Search Results (3,547)

The mechanical behavior of a composite is determined by the value of the engineering constants for the composite under consideration. If we study a homogeneous and isotropic composite, then two engineering elastic constants are needed to characterize the material; if we refer to a transversely isotropic composite, five elastic constants are needed. For more complex materials it can be necessary to determine more elastic constants in order to obtain the behavior of the composite in practical applications. In this paper, the authors present the main classic methods for calculating the engineering constants of a fiber composite material that are used in parallel with the finite element method (FEM) and highlight the advantages (and disadvantages) of using direct FEM to achieve this. The arrangement of identical fibers provides regularities that allow for easier calculations and, in some cases, the application of simple methods. The results that have already become classics, current results, and unusual examples are all critically presented in this study. All of these findings are discussed in relation to the use of the FEM, either as the primary calculation method or as a useful aid in the application of classical methods. The paper focuses on presenting research on the use of FEM for this purpose. For the different approaches discussed and for the area overall, future research directions are emphasized. Full article

Two reconstruction methods for constraint and load parameters of aero-engine pipelines based on intelligent optimization algorithms are proposed in this paper. First, a simplified finite element model (FEM) of the aero-engine pipeline structure is established, and its reliability is validated by comparing simulation data with experimental data. Second, a reconstruction algorithm for spring constraint parameters and pipeline load parameters based on the improved particle swarm optimization (IPSO) algorithm is developed on the MATLAB data analysis and ANSYS simulation platforms, which completes the reconstruction calculation of parameters such as spring constraint stiffness and applied harmonic excitation. For harmonic excitation parameter reconstruction, the maximum error of this algorithm reaches 24.9%, revealing its significant inapplicability to load parameter reconstruction. To solve this problem, a load reconstruction method based on the conjugate gradient method (CGM) is further proposed to achieve accurate reconstruction of pipeline load parameters, which mitigates the large reconstruction error of the IPSO algorithm under working conditions with multiple loads. Under 5% noise interference, the maximum error of the CGM is merely 5.16%. Finally, experimental verification of harmonic excitation amplitude reconstruction is performed using the CGM with lower reconstruction errors. Experimental results indicate that the maximum error is 14.24% for harmonic excitation amplitude reconstruction, which verifies the high applicability of the conjugate gradient algorithm to load reconstruction of aero-engine pipelines. Full article

In this work, plasmonic photoconductive antenna (PCA) structures with different grating-width and gap configurations were numerically investigated to evaluate their influence on transient-current generation and terahertz (THz) emission performance. Two geometrical scaling strategies were considered: a fixed-gap configuration with a constant 100 nm photoconductive gap and a proportional-gap configuration in which the gap size was equal to the grating width. Three-dimensional finite element method (FEM) simulations were performed to analyze transient carrier dynamics, THz pulse electric-field behavior, and frequency-domain spectral response under 800 nm optical excitation. The results demonstrate that reducing the inter-grating gap enhances plasmonic near-field confinement and carrier localization near the metal–semiconductor interface, leading to stronger transient-current responses and enhanced THz characteristics. Spatial field and carrier-distribution analyses further confirmed improved electric-field localization and carrier confinement for the fixed-gap structures. In addition, voltage-dependent investigations showed that increasing the applied bias voltage strengthens carrier acceleration and enhances the simulated THz response within the investigated operating range. The results further demonstrate that the observed enhancement is governed not only by grating periodicity but also by the grating-width/gap-size ratio, highlighting the importance of geometrical fill-factor optimization. Polarization-dependent simulations confirmed the plasmonic origin of the enhanced transient-current generation and THz emission. These findings demonstrate that optimal THz performance arises from a balanced interplay between plasmonic field localization, optical absorption, and carrier-transport dynamics, providing design guidelines for the optimization of plasmonic THz PCAs. Full article

In this study, an advanced surrogate model is proposed to simultaneously predict five key output variables, including deformation, stress, temperature, current density, and resonance frequency. This study used two models: Gaussian Process Regression (GPR) and an ensemble model based on Random Forest and XGBoost. By generating 10,000 design samples using the Latin Hypercube sampling method and performing simulations in COMSOL Multiphysics, as well as applying eight preprocessing methods, GPR achieved a mean absolute percentage error (MAPE) between 0.81% and 2.58%, whereas the ensemble model’s MAPE ranged from 3.05% to 9.20%. The ensemble model offers substantially faster training, whereas GPR achieves higher prediction accuracy across all output variables. Additionally, a 5-fold cross-validation scheme was implemented to ensure reliable model evaluation. This surrogate model, achieving multi-objective prediction with strong scalability due to efficient preprocessing and sampling strategies, is an effective step in reducing computational costs and accelerating the design process of MEMS actuators. Full article

