The Material Field Series Expansion (MFSE) method has gained attention in structural topology optimization problems owing to its advantages, including well-defined structure boundaries and dimensionality reduction. In this work, we propose a fluid topology optimization method based on MFSE to address a entropy variation minimization problem governed by the equations of compressible flow. The proposed method defines a bounded material field with spatial correlation to characterize flow channel topology and expands it into a linear combination of eigenvectors and coefficients through a series expansion approach, employing the modal truncation strategy to truncate low-order modes to reduce the dimension of the optimization design space. This method inherently prevents checkerboard patterns and mesh dependency while effectively decoupling design variables from computational grids. The effectiveness of the proposed method is evaluated through several 2D and 3D optimization cases. Numerical experiments demonstrate that MFSE achieves comparable objective function values and topological configurations to conventional density-based methods under identical volume constraints. Concurrently, this approach accomplishes an order of magnitude reduction in design variables, decreasing from O(104) to O(103), which directly improves computational efficiency. Parameter studies on truncation error and correlation length further demonstrate their controllability over both the number of optimization variables and the intricacy of resulting topological features. Crucially, these studies provide practical guidance for balancing optimization computational cost against solution accuracy.

To achieve continuous, linear, and reversible torque-limiting regulation in transmission systems, a multichamber torque-limiting magnetorheological coupling (MCTL-MRC) was developed. An equivalent current-torque model was established based on the rheological properties of magnetorheological fluid, and the key structural parameters and magnetomotive force were optimized using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) multi-objective optimization algorithm. Finite element simulations were performed to analyze the magnetic flux density and temperature rise of the MCTL-MRC under overload conditions. The peak temperature after one minute of overload slipping reached 83.87 degrees C, indicating that the equipment's temperature rise performance meets engineering requirements. Experimental results verified the feasibility of the proposed structure. The optimized MCTL-MRC achieved a maximum output torque of 56.25 N center dot m, a viscous torque of 5.60 N center dot m, and a response time of 980 ms. However, the measured maximum torque was lower than the design target of 70 N center dot m, indicating that further improvements in magnetic circuit design, chamber structure, and magnetorheological fluid distribution are required. The proposed design and optimization framework provides a useful reference for developing magnetorheological torque-limiting devices.

The elastic, plastic, and viscous properties of particle-reinforced polymer composites (PRPCs) serve as critical indicators for their mechanical behavior characterization and engineering applications. The indentation technique has gained prominence in assessing mechanical properties across various materials owing to its versatility and applicability. Moreover, the finite element model updating (FEMU) approach has been developed in conjunction with indentation techniques to elucidate elastoplastic properties, thereby providing insights for mechanical property optimization during material design. However, the commonly used load-displacement curve cannot capture the spatial distribution of deformation responses, thus limiting its ability to enhance the understanding of material properties and improve the calibration of constitutive models. In this study, an in-situ spherical indentation test was performed on a particle-reinforced polymer composite, integrated with X-ray micro-computed tomography (CT) imaging. A self-adaptive digital volume correlation (SA-DVC) approach was employed to measure the 4D spatiotemporal internal deformation fields. The elastic, plastic, and viscous parameters were identified with enhanced accuracy through enriching the cost function in the FEMU approach with the 4D deformation fields. The identification accuracy of typical elastic and viscous constitutive parameters was significantly improved, and the identification errors decreased by 11 % compared to the traditional identification method relying on the force-displacement curve. This work provides a framework capable of simultaneously identifying the elastic-viscoplastic parameters and reconstructing the internal viscous-plastic stress fields, facilitating a better understanding of the complex mechanical behaviors in PRPCs.

