Acta Mechanica Sinica最新文献

The two-dimensional Rayleigh-Bénard convection under external vibration at varying orientations has been systematically studied using direct numerical simulations. The vibration angle (β) relative to the horizontal axis spans from 0° to 90°, and the Rayleigh number (Ra) ranges from 10⁶ to 10⁸ with the Prandtl number fixed at Pr = 4.38. The dimensionless vibration frequency (ω) varies from 0 to 1000, with the dimensionless amplitude fixed at A_m = 1.52 × 10⁻³. The dependence of the Nusselt number (Nu) and Reynolds number (Re) on the vibration angle (β) exhibits a non-monotonic relationship, with an optimal vibration direction identified that maximizes heat transport enhancement. At high vibration frequencies, a critical vibration angle of β_c demarcates the transition between vibration-induced enhancement and suppression of Nu. For β < β_c, the horizontal component of vibration is dominant, leading to the detachment of thermal plumes from the conducting plates due to vibration-induced boundary layer (BL) destabilization, thereby enhancing convective heat transport. In contrast, for β < β_c, the vertical component of vibration dominates, stabilizing the thermal BLs and suppressing turbulent fluctuations, which reduces the global heat transport. Additionally, the action of vibration modifies the shape of the large-scale circulation, making it more circular and generating new vortex structures in the bulk for intermediate β. Vibration also influences the growth of corner rolls, reducing their size at small β and potentially inducing the formation of new corner rolls along the opposite diagonal direction at large β.

Inspired by the walking, jumping, and running of quadrupeds, a novel vibration isolation-absorption (BVIA) platform is proposed by applying a bistratal X-shaped structure and a multi-vertebra structure. Based on the mechanical-constitutive relationship, the static and dynamic models of the BVIA platform are established, and the force/stiffness-displacement curves are applied to reveal the loading capacity and quasi-zero stiffness characteristics. The vibration suppression performances of different parameters are investigated by amplitude-frequency curve and displacement transmissibility, and the results are verified by numerical methods. From the results, it can be found that the resonance peak significantly decreases due to the mutual promotion of vibration isolation and vibration absorption. The vibration suppression performance of the BVIA structure can be tuned flexibly by initial installation angle, rod length ratio, layer number, absorbed mass, stiffness coefficient, horizontal spring length, and excitation amplitudes. The proposed BVIA structure provides a useful reference for reducing the resonance peak and improving the vibration suppression performance in practical engineering applications.

Data-driven approaches have shown great advantage in rapidly and accurately predicting pressure coefficient distributions, which is of crucial importance to efficient aircraft design. Nevertheless, most data-driven approaches still encounter limitations in characterizing diverse aerodynamic configurations and adapting to varying grid densities, which have hindered their engineering applicability. In response to these challenges, this work adopts point clouds, a specific type of geometric data structure that is inherently suitable for uniformly characterizing diverse 2D/3D geometric shapes as the input for deep learning-based prediction of pressure coefficient distribution. By augmenting the dimensions of point cloud coordinates for local feature enhancement and utilizing the symmetric function “max pooling” to extract global features, the proposed aerodynamic model establishes the mapping between point cloud coordinates and pressure coefficients. Basic aerodynamic configurations like airfoils and wings are employed as test cases, the results demonstrate that the proposed model achieves both high accuracy and robust generalizability across variable geometries. For class-shape transformation-perturbed airfoils, the prediction error can be reduced to one-third of that of the conventional parameterization-based model. For airfoils selected in the University of Illinois Urbana-Champaign airfoil dataset, among which airfoil profiles are widely distributed, the average error of the proposed approach remains approximately 1.5%, whereas the parameterization-based model may fail. For wings, the prediction error still stays below 2.5%. Finally, the model exhibits strong robustness and generalizability across different point cloud densities. In conclusion, this work makes a breakthrough in predicting pressure coefficient distribution for variable geometric configurations, establishing the foundational framework for designing a large model capable of predicting distributed aerodynamic loads in aerospace applications.

Due to their lightweight and monolithic forming characteristics, selective laser melted (SLM) nickel-based superalloys exhibit broad potential applications in aerospace and automotive industries. However, their unique microstructure leads to distinctive creep behavior. It is crucial to clarify the microstructural characteristics and underlying creep mechanisms of these alloys. In this work, creep experiments of SLM IN718 were conducted under different temperatures and holding stresses. And the evolution of grain size, precipitate state, and dislocation density in IN718 was investigated by scanning electron microscope and electron backscatter diffraction. Both carbide precipitation and dislocation density were identified as the main factors weakening the creep life and ductility of SLM IN718. Experimental results indicated that higher initial dislocation density and finer grain size lead to a reduction in creep life. Moreover, the precipitation of carbides and M23C6 under elevated temperature and high holding stress promotes micro-crack propagation and weakens creep strength. To simulate the mechanical behavior observed in high-temperature creep experiments, a crystal plasticity model coupling dislocations and precipitated solutes was developed based on the microstructural characteristics and deformation mechanisms of SLM IN718. This model enhances the fundamental understanding of the micro-mechanisms underlying the creep behavior of SLM IN718 and provides valuable insights for optimizing the design of high-performance components under extreme conditions.

This study focuses on the application of physically informed neural networks (PINNs) in three-dimensional (3D) airfoil flow field computation. Given that PINNs face challenges such as high dimensionality, network architecture design, data requirements, numerical stability, and physical constraints formulation when dealing with this problem, the fluid dynamics PINN with DeepONet (FDPI-DeepONet) network is proposed. It combines the advantages of PINN physical constraint modeling and DeepONet data-driven learning, utilizes branch networks with different functions of the two to collaborate with the backbone network, represents the 3D flow field through Cartesian coordinates, and is trained based on the DeepONet framework and the loss function of physical information. The experiments are tested with M6 and NACA0012 airfoil data, and the results show that FDPI-DeepONet performs excellently in terms of prediction accuracy and computational resource consumption, e.g., it outperforms the comparative methods in terms of average R² metrics, and the computation time is reduced significantly. The network effectively overcomes challenges and provides efficient solutions to complex fluid dynamics problems.