Integrating neural and muscular signals into wearable robotics enables adaptive assistance during real-world tasks. This study proposes a multimodal neural interface for passive exoskeletons that combines electroencephalography (EEG) and electromyography (EMG) signals to classify motor gestures and estimate real-time cognitive and muscular effort, supported by finite-element-based biomechanical modeling. The system was implemented on the Ottobock Shoulder X passive exoskeleton© and validated using synchronous EEG–EMG acquisition via the LiveAmp platform©, a commercially available platform that was not developed specifically for this study. A hybrid CNN–LSTM architecture with deep fusion was employed to enhance robustness and responsiveness under realistic operating conditions. This study proposes a multimodal neural interface for the software-level adaptive assistance of passive upper-limb exoskeletons. While the physical device maintains a static mechanical profile, the proposed digital framework achieves adaptation by interpreting the user’s physiological and motor states. Ten healthy participants performed three functional tasks (screwing, moving the box, and lifting the box) under five assistive conditions. Finite element modeling (FEM) was used to characterize the torque–angle relationship of the passive exoskeleton and to support the interpretation of experimentally observed assistive torque profiles. The FEM model, used as an offline biomechanical analysis tool to aid in the interpretation of experimental results, has not been integrated into the real-time control loop. Results showed an average classification accuracy of 90%, an F1-score of 0.85, and inference latency below 180 ms, confirming real-time applicability. Cognitive indices such as the Cognitive Load Index (CLI) and Frontal Asymmetry Index (FAI) enabled adaptive modulation of assistance strategies without requiring active actuation, thereby preserving the device’s intrinsic passive nature. Comparative torque analysis highlighted the ergonomic benefits of passive systems in mid-range postures, while Finite Element Method (FEM) supported analysis clarified their limitations under highly dynamic loads compared to active solutions. These findings advance multimodal brain–machine interfaces for wearable robotics by integrating physiological sensing, deep learning, and biomechanical modeling, offering a safe, energy-efficient, and adaptive approach with potential rehabilitation, occupational ergonomics, and human–robot applications. Full article

This study presents a closed-form analytical solution for large-deformation pressure-induced stress and displacement fields in thick-walled, functionally graded (FG) hyperelastic polyvinyl chloride (PVC) cylinders subjected to internal pressure. The formulation inherently satisfies incompressibility—an aspect not guaranteed by standard finite element methods (FEMs)—and provides explicit expressions for all stress and deformation components. Using a Mooney–Rivlin model with an exponential–logarithmic gradation law, the study examines bi-layer and tri-layer configurations under varying property-changing scenarios. The governing equations are reduced to a single nonlinear scalar relation for the radial mapping constant, ensuring computational efficiency. Analytical predictions demonstrate excellent agreement with FEM results (errors < 1%) and recover homogeneous limits, and demonstrate that continuous gradation significantly reduces stress concentrations compared to discrete layering. The proposed model offers an efficient tool for designing pressure-resistant FG hyperelastic components for engineering applications such as pipes, hoses, biomedical devices, and protective casings. Full article

Time-fractional diffusion equations (TFDEs) are essential for modeling anomalous transport in heterogeneous media, but high-fidelity long-time simulations face two bottlenecks: the $O\left(N^2\right)$ complexity of non-local fractional derivatives, and the spatial truncation error of polynomial-based finite element methods (FEMs) when resolving oscillatory plumes or singular sources. We propose a framework combining a C-Bézier FEM for spatial approximation with a fast L1 temporal discretization. By coupling the shape parameter of the C-Bézier basis to the mesh size ($\mu =\pi h$), the scheme reproduces trigonometric profiles of the corresponding frequency exactly; for solutions whose spatial part lies in the C-Bézier space this eliminates the spatial truncation error and drives the associated error constant to near zero. A sum-of-exponentials (SOE) approximation reduces the temporal complexity from $O\left(N^2\right)$ to $O\left(N\right)$ and storage to $O\left(1\right)$, enabling scalable 3D simulation. We prove the optimal $O({\tau }^{2-\alpha }+h^{k+1})$ convergence, and numerical experiments confirm these rates. For profiles matched by the basis, the method yields substantially smaller errors than Lagrange FEM; for a general solution outside the C-Bézier space, the two methods share the same order and comparable error magnitudes, so the gains are specific to fields reproduced by the basis. We further examine low-regularity scenarios, including discontinuous interfaces and Dirac-delta injections. Full article

