is the first theoretical work regarding the free vibrational and aerothermoelastic stability characteristics of a three-directional FGM conical panel subjected to a yawed supersonic fluid flow.
Acoustic impulse response measurement campaigns conducted in performance venues under ISO 3382-1 are susceptible to a class of systematic field errors, including receiver position labelling swaps, spatial co-ordinate entry mistakes, signal-to-noise ratio failures at low frequencies, and level calibration interruptions, that have received little systematic documentation and whose downstream impact on quantitative analyses has not previously been characterised. This study presents a systematic data quality audit framework demonstrated on a 171-seat conference hall (Başöğretmen Atatürk Conference Hall, V = 1511 m 3 ); the methodology is applicable in principle to any ISO 3382-1 campaign pending multi-venue validation. Six independent error categories were identified and corrected through cross-checking between recorded source-receiver distances, architectural co-ordinate databases, Barron’s revised room acoustics theory, and physical parameter range constraints. Five of these errors were genuine field issues discovered through the audit; one (the G calibration interruption) was deliberately introduced as a blinded controlled-validation case to verify the detection mechanism against known ground truth. The impact of each error category on downstream machine learning prediction accuracy was quantified using a gradient boosting ensemble framework with cross-validated R 2 and held-out validation R 2 as performance metrics. Correcting these errors improved downstream cross-validated prediction performance, most clearly for the spatially-driven parameters: cross-validated R 2 for T 30 and ST rose by amounts exceeding the cross-validation uncertainty, and the calibration correction improved G prediction. Given the single-venue sample (54 training rows, 3-fold cross-validation, with fold standard deviations of 0.2–0.5), these effects are reported as directional evidence that undetected field errors propagate into spatial prediction models, rather than as precise effect sizes. The audit framework is formalised as six detection algorithms and an on-site verification protocol, providing a systematic illustration of how each measurement error category propagates through a machine learning acoustic prediction pipeline. All quantitative results derive from a single 171-seat conference hall; the reported Δ R 2 values are venue-specific effect sizes that carry substantial cross-validation uncertainty and await cross-venue replication.
Vibration analysis plays a crucial role in ensuring the reliability, safety and efficient operation of rotating machinery by enabling early fault detection and condition monitoring. In the present study, vibration characteristics of a high-speed vapor blower-gearbox assembly operating at speed from 1,000 to 16,000 rpm were investigated. Frequency-domain analysis and impact testing were performed using multi-axial acceleration data collected from key bearing locations at both the drive-end and non-drive-end to identify the primary excitation mechanisms. Experimental data reveal that structural resonance, rather than rotational imbalance, predominantly influences the peak vibration amplitudes. The critical natural frequencies, which are linked to the casing support, low-speed shaft coupling and base structure, have been identified in the range of 12,000 to 15,000 rpm. To address the issue of resonance amplification, ribs were strategically integrated into the structure, resulting in an additional mass of 2.5-3.0 kg to enhance stiffness. This modification effectively shifted the natural frequencies of the system away from the operational velocity range, yielding a 30–40% reduction in vibration levels. These findings demonstrate that stiffness optimization enhances dynamic stability, providing a robust foundation for condition monitoring and prognostics of high-speed turbo machinery.
In this paper, an exact analytical solution is presented to study the vibration of isotropic single-span and multi-span beams based on Levinson’s third-order shear deformation theory. The displacement field of the beam, which accounts for the variation of shear stress along the beam thickness, is defined. Based on Hamilton’s principle, the governing equations and boundary conditions of the problem are derived. The dynamics of beam vibration is captured by two coupled partial differential equations. These equations are solved by first separating the time and space response and then decoupling the two equations to obtain a single, sixth-order differential equation for beam deflection. An analytical solution is presented for this equation, which is able to exactly satisfy the beam’s boundary conditions. The natural frequencies and mode shapes are calculated for different boundary conditions and geometries of the beam and are compared with the results of Euler-Bernoulli, Rayleigh, and Timoshenko beam theories. The effectiveness of this solution for analysis of multi-span beams has been demonstrated by applying it to a two-span beam with various support arrangements. Finally, based on the results obtained, some conclusions are presented.
