国际机械系统动力学学报(英文)最新文献

This paper presents a new multiobjective discrete optimization method for the engineering design of dynamic problems. A discrete combinatorial optimization problem is solved using a particle swarm optimization algorithm coupled with a stair-form interpolation model. To address multiobjective optimization issues, a weighted average approach is implemented to convert the multiobjective optimization problem into an equivalent single-objective optimization problem. Design constraints are taken into consideration by using the penalty function strategy. The proposed method is first verified with a 10-bar truss structure design problem, where the cross-sectional area of each bar is optimized to minimize both volume and node displacement. Second, the dynamic issue for hybrid composite laminates is investigated by maximizing the fundamental frequency and minimizing the cost. The results reveal that the optimized results generated by the proposed method agree well with those from other approaches.

A machine learning-based method for the precise landing of an unmanned aerial vehicle on a moving mobile platform is proposed. The proposed approach attempts to predict the mobile platform's future trajectory based on the past states of the mobile platform. To that end, it combines a long short-term memory-based neural network with a Kalman filter. Hence, it aims at combining the advantages of a machine learning method with those of a state estimation method from established control theory. Based on the predicted trajectory, the unmanned aerial vehicle attempts to land precisely on the moving mobile platform. The experiment is conducted in the Gazebo simulation platform with a quadrotor and an omnidirectional mobile robot, and the proposed method is compared with the single-method approaches of using only either the Kalman filter or the machine learning method alone.

Wind energy harvesting technology can convert wind energy into electric energy to supply power for microelectronic devices. It has great potential in many specific applications and environments, such as remote areas, sea surfaces, mountains, and so on. Over the past few years, flutter-based wind energy harvesting, which generates electric energy based on the limit cycle oscillation created by structural aeroelastic instability, has received increasing attention, and as a consequence, different energy harvesting structures, theories, and methods have been proposed. In this paper, three types of flutter-based energy harvesters (FEHs) including airfoil-based, flat plate-based, and flexible body-based FEHs are reviewed, and related concepts and theoretical models are introduced. The recent progress in FEH performance enhancement methods is classified into structural improvement and optimization, the introduction of nonlinearity, and hybrid structures and mechanisms. Finally, the main FEH challenges are summarized, and future research directions are discussed.

Since the dynamics of thin-walled structures instantaneously varies during the milling process, accurate and efficient prediction of the in-process workpiece (IPW) dynamics is critical for the prediction of chatter stability of milling of thin-walled structures. This article presents a surrogate model of the IPW dynamics of thin-walled structures by combining Gaussian process regression (GPR) with proper orthogonal decomposition (POD) when IPW dynamics at a large number of cutting positions has to be predicted. The GPR method is used to learn the mapping between a set of the known IPW dynamics and the corresponding cutting positions. POD is used to reduce the order of the matrix assembled by the mode shape vectors at different cutting positions, before the GPR model of the IPW mode shape is established. The computation time of the proposed model is mainly composed of the time taken for predicting a known set of IPW dynamics and the time taken for training GPR models. Simulation shows that the proposed model requires less computation time. Moreover, the accuracy of the proposed model is comparable to that of the existing methods. Comparison between the predicted stability lobe diagram and the experimental results shows that IPW dynamics predicted by the proposed model is accurate enough for predicting the stability of milling of thin-walled structures.

This paper proposes a semi-analytical and local meshless collocation method, the localized method of fundamental solutions (LMFS), to address three-dimensional (3D) acoustic inverse problems in complex domains. The proposed approach is a recently developed numerical scheme with the potential of being mathematically simple, numerically accurate, and requiring less computational time and storage. In  LMFS, an overdetermined sparse linear system is constructed by using the known data at the nodes on the accessible boundary and by making the remaining nodes satisfy the governing equation. In the numerical procedure, the pseudoinverse of a matrix is solved via the truncated singular value decomposition, and thus the regularization techniques are not needed in solving the resulting linear system with a well-conditioned matrix. Numerical experiments, involving complicated geometry and the high noise level, confirm the effectiveness and performance of the LMFS for solving 3D acoustic inverse problems.

Imaging techniques have been widely implemented to study the dynamics of chip formation. They can offer a direct method and a full field measurement of the cutting process, providing kinematic information of the chip formation process. In this article, the state of the art of the imaging techniques reported in the literature has been summarized and analyzed. The imaging techniques have been applied to study the chip formation mechanism, friction behavior, strain/strain rate, and stress fields. Furthermore, the study of surface integrity has been advanced by deriving the thermo-mechanical loading, subsurface deformation, and material constitutive model from the imaging technique. Finally, achievements in the area of imaging techniques have been summarized, followed by future directions for their application in the study of surface integrity.

Considerable research has indicated that fiber-reinforced textile composites are significantly beneficial to the aerospace industry, especially aero engines, due to their high specific strength, specific stiffness, corrosion resistance, and fatigue resistance. However, damage caused by high-velocity impacts is a critical limitation factor in a wide range of applications. This paper presents an overview of the development, material characterizations, and applications of fiber-reinforced textile composites for aero engines. These textile composites are classified into four categories including two-dimensional (2D) woven composites, 2D braided composites, 3D woven composites, and 3D braided composites. The complex damage mechanisms of these composite materials due to high-velocity impacts are discussed in detail as well.

The physical mechanism of the dynamics in laser–material interaction has been an important research area. In addition to theoretical analysis, direct imaging-based observation of ultrafast dynamic processes is an important approach to understand many fundamental issues in laser–material interaction such as inertial confinement fusion (ICF), laser accelerator construction, and advanced laser production. In this review, the principles and applications of three types of commonly used ultrafast imaging methods are introduced, including the pump–probe, X-ray diagnosis, and single-shot optical burst imaging. We focus on the technical features such as the spatial and temporal resolution for each technique, and present several conventional applications.

The normal contact force determines the behavior of a particle system. To investigate the normal contact force in a one-dimensional sphere chain subjected to impact load, by comparing the simulation results of the existing typical normal contact force models embedded in the discrete element program, an improved normal contact force model was proposed in this paper. The improved model consists of two parts: the Cundall model for loading and the Daniel model for unloading. Moreover, a systematic test was designed to verify the accuracy and applicability of the improved model. The results showed that the calculated contact force curves agree well with the experimental results. Furthermore, the improved model is implemented in the solution algorithm without need for complex numerical methods and parameters fitting, leading to more efficient simulations.

Six-phase permanent magnet linear synchronous motor (PMLSM) for electromagnetic launch (EML) system presents the characteristics of a high order, nonlinearity, multivariable, strong coupling, and nonperiodic transient operation in the synchronous rotating coordinate system, posing a great challenge to the dynamic response ability of the current loop. Existing research on current decoupling control (CDC) mainly focuses on cross decoupling within a three-phase system, even though there are neither decoupling methods for multiphase systems nor effective evaluation criteria for the decoupling and dynamic response performances. From this perspective, this paper first presents an equivalent reduced-order complex-matrix dynamic mathematical model of six-phase PMLSM and analyze its transient coupling characteristics during the process of EML. Then, the CDC methods of six-phase PMLSM based on direct compensation and matrix diagonalization principles are realized, respectively, to accomplish the cross decoupling and back electromotive force decoupling within and between different three-phase windings. Finally, an all-round method is proposed, for the first time, to evaluate the decoupling performances and dynamic response performances of different CDC strategies for six-phase PMLSM. Significant superiority of deviation decoupling regulator in decoupling performance and robustness are verified based on high-speed EML experimental platform of six-phase PMLSM.