Triply periodic minimal surface (TPMS) lattices are promising lightweight impact absorbers, but high-fidelity finite element modeling is costly, and uniform designs can develop high stress peaks during densification. This study presents a voxel-based finite element framework and a topology–density dual-gradient (TDDG) TPMS absorber. A convergence study selected a voxel size of 0.33 mm, yielding a 1.11% relative-density error and converged mechanical responses. Compared with a converged C3D4 tetrahedral model, the C3D8R voxel model reduced wall-clock time from 4222 to 497 s. Following validation against quasi-static compression tests, P, G, and IWP topologies at 20%, 30%, and 40% relative densities were screened. IWP20 and P40 were assigned to the impact- and support-facing regions and connected by normalized sigmoid interpolation. Under a 125 J impact, simulations predicted that TDDG-IWP20-P40 reduced peak nominal impact stress by 32.2% and 24.6% relative to U-P30 and DG-P20-P40, respectively, while maintaining comparable SEA. Additional simulations at 62.5 and 160 J confirmed lower peak stress than U-P30, with the added benefit over density-only grading becoming more pronounced at higher impact energy. Progressive crushing and delayed densification demonstrate the potential of TDDG TPMS absorbers for impact-load mitigation and future aerospace buffer designs.

Fission-gas-induced porosity significantly affects the irradiation creep behavior of U-10Mo alloy, which is critical for the safe and reliable design of fuel elements. In this study, a representative volume element (RVE) model with randomly distributed pores is developed to investigate the porosity-dependent irradiation creep of U-10Mo alloy. Based on homogenization theory and macroscale dimensional variation, the effective creep strain and creep rate are obtained, and the contributions of the U-10Mo skeleton and pores to the macroscale creep are quantitatively distinguished. Systematic parametric studies examine the effects of fission rate, tensile stress, and porosity on the effective irradiation creep response. The results show that (1) the effective creep strain scales linearly with both fission rate and applied stress and increases significantly with porosity; (2) the creep deformation of the U-10Mo skeleton dominates the macroscale creep, contributing from approximately 85% at 10% porosity to approximately 63% at 25% porosity, while the pore contribution increases with increasing porosity; (3) a porosity-dependent effective irradiation creep rate model is established, with only two fitting parameters, and it exhibits good internal consistency and extrapolability. Additional simulations at porosities of 5%, 18%, and 28% agree well with the fitting curve. The model is further validated by a U-10Mo/Al monolithic fuel element irradiation-induced deformation simulation, where the predicted thickness deformation shows reasonable agreement with experimental data. This work provides a practical and physically grounded model for multi-scale thermo-mechanical coupled analyses and engineering-scale fuel performance simulations of porous U-10Mo fuels.

The quality factor is a key metric for evaluating the performance of micro-electro-mechanical and nano-electro-mechanical system resonators. Although reducing device dimensions enhances resonator sensitivity, it also intensifies energy dissipation, thereby degrading the quality factor. Among the dominant dissipation mechanisms in micro-/nano-resonators, support loss is strongly influenced by surface effects at small scales, particularly the surface elastic modulus and initial surface stress. In this study, support loss in a double-clamped micro-/nano-beam resonator with surface effects incorporated is investigated. A dynamic model incorporating the surface elastic modulus and initial surface stress is developed based on Euler–Bernoulli beam theory and Gurtin–Murdoch surface elasticity theory. A quality-factor calculation method is then established by combining elastic wave radiation in the supports with an energy-based formulation. The theoretical predictions are validated using a three-dimensional finite element model comprising the resonator with a surface layer, supports, and a perfectly matched layer. Further, the effects of the surface elastic modulus, initial surface stress, characteristic size, and dimensionless geometric parameters on support loss are examined. The results show that the surface elastic modulus and initial surface stress increase support loss and reduce the quality factor, with the initial surface stress exhibiting the stronger influence. The influence of these parameters becomes more pronounced as the characteristic size decreases, and variations in the length-to-thickness and width-to-thickness ratios further modify their contribution to support loss. Mechanistically, surface effects alter the effective bending stiffness and axial force, thereby changing the dynamic loads transmitted to the supports and resulting elastic-wave radiation. These findings provide theoretical insights for the design of high-quality-factor micro-/nano-devices.

Real-time monitoring of wind turbine aeroelastic damping is crucial for dynamically adjusting operational strategies and enhancing turbine stability and economic efficiency. However, since aeroelastic damping cannot be directly measured and effective industrial methodologies remain limited, this study proposes an innovative hybrid prediction framework for aeroelastic damping of wind turbine blades based on field-measured turbine data. First, a general model for calculating blade root reaction forces was developed using blade element momentum theory, considering multiple influencing factors such as motor torque, gravitational force, and centrifugal force. Linear regression and decision tree algorithms were employed to identify key coefficients in the theoretical model, thereby providing accurate hub load inputs for finite element (FE) calculations of tower aeroelastic damping through blade physical modeling. Second, a full-scale FE model of the wind turbine was constructed in Abaqus, where dynamic responses were computed using hub axial forces as inputs and compared with field data to obtain aeroelastic damping values, yielding high-quality labeled data for training machine learning models. Finally, an aeroelastic damping dataset was generated through data analysis and downsampling, and a long short-term memory recurrent neural network was trained as the prediction model. Simulation results demonstrated high accuracy, with mean prediction errors below 2.2% and maximum errors below 2.5% on real turbine datasets. In addition, experimental validation further confirmed the effectiveness of the proposed method. The model features a computationally efficient architecture, strong real-time applicability for high-dimensional inputs, and considerable potential for practical implementation.