Search Results (319)

This paper introduces a topology optimization framework for steady incompressible Stokes flow based on the non-conforming Virtual Element Method, VEM. The proposed framework combines the geometric flexibility of VEM with an optimality criteria update scheme to minimize viscous and Darcy dissipation under a prescribed volume constraint. The method is applied to the Stokes-flow pipe bend benchmark with parabolic inlet velocity, no-slip wall, and prescribed outlet velocity boundary conditions. By allowing general polygonal elements, including concave and semi-structured polygonal meshes, the method alleviates mesh-related restrictions commonly encountered in conventional finite element discretizations. The methodology is demonstrated through Stokes-flow benchmark problems on different polygonal meshes. The numerical results show that the proposed VEM-based formulation can obtain stable and mesh-insensitive optimized flow channels for Stokes-flow topology optimization. This work offers a systematic approach to obtaining accurate, efficient, and mesh-independent optimal designs for complex fluid systems, providing a stable numerical tool for low-energy-consumption flow channel design in microfluidics, heat exchangers, and biomedical engineering. Extensions to Navier–Stokes and non-Newtonian flow models are left for future work. It should be clarified that the proposed method is only validated for steady Stokes flow and has not been validated for complex fluid models including unsteady Navier–Stokes and non-Newtonian flow models; extensions to these complex models are left for future work. Full article

The open fan as a highly efficient propulsion concept is a promising approach to reduce climate-damaging emissions in aviation. However, the increased vibroacoustic emissions of the fan resulting from the open design lead to elevated cabin noise. Energy harvesting resonators can be used to leverage the piezoelectric effect and to attenuate structural vibrations caused by the acoustic loading simultaneously. To evaluate the potential of a specific configuration of energy harvesting resonators, an investigation of the dynamic interaction between the airframe and the resonators is necessary. Therefore, the eigenmodes and eigenfrequencies of a representative stiffened plate are determined experimentally using modal analysis via laser scanning vibrometry. A finite element model of the stiffened plate with the resonator idealized as a mass–spring element is implemented. The stiffness of this simplified resonator model is calibrated by correlating simulated with experimental results following a model updating approach. Finally, an optimization framework designed to determine the optimal quantity and placement of resonators using the experimentally validated model and representative loads is implemented to maximize both vibroacoustic attenuation and energy harvesting efficiency. The resulting framework serves as a generalized optimization tool capable of systematically optimizing the resonator configuration based on airframe geometry and specified vibroacoustic loading scenarios. Full article

This paper studies a nonlinear Schrödinger equation with a variable-order time-fractional derivative. Because classical L1 and L1-2 schemes are not directly applicable to variable-order fractional operators, an improved L1-2 discretization with dynamically updated convolution weights is developed based on the Coimbra-type definition, in which the fractional order is evaluated at the current time. By combining the proposed temporal approximation with the Galerkin finite element method for spatial discretization and a linearized extrapolation technique for the nonlinear terms, a fully discrete numerical scheme is constructed. The unconditional stability of the scheme is rigorously proven, and optimal error estimates are established under a mild time step restriction. Numerical experiments are presented to confirm the theoretical results and to demonstrate the effectiveness of the method in capturing the influence of time-dependent memory effects on wave propagation. A key numerical observation is that stronger memory effects may suppress wave packet evolution, which is qualitatively reminiscent of a Zeno-like inhibition phenomenon. Full article

Structural health assessment of historic buildings frequently relies on finite element (FE) model updating, yet high-dimensional parameter identification under sparse, noise-contaminated modal data can reduce robustness and lead to prohibitive computational cost. This paper proposes an application-oriented integrated workflow that improves identification stability while accelerating the updating process. A multi-indicator objective function is formulated by combining residuals of natural frequencies and mode shapes with sensitivity-based consistency relations. The inverse problem is solved using the Mayfly Algorithm (MA), and a deep neural network (DNN) surrogate is introduced to replace repeated FE modal analyses during the optimization, thereby reducing the overall computational burden. The proposed workflow is demonstrated on the Christian Lutheran Church in Wuhan, China, constructed from 1923 to 1924, using operational modal testing data collected at 25 measurement points. A refined FE model is updated by identifying 24 grouped stiffness reduction coefficients that represent columns, beams, walls, and slabs across different floors. The updated model shows substantially improved agreement with the measured first four natural frequencies and corresponding mode shapes, enabling a quantitative diagnosis of stiffness degradation and supporting stiffness-oriented reinforcement planning. A stiffness enhancement target of 20% is adopted to guide intervention measures, and an analytical modal enhancement check is provided to relate the stiffness target to the expected frequency gain. The workflow offers a reproducible route for data-informed decision support in heritage building assessment and rehabilitation, while uncertainty quantification and post-intervention validation are identified as key priorities for future work. Under the available sparse modal information, the inverse problem is underdetermined; therefore, the reported stiffness-reduction coefficients should be interpreted as non-unique grouped solutions affected by modelling and measurement uncertainty, and the reinforcement measures are presented only as planning-level design proposals requiring post-intervention verification. Full article

