This paper investigates an innovative negative-stiffness device (NSD) that modifies the apparent stiffness of the supported structure for seismic isolation. The NSD comprises a lower base on the bottom and a cap on the top, together with a connecting rod, vertical movable wall, and compressed elastic spring, as well as circumferentially arranged, pretensioned external ropes, and inclined shape memory wires. This configuration can deliver negative stiffness and energy dissipation in any direction within the horizontal plane. A numerical model of the device is developed through a two-step semirecursive method to obtain the force–displacement characteristic relationship. Such a model is first validated through comparison with the results obtained via the commercial software ADAMS. Finally, a large parametric study is performed to assess the role and the influence of each design variable on the overall response of the proposed device. Useful guidelines are drawn from this analysis to guide the system design and optimization.
To obtain accurate fatigue life results for construction machinery components, acquiring load spectra is crucial, as their authenticity and validity directly determine the precision of the analysis. In working conditions, component attitudes change continuously, but they remain static on the vibration test rig (VTR), so the acquired target signals should match with the actual component attitudes in the driving signal generation. This paper proposes an efficient and economical simulation-based virtual VTR for fatigue analysis of dozers. First, the relationship between the push arm rotation angle and the cylinder stroke is established, since the cylinder strokes can be measured easily in data acquisition experiments. Second, load decomposition is used to determine the attitude relationship between virtual VTR conditions and actual conditions, and target signals are calculated based on this attitude relationship and measured data. According to the system’s frequency response function, the driving signals are iterated until the system’s response signals converge with the target signals. Finally, the iteratively obtained load spectra are utilized for fatigue life analysis. The results show that the virtual VTR can effectively and accurately obtain the results of fatigue analysis and has engineering application significance.
In response to the limitations of the single-chamber water jet thruster used in underwater vehicles mimicked by natural cephalopods, a novel approach involving a double-chamber water jet thruster has been proposed. This thruster utilizes electro-magnetic force to manipulate the diaphragm, thereby altering the volume of the upper and lower chambers to achieve water jet propulsion. Experimental investigations were conducted to determine the tensile length-force characteristics of the diaphragm made of Agileus30. Subsequently, key parameters of essential propulsion components, such as solenoid coils, electromagnets, and currents, were established based on the tensile length-force curve, and the propulsion capabilities of the system were evaluated through theoretical analysis. Theoretical assessments indicate that the system does not produce reverse thrust regardless of whether the coil moves up or down. Further experimental results demonstrate that the maximum peak propulsion force generated by the dual-chamber water jet thruster within a 3-s cycle is 0.253N.
The operation of symmetric double-sided and asymmetric single-sided vibro-impact nonlinear energy sinks (DSVI NES and SSVI NES) is considered in this study. The methodology of optimization procedures is described. It is emphasized that the execution of optimization procedures is ambiguous, allows for a great deal of arbitrariness, and requires experience and intuition on the part of the implementer. There are a lot of damper parameter sets providing similar attenuation of the primary structure (PS) vibrations. It is shown that the efficiency of such mitigation for both VI NES types with optimized parameters is similar. However, their dynamic behavior differs significantly. The system with the attached DSVI NES exhibits calm dynamics with periodic motion and symmetrical bilateral impacts on both obstacles. The system with attached SSVI NES exhibits rich complex dynamics when the exciting force frequency is varied. Periodic modes of different periodicity with different numbers of asymmetric impacts per cycle on the PS directly and on the obstacle alternate with various irregular regimes, namely, chaotic mode, intermittency, and crisis-induced intermittency. The regions of bilateral impacts are narrow and located near resonance; they are narrower for a system with an attached DSVI NES. In a system with an attached SSVI NES, there are wider areas of asymmetric unilateral impacts.
The drag coefficient, as the most important parameter that characterizes particle dynamics in flows, has been the focus of a large number of investigations. Although good predictability is achieved for simple shapes, it is still challenging to accurately predict drag coefficient of complex-shaped particles even under moderate Reynolds number (Re). The problem is that the small-scale shape details of particles can still have considerable impact on the drag coefficient, but these geometrical details cannot be described by single shape factor. To address this challenge, we leverage modern deep-learning method's ability for pattern recognition, take multiple shape factors as input to better characterize particle-shape details, and use the drag coefficient as output. To obtain a high-precision data set, the discrete element method coupled with an improved velocity interpolation scheme of the lattice Boltzmann method is used to simulate and analyze the sedimentation dynamics of polygonal particles. Four different machine-learning models for predicting the drag coefficient are developed and compared. The results show that our model can well predict the drag coefficient with an average error of less than 5% for particles. These findings suggest that data-driven models can be an attractive option for the drag-coefficient prediction for particles with complex shapes.