The combined influences of porosity, elasticity of tangential constraints of ends, initial geometric imperfection, elastic foundations, and elevated temperatures on the nonlinear free vibration of sandwich beams made of functionally graded material (FGM) and homogeneous layers are investigated in this paper. Two sandwich models corresponding to FGM face sheets and core layer are considered. The pores are assumed to distribute into FGM layers according to even and uneven types. The effective properties of porous FGM are determined using a modified rule of mixture. The motion equations are established based on the Timoshenko beam theory including von Kármán nonlinear terms, initial geometric imperfection and interaction from elastic foundations. These equations are solved by analytical solutions in combination with Galerkin method to obtain a nonlinear ordinary differential equation. Fourth–order Runge–Kutta scheme is applied to solve nonlinear differential equation and determine the nonlinear frequencies. Parametric studies are carried out to assess various effects on both natural frequencies and frequency ratios of FGM sandwich beams. The results demonstrate that the support of elastic foundations makes the natural frequencies higher and frequency nonlinearity less significant. The study also find that the existence of geometric imperfection increases the natural frequencies and weakens the frequency nonlinearity in the deep region of dynamic deflection of the sandwich beams.
Energy based schemes for damping ratio identification in linear vibrating systems are proposed for the cases of both underdamped and overdamped systems, together with an energy based settling criterion. For the underdamped case, the total energy obtained from the free response to initial conditions is used to estimate the damping ratio. The method was validated numerically and compared with the logarithmic decrement and half power bandwidth method. The energy based damping identification method estimates resulted in errors below 1%, which were significantly lower than those of the comparison methods. The robustness of the damping ratio estimation was studied as well. For overdamped systems, two formulas for damping identification were proposed. The overdamped formulation applies to free responses initiated by an initial displacement, and numerical results showed errors under 2%. In addition, three energy based settling criteria that are insensitive to initial conditions were introduced. When compared with the standard 2% settling-time criterion, it showed consistent agreement with the 2% vibration amplitude band. The results indicate that the proposed energy based measures provide accurate and practical tools for damping identification and settling assessment.
Tool wear during turning operations can compromise surface integrity, dimensional precision, and production efficiency. This study introduces a machine learning-based framework for identifying tool wear conditions using the Support Vector Machine (SVM) algorithm. Vibration signals were acquired through impact excitation and analyzed in both time and frequency domains to extract statistical features representative of wear stages. To improve classification accuracy, Recursive Feature Elimination (RFE) was applied for optimal feature selection. The model was evaluated using a 5-fold cross-validation technique. Results indicate that the highest accuracy was achieved using frequency-domain features with RFE, yielding 100% accuracy in binary classification and 97.2% in multiclass classification. These outcomes emphasize the potential of the proposed approach as a reliable and non-invasive solution for tool wear monitoring in turning applications.
To analyze the vibration response of the catenary under strong wind and icing conditions caused by wind speed variations, a 7-span catenary finite element model was established based on the ANSYS platform. The pulsating wind field acting on the contact network was numerically simulated by combining the spectral representation method. Firstly, wind-induced vibration analysis is conducted under strong wind conditions to reveal the correlation between wind speed and vibration characteristics. Secondly, an ice thickness calculation model is developed to evaluate the impact of icing on the catenary. Finally, typical icing wind speeds are applied to comprehensively analyze the response characteristics under wind-ice coupling. The results indicate that under strong wind conditions, lateral displacement dominates the catenary vibration, with amplitudes significantly larger than vertical displacements (maximum lateral: 249.63 mm, maximum vertical: −4.94 mm). The dominant frequencies are 1.14 Hz (lateral) and 1.04 Hz (vertical), respectively. Under icing conditions, vertical displacement of the catenary increases significantly with ice thickness (sag exceeds 0.1 m with 25 mm of ice). Under wind-ice coupling, within typical icing wind speed ranges, lateral displacement changes insignificantly with ice thickness, but vertical sag becomes the primary risk. The findings provide a theoretical basis for differentiated safety assessment and the development of protective strategies for catenary systems under complex climatic conditions.
