Search Results (308)

In the thermal insulation winding process of solid rocket motors, the roller-tape contact pressure is a critical factor determining bonding quality. However, accurately predicting this pressure is challenging due to the complex three-dimensional contact involving a thin, nearly incompressible rubber tape and an elliptical concave press roller. This paper proposes a theoretical model extending the classical elastic foundation model by incorporating correction strategies to account for material incompressibility and geometric confinement. A finite element (FE) model was developed to simulate the contact and verified against Hertz theory. Two key parameters of the theoretical model were calibrated using the FE results and justified through a parametric study on Poisson’s ratio and a theoretical analysis of the contact half-width. The theoretical predictions of deformation, contact pressure distribution, and pressing force agree well with the FE results under different applied displacements and mandrel radii without parameter recalibration, demonstrating the model’s generality. Experimental validation employing hybrid inverse analysis confirms the model’s global accuracy, yielding <11% relative error between the predicted and measured pressing forces. This study establishes a theoretical foundation for pressure control in the winding process and provides insights into contact problems for thin elastic layers with high Poisson’s ratios (≥0.45). Full article

High-frequency induction hardening enhances the surface wear resistance and contact fatigue life of high-speed linear guides, but simultaneously produces an inhomogeneous, layered cross-sectional structure comprising a high-strength, low-ductility outer hardened layer and a low-strength, high-ductility inner core. This structural heterogeneity renders conventional straightening stroke prediction models—predicated on homogeneous material assumptions—fundamentally inadequate. Moreover, the iterative trial-bending operations ubiquitous in industrial practice progressively accumulate plastic strain, causing guide rails to exhibit erratic positive-to-negative deflection reversal during sequential straightening passes. To address these critical challenges, this study proposes a novel two-stage hybrid straightening strategy based on inverse finite element analysis (FEA) and closed-loop experimental feedback. An equivalent hardened layer depth (HD₀) is introduced as a parametric descriptor to construct a layered elastoplastic finite element model, and an inverse simulation strategy is developed to generate a comprehensive three-dimensional stroke–residual deflection prediction dataset encompassing both vertical and lateral straightening conditions across multiple support spans. Displacement-controlled three-point bending experiments validate the layered model and elucidate the mechanism by which cumulative plasticity progressively amplifies cross-sectional plastic sensitivity under repeated loading. Grounded in this physical insight, a hybrid straightening algorithm is formulated, combining dataset-driven initial stroke prediction for rapid large-deformation elimination with an upper-bound constraint and a measurement-feedback-driven sequential reduction compensation scheme for fine-tuning. Comparative experiments demonstrate that the proposed strategy effectively suppresses the oscillatory over-straightening characteristic of conventional empirical trial-and-error approaches, consistently reducing residual deflection below 0.05 mm within two to three loading cycles. This work bridges the gap between theoretical simulation and the complex physical state of actual machining, substantially improving both the efficiency and precision of straightening for guide rails with induction-hardened layers. Full article

In this work, we study the joint performance of the quality and reliability in terms of quality–reliability (JQR) performance of an intelligent reflecting surface (IRS)-assisted wireless communication system under severe multipath fading and shadowing. The wireless channel model is given by the Inverse Power Lomax (IPL) fading model, representing a heavy-tailed fading channel, which can describe the hyper-Rayleigh fading and is verified using two different experimentally obtained measurement scenarios, namely, the LTE-case for high-frequency, long-range cellular communications and the device-to-device (D2D) case for lower-frequency short-range communications. For the considered channel model and communication scheme, analytical expressions for the outage probability (a metric related to the reliability) and the average bit error rate for both coherent and non-coherent modulation schemes (metrics associated with the quality of the communication system) are provided. By combining the aforementioned expressions, a unified JQR curve, together with its asymptotic forms in the high signal-to-noise ratio regime and asymptotically large number of IRS elements, is derived. It is proved analytically that the use of IRS with infinite elements can remove fading, while for a finite number of IRS elements, a closed-form signal-to-noise ratio (SNR) penalty factor is presented. The numerical analysis demonstrates that coherent modulations outperform non-coherent ones, higher-order quadrature amplitude modulation (QAM) systems are highly sensitive to the multipath fading, and the LTE-case exhibits better performance compared to the D2D-case for equal settings. Moreover, the joint quality–reliability approach highlights the existence of regions where quality is more preferable than reliability, allowing the allocation of resources based on these regions. All expressions have been verified using Monte Carlo simulations with excellent agreement. Full article

