Search Results (3,368)

Fatigue strength is vital for engineering applications of aluminum alloys. Accurate models incorporating mesoscopic defect-representing features are one of the issues for accurate fatigue strength prediction. A fatigue life prediction method based on meso-defect-representing features is proposed in this study. Based on staged fatigue damage, meso-defect data was obtained by X-ray CT. After 3D reconstruction and simplification, porosity, shape, and location were selected as the meso-defect-representing features using correlation coefficient analysis. Weights of meso-defect features were determined through FEM simulation. A mesoscopic damage variable incorporating the weights of porosity, shape, and location for meso-defect was defined. Correlation between fatigue life and meso-defect features was established through the mesoscopic damage variable. Experimental verification results showed that the prediction method is an effective method for fatigue life assessment. Full article

This paper presents the results of a numerical study on the influence of the tooth root fillet manufacturing method on the bending strength of spur gears with straight teeth. A mathematical model describing the gear tooth geometry was developed, in which the transition curve at the tooth root was directly related to the applied machining process—either rack-type gear shaping or pinion-type gear shaping. Based on this model, a numerical procedure for calculating the bending stresses at the tooth root was formulated and verified using the finite element method (FEM). The results demonstrated high consistency between the proposed approach and FEM analysis, confirming the accuracy of the developed mathematical model and numerical methodology. The study also examined the effect of the tool fillet radius on the stress distribution in the root region. It was found that increasing the tool radius leads to a reduction in bending stresses, while the differences between the two machining methods gradually diminish. The proposed methodology offers a reliable numerical framework for assessing the strength of spur gears and can be effectively used in the design of lightweight, high-performance gear transmissions for aerospace and automotive applications. Full article

This study presents an optimal design combined with comprehensive multiphysics validation to enhance the torque density of a coaxial magnetic gear (CMG) incorporating an overhang structure. Four high non-integer gear-ratio CMG configurations exceeding 1:10 were designed using different pole-pair combinations, and three-dimensional finite element method (3D FEM) was employed to accurately capture axial leakage flux and overhang-induced three-dimensional effects. Eight key geometric design variables were selected within non-saturating limits, and 150 sampling points were generated using an Optimal Latin Hypercube Design (OLHD). Multiple surrogate models were constructed and evaluated using the root-mean-square error (RMSE), and the Kriging model was selected for multi-objective optimization using a genetic algorithm. The optimized CMG with a 1:10.66 gear ratio achieved a 130.76% increase in average torque (65.75 Nm) and a 162.51% improvement in torque density (117.14 Nm/L) compared with the initial design. Harmonic analysis revealed a strengthened fundamental component and a reduction in total harmonic distortion, indicating improved waveform quality. To ensure the feasibility of the optimized design, comprehensive multiphysics analyses—including electromagnetic–thermal coupled simulation, high-temperature demagnetization analysis, and structural stress evaluation—were conducted. The results confirm that the proposed CMG design maintains adequate thermal stability, magnetic integrity, and mechanical robustness under rated operating conditions. These findings demonstrate that the proposed optimal design approach provides a reliable and effective means of enhancing the torque density of high gear-ratio CMGs, offering practical design guidance for electric mobility, robotics, and renewable energy applications. Full article

A new type of transportation vehicle, the maglev car, is gaining attention in the automotive and maglev industries due to its potential to meet personalized urban mobility and future travel needs. To optimize the chassis layout of maglev cars, this paper proposes a compact powertrain integrating electrodynamic suspension with in-wheel motor technology, in which a permanent magnet electrodynamic in-wheel motor (PMEIM) enables integrated propulsion and levitation. First, the PMEIM external magnetic field distribution is characterized by analytical and finite element (FEM) approaches, revealing the magnetic field distortion of the contactless powertrain. Subsequently, the steady-state electromagnetic force is modeled and the operating states of the PMEIM powertrain are calculated and determined. Next, the PMEIM electromagnetic design is conducted, and its electromagnetic structure rationality is verified through magnetic circuit and parametric analysis. Finally, an equivalent prototype is constructed, and the non-contact electromagnetic forces of the PMESM are measured in bench testing. Results indicate that the PMEIM powertrain performs propulsion and levitation functions, demonstrating 14.2 N propulsion force and 45.8 N levitation force under the rated condition, with a levitation–weight ratio of 2.52, which hold promise as a compact and flexible drivetrain solution for maglev cars. Full article

