Search Results (1,028)

Mechanical models are capable of simulating the deformation and stress distribution of oocytes under external forces, thereby providing insights into the underlying mechanisms of intracellular mechanical responses. Interactions with micromanipulation tools involve forces like compression and punction, which are effectively analyzed using principles of solid mechanics. Alternatively, fluid–structure interactions, such as shear stress at fluid junctions or pressure gradients within microchannels, are best described by a multiphase flow model. Developing the two models instead of a single comprehensive model is necessary due to the distinct nature of cell–tool interactions and cell–fluid interactions. In this study, we developed a finite element (FE) model of porcine oocytes that accounts for the viscoelastic properties of the zona pellucida (ZP) and cytoplasm for the case when the oocytes interacted with a micromanipulation tool. Atomic force microscopy (AFM) was employed to measure the Young’s modulus and creep behavior of these subcellular components that were incorporated into the FE model. When the oocyte was solely interacting with the fluids, we simulated oocyte deformation in microfluidic channels by modeling the oocyte-culture-medium system as a three-phase flow, considering the non-Newtonian behavior of the oocyte’s components. Our results show that the Young’s modulus of the ZP and cytoplasm were determined to be 7 kPa and 1.55 kPa, respectively, highlighting the differences in the mechanical properties between these subcomponents. Using the developed layered FE model, we accurately simulated oocyte deformation during their passage through a narrow-necked micropipette, with a deformation error of approximately 5.2% compared to experimental results. Using the three-phase flow model, we effectively simulated oocyte deformation in microfluidic channels under various pressures, validating the model’s efficacy through close agreement with experimental observations. This work significantly contributes to assessing oocyte quality and serves as a valuable tool for advancing cell mechanics studies. Full article

To enhance the structural performance of concrete-filled steel tube (CFST) columns, various strengthening techniques have been proposed, including the use of internal steel stiffeners, external wrapping with carbon fiber-reinforced polymer (CFRP) sheets, and embedded steel elements. However, the behavior of concrete-filled stainless-steel tube (CFSST) columns remains insufficiently explored. This study numerically investigates the axial performance of square CFSST columns internally strengthened with embedded I-section steel profiles under biaxial eccentric loading. Finite element (FE) simulations were conducted using ABAQUS v. 6.2, and the developed models were validated against experimental results from the literature. A comprehensive parametric study was performed to evaluate the effects of several variables, including concrete compressive strength (f_cu), stainless-steel yield strength (f_y), the depth ratio between the stainless-steel tube and the internal I-section (D_st/D_si), biaxial eccentricities (e_x and e_y), and tube thickness (t). The results demonstrated that the axial performance of CFSST columns was most significantly influenced by increasing the D_st/D_si ratio and load eccentricities. In contrast, increasing the concrete strength and steel yield strength had relatively modest effects. Specifically, the ultimate axial capacity increased by 9.97% when the steel yield strength rose from 550 MPa to 650 MPa and by 33.72% when the tube thickness increased from 3.0 mm to 5.0 mm. A strength gain of only 10.23% was observed when the concrete strength increased from 30 MPa to 60 MPa. Moreover, the energy absorption index of the strengthened columns improved in correlation with the enhanced axial capacities. Full article

The skeleton curve plays a crucial role in evaluating the seismic capacity of damaged structures. The research explored the application of data-driven machine learning approaches to predict the skeleton curves of earthquake-damaged reinforced concrete (RC) columns. Various machine learning methods, including Lasso regression, K-nearest neighbor (KNN), support vector machine (SVM), decision tree, and AdaBoost, were employed to develop a machine learning prediction model (MLPM) for seismic-damaged RC columns. A substantial dataset for the MLPM was derived from finite element (FE) analysis results. The input parameters for the machine learning models included the design specifications of the numerical column model and the damage index (DI), while the coordinates of key points on the skeleton curves served as the output parameters. The findings indicated that the K-nearest neighbor algorithm exhibited the best predictive performance, particularly for the yielding and peak points. The most influential input feature for predicting peak strength was the shear span-to-effective depth ratio, followed by the DI. The ML-based models demonstrated higher efficiency than numerical simulations and theoretical calculations in predicting the skeleton curves of damaged RC columns. Full article