With the rapid advancements in high-speed train technology, the importance of ensuring the safety of train operations has become paramount. Bearings, being a critical component of train bogies, have garnered significant attention for their role in maintaining safety standards. Monitoring the temperature of bearings to evaluate their motion state is a common practice in high-speed trains, emphasizing the need for further research into temperature fluctuations. In this study, a dynamic model is developed for the bearing rotor system of high-speed trains. By considering the contact points between raceways and rolling elements, the power loss in the bearing is obtained and a transient temperature-field model of the system is established. The relationship between node temperature and factors such as ambient temperature, train running speed, and load is illustrated, with a detailed presentation of the influence of bearing fault type and size on node temperature. The analysis results reveal that the node temperature increases with higher values corresponding to those quantifiable factors and is most affected by rolling element fault. Additionally, it is observed that the temperature rises rapidly in the initial stage and gradually flattens out over time. The comparative analysis of temperature under different fault conditions shows that the node temperature is most affected by the rolling element fault. Experiments and actual line temperature data are used to verify the validity of the model. The comparison results show that the simulation aligns well with experimental and line data. The transient temperature-field model of the bearing rotor system in high-speed trains can effectively simulate and predict the temperature change process of each node of the system. The simulation results hold certain theoretical guiding significance for further research and practical applications in ensuring train operation safety.
To enable message transmission among sensors and equipment, power line communication (PLC) is a widely adopted smart grid. However, due to the occurrence of impulsive noise (IN), reliable transmissions over PLC channels in the smart grid are challenging. Therefore, in this paper, we propose an adaptive noise mitigation scheme to clip the IN with the sliding window-based method, where the altitude of the received signal in the current time slots is obtained by computing the average altitude of signals in the previous and next time slots. To detect the states of IN and dynamically estimate the power threshold of signals for the IN mitigation scheme, we develop an intelligent algorithm based on the long short-term memory network. To prevent the useful signals from being eliminated as IN signals, we propose the accelerated proximal gradient method (APGM) based on tone reservation to reduce the peak-to-average power ratio (PAPR) for the transmitting signals with low computational complexity. In addition, the closed-form expression of the bit error rate (BER) is derived for the proposed sliding window-based IN mitigation scheme according to the probability density function of the IN. Simulation results demonstrate that the proposed IN mitigation scheme achieves a better BER performance than the conventional IN mitigation schemes. In addition, the APGM aided by IN mitigation can further improve BER performance due to the PAPR reduction.
In this study, we present for the first time the application of physics-informed neural network (PINN) to fretting fatigue problems. Although PINN has recently been applied to pure fatigue lifetime prediction, it has not yet been explored in the case of fretting fatigue. We propose a data-assisted PINN (DA-PINN) for predicting fretting fatigue crack initiation lifetime. Unlike traditional PINN that solves partial differential equations for specific problems, DA-PINN combines experimental or numerical data with physics equations as part of the loss function to enhance prediction accuracy. The DA-PINN method, employed in this study, consists of two main steps. First, damage parameters are obtained from the finite element method by using critical plane method, which generates a data set used to train an artificial neural network (ANN) for predicting damage parameters in other cases. Second, the predicted damage parameters are combined with the experimental parameters to form the input data set for the DA-PINN models, which predict fretting fatigue lifetime. The results demonstrate that DA-PINN outperforms ANN in terms of prediction accuracy and eliminates the need for high computational costs once the damage parameter data set is constructed. Additionally, the choice of loss-function methods in DA-PINN models plays a crucial role in determining its performance.
In this study, a theoretical model of the vibration isolation system of the gas turbine is developed and numerically solved. A simplified finite element (FE) model was also established to determine the response under the shock load. The results of the FE model are used to verify the effectiveness of the theoretical model and the numerical solution. The influence of isolator stiffness, vibration isolator damping, and vibration isolator nonlinear stiffness coefficient on the shock response of the vibration isolation system is studied using the controlled-variable method. These parameters (stiffness, damping, and nonlinear coefficient) enter into the shock resistance design of gas turbine vibration isolators.