Thin-walled cylindrical shells in launch-vehicle structures are sensitive to geometric imperfections, making reliable lower-bound knockdown factor (KDF) prediction a requirement in preliminary design. Existing numerical approaches, including perturbation-based analyses and direct imperfection modeling, are applied on a case-by-case basis and require computational effort for each new geometry or tolerance level, which limits their efficiency in early-stage design. In parallel, widely used empirical KDF curves derived from historical databases, such as NASA SP-8007, were established under manufacturing and testing conditions that differ from those of aerospace structures, motivating complementary investigations. To address these challenges, this study develops a framework for predicting the lower-bound KDF of axially compressed cylindrical shells. The framework combines perturbation-driven sampling of imperfection-sensitive buckling responses using the Worst Multiple Perturbation Load Approach with surrogate-assisted finite element evaluations, followed by the construction of explicit KDF envelope relations through multi-gene genetic programming. This process enables generation of tolerance-aware datasets and their transformation into compact analytical expressions describing KDF variations across wide ranges of radius-to-thickness ratios, length-to-radius ratios, and imperfection amplitudes. Comparisons with benchmark experiments indicate that the proposed framework captures conservative lower-bound trends while maintaining consistency with test observations, providing a scalable and cost-effective tool for preliminary shell design.

Due to the lack of external installation space, energy harvesters for condition monitoring need to be embedded inside the hollow helicopter tail shaft. However, the absence of a stationary reference frame renders traditional separated schemes infeasible, while extreme centrifugal loads make conventional self-contained designs (e.g., cantilever beam or in-pipe sliding oscillator) highly susceptible to structural failure or friction jamming. Consequently, this paper proposes an embedded electromagnetic energy harvester based on a clamped-clamped beam architecture, using large-deformation geometric nonlinearity to reduce structural failure risk. A nonlinear electromechanical coupling model under composite motion conditions is established using the Euler-Bernoulli beam theory and Lagrange equations. The analysis demonstrates that rotational motion modulates gravity into periodic excitation. The competition between geometric nonlinear stiffness and centrifugal softening induces a pronounced hardening effect that significantly broadens the response bandwidth within the operating range. These competing effects lead to a dynamic bifurcation into a bistable regime at higher rotational velocities. Within the effective rotational speed range of 1000 to 2300 rpm, the prototype achieves a maximum steady-state load power of 108 mW. At 2024 rpm, the harvested energy supported a fully integrated real-time wireless vibration sensing node with a stable transmission distance exceeding 10 m, providing a promising technical solution for battery-free monitoring of helicopters.

A three-dimensional (3D) serpentine configuration is widely used in stretchable inorganic electronics, whose dynamic behaviour is essential in various service environments. In this study, a nonlinear dynamic model of a buckled 3D serpentine structure under base excitations is derived from the extended Lagrangian principle, with consideration of the specific local deformations. The obtained first order resonance frequency is consistent with previous results. The symplectic Runge-Kutta method is used to simulate the system, which has both high accuracy and long-term numerical stability. Rich nonlinear dynamic behaviours are revealed, including coexisting periodic and chaotic vibrations. Particularly, an essential structure parameter is identified as the ratio of the length of the unit cell to that of the serpentine configuration, which can effectively regulate the mechanical characteristics of the potential well of the structure. Various nonlinear dynamic behaviours under different applied loads and different structure parameters are found through calculating the fraction of initial conditions leading to chaos. The key geometric parameter can significantly affect the tendency of the serpentine interconnections to generate chaotic vibrations, and the change of this parameter will expand or shrink the chaos region in the phase space of the structure, indicating that complex dynamical behaviours in the buckled serpentine configuration can be tuned by the simple structure parameter. Consequently, the results of this investigation are useful for understanding complex dynamics of the buckled serpentine configuration, and for tuning the sensitivity of novel 3D serpentine-structured devices.

