国际机械系统动力学学报(英文)最新文献

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.

Adolescent idiopathic scoliosis (AIS) is a sideway curvature of the spinal column combined with a vertebral rotation that usually occurs in adolescents without any known causes. Bracing, the most common conservative treatment of AIS, has not fully exploited the benefits of the active control approaches powered by artificial intelligence (AI), although AI has entered a wide range of applications. The correction forces exerted by the brace are controlled passively by regulating the tightness of the brace's strap. Besides, training the learning-based control methods using a virtual model is of high importance in the AIS brace treatment, since training using trial and error on human subjects may result in unexpected pressure and injuries on the patient's torso. However, digital twin (DT) modeling, an emerging technology, has not been implemented into the AIS brace treatment yet. In this paper, reinforcement learning-based position-based impedance control (RLPIC) is proposed to enable a robotic brace to learn the desired physical interaction between the robotic brace and the human torso. A five-dimensional (5D) three-layer DT is also developed to be used for training the RLPIC in a simulated environment. The 5D three-layer DT consists of a physical system, a three-layer digital model of the physical system, including the robotic brace, human torso, and the physical human–robot interaction (HRI), a bidirectional connection between them, and an optimization dimension. A neural network-based regression model is also proposed to estimate the unknown parameters of the digital model. Numerical simulations and real-time experiments are performed to validate the 5D three-layer DT model. The proposed RLPIC trained using the 5D three-layer DT is verified using numerical simulations in terms of position tracking, velocity tracking, and HRI control. It is concluded that the proposed learning-based interaction control approach can improve the HRI control by learning the desired interaction in a simulated environment.

Traditional boundary element method (BEM) faces significant challenges in addressing dynamic problems in thin-walled structures. These challenges arise primarily from the complexities of handling time-dependent terms and nearly singular integrals in structures with thin-shapes. In this study, we reformulate time derivative terms as domain integrals and approximate the unknown functions using radial basis functions (RBFs). This reformulation simplifies the treatment of transient terms and enhances computational efficiency by reducing the complexity of time-dependent formulations. The resulting domain integrals are efficiently evaluated using the scaled coordinate transformation BEM (SCT-BEM), which converts domain integrals into equivalent boundary integrals, thereby improving numerical accuracy and stability. Furthermore, to tackle the challenges inherent in thin-body structures, a nonlinear coordinate transformation is introduced to effectively remove the near-singular behavior of the integrals. The proposed method offers several advantages, including greater flexibility in managing transient terms, lower computational costs, and improved stability for thin-body problems.

The performance and lifespan of Li-ion batteries used in electric vehicles are influenced by operating and environmental conditions. An understanding of the mechanisms leading to performance degradation and capacity fading can aid in the design of better battery systems. In the present study, numerical models are developed to estimate the capacity fading, battery performance, and residual life. Furthermore, key associated parameters are identified as state of charge, charging protocols, and temperature. Later on, a deep machine learning (DML) model consisting of one input, four hidden, and one output layer is developed to estimate the residual life of a battery system. The five input parameters considered include voltage, current, temperature, number of cycles, and time, apart from residual life as the output parameter. The proposed DML model consists of five dense layers and three dropout layers with 2889 trainable parameters in total, with higher neuron counts in initial layers to process diverse inputs and fewer neurons in later layers to ensure compact feature representation as well as to make better and faster predictions. Results from the numerical and DML models are compared to the reported experimental results, where good agreement is observed. Thus, the developed model is tested on Lithium based Nickel Manganese Cobalt Oxide and Nickel Cobalt Aluminum Oxide batteries, for which parametric studies are performed to investigate the influence of the operating temperature, rate of charge/discharge, and pulse charging on the battery life. Therefore, the technologies proposed in this study can contribute to the development of intelligent battery management systems, enabling enhanced performance, and hence prolonged life of battery systems.

This study proposes a computational framework for developing a multibody dynamics (MBD) model to accurately predict the vibration behavior of front-loading washing machines. The framework integrates component-level experiments and mathematical modeling to characterize the dynamic behavior of key components, including the free-stroke damper, connecting bushing, and gasket, which significantly influence the machine's vibration. Simplified, yet precise, mathematical models were developed and validated against experimental data to represent these components' dynamic characteristics. The validated models were then integrated into a comprehensive MBD model of a front-loading washing machine. This model was further verified by comparing its predicted vibrations with experimental results obtained from actual washing machines. A parametric study assessed the model's accuracy under various unbalanced mass conditions and revolutions per minute ranges, which revealed that the model is capable of generalization across different operating scenarios. Although some errors remain in specific cases involving phase differences, the overall average error is 20.11%, with a standard deviation of 4.10%. These results demonstrate that the proposed framework effectively captures the vibration behavior of front-loading washing machines, offering a reliable tool for enhancing design and operational efficiency.