Thin-walled structures have been extensively adopted as energy absorbers in various engineering fields. The energy accumulated in the coal and rock is released instantly, resulting in varying degrees of damage and failure to support equipment. To improve the crushing performance of underground support equipment, a metal thin-walled tube with high-bearing capacities is placed in the column as an energy-absorbing column. Based on the characteristics of non-dimensional parameters governing the crashworthiness of thin-walled tubes by the author’s team, a type of high-performance bi-tubular tube (HPBT) with mixed multicellular configurations is innovatively proposed. First, the finite element models of the HPBTs are established in LS-DYNA, and the accuracy of the FE model is verified by crushing tests. Second, the theoretical model of the mean crushing force (MCF) is derived. Moreover, the effects of the cross-sectional shapes and the wall thickness gradient distribution on the deformation modes and crashworthiness are investigated. The results show that the design strategies of the bi-tubular structures mixed multicellular configurations significantly improve the values of ω. The MCF of HPBT_C2 is 4458.0 kN, which is 28% and 56% higher than those of the conventional circular tube and square tube. The theoretical MCF is consistent with the simulated MCF, with a maximum discrepancy of 6.0%. The gradient distribution (k) of wall thickness significantly affects the crushing behaviors of the HPBT. Considering the energy absorption efficiency, the crushing stability, and the wall thickness gradient distribution, the HPBT_C2 with k = 0.6 has the best overall performance. The results can provide insights and guidelines for designing energy absorption devices with superior crashworthiness for support equipment. Full article

Understanding the mechanical behavior of implanted neural electrode arrays is crucial for BCI development, which is the foundation for ensuring surgical safety, implantation precision, and evaluating electrode efficacy and long-term stability. Therefore, a reliable FE models are effective in reducing animal experiments and are essential for a deeper understanding of the mechanics of the implantation process. This study established a novel finite element model to simulate neural electrode implantation into brain tissue, specifically characterizing the nonlinear mechanical responses of brain tissue. Synchronized electrode implantation experiments were conducted using ex vivo porcine brain tissue. The results demonstrate that the model accurately reproduces the dynamics of the electrode implantation process. Quantitative analysis reveals that the implantation force exhibits a positive correlation with insertion depth, the average implantation force per electrode within a multi-electrode array decreases with increasing electrode number, and elevation in electrode size, shank spacing, and insertion speed each contribute to a systematic increase in insertion force. This study provides a reliable simulation tool and in-depth mechanistic analysis for predicting the implantation forces of high-density neural electrode arrays and offer theoretical guidance for optimizing BCI implantation device design. Full article

The prestressed concrete-filled double skin steel tube (CFDST) lattice tower has emerged as a promising structural solution for large-capacity wind turbine systems due to its superior load-bearing capacity and economic efficiency. The steel–concrete composite adapter (SCCA) is a key component that connects the upper tubular steel tower to the lower lattice segment, transferring axial loads. However, the compressive behaviour of the SCCA remains underexplored due to its complex multi-shell configuration and steel–concrete interaction. This study investigates the axial compression behaviour of SCCAs through refined finite element simulations, identifying diagonal extrusion as the typical failure mode. The analysis clarifies the distinct roles of the outer and inner shells in confinement, highlighting the dominant influence of outer shell thickness and concrete strength. A sensitivity-based parametric study highlights the significant roles of outer shell thickness and concrete strength. To address the high cost of FE simulations, a 400-sample database was built using Latin Hypercube Sampling and engineering-grade material inputs. Using this dataset, five neural networks were trained to predict SCCA capacity. The Dropout model exhibited the best accuracy and generalization, confirming the feasibility of physics-informed, data-driven prediction for SCCAs and outperforming traditional empirical approaches. A graphical prediction tool was also developed, enabling rapid capacity estimation and design optimization for wind turbine structures. This tool supports real-time prediction and multi-objective optimization, offering practical value for the early-stage design of composite adapters in lattice turbine towers. Full article

This study aimed to determine optimal forming conditions by comparing the compaction behavior and microstructural characteristics of two Fe-based Soft Magnetic Composite (SMC) powders, Somaloy 700HR 5P and Somaloy 130i 5P. A full factorial design was employed with powder type, compaction temperature, and punch speed as variables. Finite element modeling (FEM) using experimentally derived properties predicted density and stress distributions in toroidal geometries. 700HR 5P exhibited higher stress under most conditions, while both powders showed similar axial density gradients. Experimental results validated the simulations. SEM analysis revealed that 130i 5P had fewer microvoids and clearer particle boundaries. As revealed by TEM-EDS analyses, after heat treatment, both powders exhibited a tendency for the insulation layers to become more uniform and continuous. The insulation layer of 700HR 5P was relatively thicker but retained some pores, whereas that of 130i 5P was thinner yet exhibited smoother and more continuous coverage. XRD analysis indicated that both powders retained an α-Fe solid solution. These results demonstrate that powder properties, composition, and insulation stability significantly influence compaction and microstructural evolution. This work systematically compares the formability and insulation stability of two commercial Somaloy powders and elucidates process–structure–property relationships through an application-oriented evaluation integrating experimental design, FEM, and microstructural characterization, providing practical insights for optimal process design. Full article

