Founded in 1895 as Peiyang University,Tianjin University is the oldest institution of higher education in modern Chinese history.After 130 years of growth and development,it has now become one of the leading research and teaching univer-sities,pioneering various aspects of engineering education and applications.As an integral part of the university,the Department of Mechanics has grown from a mechanics lab-oratory in the 1920s to its current status as one of the leading research and teaching departments,covering solid mechan-ics,fluid mechanics,engineering mechanics,and dynamics and control.Its distinctive research in solid mechanics and interdisciplinary fields ranges from elastic waves and con-trol,experimental mechanics,nonlinear dynamics,contact mechanics and tribology to nanomechanics,contributing both to the fundamental understanding of nature and to engi-neering applications.
Picosecond laser ultrasonics (PLU) has emerged as a powerful non-destructive technique for characterizing the mechanical properties of materials at the nanoscale, providing unprecedented insights into key parameters such as elastic constants, film adhesion, and subsurface defects. This review provides a comprehensive overview of the advances in PLU for mechanical analysis. We begin with the fundamental principles of opto-acoustic generation and detection, discuss various technical methodologies, and then delve into its diverse applications in material properties characterization and non-destructive mechanical imaging. Finally, we discuss the current challenges and future prospects, highlighting the path toward industrial integration through automation and AI-driven analysis.
With the continuous advancement of global rehabilitation technologies, home-based lower-limb rehabilitation exoskeletons have demonstrated broad application prospects due to their low cost and high convenience. However, significant inter-patient variability, the complex and dynamic nature of the rehabilitation process, and the time-varying characteristics of knee joint torque present major challenges for these exoskeletons in accurately matching torque demands during training. Designing scientifically-grounded, personalized rehabilitation programs and precisely regulating training intensity in home settings remains a highly complex and challenging task. To address this challenge, we propose a home-use knee health monitoring system (KHMS) grounded in embodied intelligence, featuring three key innovations: (i) a four-bar variable-stiffness mechanism that provides adjustable stiffness over 10–40 N·m/rad; (ii) a self-powered electromagnetic module that delivers a stable 3.4 V output to reliably energize low-power wireless sensors; and (iii) an LSTM-based adaptive monitoring model that estimates knee joint torque with 95.36
The era of big data has profoundly transformed mechanics research, with data-driven approaches playing a vital role in modeling and optimization. This study focuses on tunnel boring machine (TBM), where the thrust-torque ratio is a key determinant of their tunneling energy efficiency. However, due to the complexity of experiments and the testing requirements, obtaining sufficient high-quality data under varying geological conditions remains a major challenge in optimizing the tunneling energy efficiency of TBM. To address this, multi-cutter rotary cutting machine experiments and numerical simulations were conducted on 22 different rock types. Comprehensive datasets of normal and rolling forces were systematically collected. Using specific energy (SE) as the rock-breaking efficiency metric, we integrated physical and numerical data through a CatBoost-based fusion framework. The predictive model was initially trained on simulation data to capture the relationships among penetration, uniaxial compressive strength, tensile strength, and SE, and was subsequently fine-tuned with experimental data to develop the final fused model. Compared to models trained solely on experimental or simulated data, the fused model reduced RMSE by 37.1% and 58.6%, respectively, and improved R2 by 19.0% and 44.6%, thereby enhancing both prediction accuracy and generalization capability. Furthermore, Bayesian optimization was employed to minimize SE and identify the optimal penetration. The results indicate that as rock strength increases, the optimal penetration decreases, while the corresponding minimal SE increases. These findings provide theoretical and engineering insights for improving TBM energy efficiency and parameter optimization, while establishing a robust data fusion framework for mechanical data analysis.
The pre-twisted straight fiber exhibits exceptional mechanical properties, including high tensile stiffness and remarkable flexibility. In applications such as artificial muscles and fiber-reinforced composites, these fibers are typically embedded in an elastic matrix, functioning as key reinforcing or deformation-driven structural components. In this study, a shear-lag-based model is developed to describe the pullout behavior of a pre-twisted straight fiber from an elastic matrix, incorporat-ing geometric nonlinearity and tension-twist coupling induced by large pre-twist angles. Based on this model, the stress transfer mechanism between the twisted straight fiber and the surrounding matrix is systematically analyzed. Furthermore, the derived force-displacement relationship during fiber pullout is employed to perform crack-bridging analysis, revealing the toughening mechanisms in twisted fiber-reinforced composites. Results show that pre-twist of fiber introduces dis-tinct tension-twist coupling, which generates hoop interfacial shear stresses and allows the fiber to undergo larger tensile deformation. It leads to greater crack-opening displacements in the bridging zone and a significantly enhanced toughen-ing effect. The present work provides new insights into the stress transfer and toughening mechanisms of twisted fiber-reinforced composites, offering valuable guidance for the design and fabrication of high-performance composite materials