To address the performance degradation of heat sinks under uncertain operating conditions, this paper proposes a Robust Topology Optimization (RTO) framework for thermal–fluid system that integrates the Parametric Level Set Method (PLSM) with Non-Intrusive Polynomial Chaos Expansion (NIPCE). By interpolating the level-set function with Gaussian radial basis functions, the method transforms the Hamilton–Jacobi equation into ordinary differential equations, preserving smooth channel boundaries without re-initialization and enabling automatic hole nucleation. NIPCE models random inputs and computes statistical moments, which are combined into a robust objective as a weighted sum of expectation and standard deviation, with gradients evaluated via the discrete adjoint method. The framework is validated through three numerical examples. First, PLSM-based RTO produces clear, grayscale-free topologies and reduces the standard deviation by over 16% compared with deterministic designs. Second, studies on non-Gaussian distributions — bimodal, uniform, and lognormal — demonstrate intrinsic adaptability beyond conventional Gaussian assumptions. Third, under multi-source uncertainties (heat source intensity and inlet velocity), the optimizer identifies the dominant source and reconfigures the solid–fluid interface accordingly, achieving a distinct trade-off between nominal performance and robustness. Monte Carlo validation with 10,000 samples confirms that NIPCE predicts both expectation and standard deviation with relative errors below 1%, meeting engineering accuracy. These results establish the PLSM–NIPCE framework as a versatile and efficient tool for robust thermal–fluid design under diverse and multi-source uncertainties.

During the casting-rolling process of aluminum alloy for aircraft interior components, product quality often faces significant challenges due to uneven melt flow and heat transfer inside the casting nozzle. These issues can easily lead to defects such as thermal streaks. To achieve defect suppression and quality optimization, this paper innovatively developed a data-driven method for rapid optimization of process parameters. This approach integrates high-fidelity simulation, machine learning, and intelligent decision-making into a multi-objective optimization framework. A Computational Fluid Dynamics (CFD) model for the fluid-thermal coupling of the melt within the nozzle cavity was established, which accurately reveals the melt flow and heat transfer behavior, with its high fidelity being validated experimentally. A dataset encompassing key process parameters was constructed via Latin Hypercube Sampling (LHS). Subsequently, high-precision surrogate models (R-2 > 0.9) for four key indicators were developed using the Kriging method, effectively replacing the time-consuming high-fidelity numerical simulations. Building upon this, the Non-dominated Sorting Dung Beetle Optimizer (NSDBO) was introduced to efficiently solve the Pareto front, which was then coupled with the entropy-weighted TOPSIS method for objective decision-making to identify the optimal process parameter combination. The results demonstrate that the prediction errors for all objectives of the optimal solution are below 5%. The integrated performance of the fluid-thermal field inside the nozzle is significantly improved after optimization: the outlet velocity uniformity index increases from 0.839 to 0.865, the normalized Q-criterion value decreases by 19.04%, both the system temperature difference and filling time are considerably better than those under the initial conditions, and the engineering reliability of the optimized scheme is further validated through experimental testing. This research provides a robust data-driven framework and methodological support for transitioning the casting-rolling process from "high-fidelity simulation" toward "rapid and precise design".

This paper systematically regulates the microstructure and mechanical properties of a Co40Ni30Cr15.9Al7Ti7B0.1 (at.%) high-entropy alloy (HEA) through cold-rolling, recrystallization, and aging heat treatment processes. Recrystallization produces fine and uniform equiaxed grains (an average grain size: ∼26.2 μm), accompanied by a sparse dispersion of coherent L12 nanoprecipitates (25–70 nm, 23 vol%) within the FCC matrix. Subsequent aging at 800 °C for 4 hours promotes grain growth to about 31.1 μm and introduces a markedly higher density of L12 nanoprecipitates (15–90 nm, 58 vol%) into the FCC matrix. Mechanical property tests show that the CR alloy has a yield strength of 685 MPa, an ultimate tensile strength of 1238 MPa, and an elongation of 24.7%. In contrast, the CRA800-4 alloy exhibits significantly higher values of 1107 MPa, 1412 MPa, and 11.1%, respectively. Notably, the enhanced yield strength of the CRA800-4 alloy is mainly attributed to the greater volume fraction of L12 precipitates (58 vol%), which accounts for ∼73% of the total yield strength via precipitation strengthening. Deformation mechanism analysis reveals that the plastic deformation of the alloy is synergistically dominated by stacking faults and dislocation slip. Therefore, high-volume-fraction L12 nanoprecipitates can result in a good strength-ductility synergy in CoCrNi-based HEAs.