Reinforced concrete (RC) beams strengthened with carbon-fabric-reinforced cementitious matrix (CFRCM) systems have shown potential for restoring flexural performance, yet their effectiveness under different corrosion levels remains insufficiently understood. This study presents a numerical investigation of the flexural behaviour of simply supported RC beams externally strengthened with CFRCM plates. Refined finite element models (FEMs) were developed by explicitly incorporating the steel–concrete bond-slip behaviour, the carbon fabric (CF) mesh–cementitious matrix (CM) interface, and the CFRCM–concrete substrate interaction and were validated against experimental results in terms of failure modes, load–deflection responses, and flexural capacities. A parametric study was then conducted to examine the effects of CFRCM layer number, steel corrosion level, and longitudinal reinforcement ratio. The results indicate that the baseline flexural capacity can be fully restored only when the corrosion level remains below approximately 15%; beyond this threshold, none of the CFRCM configurations achieved full recovery. The influence of the reinforcement ratio was found to depend on corrosion severity, while increasing CFRCM layers enhanced flexural performance but exhibited saturation effects for thicker configurations. In addition, corrosion level and CFRCM thickness jointly influenced the failure mode. Comparisons with design predictions show that bilinear CFRCM constitutive models are conservative, whereas existing FRP-based design codes provide closer agreement with numerical and experimental results. Full article

Background: Adult patients with a thin buccal cortical plate and fragile periodontal phenotype are at high risk of dehiscence, fenestration and recession during transverse orthodontic expansion. Conventional mechanics often create a cervical compression-dominant environment that exceeds the adaptive capacity of the periodontal ligament (PDL)–bone complex. Objectives: This study proposes an integrative mechanobiological model in which a skeletal-anchorage-assisted loading protocol (Bone Protection System, BPS) transforms expansion into a tension-dominant regime that favours buccal phenotype preservation. Methods: Patient-specific finite element models were used to compare conventional expansion with a BPS-modified force system. Regional PDL stress patterns and crown/apex displacement vectors were analysed to distinguish tipping-dominant from translation-dominated mechanics. A pilot CBCT proof-of-concept (n = 1 thin-phenotype adult) with voxel-based registration quantified changes in maxillary and mandibular alveolar ridge width and buccal cortical plate thickness before and after BPS-assisted expansion. The mechanical findings were integrated with current evidence on compression- versus tension-driven inflammatory and osteogenic pathways in the PDL and cortical bone. Results: FEM demonstrated that conventional expansion concentrates high cervical compressive stress along the buccal PDL and cortical surface, accompanied by bending-like crown–root divergence. In contrast, the BPS protocol redirected forces to create a buccal tensile-favourable region and a more parallel crown–apex displacement pattern, indicative of translation-dominated movement. In the proof-of-concept (n = 1) CBCT case, BPS-assisted expansion was associated with preservation or increase of buccal ridge dimensions without radiographic signs of cortical breakdown. Conclusions: A tension-dominant orthodontic loading environment generated by a skeletal-anchorage-assisted force system may support buccal cortical preservation and vestibular phenotype reinforcement in thin-phenotype patients. The proposed mechanobiological model links these imaging and FEM findings to known molecular pathways of inflammation, angiogenesis and osteogenesis. It suggests a functional biomaterial-based strategy for widening the biological envelope of safe tooth movement. Full article

This study explores the viability of ultra-high-performance concrete (UHPC) as a structural material for compressed air storage (CAES) systems, combining comprehensive experimental testing and numerical simulations. Scaled (1:20) CAES tanks were designed and tested experimentally under controlled pressure conditions up to 4 MPa (580 psi), employing strain gauges to measure strains in steel cylinders both with and without UHPC confinement. Finite element models (FEMs) developed using ANSYS Workbench 2024 simulated experimental conditions, enabling detailed analysis of strain distribution and structural behavior. Experimental and numerical results agreed closely, with hoop strain relative errors between 0.9% (UHPC-confined) and 1.9% (unconfined), confirming the numerical model’s accuracy. Additionally, the study investigated the role of a rubber interface layer integrated between the steel and UHPC, revealing its effectiveness in mitigating localized stress concentrations and enhancing strain distribution. Failure analyses conducted using the von Mises criterion for steel and the Drucker–Prager criterion for UHPC confirmed adequate safety factors, validating the structural integrity under anticipated operational pressures. Principal stresses from numerical analyses were scaled to real-world operational pressures. These thorough results highlight that incorporating rubber enhances the system’s structural performance. Full article