This work proposes a mid-fidelity load-mapping method for the structural analysis of semisubmersible floating offshore wind turbine substructures. Building on a hybrid linear potential flow and strip-theory dynamic analysis, the method maps hydrodynamic, current, hydrostatic, gravitational, inertial, mooring, and turbine loads onto a shell-based finite element (FE) model. The functionality of the proposed method is demonstrated through two case studies involving ultimate limit state analysis of a structurally reinforced OC4 DeepCwind semisubmersible platform. The analyses were conducted for two design load cases (DLCs) formulated to represent the metocean conditions at the Utsira Nord site, located off the coast of Norway. The accuracy of the mapped hydrostatic and potential flow loads is validated against dynamic simulation data, while a mesh convergence study is used to ensure reliable FE model performance. Results show that the highest von Mises stresses occur at unsupported heave-plate regions, internal stiffeners, and welded joints, with peak stresses safely below the steel’s yield strength. The more severe conditions of DLC 6.1 lead to a broader distribution of high-stress locations compared to DLC 1.6 but only a modest increase in peak stress. Full article

Finite element (FE) modeling has emerged as a powerful computational approach in cardiovascular biomechanics, enabling detailed simulations of myocardial stress, strain, and hemodynamics, which are challenging to measure with conventional imaging techniques. This narrative review explores the progression of cardiac FE modeling from research-focused applications to its increasing integration into clinical practice. Specific attention is given to the mechanical effects of myocardial infarction, the limitations of conventional LV volume-reduction surgeries, and novel therapeutic approaches like passive myocardial reinforcement via hydrogel injections. Furthermore, the review highlights the critical role of patient-specific FE simulations in optimizing LV assist device parameters and guiding targeted device placements. Cutting-edge developments in artificial intelligence-enhanced FE modeling, including surrogate models and precomputed simulation databases, are examined for their potential to facilitate real-time, personalized therapeutic decision-making. Collectively, these advancements position FE modeling as an essential tool in precision medicine for structural heart disease. Full article

The interaction between tires and snow layer is fundamental for vehicle safety on snowy roads. Due to the instantaneous high torque output characteristics of electric vehicles, they are more prone to slipping when driving in snow, which exacerbates the complexity of tire–snow interaction. In order to construct a more accurate tire–snow interaction model in Northern China, the Bekker formula is introduced to establish the snow pressure–sinkage relationship formula, and the parameters are calibrated by disk experiments. Then the improved tire–snow interaction model is proposed by combining the use of the brush model on the rigid road surface and the dynamic discussion of the tire’s motion behavior on the snow. A coupled finite element (FE) tire model and discrete element (DE) snow terrain model are established, with interactions governed by snow–rubber contact mechanics. The simulation tests the sinking depth of tires on snowy road surface under different slip rates and different loads, as well as the force on tires. The model provides high-precision input to the EV snow traction control algorithm to optimize motor torque distribution to improve energy efficiency. By comparing and analyzing with theoretical values, the traditional empirical model, and the modified physical model, it is finally concluded that the modified model has better reliability than the original model. Compared with the empirical model, the improved model reduces the vertical stress prediction error from 5% to less than 1%, and the motion resistance error from 6% to approximately 2%, providing high-precision input for the snow traction control of electric vehicles. Full article

Predicting buckling in large-scale composite structures is hindered by the need for highly detailed Finite Element (FE) models, which are computationally expensive and impractical for early-stage design iterations. This study introduces a macromodelling buckling framework that reduces those models to plate-level size without sacrificing accuracy. An equivalent bending stiffness matrix is derived from strain–energy equivalence, rigorously retaining orthotropic in-plane terms, bending–extensional coupling, and—crucially—the eccentricity of compressive loads about an unsymmetrically stiffened mid-plane, effects overlooked by conventional Parallel-Axis smearing. These stiffness terms contribute to closed-form analytical solutions for homogeneous orthotropic plates, providing millisecond-level evaluations ideal for gradient-based design optimisation. The method is benchmarked against detailed FE simulations of panels with three to ten stringers under longitudinal and transverse compression, showing less than 5% deviation in critical load prediction. Its utility is demonstrated in the sizing optimisation of the upper cover of a scaled Airbus A330 composite wing-box, where the proposed model explores the design space in minutes on a standard workstation, orders of magnitude faster than full FE analyses. By combining analytical plate theory, enhanced smearing, and rapid optimisation capability, the framework provides an accurate, ultra-fast tool for buckling analysis and the preliminary design of large-scale stiffened composite structures. Full article