Automatic fiber placement (AFP) is an advanced composite manufacturing technology with precise fiber steering and rapid in-situ forming capability. However, out-of-plane wrinkling caused by geometric incompatibility during curved-surface placement remains a major limitation. This study proposes an integrated optimization framework combining geometric contact mechanics, high-fidelity finite element simulation, and deep learning to investigate wrinkle suppression under curved-surface conditions. A finite element model incorporating the nonlinear shear behavior of thermoplastic prepregs and interfacial viscous contact was developed. A MeshGraphNets-based graph neural network was further established as a physics-aware surrogate model for the placement process. The results indicate that the roller yaw attitude reshapes the stress state in wrinkle-prone regions by regulating shear deformation and stress paths, while compaction force enhances interlaminar constraint and consolidation. Their synergistic effect suppresses fiber buckling and interrupts wrinkle formation. The surrogate model achieved a mean prediction error of 3.95% and improved computational efficiency by 1224 times, providing a rapid analysis method for wrinkle suppression in curved-surface AFP. This method broadens the wrinkle-free processing window and improves placement quality.

In aerospace components, the optimization design of the triply periodic minimal surface (TPMS) lattice structure encounters issues of unified parameterization for discrete design variables and multiphysics coupling. A deep learning-driven multiobjective inverse optimization design framework for lightweight TPMS lattice structures with targeted thermal insulation and load-bearing performances was proposed in this study to circumvent non-differentiability challenges inherent in transitions across different unit cell types. Functionally graded TPMS lattice structures with adjustable structural parameters were constructed, and the coupled thermal-fluid-mechanical finite element analyses were performed to estimate their corresponding thermal and mechanical performances. A back propagation neural network (BPNN) was established as a surrogate model to capture the relationship between structural parameters and the resulting thermo-mechanical properties. The BPNN model was then integrated with the genetic algorithm (GA) for inverse design targeting required structural, thermal, and mechanical characteristics within discrete-continuous mixed design spaces. An adaptive dataset expansion (ADE) strategy was developed to generate a "deep learning-inverse design-coupled analysis" closed-loop error feedback system. Key findings revealed that the presented BPNN-GA framework achieves high accuracy (error: similar to 1%) in the multi-objective inverse optimization design of TPMS lattice sandwich structures under multiphysics coupling scenarios. The thermomechanical performances of the designed structure were further validated through thermal-fluid coupling tests. This study establishes a transferable closed-loop framework that bridges deep learning surrogate modeling with multiphysics-coupled inverse optimization design, enabling simultaneous targeting of thermal, mechanical, and structural requirements, thereby offering practical pathways for thermomechanical protection components in lightweight aerospace structures.

BACKGROUND:with the increasing prevalence of refractive surgery, its impact on intraocular pressure (IOP) measurement has gained significant attention. This study aimed to elucidate the mechanism by which femtosecond laser-assistedin situkeratomileusis (FS-LASIK)-induced geometric alterations of the cornea affect IOP measurements obtained with the Goldmann applanation tonometer (GAT). METHODS:IOP was measured with the GAT in patients undergoing FS-LASIK preoperatively and at 3 and 6 months postoperatively. A corneal finite element (FE) model, including aqueous humor for both pre- and post-operative states, was created to simulate the surgical volume required for correcting myopia of -2, -4, and -6 diopters (D). IOP values were calculated based on the principle of the GAT, and FE simulation results were compared with clinical IOP data measured using the GAT to validate the model. RESULTS:Clinical data from GAT measurements demonstrated a consistent decrease in IOP following surgery. FE results indicated that the postoperative IOP, ranging from 8.26 mmHg to 13.04 mmHg, decreased by 23%-51% compared to the preoperative IOP of 17.04 mmHg. Deeper ablation depths and larger optical zone diameters were associated with lower postoperative IOP. CONCLUSION:The FE analysis demonstrated a decreasing trend in postoperative IOP with increasing ablation depth and optical zone diameter. These findings provide valuable insights for guiding postoperative IOP monitoring, optimizing surgical parameters, and understanding postoperative corneal biomechanical behavior.

