This study focuses on the integrated manufacturing process of sensors in unbonded flexible pipes for deep-sea mining engineering, investigating the sensor integration process with compensation reinforcement layers and their mechanical performance. A simulation model for the winding process of the compensation reinforcement layer in flexible pipes was established, systematically analyzing the influence patterns of winding tension and winding angles on sensor integration processes. Additionally, the effects of sensor quantity and winding angles on the tensile performance of integrated compensation reinforcement layers were explored. The results demonstrate that winding tension significantly affects sensor elongation rates, while winding angles predominantly influence stress distribution in the lining layer. The tensile performance of integrated compensation reinforcement layers shows minimal sensitivity to sensor quantity, and variations in winding angles within a specific range exhibit limited impact on anti-tensile properties. These findings reveal the correlation between sensor integration parameters and structural performance of flexible pipes, providing theoretical guidance for optimizing sensor integration processes in deep-sea flexible pipes.

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.

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.

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".

This study systematically investigates the influence of the wall slip length (L_s) on the statistical properties and flow structures of turbulent channel flow. The Navier slip boundary condition is applied at the boundary of turbulent channel flow, and direct numerical simulation (DNS) is employed to numerically explore the evolution of turbulence for L_s ranging from 0 (no-slip) to 0.1. The results reveal that as L_s increases, the viscous damping effect at the wall is substantially reduced, resulting in an overall elevation of the mean velocity profile. Within the viscous sublayer, the mean velocity increment exhibits a linear relationship with L_s, satisfying the relation . In the near-wall region, the turbulence fluctuation intensity demonstrates an enhanced dependence on L_s, with the intensification of Q2 (ejection) events leading to an elevated Reynolds stress peak that shifts closer to the wall. Analysis of wall-attached low-speed streaks indicates that, for a dimensionless wall-normal structure scale , both their number and volume increase significantly with rising L_s. Furthermore, it is found that the effects of the wall slip condition are confined to the near-wall region, while the outer inertia-dominated region continues to follow the scaling laws of no-slip wall turbulence.

Interface consistency and error convergence are central issues in concurrent multiscale computational methods, particularly critical for atomistic-to-continuum coupling models. However, existing theoretical studies remain limited and are mostly confined to one-dimensional settings. This work focuses on the multiresolution molecular mechanics (MMM) approach and systematically investigates the impact of various energy sampling schemes on interface consistency and error convergence. Two-dimensional square and triangular lattice models containing both atomistic and coarse-grained regions are constructed under bilinear element interpolation. The results show that interface secondary sampling schemes can significantly improve consistency in the interfacial region, with the scheme incorporating all neighboring layers achieving the best performance. Error analysis reveals that discretization error dominates the total error, and increasing the number of secondary sampling points effectively reduces the sampling error, particularly under tensile loading conditions. Moreover, both lattice types exhibit consistent error convergence behavior, demonstrating high generality of the method to different structures. This study highlights the advantages of energy sampling strategies in improving interface treatment and convergence behavior in MMM, providing theoretical support for the development of high-accuracy multiscale computational mechanics methods.

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.

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.

For the lightweight design problem of composite grid-stiffened sandwich cylindrical shells, this paper proposes an optimization strategy that couples the differential quadrature method (DQM) with intelligent algorithms. First, the buckling governing equations are established based on the smeared stiffener method and energy principle, which are efficiently solved using DQM and validated through finite element analysis. Subsequently, an artificial neural network (ANN) surrogate model and genetic algorithm (GA) are employed to achieve structural optimization. The results demonstrate that the critical buckling loads obtained via DQM agree well with finite element results, confirming the accuracy of DQM in analyzing the buckling of sandwich cylindrical shells. The ANN-based surrogate model exhibits high reliability in predicting the critical buckling loads of grid-stiffened sandwich shells. Moreover, the genetic algorithm, combined with theoretical results, efficiently yields lightweight design parameters. Case studies show that the optimized structure not only achieves significant weight reduction but also exhibits a substantial increase in critical buckling load.

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..