Due to the low strength of putty, it is impossible to directly measure its fracture toughness using the standard tensile fracture test. In order to accurately measure the tensile fracture toughness of low-strength materials such as putty, this study presents an improved compact tension test method and derives a tensile fracture toughness formula through numerical analysis. The formula is further refined by accounting for boundary effects, and its accuracy is validated through systematic testing with varying initial crack lengths. The results demonstrate that, the modified formula enables direct calculation of material fracture toughness. The stress intensity factor depends on relative boundary conditions rather than absolute boundary effects, with boundary effects becoming more pronounced with increasing initial crack length. The specimens exhibit pure Mode I fracture, with the modified formula yielding a stable fracture toughness of approximately 42 kN/m^3/2. Experimental validation confirms the accuracy of both the improved test method and the modified formula, establishing their applicability for measuring fracture toughness in low-strength materials like putty and contributing to putty material development.

Split-sleeve cold expansion technology is a critical process for improving the fatigue life of aerospace structural components. However, the influence of its parameter variations on fatigue performance remains insufficiently studied. This paper systematically investigates the sensitivity of initial hole diameter, thickness of split-sleeve, and diameter of the extrusion zone of the mandrel on the fatigue life of 7050-T7451 aluminum alloy through integrated fatigue testing, finite element simulation, and machine learning methods. Based on the S-N curve model of 7050-T7451 aluminum alloy and the critical distance line method, a fatigue life prediction model for cold-expanded holes was established. The model was then used to generate datasets for training an intelligent fatigue life prediction model. Leveraging 400 000 data points obtained from the intelligent model, Sobol global sensitivity analysis was conducted to quantify the independent and interactive contributions of these parameters to fatigue life. Results indicate that the initial hole diameter has the most significant impact on fatigue life, dominating both independent effects and synergistic interactions, while the influence of Thickness of split-sleeve and mandrel diameter primarily manifests through interactive mechanisms. The study proposes prioritizing tolerance optimization for initial hole diameter while adopting collaborative design strategies for sleeve thickness and diameter of the extrusion zone of the mandrel. This methodology provides an efficient and economical approach for identifying critical process parameters and optimizing designs, demonstrating significant advantages over traditional physical experimentation and finite element analysis..

To elucidate the complex bonding and fracture mechanisms at the interface between Ultra-High Performance Concrete (UHPC) and Normal Concrete (NC), this study systematically investigates how interfacial roughness and mesoscale structural characteristics influence interface mechanical performance. Four interface treatments (i.e., smooth surface, high-pressure water jetting, sandblasting, and chiseling) were comparatively analyzed through direct tension and shear tests, complemented by quantitative surface roughness characterization using laser scanning. Furthermore, X-ray Computed Tomography (X-CT) facilitated the three-dimensional reconstruction of UHPC-NC mesoscale structures, enabling advanced segmentation of pores, fibers, and other structural phases via deep learning algorithms. Multi-scale finite element modeling based on X-CT data simulated the damage evolution and crack propagation at the interface. Results indicate that chiseling significantly increased interfacial roughness, yielding substantial improvements in direct tensile and shear bond strengths by 123% and 126%, respectively, relative to the smooth surface. X-CT analysis revealed a distinct hydration transition zone at the interface, significantly influencing chemical bonding and exhibiting notably lower porosity compared to the NC matrix. Steel fibers from UHPC penetrated into the NC substrate, creating enhanced mechanical interlocking effects. Numerical simulations demonstrated that interface failure mechanisms are jointly governed by tensile failure within the NC substrate and crack propagation through the interfacial transition zone (ITZ), consistent with experimental observations of mixed-mode fractures. Overall, enhanced interfacial roughness improved bonding strength through both mechanical interlocking and chemical adhesion, while mesoscale structural defects critically influenced crack development pathways. The proposed multi-scale analytical approach provides comprehensive methodological support for optimizing the design and rehabilitation of concrete interfaces in engineering practice.