For hot-pressed carbon fiber fabric-reinforced poly (ether-ether-ketone) (CF/PEEK) composites, defects such as void induced by geometry design and processing parameters significantly impact mechanical performance. Studies have shown that the internal void size ranges from a few microns to several hundred microns, making it difficult to accurately evaluate the influence of void on structural integrity. This study proposes a multiscale analysis framework for evaluating the mechanical properties of porous fabric-reinforced composites. CF/PEEK composite specimens with different void content were prepared. X-ray computed tomography (CT) showed that there were almost no voids between the fiber bundles, while the voids inside the fiber bundles showed strong connectivity along the fiber direction. Increasing the hot-pressing time can significantly reduce the void size and void content. A typical representative volume element (RVE) model with actual fiber distribution was established based on cross-sectional scanning electron microscopy (SEM), and microstructural features such as void content and interface discontinuity were also considered. The mechanical behavior of materials at the microscale was studied by homogenization method and progressive damage analysis; a method for extracting voids from CT images and a gradient material distribution method for the representative volume cell (RVC) model were proposed. The progressive damage results of the established gradient RVC model, with the maximum deviation from the experimental average strength within 5.3 %. In summary, this framework enables efficient and accurate evaluation of microscale defects such as void content and interfacial discontinuities. It provides a novel approach for incorporating microstructural imperfections into the structural design of fabric-reinforced composites.

This study proposes a novel theoretical model to efficiently and accurately evaluate the dynamic pile-soil-pile interaction of inclined pile groups in layered soil by coupling the finite element method (FEM) and thin-layer method (TLM). The dynamic governing equations for the inclined pile group are formulated based on the three-dimensional Euler-Bernoulli beam theory and a local-global coordinate transformation. Subsequently, the TLM is adopted to describe the complex wave propagation within the layered soil, while Perfectly Matched Layers (PMLs) are incorporated to simulate the underlying semi-infinite elastic space to eliminate spurious reflections at the truncated boundaries. The dynamic Green’s functions of the layered soil are efficiently derived based on this coupled TLM-PMLs framework. By enforcing strict displacement compatibility and force equilibrium conditions at the pile-soil interface, the coupling of the inclined pile group and the layered soil is achieved, allowing for the solution of the dynamic impedance. The proposed model is validated by comparing its predictions with results of existing numerical solutions. An in-depth investigation is further conducted to reveal the influence of the geometric parameters on the dynamic bearing characteristics of the inclined pile group. The obtained conclusions can not only deepen the understanding of the spatial dynamic synergistic behavior of inclined pile groups, but also provide a reliable theoretical basis for foundation configuration optimization and refined anti-vibration design in practice.

Origami-inspired architectures offer a powerful route toward lightweight, reconfigurable, and programmable robotic systems. Yet, a unified mechanics framework capable of seamlessly bridging rigid folding, elastic deformation, and stability-driven transitions in compliant origami remains lacking. Here, we introduce a geometry-consistent modeling framework based on discrete differential geometry (DDG) that unifies panel elasticity and crease rotation within a single variational formulation. By embedding crease-panel coupling directly into a mid-edge geometric discretization, the framework naturally captures rigid-folding limits, distributed bending, multistability, and nonlinear dynamic snap-through within one mechanically consistent structure. This unified description enables programmable control of stability and deformation across rigid and compliant regimes, allowing origami structures to transition from static folding mechanisms to active robotic modules. An implicit dynamic formulation incorporating gravity, contact, friction, and magnetic actuation further supports strongly coupled multiphysics simulations. Through representative examples spanning single-fold bifurcation, deployable Miura membranes, bistable Waterbomb modules, and Kresling-based crawling robots, we demonstrate how geometry-driven mechanics directly informs robotic functionality. This work establishes discrete differential geometry as a foundational design language for intelligent origami robotics, enabling predictive modeling, stability programming, and mechanics-guided robotic actuation within a unified computational platform.

