力学专业英语

This paper designed a kind of satellite deployment mechanism with a boxed structure and passive torsion joints. This deployment mechanism has significant strengths, including a high base frequency and stiffness, a high ratio of deployed and folded space occupation ratios, and self-actuated joints without needing any external power to drive. In order to analyze the dynamic characteristics of this mechanism, a simplified governing equation is proposed and dynamic behavior is studied systematically, including impact response, harmonic response, and modal analysis. Through systematic research, several conclusions are drawn. Firstly, when the deployment mechanism reached the ending stage of unfolding driven by passive torsional joints, the load base installed on top of the deployment mechanism generated a first-order sharp reaction force and multiple low-order shocks followed, and the system entered a stable state after a certain time of vibration. Secondly, the system can generate a high vibration magnitude at low-frequency excitation when the mechanism is in a fully deployed state and at high-frequency excitation when the mechanism is in a folded state. Thirdly, the first sixth-order natural frequency and vibration shape with different wall thicknesses are obtained by modal analysis. The result shows that only with a 2.5 mm wall thickness can the connecting rod satisfy the design requirement. Through dynamic behavior research, the structure characteristics are obtained which can be used for structure optimization and to provide an effective solution for the design of a box-structured satellite-unfolding mechanism with self-actuated torsion joints.

In this paper, a wheel–soil coupling simulation system was constructed to simulate the interaction between the flexible metal wheels of a staffed lunar rover during steering maneuvers. The wheel model was developed using the finite element method, and the lunar soil simulant model was created using the discrete element method. This system can accurately reproduce the discontinuous characteristics of lunar soil simulant and the deformation characteristics of flexible wheels, and simulations were conducted under Earth gravity ( 1 g ) and Moon gravity ( 1 / 6 g ) conditions. The results indicated that under these gravity conditions, the average differences in sinkage, drawbar pull, and lateral forces were 12.35 %, 76.60 %, and 83.23 %, respectively. These findings suggest that the impact of gravity on sinkage is limited, whereas its influence on drawbar pull and lateral force is significant. This phenomenon occurs because, as gravity decreases, both the wheel load and the bearing capacity of the lunar soil diminish, leading to a cancellation of their effects on the sinkage amount.

The real-time forecasting of flood dynamics is a long-standing challenge traditionally addressed through numerical solutions of the Shallow Water Equations (SWEs). Numerical solutions of realistic flow problems using numerical schemes are often hindered by high computational costs, particularly due to the need for fine spatial and temporal discretization, complex boundary conditions, and the resolution of non-linearities inherent in the governing equations. In this study, we investigate the use of Physics-Informed Neural Networks (PINNs) to solve 1D and 2D SWEs in dam-break scenarios. The proposed PINN framework incorporates the governing partial differential equations along with the initial and boundary conditions directly within the training process of the network, ensuring physically consistent solutions. We conduct a systematic comparison of the solutions of SWE using the classical numerical scheme (Lax-Wendroff) with estimates of physics informed neural networks. For 1D SWE, a neural network is trained and validated on a dam-break problem, revealing that physics-informed models produce smoother but still acceptable estimates of wave propagation compared to standard numerical results. For 2D SWE, we consider various configurations of dam geometries along with varying initial profiles for water heights. Across all scenarios, reproduce the numerical baselines, albeit with limited accuracy, while avoiding spurious oscillations and numerical artifacts. Further tuning, achieved by incorporating numerical solutions into the PINN training, improved accuracy. This proof of concept demonstrates the potential of hybridized PINNs as a mesh-free, scalable, and generalizable framework for approximating solutions to nonlinear hyperbolic systems. Our results indicate that pre-trained, physics-informed models could serve as a viable alternative for real-time flood forecasting in complex domains.

The gear system with backlash is a strong nonlinear system. The generalized harmonic balance method is capable of dealing with most strong nonlinear problems. The analytical solutions of unstable periodic motion and quasi-periodic motion can also be obtained, and the principle of chaos generation can be further explained. The present study employs the generalized harmonic balance method to obtain an approximate analytical solution for a nonlinear gear dynamic system, which can be used to analyze the dynamics models of gear system with backlash. The accuracy of the analytical solution can be controlled by changing the number of harmonic terms. A characteristic diagram of harmonic amplitude versus the amplitude of the dynamic transmission error (DTE) is obtained using the generalized harmonic balance method, and the influence of DTE on the stability and bifurcation of the system is discussed. The stable intervals and bifurcation of periodic motion of the system are discussed in detail through the analysis of eigenvalue structures. It is found that the existence of Hopf bifurcation at the intersection of stable and unstable branches of periodic solutions leads to changes in the topology of periodic motion of the system.

This paper presents the potential of applying physics-informed neural networks for solving nonlinear multiphysics problems, which are essential to many fields such as biomedical engineering, earthquake prediction, and underground energy harvesting. Specifically, we investigate how to extend the methodology of physics-informed neural networks to solve both the forward and inverse problems in relation to the nonlinear diffusivity and Biot’s equations. We explore the accuracy of the physics-informed neural networks with different training example sizes and choices of hyperparameters. The impacts of the stochastic variations between various training realizations are also investigated. In the inverse case, we also study the effects of noisy measurements. Furthermore, we address the challenge of selecting the hyperparameters of the inverse model and illustrate how this challenge is linked to the hyperparameters selection performed for the forward one.

Freeze-thaw erosion is the main reason for rock mass instability in cold regions and poses major threats to public safety. In this study, the stress threshold, energy, and strain field evolution of sandstone and the variation in stress intensity factor of fractures in various stress fields were all investigated after freeze-thaw cycles by uniaxial compression tests and digital image correlation technology. The results show that the elastic modulus, crack initiation stress, and peak stress all fell by 97%, 92.5%, and 89.9%, respectively, as the number of freeze-thaw cycles approaches 80. Elastic energy’s storage capacity also dropped from 0.85 to 0.17 . Sandstone’s strain was increased by freeze-thaw erosion, which also improved ductility and shortened the cracking time. The stress intensity factor at the crack tip was positively correlated with the tip inclination angle and negatively correlated with the number of freeze-thaw cycles. This study provides a useful reference for understanding the stability of rock masses and the characteristics of crack derivation in cold regions.