Data-driven surrogate methods are increasingly applied to real-time structural health monitoring of critical engineering systems, but their use is often limited by the computational cost of high-fidelity finite element (FEM) simulations. This work proposes an FEM-based machine learning framework combining FEM, Principal Component Analysis (PCA), and Random Forest regression to reconstruct full-field von Mises stress distributions of a hydropower regulating ring from a reduced set of proximity sensor measurements. A calibrated 3D FEM model generated a representative dataset of operating conditions using a Design of Experiments (DOE) sampling strategy, reducing the simulation space. The resulting stress fields were reduced using a single global PCA model, and the retained principal components were predicted by a single Random Forest model trained on the guide vane opening and four displacement sensors installed on the turbine unit. Stress reconstruction was obtained via inverse PCA transformation and validated against FEM results through a leave-one-opening-out cross-validation, in which each guide vane opening was entirely withheld from training. The method achieved an average PSNR of 17.4 dB and SSIM of 0.837 across the eleven withheld openings, with 8 to 9 PCA components sufficient to preserve over 95% of the cumulative explained variance. A sensitivity analysis of the ensemble size showed that 100 decision trees provide accuracy comparable to larger ensembles at lower computational cost, and a feature importance analysis revealed the guide vane opening as the dominant predictor, with the four sensors providing complementary, fine-grained corrections. The framework enables near real-time reconstruction, requiring approximately 1.5 s per condition versus several hours for FEM. These results show that combining physics-based modeling with machine learning enables efficient structural monitoring for predictive maintenance and operational decision-making in hydroelectric systems. Full article

In the preliminary vehicle design process, Finite Element Analysis (FEA) and Multi-Body Dynamics (MBD) simulations require detailed physical parameters that are not available during the conceptual design phase. This study proposes a novel analytical algorithm that uses a virtual weighting approach to rapidly establish a reasonable baseline for these parameters, serving as an efficient analytical precursor to the physical tests, complex dynamic simulations, and optimization methods conventionally applied in later stages. In this study, a dual-layer model for rapid geometric assessment of stability indices and vertical loads in hyperstatic 8 × 8 vehicles is proposed. The model consists of a prognostic Weighted Singular Value Decomposition (SVD) layer and an operative Weighted Pseudo-Inverse (WPI) layer. In the SVD layer, a spectral mode alignment technique is proposed to evaluate the load transmission capacity to predict the stability limits under worst-case operating conditions including extreme maneuvers and wheel failures. In the WPI layer, the optimal distribution of wheel loads is computed under different operating conditions. For a uniform vehicle configuration, a high-resolution continuous sweep of lateral acceleration identifies the exact wheel lift-off point at 0.8621 g, perfectly aligning with the theoretical Static Stability Factor (SSF). By employing a virtual weighting strategy instead of relying on traditional exhaustive physical parameters, this parameter-independent framework provides an analytical load-boundary evaluation and determines the theoretical topological capacity, thereby acting as an essential tool for preliminary conceptual design prior to detailed MBD and FEA analyses. Full article

