国际机械系统动力学学报(英文)最新文献

Time delays frequently arise in active control systems due to sensor sampling, signal transmission, and actuator response, making their effects on system dynamics non-negligible. This paper investigates how velocity feedback time delay influences the nonlinear dynamic characteristics of a maglev train subjected to unsteady aerodynamic forces. First, a time-delay dynamic model of the maglev system under unsteady aerodynamic forces is developed. Then, using the method of multiple scales (MMS), the frequency response equations for the maglev train are derived, and the steady-state solutions are evaluated for a stability assessment. Finally, the influence mechanism of time delay on the system's nonlinear vibration is explored under various parameters, such as unsteady aerodynamic force, train mass, displacement, and velocity feedback gain coefficients, with a particular focus on mitigating adverse effects stemming from the time delay. The results reveal that time delay plays a pivotal role in determining the vibration amplitude and overall system stability and that its influence exhibits periodic characteristics. In practical applications, judiciously tuning the time delay can help avoid its adverse impact. This study offers theoretical insights into the severe vibrations observed in real maglev operations and offers guidance for designing and optimizing control strategies to enhance ride comfort and system reliability.

The advancement of artificial intelligence (AI) in material design and engineering has led to significant improvements in predictive modeling of material properties. However, the lack of interpretability in machine learning (ML)-based material informatics presents a major barrier to its practical adoption. This study proposes a novel quantitative computational framework that integrates ML models with explainable artificial intelligence (XAI) techniques to enhance both predictive accuracy and interpretability in material property prediction. The framework systematically incorporates a structured pipeline, including data processing, feature selection, model training, performance evaluation, explainability analysis, and real-world deployment. It is validated through a representative case study on the prediction of high-performance concrete (HPC) compressive strength, utilizing a comparative analysis of ML models such as Random Forest, XGBoost, Support Vector Regression (SVR), and Deep Neural Networks (DNNs). The results demonstrate that XGBoost achieves the highest predictive performance ($$), while SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) provide detailed insights into feature importance and material interactions. Additionally, the deployment of the trained model as a cloud-based Flask-Gunicorn API enables real-time inference, ensuring its scalability and accessibility for industrial and research applications. The proposed framework addresses key limitations of existing ML approaches by integrating advanced explainability techniques, systematically handling nonlinear feature interactions, and providing a scalable deployment strategy. This study contributes to the development of interpretable and deployable AI-driven material informatics, bridging the gap between data-driven predictions and fundamental material science principles.

Ultra-precision machining (UPM) has been extensively employed for the production of high-end precision components. The process is highly precise, and the associated cost of production is also high. Optimization of machining parameters in UPM can significantly improve machining efficiency and surface roughness. This study proposes an innovative approach that couples transfer matrix methods for multibody systems (MSTMM) and particle swarm optimization (PSO) to optimize the machining parameters, aiming to simultaneously improve the machining efficiency and surface roughness of UPM machined components. Initially, the dynamic model of an ultra-precision fly-cutting (UPFC) machine tool was developed using MSTMM and validated by machining tests. Subsequently, the PSO algorithm was employed to optimize the machining parameters. Based on the optimized parameters, a 40% reduction in machining time and an 18.6% improvement in surface roughness peak-to-valley (PV) value have been achieved. The proposed method and the optimized parameters were verified through simulations using the MSTMM model, resulting in a minimal error of only 0.9%.

Predicting fatigue life with precision requires more than isolated evaluations of mechanical properties; it requires an integrated approach that captures the interdependencies between various parameters, including elastic modulus, tensile strength, yield strength, and strain-hardening exponent. Neglecting these correlations in sensitivity analyses can compromise prediction accuracy and physical interpretability. In this study, we introduce a dependency-aware sensitivity analysis framework, assisted by machine learning-based surrogate models, to evaluate the contributions of these mechanical properties to fatigue life variability. Tensile strength emerged as the most influential parameter, with significant second-order interactions, particularly between tensile and yield strength, highlighting the central role of coupled effects in fatigue mechanisms. By addressing these interdependencies, the proposed approach improves the reliability of fatigue life predictions and offers a solid foundation for the optimization of metallic components subjected to cyclic stresses.

The supercritical drive shaft is becoming increasingly popular in helicopter transmission system. Dry friction dampers are specially employed to ensure the supercritical shafts crossing the critical speed safely. Due to design tolerances, manufacturing errors and time-varying factors, the parameters of the damper are inherently uncertain, affecting the safety performance of the rotor system. This paper incorporates these parameter uncertainties to investigate the dynamic response uncertainties of a supercritical shaft and dry friction damper system, which is characterized by its high dimensionality and nonlinear behaviors of rub-impact and dry friction. The nonintrusive Polynomial Chaos Expansion (PCE) is adopted to achieve the propagation of uncertainties in the rotorsystem. To achieve efficient uncertainty quantification for this high-dimensional nonlinear system, a double-layer dimensionality reduction algorithm combining modal superposition with sparse grid technique has been applied. In the computational workflow, the inner layer uses modal superposition and the outer layer uses sparse grid techniques. The stochastic dynamic response of the rotorsystem is analyzed considering the uncertainty of five design parameters of the damper. Furthermore, as a post-processing of the PCE coefficients, the Sobol global sensitivity analysis is conveniently conducted. The influence of individual parameters or groups of parameters on the dynamic response is studied. A multi-objective optimization design for the key parameters is then carried out based on the established PCE model. The dynamic model and optimization design method are verified by experiments. The results will benefit uncertainty quantification analysis of high-dimensional nonlinear rotorsystem.