Assessing the stress–strain state around interacting mining excavations using the finite element method (FEM) is computationally expensive for parametric studies. This study evaluates tabular machine-learning surrogate models for the rapid prediction of full stress–strain fields in fractured rock masses treated as an equivalent continuum. A dataset of 1000 parametric FEM simulations using the elastoplastic generalized Hoek–Brown constitutive model was generated to train Random Forest, LightGBM, CatBoost, and Multilayer Perceptron (MLP) models based on geometric features. The results show that the best models achieve R² scores of 0.96–0.97 for stress components and 0.99 for total displacements. LightGBM and CatBoost provide the optimal balance between accuracy and computational cost, offering speed-ups of 15 to 70 times compared to FEM. While Random Forest yields slightly higher accuracy, it is resource-intensive. Conversely, MLP is the fastest but less accurate. These findings demonstrate that data-driven surrogates can effectively replace repeated FEM simulations, enabling efficient parametric analysis and intelligent design optimization for mine workings. Full article

Phased array speakers are often designed with uniform element spacing, which limits beam steering capability and sidelobe control under practical aperture and hardware constraints. This study presents an optimization-driven modelling framework for parametric array loudspeakers (PALs) that systematically links array layout synthesis with high-fidelity directivity prediction, by combining a frequency-domain convolution model with a finite element method (FEM) pipeline. We formulate array layout synthesis as a constrained optimization problem and employ particle swarm optimization (PSO) to determine non-uniform element positions that suppress sidelobes while preserving mainlobe integrity across steering angles. By integrating linear acoustic field simulation with far-field directivity prediction, the framework serves as a computationally efficient surrogate model suitable for iterative design under non-ideal spacing conditions. Simulation results and laboratory measurements demonstrate that the optimized non-uniform arrays achieve consistently lower sidelobe levels and more concentrated mainlobes than conventional uniform configurations. These results validate the proposed framework as a practical and reproducible solution for steering-capable PAL design when the conventional λ/2 spacing constraint cannot be satisfied and establish a foundation for subsequent robustness and sensitivity analyses. Full article

The detection of titanium dioxide (${\mathrm{TiO}}_2$) nanoparticles is a significant challenge due to their extensive industrial use and potential health and environmental impacts, which demand accurate, label-free approaches. This work presents an automatic detection system based on spectroscopy with optical frequency combs (OFC) in dual-coupled microresonators. The OFC generation was modeled through the Lugiato-Lefever equation, while propagation in distilled water containing ${\mathrm{TiO}}_2$ was simulated using the finite element method (FEM). The water–${TiO}_2$ mixture was described with the Yamaguchi model in a 5 × 5 mesh to represent non-uniform concentrations. From the norm of the electric field at a probe, a database of 11 classes (0–100%) with controlled Gaussian noise was constructed. A Transformer-based classifier was trained and compared with 1D-CNN and SVM under Monte Carlo validation (100 random 70/30 splits). The Transformer achieved 99.84 ± 0.01% accuracy with an inference time of 0.793 ± 0.05 s, while the 1D-CNN reached 99.64 ± 0.09% and the SVM 84.73 ± 1.48%. A repeatability test with 200 iterations confirmed deterministic DKS trajectories. The results demonstrate that combining dual-coupled microresonators, FEM, and Transformer architectures enables precise and efficient detection of ${\mathrm{TiO}}_2$ nanoparticles in aqueous solutions. Full article

This work presents a combined numerical and experimental investigation into the laser machining of aluminum alloy Al 1050 H14 using a high-power Continuous Wave (CW) fiber laser. Advanced three-dimensional, coupled thermal–structural Finite Element Method (FEM) simulations are developed to model key laser–material interaction processes, including laser-induced plastic deformation, laser etching, and engraving. Cases for both static single-shot and dynamic linear scanning laser beams are investigated. The developed numerical models incorporate a Gaussian heat source and the Johnson–Cook constitutive model to capture elastoplastic, damage, and thermal effects. The simulation results, which provide detailed insights into temperature gradients, displacement fields, and stress–strain evolution, are rigorously validated against experimental data. The experiments are conducted on an integrated setup comprising a 2 kW TRUMPF CW fiber laser hosted on a 3-axis CNC milling machine, with diagnostics including thermal imaging, thermocouples, white-light interferometry, and strain gauges. The strong agreement between simulations and measurements confirms the predictive capability of the developed FEM framework. Overall, this research establishes a reliable computational approach for optimizing laser parameters, such as power, dwell time, and scanning speed, to achieve precise control in metal surface treatment and modification applications. Full article