Low-frequency sound insulation remains a major challenge for conventional passive materials, as improved attenuation is usually achieved at the expense of increased thickness and mass. In this work, a smooth fixed third-order ring-curve fractal-maze acoustic metamaterial is proposed for compact low-frequency sound insulation, and a physics-guided long short-term memory–physics prediction surrogate network (LSTM–PPS-Net) tandem framework is developed for its full-spectrum inverse design. Different from conventional Hilbert-type, right-angled, or sharply folded labyrinthine structures, the proposed topology uses recursively arranged curved channels to extend the effective acoustic propagation path and enhance phase accumulation within a limited space. Based on this mechanism, four physically meaningful parameters, namely slit width d, characteristic radius R₃, wall thickness t_w, and inter-column spacing l_E, are selected to construct a low-dimensional design space. A COMSOL–MATLAB automated finite-element method (FEM) workflow is established to generate 1000 valid transmission-loss (TL) spectra over 100–1700 Hz with a 5 Hz interval. For forward prediction, PPS-Net is developed by integrating geometry encoding, frequency-conditioned spectral decoding, and peak-weighted learning. The proposed PPS-Net achieves the best prediction accuracy among the tested models, with a mean absolute error (MAE) of 0.75 dB, a root mean square error (RMSE) of 1.88 dB, and a coefficient of determination (R²) of 0.96, outperforming multi-layer perceptron (MLP), convolutional neural network (CNN) and Transformer models under the same dataset and training protocol. For inverse design, the LSTM encoder extracts frequency-ordered spectral features from the target TL curve, while the frozen PPS-Net decoder provides differentiable acoustic-response feedback, thereby addressing the non-unique mapping from acoustic response to structural parameters. Furthermore, a compactness-oriented optimization strategy is introduced to balance spectral consistency, peak alignment, bandwidth preservation, and occupied-area reduction. In two representative cases, the optimized designs reduce the occupied area by approximately 21% in both representative cases, while maintaining the target attenuation characteristics after FEM verification. These results demonstrate that the proposed framework provides an efficient and physically interpretable route for the full-spectrum inverse design and compact optimization of low-frequency acoustic metamaterials. Full article

The motion of AC electromagnetic actuators exhibits complex electrical–magneto–mechanical coupling characteristics. A three-phase AC contactor is taken as the typical research object in this paper. Using the finite-element method (FEM) and mesh deformation technique, the commercial software COMSOL Multiphysics is adopted to analyze its static electromagnetic characteristics, together with the operational coil current response and movable core displacement. In addition, the static correlation between the magnetic force, air gap, and time-varying magnetic force curves in the movement process are obtained. An experimental platform is established to measure the magnetic force of electromagnetic actuators. The experiment results demonstrate the feasibility of the proposed simulation method. The normalized root mean square errors between simulated and measured static magnetic forces are below 8% under all tested coil voltages. Furthermore, the effect of coil voltage phase angle on dynamic operational characteristics is thoroughly investigated. Combined with the closing time and final velocity of the movable core, the recommended operating window and its corresponding phase angle are determined. Full article

This study investigates the application of physics-informed neural networks (PINNs) to two-dimensional reaction–diffusion biological models with singular terms. Motivated by the classical prey–predator framework, we propose an improved model that incorporates human influence into the ecological environment. The stability of the reaction–diffusion system is analyzed, and an analytical solution is derived for a specific case to provide theoretical support for the numerical model. In addition, a PINN–Adam deep learning algorithm is developed to effectively handle the singular characteristics of the system. Unlike traditional finite element methods (FEMs), which rely on grid-based discretization, the proposed method utilizes random spatiotemporal sampling, leading to improved computational flexibility and prediction accuracy. The proposed approach is validated using real-world fish population data from coral reef ecosystems along the South Australian coast, demonstrating its effectiveness in modeling complex ecological dynamics. Full article

Digital tools in aluminum alloy processing are computational, sensing-based, and data-driven methods used to understand, predict, monitor, optimize, and control how aluminum alloys are transformed into components. They support decisions across casting, deformation processing, heat treatment, welding, surface engineering, and additive manufacturing by linking processing conditions with geometry, microstructure, defects, properties, and service performance. In technical use, the term includes finite element method (FEM), computational fluid dynamics (CFD), CALculation of PHAse Diagrams (CALPHAD), microstructure models, machine-learning regressors, surrogate models, nondestructive digital inspection, image-analysis tools, and digital twins. These tools are most effective when they establish links among controllable processing variables, underlying metallurgical mechanisms, measurable quality indicators, and service-relevant performance outcomes. Full article