The rack-and-pinion drive mechanism (RPD) is a critical transmission component in the battery swap system (BSS) of electric heavy trucks (EHTs). The transmission mechanism in the RPD typically operates under conditions of low speed, speed fluctuations, and short-term sampling, posing challenges for accurate fault diagnosis. To address these issues, this study proposes a fault diagnosis method based on the Heterospectral-Symmetric-Derived Point Cloud Feature Tree (HS-PCFT): First, multiple SDP (symmetric point pattern) variants are generated using Hybrid-dimensional vibration signals to construct multi-perspective symmetric patterns. Based on this, these images are converted into coordinate points and a structured point cloud feature tree is constructed to capture geometric differences across different fault modes. Subsequently, the point cloud is voxelized and input into a lightweight 3D convolutional neural network (3D CNN) for classification. The fault diagnosis algorithm was validated on the BSS platform, achieving an accuracy rate of 97.83%. Comparative studies indicate that the proposed method outperforms traditional 2D/3D networks and conventional machine learning methods. This research proposes an effective representation path from signals to structured geometry, providing an effective solution for fault diagnosis in low-speed and complex industrial environments.
The variability in whole-body vibration (WBV) of a seated person measured in the passenger car was evaluated. The vibration variability was evaluated as a function of the International Roughness Index (IRI). Vibration was measured on 58 test vehicles of 6 different categories. A two-parameter regression function relating the overall vibration total value to its 95% confidence interval and IRI was estimated. The identified regression function may help detect the IRI threshold. The IRI threshold could be derived from the boundaries of comfort reactions to vibration environments, as specified in ISO 2631-1: 1997. Calculation of a 95% confidence interval for the measured overall vibration total value at the seat surface showed that a single IRI value can correspond to vibration values that span up to comfort levels according to ISO 2631-1:1997.
The failure due to flow induced vibrations in heat exchangers occurs when shell side cross flow velocity exceeds critical velocity at instability. This ultimately results in excessive vibrations and failure of the tubes because of fretting wear. It is necessary to know the critical velocity of heat exchanger tube arrays to avoid the risk of failure as a result of flow induced vibrations. This is possible by designing the shell and tube heat exchangers so that they reach instability at higher velocities. The study aims at conducting an experimental program to determine critical velocity at instability for three normal triangular tube bundles having pitch ratio of 2.1. Plain tube configuration, a finned tube configuration having fin density of 4 fpi and a finned tube configuration having fin density of 10 fpi are tested in the water. The vibration characteristics of normal triangular tube bund are compared with available results for other tube array patterns, to compare their instability thresholds. The design changes made in the outer box are also assessed to find if fluid elastic instability is reached at lower flow rates. The research outcomes indicate that design changes in the setup have resulted in reaching clear instability for all the normal triangular tube bundles. The instability thresholds for plain bundles when compared against finned bundles show that instability is reached at higher velocity for finned bundles. The comparison of vibration characteristics of different tube array patterns indicate that instability thresholds for normal triangular tube array configuration are delayed than other tube array configurations including normal square, rotated square and parallel triangular.
This study presents the design, development, and acoustic characterisation of a tessellated polyform absorber composed of perforated panels (PPs) made from jute-reinforced rigid composites. A full factorial optimisation involving 72 combinations—spanning 12 geometric variants, six cavity depths, multiple perforation ratios, and orifice diameters—was conducted using impedance tube measurements. Integration of jute fleece within the backing cavity, combined with an L-tromino tessellation, enabled noise absorption (NAC ≥0.9) across 400–6300 Hz in a single modular structure. To explain deviations from the classical Maa model, the transfer matrix was progressively refined: T 1 accounted for orifice irregularities caused by jute-fibre fraying; T 2 incorporated additional damping from jute fleece in the cavity. CFD simulations were conducted to qualitatively examine the influence of tessellated geometry on local airflow patterns and edge-induced vortical structures around the L-tromino elements. The analysis highlights how geometric discontinuities influence local viscous interaction, supporting the proposed topology-driven acoustic design. The resulting tessellated polyform structure demonstrates high acoustic efficiency along with favourable mechanical strength and fire-retardant characteristics of SMC-based natural fibre composites. This integrated approach offers a sustainable, geometry-driven solution suitable for precision acoustic environments such as recording studios and controlled architectural spaces.