The mechanical interpretation of impact traces on historical masonry structures offers a promising pathway for identifying the typology of ancient projectiles used in past conflicts. In recent years, inverse analysis approaches have been increasingly employed to infer projectile characteristics from residual damage patterns observed on archaeological remains. However, the reliability of such reconstructions strongly depends on the mechanical representation adopted for the impacted masonry. Ancient masonry walls, particularly those composed of tuff blocks and mortar joints, exhibit a marked heterogeneity that cannot always be adequately captured through simplified homogeneous material models. In this study, a numerical framework is developed to investigate how different assumptions regarding masonry homogenization influence the identification of projectile parameters derived from impact evidence. The mechanical response of the masonry is modeled through a homogenization procedure based on representative volume elements (RVE), allowing the heterogeneous brick—mortar assemblage to be translated into an equivalent macroscopic constitutive description. The resulting elastic and limit mechanical properties are then employed within a Finite Element Limit Analysis (FELA) formulation grounded in Melan’s lower bound theorem to evaluate collapse mechanisms and energy dissipation during impact.The methodology is applied to a case study inspired by the masonry walls of Pompeii, where parametric variations in mortar thickness are considered to assess their influence on the homogenized stiffness and strength domain. The results highlight how even simplified yet mechanically consistent models are capable of capturing the anisotropic behavior of masonry and its implications for energy absorption. In particular, the study shows that adopting a homogenization-based representation leads to more reliable inverse estimates of projectile velocity and momentum compared to purely homogeneous approximations. Overall, the proposed approach provides a computationally efficient yet mechanically grounded framework for supporting archaeological interpretations of impact traces, contributing to a more quantitative understanding of ancient warfare technologies. Full article

Accurate prediction of retaining wall displacements is essential to ensure safety and protect adjacent structures in large-scale urban excavations. However, inherent uncertainties in soil parameters often limit prediction accuracy, and cumulative deviations may result in significant overestimation or underestimation of wall displacements at the final excavation stage. Although observational construction methods and inverse analysis approaches have been proposed to sequentially update parameters using early-stage monitoring data, these studies have primarily focused on numerical values, such as deformation modulus. However, limited attention has been paid to the spatial extent over which these parameters are assigned. This study introduces a method for optimizing the zoning width of the soil deformation modulus by incorporating monitoring data from the initial excavation stages into finite element analysis. A Mohr–Coulomb constitutive model was adopted, and the maximum wall displacement was used as the primary evaluation index. The optimal zoning width was defined as the condition that minimized the difference between measured and simulated displacements. The results demonstrate that the proposed optimization approach improves the predictive accuracy of wall displacements in later excavation stages. The proposed method was validated using a single excavation case; therefore, further verification using multiple case histories is required to confirm its broader applicability. Full article

We establish explicit direct and inverse approximation estimates for biorthogonal multiresolution projections on weighted Besov spaces over Muckenhoupt A_p weights. A Jackson-type direct estimate bounds the weighted L^p projection error by 2^−Js times the weighted Besov norm, and a matched Bernstein-type inverse estimate bounds the weighted Besov seminorm of a resolution-space element by 2^Js times its weighted L^p norm. Every constant is displayed in factorized form: each factor is either given in closed form or is the operator norm of the Hardy–Littlewood maximal operator on the weighted Lebesgue space, through which the entire dependence on the Muckenhoupt characteristic is routed. For a piecewise regular class combining a Besov-smooth component with finitely many net-zero jumps, the projection error separates into a smooth part decaying at 2^−Js and a localized jump part carrying the weighted measure of a shrinking interval about each jump; when the weight is locally comparable to Lebesgue measure near the jumps, this yields the effective rate $\min (s,1/p)$. A deterministic numerical experiment confirms this rate within one fixed biorthogonal analysis, and the same projection is illustrated on an empirical higher-education finance series. Full article