Background/Objectives: Image-based finite element analysis (FEA) is increasingly used in dental biomechanics; however, its reliability is often limited by insufficient experimental benchmarking and a lack of standardized workflows. This study aimed to quantitatively benchmark a Cone beam computed tomography-based (CBCT) finite element pipeline using experimentally measured strain in restored human molars. Methods: Extracted human mandibular molars were restored using a total-etch adhesive system and bulk-fill composite resin. Specimen-specific finite element models were generated from CBCT data using a standardized segmentation and meshing workflow. Numerical simulations were compared with experimentally measured strain obtained during mechanical loading using Digital Image Correlation. Agreement between numerical and experimental data was assessed using regression analysis, Bland–Altman analysis, and equivalence testing. Results: A total of 304 spatially clustered paired measurements nested within 16 specimens were analyzed. FEM predictions showed strong correlation with experimental data (r = 0.91–0.97; R² up to 0.937) and low relative error (~5–6%). The model systematically overestimated deformation by approximately 10–15%. Equivalence was confirmed within ±15% for dentin and composite, and within ±20% for enamel. Bland–Altman analysis revealed proportional bias and heteroscedasticity, particularly in dentin. Conclusions: The proposed CBCT-based finite element workflow demonstrates strong benchmarking agreement with experimental measurements and provides reproducible estimates of mechanical behavior within defined tolerance limits under controlled experimental conditions. Despite systematic overestimation, the model exhibits stable and reproducible behavior under controlled conditions. These findings support the use of experimentally benchmarked, image-based FEA workflows in dental biomechanical research. Full article

This paper presents a new process for forming stepped shafts by upsetting with two movable deformation zones. The developed technology enables several shaft steps to be formed at the same time, thereby increasing process efficiency and reducing material consumption. A distinctive feature of the process is that it uses two forming sleeves, each with a variable cross-section of the impression, which move in an opposite direction to that of the punches during operation. This results in a simultaneous occurrence of upsetting and extrusion, thus leading to intensified plastic deformation and stabilized metal flow. The practical applicability of the process is demonstrated on the example of a forged railway axle. An analysis is carried out by the finite element method (FEM) using specimens of hot-formed C35 steel. The obtained results reveal proper material flow and the correct filling of the tool impressions. The examination of strain and stress distributions confirms favorable forming conditions. The calculated values of the Cockcroft–Latham integral indicate favorable forming conditions and a low risk of fracture initiation during the analyzed process. The results demonstrate the potential of the proposed technology and provide a basis for future experimental verification and industrial assessment. Full article

This study investigates the shear damage mechanisms in reinforced concrete (RC) beams through non-linear numerical modeling. Using the Finite Element Method (FEM) in ABAQUS, a Concrete Damaged Plasticity (CDP) framework was validated against experimental results and subsequently utilized for a 36-model parametric investigation. The study isolated the influence of stirrup spacing, diameter, and yield strength to evaluate their roles in ultimate shear capacity. The results indicated that while increasing stirrup diameter yielded modest capacity enhancements of approximately 7%, the impact of increasing yield strength was negligible, as the failure modes were primarily governed by concrete web crushing before reinforcement yielding could occur. These physical limit states were compared against the linear Truss Analogy adopted by major design standards—including ACI 318-19, Eurocode 2, and TS 500—to quantify discrepancies in heavily reinforced sections. The findings reveal that, strictly within the investigated parameter space (a/d = 2.67, f’_c = 28.5 MPa), current linear equations can significantly overestimate the physical capacity gains provided by reinforcement modifications. These observations are configuration-specific and highlight the need for cautious application of linear models in heavily reinforced scenarios. Furthermore, the study suggests that utilizing 3D beam elements for transverse reinforcement provides a more nuanced representation of shear transfer mechanisms, such as dowel action, compared to standard truss models. Full article

Conventional finite element method (FEM) has a complex model and a long optimization time for Halbach array PM machines. This paper proposes a hybrid analytical method that combines the subdomain method (SM) and the magnetic circuit method (MEC) for analyzing a high-torque-density, three-segment Halbach array rotor permanent magnet (PM) machine, accounting for Halbach array magnetization and end leakage flux. Firstly, to address the challenge posed by complex PM shapes in the Halbach array PM machine, a novel subdivision equivalence method is conducted. Then, the magnetic equivalent circuit (MEC) of the stator and rotor is established, and the axial leakage flux and nonlinearity of the iron core are taken into account. In addition, electromagnetic performance, such as air gap flux density, cogging torque, electromagnetic torque, and back electromotive force (back-EMF), is obtained based on the proposed hybrid analytical model. The analytical results are verified by using the finite element method (FEM), and the results show that the error is less than 2%. Finally, a 15 kW prototype PM machine with a Halbach array PM rotor is manufactured and tested, and the results validate the accuracy and efficiency of the analytical method. Full article