Public health suffers from noxious gas emitted by massive beached Sargassum algae. Net-barrages deployed in near-shore seas can contain Sargassum, provided they efficiently resist the additional hydrodynamic pressure induced by the catch. Nowadays, the design and installation of net-barrages are empiric. Structural breaks and anchor and mooring chain drifts can arise. We provide a mechanical model to evaluate stresses and loads on a structure made of fishing nets and buoy moorings. Hydrodynamic uncertainties occur through catches, fouling and sea current amplitudes appearing in lagoons or sheltered bays. This study presents a non-linear four-node finite-element model for continuous elastic membranes undergoing large displacements and small strains. The model relies on the Lagrangian linearly elastic membrane theory, employing the non-linear Green strain tensor and a non-updated hydrodynamic loading. We study forcings fixed a priori on a netting section of barrage that is 50 m long and 1 m high with double layer, e.g., two net-faces. We consider low and moderate current velocities, 0.05 and 0.35 m∙s⁻¹, while assuming specific vertical and horizontal catch pressures. A barrage installed in the reef lagoon at Le François on Martinique Island that is observable by satellite imagery could benefit of the computed net and mooring tensions. Full article

Under long-term marine environmental loading, deep-water risers are highly susceptible to fatigue damage, and the accumulation of local damage may lead to global structural failure. In this study, the fatigue damage mechanism and crack growth behavior of a girth-welded riser are systematically investigated. Full-scale radial fatigue test results of risers are referenced, and the experimental process is reproduced through numerical simulation. A finite element model of a girth-welded riser is established. The fatigue crack growth process is subsequently simulated, yielding the crack propagation path and crack growth rate curves. By comparison with experimental results, the characteristics of the crack growth process are analyzed, and the feasibility and accuracy of numerical simulations in predicting fatigue crack growth in riser girth welds are verified. A relatively accurate prediction model for fatigue crack growth in risers is proposed. To further improve the accuracy of crack growth prediction, a machine learning-based correction model is developed. On the basis of available in-service inspection data, a correction strategy is proposed in which the predicted crack growth process is dynamically updated with measured crack growth data. The proposed approach establishes a theoretical foundation for accurate and forward prediction of fatigue fracture damage in riser structures. Full article

Notable advances in modal analysis in the last 50 years have paved the way for more widespread use of modal parameters, including those from in situ measurements, in Structural Health Monitoring and finite element model updating. Current state-of-the-art techniques in output-only modal analysis include Stochastic Subspace Identification techniques, such as Canonical Variate Analysis (SSI), and the Natural Excitation Technique with the Eigensystem Realization Algorithm (NExT-ERA). The former have been shown to struggle on very large systems and the latter suffers from the usual fitting problems arising in noisy environments. In this work, an output-only version of the frequency domain technique known as the Loewner Framework (LF) is pioneeringly applied to an aeronautical system. The implementation pairs the LF with NExT (NExT-LF) to exploit the fitting process efficiency of the former and robustness to noise of the latter. The thus-defined NExT-LF is then applied to the well-known experimental benchmark of the eXperimental BeaRDS 2 high-aspect-ratio wing main spar. The results are compared to the known experimental values and those obtained from SSI and NExT-ERA. Full article

Electrical Impedance Tomography (EIT) is a promising non-invasive sensing technique, yet its practical application in resource-constrained environments is often limited by the high computational cost of inverse image reconstruction. To address this challenge, we focus on specific sensing objectives rather than full image recovery. In this study, we propose a lightweight, task-oriented inference framework for object localization in EIT that bypasses the need to solve computationally expensive inverse reconstruction problems. This approach addresses the high computational demands and hardware complexity of conventional iterative methods, which often hinder real-time monitoring in resource-constrained edge computing environments. Training datasets were generated via finite element method (FEM) simulations for Opposite and Adjacent current injection configurations. A feedforward neural network was developed to independently estimate the radial and angular object positions as probability distributions. Our systematic evaluation revealed that the localization performance depends on the injection configuration and model depth; notably, the Opposite method achieved perfect classification accuracy (1.00) for radial estimation with an optimized architecture of four hidden layers, whereas the Adjacent method exhibited higher ambiguity. Results quantitatively evaluated using the Wasserstein distance show that the Opposite configuration produces more localized, unimodal probability distributions than the Adjacent configuration by utilizing current fields that traverse the entire domain. Compared with existing image-based reconstruction methods, including the conventional electrical impedance tomography and diffuse optical tomography reconstruction software (EIDORS ver.3.12), the proposed framework reduced energy consumption from 3.09 to 0.96 Wh, demonstrating an approximately 70% improvement in energy efficiency while maintaining a high localization accuracy without the need for iterative Jacobian updates. This task-oriented framework enables reliable, high-speed, and energy-efficient localization, making it well-suited for low-power EIT applications in mobile and embedded sensor systems. Full article