Stress–strain detection serves as a common inspection technique in engineering integrity management, enabling the prediction of the health status and remaining service life of materials or structures. This study systematically investigates the performance and application effectiveness of Digital Image Correlation (DIC) and Alternating Current Stress Measurement (ACSM) technologies in the field of stress–strain detection. Based on the inverse magnetostriction effect and Maxwell’s equations, an ACSM detection system was developed, achieving the conversion of stress signals into electromagnetic signals. Simultaneously, DIC technology combined with a high-resolution binocular vision system was employed to realize non-contact measurement of full-field strain distribution. Finite element analysis using COMSOL V6.1 software was conducted to simulate the stress–strain distribution of ferrous specimens under axial tensile load, identifying the range of the gauge section with uniform stress–strain distribution. An experimental platform was established to perform tensile tests on flat specimens. The results demonstrated that the stress detection error of the ACSM system was less than 42.93 MPa, the strain measurement error of the DIC system was below 2.14722 × 10⁻⁴, and the stress inversion error was less than 31 MPa. A comprehensive comparison indicates that DIC technology offers superior performance in measurement accuracy and resolution, while ACSM technology provides advantages in operational convenience and rapid response, making it suitable for rapid screening in industrial settings. Full article

This study proposes a surrogate-assisted framework to characterize system-level equivalent stiffness parameters of composite resilient floor subsystems from global resonance features, calibrated and assessed for consistency using actual building acoustic tests. Since intrinsic material characterization alone may not fully capture the contribution of resilient layers under complex in situ boundary conditions, a parametric finite element model utilizing a transversely isotropic formulation with five independent stiffness-related parameters is coupled with Gaussian Process surrogates and a genetic algorithm. Repeated laboratory impact tests provide empirical bending and torsion resonance targets near 60 Hz and 155–160 Hz. The trained surrogates predict these targets with a root mean square error (RMSE) below 0.02, enabling the inverse estimation to identify representative parameter sets within a ±5% tolerance. The optimization yields a finite admissible parameter region, reflecting the inherent non-uniqueness of the inverse problem. To establish framework consistency, the identified equivalent parameters are integrated into a hybrid vibro-acoustic scheme and assessed for consistency against sound pressure level measurements from real-world building tests. Crucially, the sensitivity analysis indicates a mechanical divergence: in-plane stiffness primarily governs resonance-frequency reproduction, whereas out-of-plane stiffness is more influential in the noise-related structural wall-response-energy assessment within the admissible solution region. This behavioral mismatch demonstrates resonance matching alone is insufficient for design prioritization. The proposed framework establishes a configuration-level tool to systematically evaluate structural improvement directions without direct intrinsic material testing. Full article

This study addresses an off-site construction (OSC) task: installing heavy prefabricated equipment modules at elevated positions inside existing structures. The task simultaneously demands multi-ton payload capacity, a lift height approaching 10 m, and holonomic maneuvering in narrow aisles; to the authors’ knowledge, no single reported platform satisfies all three. We present a heavy-duty, high-lift mobile robot that lifts 6 t to 8 m. Two active swerve-drive modules and three passive casters form a five-point asymmetric layout combining holonomic mobility with load distribution, and the lift unit functionally decouples the vertical stroke (four helical band actuators) from the lateral stiffness (four scissor-stabilizing mechanisms). Planar motion is partitioned into three driving modes with closed-form forward and inverse kinematics, and zero-velocity transitions remove the kinematic model mismatch and the instantaneous-center-of-rotation discontinuity of a single unified model. Prototype measurements confirmed the motor-sizing torque assumptions, and chassis finite element analysis showed a factor of safety above 2.0 under maximum payload and quantified the in-plane stress induced by kinematic mismatch. In two field deployments, the robot reduced personnel by 25.0–27.3%, equipment by 42.9–60.0%, and installation duration by 50.0–85.7% relative to the incumbent methods, thereby extending mobile robots from horizontal transport to vertical OSC module installation. Full article