Infrared suppression (IRS) devices for naval ships play a crucial role in reducing the infrared radiation signature of high-temperature exhaust, thereby enhancing the survivability of ships against infrared-guided weapons. This paper provides a comprehensive review of recent advancements in the design and optimization of IRS devices. The primary research problem of the devices is the need to effectively suppress infrared radiation from ship exhaust gases, which are the main targets of infrared-guided missiles. To achieve this, the paper analyzes the infrared characteristics of exhaust systems from the perspectives of fluid dynamics, radiation sources, and radiation transmission, with a detailed explanation of the associated physical mechanisms and computational methods. The working principles and structural features of commonly used IRS devices, such as eductor/diffuser (E/D) devices and DRES-Ball devices, are introduced, with a focus on the design and optimization of key components, including nozzles, mixing diffusers, and optical blocking obstacles. Advanced suppression technologies, such as water injection and aerosol particle dispersion, are also discussed as auxiliary methods to enhance the infrared stealth capabilities. The review highlights that the advanced cooling mechanisms and optical property modifications can significantly reduce the infrared radiation of exhaust plumes. Furthermore, the paper identifies several challenges and future research directions, including the performance impacts of multi-device coordinated operation, the development of intelligent adaptive control systems, and the pursuit of lightweight and modular designs to meet the high mobility requirements of modern naval ships. This review aims to provide theoretical support and technical guidance for the practical design of IRS devices, offering valuable insights for the development of next-generation infrared stealth technologies for naval vessels.

In rest-to-rest maneuvers, input shapers like the double step (DS), zero vibration (ZV), and zero vibration derivative (ZVD) are widely utilized to eliminate residual vibrations in single-mode systems. These shapers can be used to eliminate residual oscillations in multimode systems, given that the higher frequencies are odd multiples of the system's fundamental frequency. However, the natural frequencies depend on the physical properties of the system, and such ratios cannot be guaranteed. In this study, an analytical frequency modulation technique is proposed to eliminate the residual oscillations of a double pendulum using a modified single-mode shaper. The proposed technique is based on altering the natural frequencies of the system, forcing the odd multiple ratio. This involves modifying a single-mode double-step (SMDS) input shaper by adding scaled state variables, first and second angles, to the original shaper. This addition allows the user to choose the first natural frequency and force the second natural frequency to be an odd multiple of the chosen frequency. To apply the proposed technique, the double pendulum nonlinear equations of motion are derived, linearized, and then solved analytically using modal analysis. The scaling parameters used to modify the natural frequencies are then solved analytically. To prove the concept, several numerical simulations with randomly selected parameters are presented and then experimentally tested on a scaled overhead crane. The numerical and experimental results demonstrate the effectiveness of the proposed technique.

This study investigates the hydraulic performance of an Ogee spillway under varying flow rate conditions, gate opening heights, and spillway widths. Numerical simulations using Flow-3D, incorporating the (k-ε) turbulence model and Large Eddy Simulation (LES), were employed alongside surrogate models using MATLAB codes and LP-TAU to predict flow behavior. The analysis focused on pressure distribution, water velocity, and shear stress variations across seven sensor locations along the spillway. Results indicate that pressure distribution generally decreases with increasing flow rate but rises with greater gate opening height or spillway width. A reduction in gate opening height lowers the pressure in the initial region but increases it downstream. Two negative pressure zones were identified, one at the Ogee curve and another at the downstream sloping section, highlighting potential cavitation risks. Comparisons with experimental data confirmed a strong correlation, with minor discrepancies in specific sensors under varying conditions. The study demonstrates that numerical modeling, particularly using the (k-ε) turbulence model in Flow-3D, effectively assesses the hydraulic performance of controlled Ogee-type spillways.

Health monitoring and damage detection for important and special infrastructures, especially marine structures, are one of the important challenges in structural engineering because they are subjected to corrosion and hydrodynamic loads. Simulation of marine structures under corrosion and hydraulic loads is complex; thus, a combination of point cloud data sets, validation finite element model, parametric studies, and machine-learning methods was used in this study to estimate the damaged surface of retaining reinforced concrete walls (RRCWs) and the load-carrying capacity of RRCWs according to design parameters of RRCWs. After validation of the finite element method (FEM), 144 specimens were simulated using the FEM and the obtained displacement-control loading. Compressive strength, thickness of RRCWs, strength of reinforcement bars, and ratio of reinforcement bars were considered as the design parameters. The results show that the thickness of RRCWs has the most effect on decreasing the damaged surface and load-carrying capacity. Furthermore, the results demonstrate that Gene Expression Programming (GEP) performs better than all models and can predict the damaged surface and load-carrying capacity with 99% and 97% accuracy, respectively. Moreover, by decreasing the thickness of RRCWs, the damaged surface is reduced to 2.5%, and by increasing the thickness, the load-carrying capacity is increased to 51%–59%.

Operational modal analysis (OMA) is vital for identifying modal parameters under real-world conditions, yet existing methods often face challenges with noise sensitivity and stability. This study introduces NExT-LF, a novel method that combines the well-known Natural Excitation Technique (NExT) with the Loewner Framework (LF). NExT enables the extraction of Impulse Response Functions from output-only vibration data, which are then converted into the frequency domain and used by LF to estimate modal parameters. The proposed method is validated through numerical and experimental case studies. In the numerical study of a two-dimensional Euler–Bernoulli cantilever beam, NExT-LF provides results consistent with analytical solutions and those from standard methods, NExT with Eigensystem Realization Algorithm (NExT-ERA) and stochastic subspace identification with canonical variate analysis. Additionally, NExT-LF demonstrates superior noise robustness, reliably identifying stable modes across various noise levels where NExT-ERA fails. Experimental validation on the Sheraton Universal Hotel is the first OMA application to this structure, confirming NExT-LF as a robust and efficient method for output-only modal parameter identification.