国际机械系统动力学学报(英文)最新文献

This research article introduces a high-order finite element model based on the first-order shear deformation theory to analyze the hygrothermal static responses of nanoscale, multidirectional nanofunctionally graded piezoelectric (NFGP) plates resting on variable elastic foundations. The study considers the material properties of these plates, which are governed by three distinct material laws—Power, Exponential, and Sigmoid as well as various patterns of porosity distribution. The derived governing equations are formulated using Hamilton's principle and incorporate nonlocal piezoelasticity theory, employing a nine-node isoperimetric quadrilateral Lagrangian element capable of handling six degrees of freedom. A comprehensive parametric study is conducted, examining the influence of the small-scale parameter, material exponent for multidirectional grading, variable foundation stiffness, porosity-related exponent, thickness ratio, and the effects of hygrothermal and electrical loading on the NFGP plates, all while considering different boundary conditions. The findings provide valuable insights into the interaction between multidirectional graded smart structures and their foundations under varying hygrothermal and electromechanical conditions, which can significantly enhance the efficiency of designing and developing intelligent structures and systems.

This article deals with the dynamic response analysis and active vibration control of the smart functionally graded material (FGM) composite core plate with FG piezoelectric material (FGPM) surface actuators and sensors. Considering a power law distribution, the mechanical and electrical material characteristics of the FGM and FGPM layers change continually along the thickness plane. The finite element method (FEM) and the first-order shear deformation theory (FSDT) are utilized in the modeling process for the FGM and FGPM layers. In the dynamic analysis, the dynamic response of the sandwich structure under the impact of sinusoidally distributed step load and the corresponding sensor voltage is obtained. To ensure that the simulations are accurate, the findings are compared with previously published research. To analyze the control efficiency of FGPM sensors and actuators on the FGM host structure, the linear quadratic regulator (LQR) controller is utilized. The sandwich structure is considered a multiple-input multiple-output system (MIMO), so sensors and actuators are placed at different locations on the plate surface. The modal strain energy method is utilized to find the appropriate location of the FGPM layers. According to the results of the analysis, it has been determined that piezoelectric material coefficients as well as mechanical properties are extremely important for obtaining optimum control performance from FGPM sensors and actuators. In addition, it is emphasized that active vibration control of FGM plates can be performed effectively with the proper selection of sensors and actuators and their accurate distribution on the plate. These results are expected to contribute to micro-electro-mechanical system (MEMS) sensor and actuator applications, soft robotics applications, and vibration protection and vibration damping applications of nanostructures.

Constitutive modeling is crucial for engineering design and simulations to accurately describe material behavior. However, traditional phenomenological models often struggle to capture the complexities of real materials under varying stress conditions due to their fixed forms and limited parameters. While recent advances in deep learning have addressed some limitations of classical models, purely data-driven methods tend to require large data sets, lack interpretability, and struggle to generalize beyond their training data. To tackle these issues, we introduce “Fusion-based Constitutive model (FuCe): Toward model-data augmentation in constitutive modeling.” This approach combines established phenomenological models with an Input Convex Neural Network architecture, designed to train on the limited and noisy force-displacement data typically available in practical applications. The hybrid model inherently adheres to necessary constitutive conditions. During inference, Monte Carlo dropout is employed to generate Bayesian predictions, providing mean values and confidence intervals that quantify uncertainty. We demonstrate the model's effectiveness by learning two isotropic constitutive models and one anisotropic model with a single fiber direction, across six different stress states. The framework's applicability is also showcased in finite element simulations across three geometries of varying complexities. Our results highlight the framework's superior extrapolation capabilities, even when trained on limited and noisy data, delivering accurate and physically meaningful predictions across all numerical examples.