Over the last decades, progress in modal analysis has enabled the increasingly routine use of modal parameters for applications such as structural health monitoring and finite element model updating. For output-only identification, or operational modal analysis (OMA), widely adopted approaches include stochastic subspace identification (SSI) methods and the Natural Excitation Technique, combined with the Eigensystem Realization Algorithm (NExT-ERA). Nevertheless, SSI-based techniques may become cumbersome on large systems, while NExT-ERA fitting can struggle when measurements are contaminated by noise. To alleviate these, this work investigates an OMA frequency-domain formulation for aeronautical structures by coupling the Loewner Framework (LF) with NExT, yielding the proposed NExT-LF method. The method exploits the computational efficiency of LF, due to the effectiveness of tangential interpolation, together with the impulse response function retrieval enabled by NExT. NExT-LF is assessed on two experimental benchmarks: the eXperimental BeaRDS 2 high-aspect-ratio wing main spar and an Airbus Helicopters H135 bearingless main rotor blade. The identified modal parameters are compared against available experimental references and results obtained via SSI with a Canonical Variate Analysis and NExT-ERA. The results show that the modes identified by NExT-LF correlate well with benchmark data, particularly for high-amplitude tests and in the low-frequency range. Full article

Reverse time migration (RTM) exploits time-reversal symmetry and adjoint duality to focus wavefields and reconstruct subsurface reflectivity, but large surveys remain limited by the cost of forward and backward propagation. We present a Graphics Processing Unit (GPU)-accelerated adjoint RTM workflow for depth imaging of the Chicxulub impact structure using the marine A0/A1 composite profile (1996). The processed stacked section contains 14,172 traces with 6.25 m Common Depth Point (CDP) spacing, 1 ms sampling, and 18 s record length. Forward and adjoint wavefields are computed with a staggered-grid finite-difference scheme (fourth order in space, second in time) and Convolutional Perfectly Matched Layers (CPMLs), which provide stable finite-domain simulations while introducing controlled symmetry breaking through absorption. The solver is verified with the Lamb half-space analytical benchmark and applied through five interpretation-guided velocity/density updates. The final depth image improves reflector continuity and interpretability of crater-scale elements, including post-impact sedimentary fill, melt and breccia units, terrace fault blocks, and deep uplift-related structure. Compute Unified Device Architecture (CUDA) acceleration reduces runtime from ∼32.36 h on a CPU baseline to ∼34.10 min on an RTX 3070 (≈56.9×), enabling practical, reproducible iterative RTM on accessible hardware. Full article

This study aims to determine the current seismic resistance of two masonry minarets that were severely damaged during the 6 February 2023 Kahramanmaraş earthquakes, while also evaluating whether a model-updating approach based on experimental dynamic characteristics can reliably capture the actual seismic behavior and collapse mechanism of such structures under real earthquake conditions. The dynamic characteristics of the minarets were identified using Operational Modal Analysis (OMA) based on previous in-situ vibration measurements. These characteristics were used to calibrate finite element models through a model-updating process employing Multi-Criteria Decision-Making (MCDM) methods. The initial modal analyses revealed discrepancies of up to 13.7% in natural frequencies and 9.7% in mode shapes. After applying MCDM methods to a wide set of model variants, these differences were reduced to 2.0% and 9.2%, respectively, improving the agreement between numerical and experimental results. Once the most representative models were obtained, nonlinear seismic analyses were performed using actual ground motion records from the earthquake. The results included evaluations of peak displacements, base shear forces, and principal stresses. The concentration of principal stresses near the transition zone showed good qualitative agreement with the observed collapse locations, indicating a reasonable consistency between numerical results and observed damage patterns. These findings demonstrate the value of integrating OMA-based model updating with MCDM methods and support a data-driven framework for assessing the seismic performance of historical masonry structures. Full article

