Acta Mechanica Sinica最新文献

Nonlinear energy sink (NES) coupled piezoelectric devices offer dual efficacy of vibration suppression and vibration energy harvesting. This work proposes a novel two-degree-of-freedom NES-piezoelectric system that combines linear and nonlinear damping of NES (2-DOF CPNES), and focuses on the transient responses of the system of nonlinear main structure coupled with 2-DOF CPNES. The theoretical model of the system is developed, and the slowly varying dynamic flow equations for both conservative and non-conservative systems are derived using the complexification-averaging method. The nonlinear normal modes of the conservative system are then analyzed. The vibration suppression efficiency and energy transfer mechanisms of the system are examined under various external excitation intensities, and compared with those of the NES-piezoelectric system with purely linear damping (2-DOF LPNES) and purely nonlinear cubic damping (2-DOF NPNES). Additionally, the influence of circuit parameters on performance is further explored. The 1:1 transient resonance capture phenomena occur between the main structure and the first-level NES oscillator, as well as between the first-level and second-level NES oscillators, revealing the occurrence of transient 1:1:1 resonance capture phenomena among the three oscillators. The 2-DOF CPNES system combines the advantages of both the 2-DOF LPNES and 2-DOF NPNES systems, featuring a lower energy triggering threshold and maintaining high performance under larger transient excitation intensities. Although the circuit parameters have minimal impact on vibration suppression performance, they significantly affect energy harvesting efficiency. Furthermore, this study elucidates the vibration suppression and energy harvesting mechanisms of the 2-DOF NES-piezoelectric system from the perspective of dual nonlinearities in stiffness and damping. The findings demonstrate that the incorporation of nonlinear damping effectively enhances system robustness and improves vibration suppression performance under high-intensity transient excitations.

The development of a multiscale method is essential for resolving cross-regime numerical simulations with precision, particularly in hypersonic flow scenarios. Traditional approaches, such as continuum fluid dynamics equations and stochastic particle methods, encounter considerable computational accuracy or efficiency limitations under extreme flow conditions. Here, we present a joint hydrodynamics-particle (JHP) method specifically designed for multi-regime hypersonic simulations, incorporating advancements in the discrete velocity method. In this framework, the main transport fluxes computed via the collision-free discrete velocity method are replaced by stochastic particle representations, while the continuum Navier-Stokes equations are seamlessly coupled with the particle method through an integral solution strategy. Adaptive weighting factors for these methodologies are determined dynamically at each spatial cell and timestep through a competition mechanism coupling model, ensuring multiscale consistency and computational efficiency. Analysis of the flux iteration formulation reveals that the JHP method exhibits strong asymptotic-preserving properties, naturally transitioning to macroscopic solvers in the continuum regime while accurately resolving particle-dominated fluxes in rarefied flows. Validation through benchmark cases, including shock wave structures and hypersonic flow around a cylinder, demonstrates the method’s ability to capture flow physics across continuum and rarefied regimes with high accuracy and computational efficiency. These results highlight the JHP method as a robust and versatile multiscale framework with significant potential for extending its applicability to complex physical phenomena.

Simultaneously detecting hidden solid boundaries and reconstructing flow fields from sparse observations poses a significant inverse challenge in fluid mechanics. This study presents a physics-informed neural network framework designed to infer the presence, shape, and motion of static or moving solid boundaries within a flow field. By integrating a body fraction parameter into the governing equations, the model enforces no-slip/no-penetration boundary conditions in solid regions while preserving conservation laws of fluid dynamics. Using partial flow field data, the method simultaneously reconstructs the unknown flow field and infers the body fraction distribution, thereby revealing solid boundaries. The framework is validated across diverse scenarios, including incompressible Navier-Stokes and compressible Euler flows, such as steady flow past a fixed cylinder, an inline oscillating cylinder, and subsonic flow over an airfoil. The results demonstrate accurate detection of hidden boundaries, reconstruction of missing flow data, and estimation of trajectories and velocities of a moving body. Further analysis examines the effects of data sparsity, velocity-only measurements, and noise on inference accuracy. The proposed method exhibits robustness and versatility, highlighting its potential for applications when only limited experimental or numerical data are available.

One of the most popular micro-gravity ground test of space missions is planar air-bearing spacecraft simulator equipped with controllers to perform both pose stabilization and trajectory tracking in a simulated space mission. Previous studies based on the double-integrator equations neglect the geometric structures of the dynamics of the simulator. To address this problem, this paper begins with the continuous dynamic equations formulated on both left and right trivializations of Lie group SE(2), and then presents the intrinsic proportional-derivative controllers derived from these equations. Based on the discrete variational principle, this study presents a Lie group variational integrator derived for the time integration of the dynamic equations of the simulator. Then, the discrete geometric optimal controller is established by including the Lie group variational integrator as equality constraint equations into the optimal control problem. This work also prepares the proposed controllers in simulations in terms of accuracy, convergence rate, and control cost. Numerical results show intrinsic geometric characteristics of the pose trajectories obtained by using different controllers. To further investigate the real-time performances of different controllers, the paper presents a prototype of the planar air-bearing simulator and the experimental tests of pose stabilization and circle tracking. The experimental results are in good agreement with the numerical ones obtained from simulations.