The fundamental period is a crucial parameter in structural dynamics that informs the design, assessment, and monitoring of structures to ensure the safety and stability of buildings during earthquakes. Numerous machine-learning and deep-learning approaches have been proposed to predict the fundamental period of infill-reinforced concrete frame structures. However, challenges remain, including insufficient prediction accuracy and excessive computational resource demands. This study aims to provide a new paradigm for accurately and efficiently predicting fundamental periods, namely, Kolmogorov–Arnold networks (KANs) and their variants, especially radial basis function KANs (RBF-KANs). KANs are formulated based on the Kolmogorov–Arnold representation theorem, positioning them as a promising alternative to multilayer perceptron. In this research, we compare the performance of KANs against fully connected neural networks (FCNNs) in the context of fundamental period prediction. The mutual information method was employed for the analysis of dependencies between features in the FP4026 data set. Nine predictive models, including KANs, F-KANs, FCNN-2, FCNN-11, CatBoost, Support Vector Machine, and others, were constructed and compared, with hyperparameters determined by Optuna, which will highlight the optimal model amongst the F-KANs models. Numerical results manifest that the highest performance is yielded by the KANs with R² = 0.9948, which offers an explicit form of the formula. Lastly, we further dive into the explainability and interpretability of the KANs, revealing that the number of stories and the opening percentage features have a significant effect on the fundamental period prediction results.

Efficient transient analysis is critical in rotor dynamics. This study proposes the super-element (SE) differential-quadrature discrete-time transfer matrix method (DQ-DT-TMM), a novel approach that eliminates the requirement for initial component accelerations and effectively handles beam and solid finite element (FE) models with high-dimensional degrees of freedom (DOFs) in rotor systems. The primary methodologies of this approach include: (1) For the beam substructure FE dynamic equation, the Craig–Bampton method is employed for the order reduction of internal coordinates, followed by the differential-quadrature method for temporal discretization. Using SE technology, the internal accelerations are condensed into the boundary accelerations, and the transfer equation and matrix for beam SEs are derived. (2) For the solid substructure FE dynamic equation formulated in the rotating reference frame, in addition to applying the procedures used for beam substructures, rigid multipoint constraints are introduced to condense the boundary coordinates for hybrid modeling with lumped parameter components. The transfer equation is subsequently formulated in the inertial reference frame, enabling the derivation of the transfer matrix for solid SEs. Comparative analysis with full-order FE models in commercial software demonstrates the advantages of the SE DQ-DT-TMM for linear rotor systems: (i) Accurately captures system dynamics using only a few primary modes. (ii) Achieves a 99.68% reduction in computational time for a beam model with 1120 elements and a 99.98% reduction for a solid model with 75 361 elements. (iii) Effectively recovers dynamic responses at any system node using recovery techniques. This research develops a computationally efficient framework for the transient analysis of large-scale rotor systems, effectively addressing the challenges associated with high-dimensional DOF models in conventional DT-TMMs.

Thin plate and shell structures are extensively used in aerospace, naval, and energy sectors due to their lightweight and efficient load-bearing properties. Structural Health Monitoring (SHM) implementations are becoming increasingly important in these industries to reduce maintenance costs, improve reliability, and ensure safe operations. This study presents an efficient triangular inverse shell element for thin shell structures, developed using discrete Kirchhoff assumptions within the inverse finite element method (iFEM) framework. The proposed inverse formulation is efficient and requires fewer strain sensors to achieve accurate and reliable displacement field reconstruction than existing inverse elements based on the First Order Shear Deformation Theory (FSDT). These features are critical to iFEM-based SHM strategies for improving real-time efficiency while reducing project costs. The inverse element is rigorously validated using benchmark problems under in-plane, out-of-plane, and general loading conditions. Also, its performance is compared to an existing competitive inverse shell element based on FSDT. The inverse formulation is further evaluated for robust shape-sensing capability, considering a real-world structural configuration under a practicable sparse sensor arrangement. Additional investigation includes defect characterization and structural health assessment using damage index criteria. This research contributes toward developing more reliable and cost-effective monitoring solutions by highlighting the potential application of the proposed inverse element for SHM frameworks designed for thin shell structures.