Electrical machine design faces growing constraints from power density, wide operating ranges, thermal and mechanical limits, acoustics, manufacturability, cost, and critical material availability. While finite element and multi-physics simulations remain essential, their direct use in population-based or multi-objective optimization is often computationally prohibitive. AI, spanning surrogate modeling, machine learning, deep learning, physics-informed networks, Bayesian optimization, and emerging generative methods, is increasingly used to accelerate analysis, enlarge design spaces, and support inverse or multi-objective tasks. This review examines AI-assisted electrical machine design from a workflow perspective, distinguishing functional approximation, performance prediction, topology-aware learning, physics-informed modeling, active learning, and robust optimization under uncertainty. It highlights current limitations, including narrow topology coverage, reliance on FEM-generated data, weak extrapolation, limited uncertainty reporting, scarce experimental validation, and insufficient attention to manufacturability and sustainability. A design-readiness framework and minimum reporting checklist are proposed to improve trustworthiness and reusability. The review concludes that AI should serve as a physics-aware, validation-dependent accelerator, complementing, not replacing, electromagnetic expertise, multi-physics simulation, and prototype testing. Full article

Multi-material structures such as carbon fiber reinforced polymer (CFRP) and epoxy resin are increasingly used in modern power equipment. However, significant differences in their thermophysical properties result in distinct defect thermal responses, which can reduce the reliability of infrared thermography inspections. To address this issue, this study investigates the thermal response mechanisms and quantitative analysis of defects in multi-material power equipment through finite element simulation and experimental validation. Three-dimensional transient heat transfer models containing air voids and heterogeneous insert defects were established using COMSOL Multiphysics for both carbon fiber reinforced polymer and epoxy resin matrices. Pulsed infrared thermography experiments were subsequently conducted to verify the simulation results. The effects of material properties, defect geometry, and cover-layer thickness on thermal response characteristics were systematically analyzed. The results show that thermal diffusivity is the key factor governing defect signal evolution. Carbon fiber reinforced polymer exhibits rapid thermal propagation and early transient responses, whereas epoxy resin produces delayed and slowly increasing thermal signals. Greater defect depth weakens thermal contrast and delays peak response time, while larger defect diameters enhance defect detectability. Increasing cover-layer thickness significantly attenuates defect signals and reduces imaging contrast. Experimental results are in good agreement with simulation predictions, confirming the validity of the proposed models. This work provides a quantitative analysis of defect thermal behavior in multi-material systems and offers a theoretical basis for adaptive infrared thermography inspection and condition assessment of power equipment. It should be noted that this study focuses on mechanistic understanding and parametric analysis rather than on proposing a dedicated quantitative defect-sizing or inversion method. Full article

Local topography critically influences seismic hazards by amplifying ground motions and altering their spectral content. This study presents a novel semi-analytical solution for modeling the time-domain scattering of SH-waves by a near-source loess yuan, idealized as an asymmetric trapezoidal ridge. To accurately represent near-source conditions, cylindrical wave incidence is incorporated. The frequency-domain solution is derived using a wave function expansion method within a multi-region framework, employing the wave field mirror method. The transient response is efficiently synthesized via the inverse Fourier transform using a Ricker wavelet source. The results reveal that the asymmetric topography induces significant, incidence-dependent amplification due to wave focusing and prolonged shaking duration caused by multiple internal reflections and scattering within the topographic feature. A key finding is that while a steeper incident slope provides surface shielding, it can generate pronounced subsurface amplification. The solution is rigorously validated against independent finite-element simulations, confirming its accuracy. Furthermore, the proposed method demonstrates a substantial computational advantage. This efficient and accurate framework provides a valuable tool for parametric analysis in site-specific seismic hazard assessment. Full article

Mechanical metamaterials are architected materials that exhibit unusual mechanical properties arising from their internal geometry. This paper reviews recent developments in the application of machine learning for the design and analysis of these structures. It categorizes common architectures, including strut-based lattices and triply periodic minimal surfaces, and details the end-to-end design workflow, from dataset preparation and preprocessing to the iterative, simulation-based validation approach. The review compares a range of model architectures. These include foundational models like deep neural networks, fully connected and convolutional neural networks, graph neural networks, and generative models such as GANs and diffusion models. Applications in mechanical property prediction and inverse design are highlighted with examples using finite element simulations and generative design models. A structured design workflow and a comparative summary of recent studies are presented to guide future research and application. This review aims to support the development of ML frameworks for next-generation metamaterial design. Full article