The expansion of industrial and logistics facilities increases the impact of environmental noise on residential areas, making it essential to apply accurate and reliable methods for noise assessment and prediction. The aim of this study is to model the dispersion of noise generated during warehouse operations and to evaluate the suitability of machine learning methods for predicting noise levels. Noise calculations were performed using Inter-Model Integration (IMMI) software according to the ISO 9613-2 methodology and the requirements of the Lithuanian hygiene standard HN 33:2011. The spatial distribution of noise was visualized in a GIS (Geographic Information Systems) environment by calculating zonal raster statistical indicators. Predictive modeling was performed using five machine learning algorithms - Random Forest, M5P, Multilayer Perceptron, SMOreg, and Linear Regression - implemented within the WEKA environment. Results indicate that noise generated by warehouse operations near the closest residential areas did not exceed the regulatory limits, with the highest noise levels observed in areas of high traffic flow. The machine learning (ML) algorithms demonstrated very high prediction accuracy - all tested models achieved a correlation coefficient (r) above 0.97. ML analysis revealed particularly high predictive accuracy when using Random Forest (r = 0.9984) and M5P (r = 0.9898) algorithms. These findings confirm that integrating zonal raster statistical indicators with machine learning methods is an effective tool for analyzing industrial noise dispersion and can be successfully applied for practical environmental noise assessment and planning purposes.
Noise levels in hospital emergency department (ED) halls frequently exceed recommended limits, adversely affecting patient recovery and staff performance. Although acoustic materials are widely used for noise control, limited guidance is available on how to place them systematically and cost-effectively in functionally complex spaces with unevenly distributed noise sources. This study addresses that gap through a three-stage framework. First, baseline noise levels and reverberation times were measured in an ED hall in China. Second, ten acoustic treatment configurations varying in material type (absorptive, diffusive, and standard reflective panels), coverage, and ceiling placement above noise hotspots were simulated using the ray acoustics module in COMSOL Multiphysics. Third, an improved Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), incorporating Mahalanobis distance and hybrid entropy–Delphi weighting, was used to rank the configurations on the basis of renovation cost, reverberation time (RT60), and noise-exposure indicators (quietness coefficient and noise coefficient). The optimal configuration combined absorptive and diffusive panels in a checkerboard pattern directly above the major noise sources. This scheme achieved the best balance between acoustic performance and cost and outperformed layouts that simply maximized absorptive coverage. Notably, the combined use of absorptive and diffusive materials substantially reduced noise exposure even though it did not yield the lowest RT60. These findings indicate that, in ED halls, strategic material placement is more important than total material quantity for acoustic optimization.
To address the problems of slow steady-state response, poor optimization accuracy, and easy falling into local optima in the parameter optimization of hydraulic electric energy-feeding suspension systems, an improved cloud particle swarm optimization-cuckoo search (CPSO-CS) algorithm was proposed. The algorithm innovatively integrated cloud theory and Logistic chaotic initialization into the traditional particle swarm optimization (PSO) and, combined with the levy flight local search mechanism of cuckoo search (CS), achieved a balance between global exploration and local development. Under Class-B, Class-C, and convex road excitations, the dynamic characteristics of passive control, sliding mode control (SMC), PSO control, and CPSO-CS control strategy applied to the hydraulic electric energy-feeding suspension were analyzed. Vertical body acceleration, suspension dynamic deflection, and tire dynamic load were selected as the evaluation indices. The results indicate that the proposed improved algorithm enhances both the optimization accuracy and convergence speed of the suspension system’s dynamic performance indices, thereby significantly improving the ride comfort of the hydraulic electric energy-feeding suspension system.