This research presents a novel approach to pipeline Structure Health Monitoring (SHM) by utilizing frequency response function signals and integrating advanced data-driven techniques to detect and evaluate vibration responses regarding loose bolts, scale deposits within pipelines, and cracks at pipeline supports, aiming to determine the effectiveness of utilizing artificial neural networks (ANN) and an ensemble learning approach in detecting the aforementioned damages through a data-driven approach. The research starts by recording 6500 samples captured by two accelerometers, related to 11 replicated pipeline structural scenarios. The research demonstrated the potential of principal component analysis (PCA) in dimensionality reduction, achieving approximately 81% reduction in data set 1 acquired by accelerometer 1 and around 79.5% in data set 2 acquired by accelerometer 2, without significant loss of information. Additionally, two ANN base models were employed for fault recognition and classification, achieving over 99.88% accuracy and mean squared error values ranging from 0.00006 to 0.00019. A significant innovation of this work lies in the implementation of an ensemble learning approach, which integrates the strengths of the base models, showcasing outstanding performance that was proved consistent across multiple iterations, effectively mitigating the weaknesses of the base models and providing a reliable fault classification and prediction system. This research underscores the effectiveness of combining PCA, ANN, k-fold cross-validation, and ensemble learning techniques in pipeline SHM for improved reliability and safety. The findings highlight the potential for broader applications of this methodology in real-world scenarios, addressing urgent challenges faced by infrastructure owners and operators.

A critical distance parameter is introduced to describe the stress gradient effect of notched metallic structures under random vibration loadings, which is the frequency domain expression of the theory of critical distance (TCD) based line method stress. Fatigue life estimation on notched metallic structures could be carried out by combining this parameter with the spectral method for random vibration fatigue life analysis. The fatigue experiment under random vibration loadings is conducted on two types of notched plate specimens of 7075-T6 aviation-grade aluminum alloy, where both circumstances of large and small stress gradients in the notch region are investigated. Good correlation between the calculated results given by the proposed model and the experimental fatigue life results shows the satisfactory prediction capability on random vibration fatigue life for notch conditions of both steep and mild stress distribution variations.

Cover Caption: The BallBot, a versatile robot system, finds applications in various domains of life. During the operating process, its performance is influenced by parametric configurations, including body mass, chassis size, and ball diameter. Based on a 3D-dynamics model of the Ballbot, a linear-quadratic regulator (LQR) controller is effectively applied to control such an underactuated MIMO system with nonlinear characteristics as the Ballbot. Subsequently, the simulation model is used to assess the effects of changing the initial parametric configuration. Obviously, the body mass of Ballbot significantly impacts the system response time and stability, whereas the ball parameters have a less pronounced effect.

This work aims to identify ways to achieve dynamic performances of a novel double-layer vibration isolation system (DL-VIS) capable of achieving multi-directional isolation and extreme environmental adaptability. A forward modeling approach applicable to complex systems has been developed and analyses of nonlinear dynamic characteristics under different working conditions are performed. First, by integrating with constitutive models in terms of individual elastic elements and the connective relationships within the structure, multidirectional constitutive models for isolation devices are established. Further, the decomposition of linear and nonlinear stiffness components in different directions is performed using the Taylor expansion method. Subsequently, the dynamic response under sinusoidal sweep frequency loading is obtained using the related stiffnesses in the dynamic model and adopting the extended harmonic balance method. The effects of stiffness, damping, and a nonlinear stiffness gradient on the DL-VIS response are thoroughly evaluated. Finally, the vibration isolation performance and nonlinear dynamics under different working conditions are examined, and the proposed dynamic model is experimentally validated. The results indicate that the response of DL-VIS varies significantly under different working conditions, particularly under overload conditions. The nonlinear characteristics lead to wide-band instability near the natural frequency and excellent vibration attenuation performance in multiple directions. The theoretical model agrees well with the experimental results in the nonresonant region and near the first resonant peak, which proves the prediction accuracy in the low-frequency range. These findings provide robust theoretical and technical support for the design and performance optimization of isolation systems.