This study elucidates a multi-applicability mechanism of elastomer-toughened brittle thermoplastics polymers through experimental methods and multiscale analysis. Polyolefin elastomer (POE) and glycidyl methacrylatemodified POE (POE-GMA) were used to toughen thermoplastic polymers polyphenylene sulfide (PPS) and polybutylene terephthalate (PBT). It was found that small amounts of POE-GMA could enhance the fracture energy of PBT and PPS by 167 % and 415 %, while only sacrificing 5.3 %-11.6 % of strength or rigidity, and the lower the inherent toughness of the polymer, the better the toughening effect, whereas POE showed no significant effect. Molecular dynamics simulations indicate that the GMA groups enhance interactions between POE-GMA and polymers, promoting POE-GMA diffusion into the polymer matrix and improving dispersion. Further finite element modeling indicates that smaller and more dispersed elastomer particles can induce more microcracks, enhancing energy absorption and consequently increasing the fracture energy, thereby improving toughness. This multi-applicability mechanism provides crucial insights for designing polymer composites that balance toughness and rigidity.

Thin plates and shells are central to emerging technologies such as deployable space structures, wearable devices, and flexible electronics, where large geometric nonlinearities are not only unavoidable but often exploited for functionality. While the widely used bar-and-hinge model in the discrete differential geometry approach offers computational simplicity, it lacks physical consistency and suffers from mesh-dependent artifacts, limiting its predictive capability. In this technical brief, we show that the mid-edge-based formulation can provide an accurate and consistent simulation for thin plates and shells. By constructing discrete analogs of the first and second fundamental forms from a mesh and its edge-adjacent neighbors, the method naturally recovers in-plane and bending strain tensors and their associated strain energy. Benchmark comparisons against finite element simulations demonstrate that the mid-edge model achieves superior accuracy, stronger consistency, and faster convergence than the bar-and-hinge formulation. Crucially, the method delivers mesh-shape-independent convergence, enabling robust modeling of geometrically nonlinear responses on arbitrary meshes. These advantages make the framework highly suitable for rapid simulation, optimization, and inverse design of morphable and programmable plate structures, with potential applications in metasurfaces, kirigami, and origami-inspired systems.

Interfacial delamination is a common failure mode in fiber fabric/flexible polymer composites. Using aramid fiber fabric/thermoplastic polyurethane composites as a representative system, this study develops a multiscale model to predict interface peel strength. Guided by the model, an interface mechanical interlock structure is designed and geometrically optimized. Experiments demonstrate that the optimized mechanical interlock structure increases the interfacial peel strength by a factor of 9.6. The proposed multiscale modeling framework and mechanical interlock design strategy provide guidance for the interfacial strengthening of fiber fabric/flexible polymer composites.

BACKGROUND:Altered occlusal mechanics are critical regulators of the structure and function of the dentoalveolar and temporomandibular joints (DAJ, TMJ); however, the multiscale biological and mechanical consequences of sustained occlusal rise on the TMJ are not understood. OBJECTIVE:This study establishes a proof-of-concept for using an occlusal rise (OR) to investigate how the resulting chronic eccentric loading influences the DAJ and TMJ across mechanical, structural, and metabolic domains. METHODS:Rats were subjected to increased occlusal forces by placing a 0.5 mm metal wire and dental composite on the maxillary molars, producing a total occlusal rise of 1-1.5 mm. The DAJ and TMJ condylar bones in OR (N = 7) and control (N = 3) groups were evaluated using in vivo micro-CT at days 1, 14, and 35. One rat/group underwent 18F-NaF PET, complemented by autoradiography and histology. Finite element modelling (FEM) based on CT-derived geometries was used to evaluate TMJ reaction forces and stress under OR-loads. RESULTS:Widened PDL space and interradicular bone remodelling were accompanied by reduced BMD with relatively preserved BV/TV, indicating redistribution rather than uniform loss of mineralized tissue in the DAJ. Progressive changes in condylar physical properties, with deviations between anatomical and mechanical axes, reflected adaptive load transmission. Increased 18F-NaF in TMJ indicated elevated metabolic activity. Histology confirmed ectopic cartilage-like tissue formation. FEM demonstrated buffering of TMJ stresses followed by temporal structural reorganisation. CONCLUSIONS:Sustained OR drives coordinated, time-dependent remodelling across the DAJ-TMJ biomechanical unit through coupled mechanobiological adaptation, providing a mechanistic framework linking eccentric occlusal loading to TMJ remodelling and pathophysiology.