The present study analyses plate structures subjected to hydrostatic load. The plates are modelled and analysed using finite element method-based software, ANSYS Workbench . The unstiffened plate structure is first examined, and responses are recorded. Further, the plate is stiffened using one, cross (two) and three flat stiffeners, considering almost the same volume of material as used in the modelling of an unstiffened plate. The static response of plate structures is presented and compared by keeping all edges of the plates either hinged or clamped. The thickness of the plate or stiffener is kept at a minimum of 6 mm as specified in IS 800:1984 (Clause no. 3.8.2). The outstand of the stiffeners are modelled based on IS 800: 2007 (Clause no. 8.7.1.2). The different configurations of stiffened plates are further compared with unstiffened plate, also the stiffeners placements are changed between horizontal and vertical (except in cross stiffened plate). The spacing between the central and extreme stiffeners is kept constant, and the extreme stiffeners are placed at the centre of the central stiffener and the extreme edge of the plate. The results highlight the influence of stiffening configurations or stiffener eccentricity in decreasing deflection and stress under hydrostatic load. It is found that the eccentric position produces a serious impact on the distribution of stiffness, transfer of stress and the overall structural response of the plate. The study offers helpful information for improving plate-stiffener systems in real, practical applications involving hydrostatic pressure.
Rolling bearing fault diagnosis under complex operating conditions forms the essential foundation for the predictive maintenance of rotating machinery. However, traditional methods are often overwhelmed by strong noise, and constrained by the empirical risk minimization (ERM) principle, leading to significant overfitting in small sample learning scenarios. To address the aforementioned limitations, a lightweight diagnostic model integrating S-Transform (ST), convolutional neural network (CNN), and support vector machine (SVM) is proposed in this paper. Time-frequency features are extracted by leveraging the multi-resolution characteristics of the ST, deep feature mapping is performed through a customized CNN, and SVM is introduced to construct the maximum-margin classification hyperplane based on the structural risk minimization (SRM) principle. The experimental results illustrate that the method exhibits exceptional diagnostic accuracy under intense noise and small sample sizes. Randomized subset cross-validation confirms that this architecture effectively eliminates the interference of sampling randomness. Consequently, the ST-CNN-SVM model demonstrates high statistical stability.
The differential transform method (DTM) has been presented for solving relativistic equation. The proposed method is easier and more reliable than usual DTM and other widely used techniques. The algebraic equations related to unknown coefficients of the proposed solution are linear, which simplifies the determination of them. The obtained results have been compared to those obtained by the numerical method (RK fourth order), the differential transform method, the modified Lindstedt–Poincare method and the harmonic balance method. The comparisons demonstrate that the proposed solution is closer to the numerical solution than the other methods.
This study presents a semi–analytical approach to deal with the large–amplitude free vibration of sandwich rectangular plates with boundary edges elastically restrained against in-plane displacements exposed to thermal environments. Sandwich plate is constructed from carbon nanotube (CNT) reinforced composite core layer and homogeneous face sheets. The volume percentage of CNTs in the core layer is varied according to functional rules. The effective properties of nanocomposite core are determined by means of an extended version of the rule of mixture. Motion and compatibility equations are established on the basis of first order shear deformation theory (FSDT) including von Kármán nonlinearity, initial geometric imperfection and interactive pressure from Winkler–Pasternak foundations. These equations are solved by applying analytical solutions and Galerkin method to derive a nonlinear ordinary differential equation of time variable. Fourth–order Runge–Kutta method is taken up for solving the nonlinear ordinary differential equation and seeking the nonlinear frequencies of sandwich plates. The results detect that there exists a particular value of thickness ratio of layers for which natural frequency and ratio of the nonlinear to linear frequencies of the sandwich plates with both CNT–rich interfaces are respectively the highest and smallest. The study also finds that the support of elastic foundations increases natural frequencies and decreases the ratio of nonlinear to linear frequencies of the sandwich plates. Besides, it is elicited that the frequency nonlinearity is more significant when the edges are restrained more rigorously and temperature is more elevated.
Vibrations are an inevitable phenomenon in various industries and human daily life. Different methods of vibration control can be divided into four categories, active, semi-active, passive and combined. Passive vibration control methods are widely used in industry due to their easier construction and easier use compared to other methods. There are several methods of passive vibration control that can be divided into different types of isolators, viscous dampers, viscoelastic dampers, metal dampers, friction devices, tuned fluid dampers, tuned mass dampers and impact dampers. Impact dampers are one of interesting passive vibration control methods. In this paper, an overview of impact dampers are provided. First, a brief history of impact damper is presented. Then, various types of impact dampers are presented and discussed. After that, applications of impact dampers in various industries are expressed. Then, various analysis methods of impact dampers are presented. After that, new developed impact dampers are introduced. Finally, conclusions are presented.
