国际机械系统动力学学报(英文)最新文献

Flaw detection in structures is crucial for ensuring structural integrity and safety across various engineering applications. Traditional nondestructive evaluation (NDE) techniques often face challenges in accurately identifying and characterizing flaws, particularly when dealing with complex geometries and strain fields. In this study, we propose a deep learning-based approach utilizing convolutional neural networks (CNNs) for the regression-based parameter identification of flaws in structures. Specifically, we focus on identifying and characterizing circular flaws and cracks. The photoelastic fringe patterns of the flawed structure are used for training and testing the model and are generated using the quadtree-based scaled boundary finite element method (SBFEM), which provides high-fidelity images. The proposed CNN model is trained on these fringe images to learn the intricate patterns associated with different types of flaws and to regress the geometric parameters of the flaws accurately. The results demonstrate that our approach achieves high accuracy, with the CNN model's predictions for both circular flaws and cracks approaching 99%, showcasing the potential of deep learning in advancing NDE methods.

Because of the surging demand for clean energy, the performance and safety of lithium-ion batteries (LIBs) for energy storage and conversion have received much attention. This study presents a battery thermal management system (BTMS) that combines air cooling with microchannel liquid cooling. The system is optimized to significantly improve heat dissipation efficiency and reduce energy consumption. The study utilizes computational fluid dynamics (CFD) simulations to analyze the effects of various air supply velocities, microchannel cross-sectional dimensions, and cooling water flow rates on the thermal performance, which leads to a step-by-step optimization and an overall improvement of the BTMS performance. The balance between BTMS thermal performance and energy consumption is achieved by expanding the thermal performance data samples using the orthogonal method and subsequent multi-objective optimization of energy consumption and heat dissipation using the entropy-weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to determine the optimal operating parameters. This study highlights the potential for optimizing LIB thermal management through parameter tuning and validates the effectiveness of a comprehensive optimized hybrid cooling strategy in improving battery performance and safety.

Marine environment is a harsh environment that can cause major issues for marine structures while operating in this environment, including fatigue cracking and corrosion damage, which can yield catastrophic consequences, such as human life losses, financial losses, environmental pollution, and so forth. Therefore, it is critical to take necessary actions before undesired situations happen. One potential solution is to install structural health monitoring systems on marine structures. Structural health monitoring is a technology to enhance the safety, stability, and functionality of large engineering structures. The inverse Finite Element Method (iFEM) is a promising technique for this purpose. In this study, the corrosion damage detection capability of iFEM is presented by introducing two new damage parameters for plates under tension and bending loading conditions. The contribution of newly introduced parameters to the accuracy of iFEM on damage detection is demonstrated for multiple corrosion scenarios and sensor configurations.

Front Cover Caption: Control of a lambda-robot based on machine learning surrogates for inverse kinematics and kinetics: Tracking control of multibody systems with closed-loop mechanisms presents significant computational challenges due to the complexity of inverse kinematics and dynamics. This study introduces an innovative approach that replaces traditional model-based methods with artificial intelligence by training surrogate models on simulation data. Using the λ-robot, a parallel mechanism, as a case study, the workspace is analyzed to ensure comprehensive data coverage for training. The trained surrogates provide control inputs that enable the use of a linear quadratic regulator (LQR) for trajectory tracking. An additional feedback loop addresses model uncertainties. Simulation results validate the effectiveness of this AI-enhanced, data-driven control framework.

Back Cover Caption: Transfer learning in Physics-informed Neural Networks: This study explores the generalization capabilities of physics-informed neural networks (PINNs) through transfer learning techniques applied to partial differential equation (PDE) problems. Traditional PINNs require retraining when problem conditions change, whereas this approach leverages full finetuning, lightweight finetuning, and low-rank adaptation (LoRA) to enhance efficiency across varying boundary conditions, materials, and geometries. Benchmark cases include the Taylor-Green Vortex, functionally graded elastic materials, and structural problems such as a square plate with a circular hole. The results demonstrate that full finetuning and LoRA significantly improve convergence and accuracy, highlighting their potential in developing more adaptable and efficient PINN-based solvers.