This research presents a novel methodology for lightweighting and cost reduction of components with high structural demands by integrating advanced design and manufacturing techniques. Specifically, it combines topology optimization (TO) with additive manufacturing (AM), also known as 3D printing. Unlike conventional approaches, the proposed method first determines the optimal geometry using an artificially stiff material, and only then evaluates real materials for structural and manufacturing feasibility. This design-first, material-second strategy enables broader material screening and maximizes weight reduction without compromising performance. The proposed workflow is applied to the design of a turbofan air intake—an aeronautical component operating under supersonic conditions—addressing both structural integrity and manufacturing feasibility. Three materials from distinct classes are assessed: two metallic alloys (aluminum alloy 6061 and titanium alloy, Ti6Al4V) and a high-performance polymer (polyetheretherketone, PEEK). This last option is preliminarily discarded after being analyzed for this specific application. Finite element (FE) simulations are used to evaluate the mechanical behavior of the optimized geometries, including bird-strike conditions. Among the evaluated manufacturing techniques, Selective Laser Melting (SLM) is identified as the most suitable for the metallic materials selected, providing an effective balance between performance, manufacturability, and aerospace compliance. This study illustrates the potential of TO–AM synergy as a sustainable and efficient design approach for next-generation aerospace components. Simulation results demonstrate a weight reduction of up to 71% while preserving critical functional regions and maintaining structural integrity in Al 6061 and Ti6Al4V cases, under the diverse loading conditions typical of real flight scenarios, while PEEK remains an attractive option for uses where mechanical demands are less stringent. Full article

This study investigates the sustainable production of NdFeB permanent magnets using powder extrusion molding (PEM) with in situ magnetic alignment, utilizing recycled powder from an end-of-life (Eol) wind turbine magnet obtained via hydrogen processing of magnetic scrap (HPMS). Finite Element Method (FEM) simulations were conducted to design and optimize alignment tool geometries and magnetic field parameters. A key challenge in the PEM process is achieving effective particle alignment while the continuous strand moves through the magnetic field during extrusion. To address this, extrusion experiments were performed using three different alignment tool geometries and varying magnetic field strengths to determine the optimal configuration for particle alignment. The experimental results demonstrate a high degree of alignment (B_r/J_s = 0.95), exceeding the values obtained with PEM without an external magnetic field (0.78). The study confirms that optimizing the alignment tool geometry and applying sufficiently strong magnetic fields during extrusion enable the production of anisotropic NdFeB permanent magnets without post-machining, providing a scalable route for permanent magnet recycling and manufacturing. Moreover, PEM with in situ magnetic particle alignment allows for the continuous fabrication of near-net-shape strands with customizable cross-sections, making it a scalable approach for permanent magnet recycling and industrial manufacturing. Full article

Background: The knee joint performs a vital function in human movement, supporting significant loads and ensuring stability during daily activities. Methods: The objective of this study was to develop and validate a subject-specific framework to model knee flexion–extension by integrating 3D gait data with individualized musculoskeletal (MS) and finite element (FE) models. In this proof of concept, gait data were collected from a 52-year-old woman using Xsens inertial sensors. The MS model was based on the same subject to define realistic loading, while the 3D knee FE model, built from another individual’s MRI, included all major anatomical structures, as subject-specific morphing was not possible due to unavailable scans. Results: The FE simulation showed principal stresses from –28.67 to +44.95 MPa, with compressive stresses between 2 and 8 MPa predominating in the tibial plateaus, consistent with normal gait. In the ACL, peak stress of 1.45 MPa occurred near the femoral insertion, decreasing non-uniformly with a compressive dip around –3.0 MPa. Displacement reached 0.99 mm in the distal tibia and decreased proximally. ACL displacement ranged from 0.45 to 0.80 mm, following a non-linear pattern likely due to ligament geometry and local constraints. Conclusions: These results support the model’s ability to replicate realistic, patient-specific joint mechanics. Full article

Cold-formed steel (CFS) channel beams are increasingly used as primary structural elements in modern construction due to their lightweight and high-strength characteristics. To accommodate building services, these members often feature perforations—typically circular and unstiffened—produced by punching. Recent studies indicate that adding edge stiffeners, particularly around circular web openings, can improve flexural strength. Extending this idea, attention has shifted to hexagonal web perforations; however, limited research exists on the bending performance of hexagonal cold-formed steel channel beams (HCFSBs). This study presents a detailed nonlinear finite element (FE) analysis to evaluate and compare the flexural behaviour of HCFSBs with unstiffened (HUH) and edge-stiffened (HEH) hexagonal openings. The FE models were validated against experimental results and expanded to include a comprehensive parametric study with 810 simulations. Results show that HEH beams achieve, on average, a 10% increase in moment capacity compared to HUH beams. However, when evaluated using current Direct Strength Method (DSM) provisions, moment capacities were underestimated by up to 47%, particularly in cases governed by lateral–torsional or distortional buckling. A reliability analysis confirmed that the proposed design equations yield accurate and dependable strength predictions. Full article