Currently, over 90% of crude steel production is achieved through continuous casting. Increasing the casting speed during continuous casting can significantly enhance production efficiency, but it also impacts the flow field, exacerbating slag entrapment and argon bubble entrainment, which lead to a series of quality defects. These issues have become critical constraints on the development of high-speed continuous casting molds. This paper establishes a multiphase numerical model of the continuous casting mold by coupling Large Eddy Simulation (LES) with the Volume of Fluid (VOF) method and a two-way coupled Discrete Phase Model (DPM). By analyzing flow field variations, steel-slag interface velocity, interface fluctuations, and slag entrapment ration under three different casting speeds, the internal correlation mechanisms are revealed. The study finds that appropriately increasing casting speed can improve production quality, providing a reference for optimizing casting speed in continuous casting processes.

With the rapid acceleration of population aging in China, the demand for feeding assistance among individuals with disabilities is becoming increasingly urgent. However, current feeding robots face several limitations such as poor adaptability to complex environments and safety concerns due to rigid structures. In this paper, a new six-degree-of-freedom feeding robot was developed that integrates obstacle avoidance path planning and compliance control. The robotic arm was designed employing a Bi-directional Rapidly-exploring Random Tree (Bi-RRT) algorithm to generate collision-avoidance trajectories, while inverse kinematics was solved using the Denavit-Hartenberg (D-H) parameter. At the control level, an impedance model-based compliance control strategy was introduced, and its compliant behavior under sudden external forces was verified through dynamic simulations. Prototype experiments demonstrated that the robot could effectively avoid obstacles and respond compliantly to external interference. While the robot achieved a 100% feeding success rate with solid and semi-liquid foods, it occasionally experienced spillage when feeding liquids due to structural limitations of the end-effector. This paper provides both a theoretical framework and practical guidance for enhancing the safety and environmental adaptability of feeding robots. Future work will focus on optimizing the end-effector design to further improve performance with liquid foods.

Deoxyribonucleic acid (DNA), as the fundamental genetic material of life, possesses diverse mechanical characteristics endowed from its unique chemical and physical properties. These properties play a pivotal role in regulating gene expression, viral infection mechanisms, disease diagnostics, and intelligent nanodevices. A profound understanding of the mechanical properties and behaviors of DNA-like material—spanning from the molecular scale to the macroscopic device level—provides a foundation for unveiling the physical mechanisms underlying biological activities, advancing biomedical detection technologies, and enabling the precise design of dynamic nanodevices. This paper systematically reviewed recent research progress on the mechanical properties of DNA-like material and their applications in biomedicine and nanotechnology. First, some significant experimental advances across different-scale DNA systems were introduced, emphasizing how experiments revealed the influence of microstructure and environmental conditions on the mechanical properties and responses of DNA-like material. Second, the developments of theoretical models for the mechanical behavior of DNA-like material were explored, elucidating the mechanisms underlying relevant experimental findings. Finally, the paper identified the challenges in the current DNA-like material mechanics research and its practical implementation, and looked forward to the prospect of achieving breakthroughs through research paradigms such as "digital and intelligent mechanics".

In the precursor impregnation pyrolysis (PIP) process of ceramic matrix composites, the viscosity of the precursor liquid significantly affects the effectiveness and efficiency of its infiltration into the fiber preform. The solute mass fraction influences the viscosity itself in the impregnation solution. To provide a theoretical basis for optimizing the impregnation process, this study establishes an apparent viscosity model for precursor liquids with different solute contents. Firstly, an order parameter describing the solid and liquid phase content is introduced, establishing the interrelationship between shear stress, shear rate, and the order parameter of the precursor liquid. Then, based on Landau phase transition theory and the variational principle of the Lagrangian energy functional, a differential equation for the order parameter is constructed and solved, yielding an approximate analytical expression for apparent viscosity, mass fraction, and shear rate in a subdomain. Furthermore, a first-order approximation of the Bernstein polynomial is employed to extend the model to the entire computational domain, resulting in a viscosity model applicable to precursor liquids with different solute mass fractions. Finally, precursor liquids incorporating two types of additives, powders and binders, are examined. By calculating the effect of different mass fractions of additives on precursor viscosity, the validity of the model is verified, and the influence of solute mass fraction and shear rate on the apparent viscosity of the precursor liquid is analyzed based on the viscosity model.