A robust control method for the uncertain vertical electric stabilization system (VESS) with flexible nonlinearity is proposed, and the mismatched uncertainty is considered and compensated based on the backstepping idea. First, based on evaluating the coupling effects of the flexible nonlinearity, the analytical dynamics model of the VESS is established. Second, the tracking error is defined as the evaluation of the system's pitch-pointing tracking control, and then the mismatched state space model with two interconnected subsystems is established as the controlled system. Third, the original mismatched system is converted to the locally matched system using the backstepping design to transform the system state variables. The robust control is proposed to handle the flexible nonlinearity and mismatched uncertainty, which can make both the original system and the reconfigured system present practical stability. Finally, the effectiveness of the proposed control is verified by numerical simulation experiments. This study should be the first to consider flexible nonlinearity coupling and two different uncertainties (matched and mismatched uncertainty) in the design of pitch-pointing tracking control for the vertical electric stabilization system (VESS).

AI for PDEs has garnered significant attention, particularly physics-informed neural networks (PINNs). However, PINNs are typically limited to solving specific problems, and any changes in problem conditions necessitate retraining. Therefore, we explore the generalization capability of transfer learning in the strong and energy forms of PINNs across different boundary conditions, materials, and geometries. The transfer learning methods we employ include full finetuning, lightweight finetuning, and low-rank adaptation (LoRA). Numerical experiments include the Taylor-Green Vortex in fluid mechanics and functionally graded materials with elastic properties, as well as a square plate with a circular hole in solid mechanics. The results demonstrate that full finetuning and LoRA can significantly improve convergence speed while providing a slight enhancement in accuracy. However, the overall performance of lightweight finetuning is suboptimal, as its accuracy and convergence speed are inferior to those of full finetuning and LoRA.

In this article, the physics informed neural networks (PINNs) is employed for the numerical simulation of heat transfer involving a moving source under mixed boundary conditions. To reduce computational effort and increase accuracy, a new training method is proposed that uses a continuous time-stepping through transfer learning. A single network is initialized and used as a sliding window function across the time domain. On this single network each time interval is trained with the initial condition for $$ iteration as the solution obtained at $$ iteration. Thus, this framework enables the computation of large temporal intervals without increasing the complexity of the network itself. The proposed framework is used to estimate the temperature distribution in a homogeneous medium with a moving heat source. The results from the proposed framework is compared with traditional finite element method and a good agreement is seen.

Nonlinearity in parallel compliance can be exploited to improve the performance of locomotion systems in terms of (1) energy efficiency, (2) control robustness, and (3) gait optimality; that is, attaining energy efficiency across a set of motions. Thus far, the literature has investigated and validated only the first two benefits. In this study, we present a new mathematical framework for designing nonlinear compliances in cyclic tasks encompassing all three benefits. We present an optimization-based formulation for each benefit to obtain the desired compliance profile. Furthermore, we analytically prove that, compared to linear compliance, using nonlinear compliance leads to (1) lower energy consumption, (2) better closed-loop performance, specifically in terms of tracking error, and (3) a higher diversity of natural frequencies. To compare the performance of linear and nonlinear compliance, we apply the proposed methods to a diverse set of robotic systems performing cyclic tasks, including a 2-DOF manipulator, a 3-DOF bipedal walker, and a hopper model. Compared to linear compliance, the nonlinear compliance leads to better performance in all aspects; for example, a 70% reduction in energy consumption and tracking error for the manipulator simulation. Regarding gait optimality, for all robotic simulation models, compared to linear compliance, the nonlinear compliance has lower energy consumption and tracking error over the considered set of motions. The proposed analytical studies and simulation results strongly support the idea that using nonlinear compliance significantly improves robotic system performance in terms of energy efficiency, control robustness, and gait optimality.

This article investigates the nonlinear vibration behavior of porous multidirectional piezoelectric functionally graded nonuniform (PFGN) plates resting on orthotropic variable elastic foundations and subjected to hygrothermal loading. The sigmoidal law is employed to define the multidirectional gradation properties, incorporating porosity along both the axial and thickness directions. The governing equations for the porous multidirectional PFGN plate are derived using the modified first-order shear deformation theory (FSDT) with nonlinear von Kármán strain and Hamilton's principle. A higher-order finite element (FE) approach, combined with a modified Newton-Raphson method, is utilized to solve the resulting equations. The study reveals that orthotropic variable elastic foundations significantly influence the vibration behavior of multidirectional PFGN porous plates compared to conventional elastic foundations under hygrothermal loading. Additionally, the effects of various parameters such as thickness ratio, tapered ratio, material exponent, boundary conditions, porosity distribution, electrical loading, temperature variation, and moisture change on the vibration behavior are comprehensively analyzed. The results of this study have direct applications in energy harvesting and structural health monitoring, offering a novel approach to designing and optimizing smart materials for engineering systems operating under hygrothermal and thermoelectrical conditions.