Theoretical analysis and numerical simulations using computational fluid dynamics (CFD) were conducted to investigate hypersonic laminar flow over a compression corner, focusing on the effects of arbitrary surface catalysis on peak surface heat transfer. Four groups of inflow conditions were considered, varying in Mach number (8–12), unit Reynolds number (2 × 10⁵-8 × 10⁵ m⁻¹), degree of dissociation (0.05–0.15), and ramp angle (20°–28°). The results indicate that while wall catalysis has a negligible effect on the flow structure, it significantly influences the peak surface heat transfer near the reattachment region, even for finite-rate catalytic walls. A predictive formula is proposed for the non-dimensional catalytic heating, considering finite-rate catalytic walls. CFD results show that the peak heat flux increases as the catalytic coefficient increases due to enhanced surface recombination of atoms, and the effectiveness of the formula is further verified by oxygen inflow. Finally, the catalytic heating ratio at the location of peak surface heat transfer is evaluated by the formula using catalytic coefficients of real materials. It is shown that the catalytic heat flux ratio may increase by approximately 50% from oxygen inflow to nitrogen inflow for copper under the same nominal freestream.

Gradient-structured (GS) metallic polycrystals exhibit significant potential for integrating strength, ductility, and toughness. Enhancing our understanding of the relationship between their microstructure and mechanical properties of GS polycrystals is essential for optimizing the properties of materials. In this paper, a discrete dislocation dynamics model within the framework of state-based peridynamics (DDD-SBPD) is developed to investigate the elastoplastic deformation and fracture in GS polycrystals. By solving typical boundary value problems and comparing the DDD-SBPD simulation results with those from traditional DDD simulations and theoretical solutions, the DDD-SBPD model is validated. The model is then applied to simulate elastoplastic fracture in GS and homogeneous-structured (HS) polycrystals. By incorporating the intrinsic interaction between grain boundaries and dislocations, the model successfully captures the crack oscillations observed in the experiments. The relationships between the mechanical properties (e.g., strength, ductility, and fracture resistance) of metallic polycrystals and their structure are systematically analyzed. The results demonstrate that GS polycrystals outperform HS polycrystals in achieving a superior balance of mechanical properties, and that gradient orientation has a significant effect on these properties.

Engineering problems such as the aerodynamics optimization of high-speed trains (HSTs) that require large-scale numerical simulations necessitate the utilization of multi-fidelity surrogate models to address the inherent conflict between accuracy and computational expense. This research presented a multi-fidelity Kriging surrogate modeling method combined with the transit strategy (TMFK), which leverages both low-fidelity and high-fidelity samples to generate the medium-fidelity surrogate model as a transit model. This model is subsequently refined using high-fidelity samples. The efficacy and precision of the TMFK model are evaluated through the single-objective and multi-objective test functions, followed by its application in the optimization of the HST aerodynamic performances. The results indicate that the TMFK surrogate model constructed with the transit model achieves higher accuracy compared to multi-fidelity surrogate models built solely using scaling functions. Moreover, a transit model with higher accuracy is particularly advantageous for establishing high-precision TMFK models. The prediction errors of the aerodynamic drag force of the head car (DH), tail car (DT), and the lift force of the tail car (LT) of the optimal model for the TMFK model are 0.10%, 0.87%, and 0.40%, respectively. In the optimal model, the surface pressure on the tail car nose exhibits an increase compared to that of the original model, accompanied by a reduction in the scale of vortices and slipstream velocity in the wake. Consequently, reductions of 1.73%, 7.04%, and 18.76% are observed in DH, DT, and LT, respectively. Furthermore, the height of the nose tip, gear region, and the profile of the lower contour line have significant impacts on optimization objectives, particularly on the DT and LT. Notably, the influence of design variables on the DH is relatively minor compared to the effects on the DT and LT.

Continuous fiber-reinforced polymers (CFRPs) have been extensively utilized in aerospace industries, making it imperative for CFRP structural optimization to consider the effects of extreme service environments. In this paper, a thermal-mechanical coupling concurrent topology and fiber distribution optimization (TM-CTFDO) method is proposed, specifically tailored for CFRP structures subjected to extreme environmental conditions. The mapping relationships between fiber design variables and material properties are deduced based on the rule of mixture to realize the analysis of thermoelastic CFRP structures. The integrated optimization model for CFRP structures is established with minimizing structural compliance, adhering to the volume constraints of structural and fiber under mechanical and temperature loads. Sensitivity analysis and optimization solution are realized by adopting the adjoint method and the method of moving asymptotes, respectively. This approach culminates in the determination of the optimal topology, fiber orientation, and content. In the post-processing phase, a fiber path planning algorithm is investigated to achieve the continuous fiber path based on the optimization results, which also effectively controls the distribution of dense and sparse fiber. Several examples under uniform and varying temperature fields are provided to verify the effectiveness of the TM-CTFDO method. The influence of temperature and mechanical loads on the optimization results is discussed, which will provide guidance on CFRP structural design and fiber path planning under thermal-mechanical coupling.

Accurate modelling of confined fluid transport requires addressing the fluid-fluid and fluid-surface interactions across multiple scales, particularly when molecular sizes are comparable to the fluid mean free path and the confinement dimension. Advances in nanotechnology have sparked tremendous interest in the combined effects of non-equilibrium, real-fluid properties, and confinement on multiscale flows, where the Knudsen number or confinement dimension can vary widely. This review discusses molecular kinetic modelling approaches for: (1) fluid density ranging from dilute, where non-equilibrium effects dominate, to dense, where real fluid effects become critical; (2) flow domains ranging from macroscale to nanoscale, necessitating confinement-specific treatments; (3) fluid-surface interactions at various densities and confinements. The pronounced non-equilibrium and confinement effects introduce both intrinsic and apparent non-hydrodynamic effects, which require careful consideration in developing a molecular kinetic model.