Although supplying extensive design space, the curse of dimensionality restricts the widespread application of large-scale topology optimization in practical engineering. Various acceleration techniques have been integrated with topology optimization, achieving significant attention and progress in large-scale problems. This work aims to investigate how much benefit can be obtained by combining parallel computing and machine learning techniques to enhance the efficiency of large-scale topology optimization algorithms. Accordingly, a parallel problem independent machine learning (PIML)-enhanced topology optimization method is proposed. The PIML model substantially reduces the dimension of the condensed stiffness matrix and its computational cost, and parallel computing reduces the workload per process and enables the application of a parallel multigrid solver. Besides, several techniques, such as matrix-free implementation, direct condensation of uniform coarse elements, and adjusting computational resource limits, have been developed to enhance computational efficiency. The weak scaling efficiency, strong scaling speedup, and maximum achievable efficiency of the proposed method are validated across multiple numerical examples, showing significant improvement in the tractable problem size and solution efficiency compared to traditional topology optimization algorithms.

This paper explores the use of sparse time-series data from flow systems, acquired through sensors or other means, to predict flow fields using deep learning techniques. This area of research holds substantial scientific significance and practical application value. The time-series data measured from different points typically contain spatial correlation and temporal features, which, when utilized effectively, can contribute to reconstructing flow fields. In this study, a convolutional autoencoder is applied to reduce the dimensionality of the flow field. Subsequently, an Informer neural network and a convolutional neural network are employed to extract low-dimensional representations of the flow field from the measurement data. A specially designed loss function bridges these latent features to establish a mapping between measurement point sequences and flow fields. The hybrid model is validated using data from both numerical simulations and experimental measurements. Results demonstrate that this method effectively predicts velocity and pressure fields from sparse data, showcasing its potential for practical flow field reconstruction tasks.

Threaded connection is a common structural form in mechanical engineering, with their complex nonlinear behavior under combined loading critically affecting structural performance. While existing simplified models and finite element analysis (FEA) methods describe force distribution under single loading conditions, accurately modeling threaded connections under complex loading remains challenging. This paper proposes a simplified theoretical model to efficiently predict contact forces and deformation distributions under tension, torsion, bending, and shear. The model treats bolt and nut bodies as Euler-Bernoulli beams and represents thread stiffness using equivalent trapezoidal cantilever beams, reducing computational complexity while retaining essential mechanical characteristics. The paper introduces reference helical curves and derives a deformation coordination relationship based on contact constraints. The model’s calculations are validated against FEA results, demonstrating both high precision and significant computational efficiency under complex loading conditions. This work provides an efficient and reliable tool for analyzing threaded connections, offering promising engineering applications.

In this paper, the load characteristics of shock wave, bubble pulsating water jet and cavitation closure are studied by carrying out underwater explosion experiments and numerical simulation of fixed square plates with 2.5, 5 and 10 g trinitrotoluene. The results show that under the combined action of multiple loads, the impulse of bubble pulsation and water jet load plays a leading role in the process of underwater explosion, and the impulse of cavitation closure load is greater than that of shock wave. The damage to the structure cannot be ignored, and the pressure time-history curve presents a “multi-peak” state, and it is pointed out that the water jet is a concentrated load. Then, the dynamic response of the full-scale model of the ship under the combined action of multiple loads is studied, and the dynamic response of the ship under different cabin water depths and different explosion distances is discussed. The results show that when the ship is empty, the damage degree of the ship is the most serious, and the influence of cavitation effect on the half cabin is weaker than that of the empty cabin, so the damage degree is the second, and the damage degree is the smallest when the cabin is full. When the distance parameter is less than 0.68, the shock wave and the after flow play a leading role in the dynamic response of the ship. When the distance parameter is between 0.68 and 1.38, the combined action of the bubble pulsating water jet and the cavitation closure multi-load causes the main damage to the ship. When the distance parameter is greater than 1.38, the bubble pulsation and the cavitation closure load play a leading role.

Central pattern generators (CPGs) are neural circuits which are found in both invertebrates and vertebrates, and are capable of generating rhythmic patterns of neural activity without receiving any rhythmic input. The mechanism by which CPG controls alternating and turning movements in vertebrates has not yet been fully understood. In this paper, a new CPG model for rat hindlimbs is proposed to unravel the above mechanism based on recent physiological experimental results. The model is obtained by adding commissural interneurons (CINs, including V0, V3, and CINi populations) and chx10 Gi neurons into Deng’s CPG model. Based on our proposed CPG model, different alternating gaits in the rat hindlimb model can be achieved by ablating different populations of commissural interneurons, which is consistent with physiological experimental findings. Then, turning movements are studied by applying unilateral excitatory and inhibitory stimuli to chx10 Gi neurons which are connected to the flexion side of the CPG with inhibitory synapses. It is found that unilateral activation leads to turning towards the same side, while unilateral inhibition results in turning towards the opposite side, which is also consistent with the results in physiological experiments. Finally, the ion-channel control mechanism of CPG burst rhythms is investigated, and it is found that the CPG burst rhythm is most sensitive to sodium ion channels, with a significantly greater impact than the influence of reciprocal inhibition on the rhythm. The proposed CPG model provides a new perspective for understanding motor neural mechanism of vertebrates, and also could be adopted in motion control of hindlimb robots.