Accurate prediction of subway-induced environmental vibrations for unopened lines remains a significant challenge due to the difficulty in determining appropriate excitation inputs. To address this issue, this study proposes an excitation modification method based on field measurements and numerical simulations. First, field measurements were conducted on a subway line in Shanghai to analyze vibration propagation characteristics and validate a two-dimensional finite element model (FEM). Subsequently, based on the validated model, frequency-band excitation modification formulas were derived. Distinct from existing empirical approaches that often rely on simple statistical scaling, the proposed method utilizes parametric numerical analyses to determine frequency-dependent correction coefficients for four key parameters: tunnel burial depth, tunnel diameter, soil properties, and train speed. The reliability of the proposed method was verified through theoretical analysis and an engineering application. The results demonstrate that the proposed method improves prediction accuracy for tunnels in similar soft soil regions, reducing the prediction error from 10.1% to 5.2% in the engineering case study. Furthermore, parametric sensitivity analysis reveals that ground vibration levels generally decrease with increases in burial depth, tunnel diameter, and soil stiffness, while exhibiting an increase with train speed. This study improves the reliability of vibration prediction in the absence of direct measurements and provides a practical tool for early-stage design and vibration mitigation for unopened lines. Full article

Uncertainty quantification in science and engineering has become increasingly important due to advances in computational mechanics and numerical simulation techniques. In this work, the relationship between uncertainty in soil material parameters and the variability of failure loads and displacements of a shallow foundation is investigated. A Drucker–Prager constitutive law is implemented within a stochastic finite element framework. The random material variables considered are the critical state line slope c, the unload–reload path slope $\kappa$, and the hydraulic permeability k defined by Darcy’s law. The novelty of this work lies in the integrated stochastic u–p finite element framework. The framework combines Drucker–Prager plasticity with spatially varying material properties, and Latin Hypercube Sampling. This approach enables probabilistic prediction of failure loads, displacements, stresses, strains, and limit-state initiation points at reduced computational cost compared to conventional Monte Carlo simulations. Statistical post-processing of the output parameters is performed using the Kolmogorov–Smirnov test. The results indicate that, for the investigated configurations, the distributions of failure loads and displacements can be adequately approximated by Gaussian distributions, despite the presence of material nonlinearity. Furthermore, the influence of soil depth and load eccentricity on the limit-state response is quantified within the proposed probabilistic framework. Full article

Side flaps are critical structural components of flat freight wagons, directly affecting cargo safety during transportation and playing an essential role in loading and unloading operations. Over the years, their reliability has been well established, with standardized designs available in UIC technical datasheets. Despite this standardization, the introduction of newly manufactured or redesigned components necessitates technological validation through Finite Element Method (FEM) simulations and/or physical testing. This requirement holds irrespective of whether the component in question adheres to existing standards or is a novel development. This study presents the creation and application of computational models for the structural sizing and strength assessment of side flaps for flat wagons. The models are verified through a series of physical tests conducted by a research team at the Technical University of Sofia. Full article

Agricultural materials frequently undergo fragmentation due to high-stress conditions during processing, storage, and transportation. Throughout these processes, the spatial arrangement and morphology of particles continuously evolve, rendering the breakage behaviour of particle groups particularly complex. Thus, an in-depth understanding of the fracture processes and breakage mechanisms within particle beds holds significant research value. This study systematically investigates the breakage behaviour of rice particle groups under confined compression through an integrated methodology combining experimental testing, X-ray CT imaging, and finite element modelling (FEM) based on the cohesive zone model (CZM). Results demonstrate that, at the granular assembly scale, external loads are transmitted through force chains and progressively attenuate. As compression proceeds, stress disseminates toward peripheral particle regions. At the individual particle level, particle breakage results from the intricate interaction between coordination number (CN) and localized contact stress, with tensile stress playing a predominant role in the fracture process. An increase in coordination number promotes a more uniform stress distribution and inhibits breakage, thereby exhibiting a “protective effect”. These findings provide valuable insights for the design and optimization of grain processing equipment, contributing to a deeper comprehension of particle breakage characteristics. Full article