The mechanical response of geotechnical granular materials exhibits multi-scale characteristics. The multi-scale simulations coupling the Finite Element Method (FEM) and Discrete Element Method (DEM) can effectively capture the multi-scale responses while maintaining high computational efficiency. A GPU-parallel FEM-DEM coupling code was developed based on the high-performance DEM software MatDEM, with computational parameters and results analyzed in conjunction with granular pore fractal characteristics. Firstly, multi-fractal theory was employed to investigate the spatial distribution characteristics of pores, identifying key fractal indices. Subsequently, the reliability of the FEM-DEM coupling code was verified through single element tests and biaxial compression tests. Finally, the meso-scopic responses of Representative Volume Elements (RVEs) at different locations were investigated based on biaxial compression tests. Results demonstrate that the pore spatial distribution within RVE exhibits multi-fractal characteristics. When the particle quantity exceeds 400, the self-similarity of pore spatial distribution ensures the stability of stress-strain responses output after homogenization of granular assemblies within RVE. The capacity dimension D₀ and singularity index α₀, which characterize the average information of pore distribution, show linear correlations with RVE volumetric strain. These indices can serve as internal variables reflecting the complexity of granular material spatial characteristics. This study provides an exploration for analyzing the macro-meso mechanical relationships in engineering-scale granular deposits.

In the process of energy storage and power generation in molten salt tanks, the ceramic particle layer at the bottom of the tank plays a critical role in load-bearing and thermal insulation. Under cyclic loading, analyzing the effects of particle compression, flow, and contact stress on the overall settlement of the ceramic particle layer provides an important basis for design. In this study, the Hertz-Mindlin contact model in EDEM software was employed to establish a discrete element particle simulation model for the ceramic particle layer at the bottom of the molten salt tank. Simulations were conducted for the compaction backfill process and full-tank working conditions, and a comparative analysis was performed to investigate the effects of pre-compaction processes and particle size distribution on the compression settlement and maximum equivalent stress of the ceramic particles, as well as the influence of randomness in discrete element analysis results. The results indicate that for a ceramic particle layer height of 1.6 m and a particle size range of 5~20 mm, adopting a segmented compaction backfill process under a 50 000-ton tank load results in a maximum internal particle stress of 18.1~21.8 MPa and an overall settlement of 20.44~29.6 mm. As the particle size increases, the maximum stress decreases, with a maximum stress of 12 MPa observed for particle sizes of 15~20 mm. However, the settlement increases significantly, reaching 184 mm. Therefore, a wide particle size distribution range is beneficial for reducing settlement. Considering these factors comprehensively, the optimal configuration is a particle size range of 5~20 mm with a segmented and repeated pre-compaction process. Accounting for the influence of particle randomness, the maximum stress of the ceramic particle layer is 23.16 MPa, and the maximum settlement is 23.5 mm, meeting the design requirements.

For the dynamic process of harvesting energy from water droplet impact by using piezoelectric beams, a water droplet impact force model was developed. Based on the Euler Bernoulli beam theory, an electromechanical coupling prediction model of piezoelectric cantilever beam was established. Droplet impact tests were conducted, the voltage output characteristics and dynamic response characteristics of the piezoelectric beams were analyzed. By comparing the experimental results and model prediction results under different impact conditions (droplet diameter D_d=2.4~4.4 mm and impact velocity V_d=1.0~3.4 m/s), the accuracy of the force electromechanical coupling model was verified. Results showed that there is a linear relationship between the maximum deformation of cantilever end and the peak voltage under the impact excitation of water droplets. Water droplets exhibit "rebound" and "splashing" characteristics under low and high Weber number conditions, respectively, and the experimental results are highly consistent with the predicted results of the model, verifying the applicability and accuracy of the model. As the cantilever length increases, the natural frequency and the bending stiffness of the system gradually decreases, the output voltage and the total energy harvested gradually increase; however, the electric energy density shows a trend of first increasing and then decreasing, reaching a maximum of d_E=4.27 mJ/m² when the cantilever beam length L=35 mm.