Real-time mechanical analysis within digital twin (DT) systems requires high-fidelity models that synchronize with the “as-built” state of physical structures. This paper proposes a technical framework for constructing a “mechanical-core” DT by integrating computer vision (CV) sensing with automated finite element model (FEM) updating. Utilizing the Midas API, we developed a platform that automates data acquisition, modeling, and parameter inversion. A momentum-based optimization algorithm is implemented to invert the instantaneous elastic modulus of bridge segments during cantilever construction. The system was validated through a case study of a continuous box-girder bridge. Quantitative results indicate that the initial theoretical model, based on design-phase assumptions, exhibited a mean relative error of approximately 21.9% in vertical displacement. Following the real-time parameter inversion, this error was significantly reduced to less than 0.2% across all monitored construction stages. The rapid convergence (typically within three iterations) and the substantial increase in predictive accuracy demonstrate that the proposed framework effectively bridges the gap between raw sensing data and structural analysis, providing a reliable basis for proactive engineering decision-making. Full article

Nonlinear structural analysis serves as a fundamental tool for accurately predicting structural bearing capacity and ultimate strength. The incremental-iterative solution scheme represents the prevailing methodology for tracing nonlinear load–displacement responses and is implemented in most commercial finite element software. To enhance the robustness and computational efficiency of existing schemes, this paper first revisits the incremental-iterative framework, providing a detailed analysis that clarifies the distinct roles of the load increment factor in the predictor and corrector phases. Subsequently, a novel framework of updated orthogonal iterative schemes (UOIS) is established. Within this framework, the current generalized stiffness parameter (CGSP) and a cumulative indicator $S_i$ are introduced in the predictor phase to adaptively control the magnitude and sign of the load increment, respectively. In the corrector phase, four enhanced orthogonal iteration strategies are formulated. Furthermore, to improve computational efficiency, a novel acceleration strategy is proposed, which embeds a secant prediction operator in the predictor phase, thereby circumventing the costly assembly and inversion of the tangent stiffness matrix. The results demonstrate that: (1) compared to the conventional generalized stiffness parameter (GSP), the proposed CGSP exhibits superior stability in tracking stiffness variations, offering a more reliable indicator for adaptive step-size control; (2) the cumulative indicator $S_i$ reliably identifies load limit points and accurately distinguishes between loading and unloading regimes; (3) the UOIS framework demonstrates strong convergence in tracing complex equilibrium paths with multiple critical points and exhibits significantly superior robustness under large increment sizes compared to the generalized displacement control method (GDCM); and (4) the secant-prediction acceleration strategy achieves substantial improvements in computational efficiency without compromising solution accuracy. Full article

This paper proposes a simplified mechanical model to estimate transverse shrinkage and angular distortion in multi-pass butt welding. The simplified mechanical model is first derived for an I-groove joint by representing the heated weld region with one-dimensional bar elements and by enforcing force equilibrium to obtain closed-form expressions for pass-by-pass deformation increments and cumulative deformation. For non-I-groove joints, the same simplified mechanical model is applied by updating the layer partition and geometric parameters for each pass based on the pass-wise high-temperature region; the inherent shrinkage of each pass is evaluated from the heat input and an equivalent heated-layer thickness. The simplified mechanical model is validated for V-groove multi-pass joints by comparison with thermo-elastic-plastic finite element (FE) analyses and available experimental data, and for X-groove multi-pass joints by comparison with thermo-elastic-plastic FE analyses. In addition, a parametric study on the V-groove angle (40°–70°) for SUS316L demonstrates that the model captures the increasing trend of final transverse shrinkage with groove angle without a pronounced degradation in prediction accuracy. The results show that the simplified mechanical model reproduces both deformation histories and final values with good accuracy while using only a small set of input parameters and negligible computational cost, making it useful for early-stage welding procedure planning and quick parameter studies. Full article

Composite bone replicas are widely used in biomechanical testing as alternatives to cadaveric specimens, with numerical models often complementing or replacing experiments. The reliability of these models depends strongly on accurate material parameters. This study investigates a fourth-generation Sawbones composite L5 vertebra, updating cortical material properties under isotropic and transversely isotropic modelling assumptions. Finite element models were calibrated using free-free experimental modal analysis, revealing differences between manufacturer-provided material properties and the measured specimen behaviour. For both models, matching the specimen mass required reducing the cortical density from 1.64 g/cm³ to 1.423 g/cm³. In the isotropic model, the Young’s modulus was reduced from 16,000 MPa to 6500 MPa. In the transversely isotropic model, longitudinal and transverse Young’s moduli were reduced from 16,000 MPa and 11,000 MPa to 6400 MPa and 5500 MPa, respectively, while the shear moduli decreased from 4370 MPa and 6350 MPa to 3500 MPa and 2540 MPa. In both models, the Poisson’s ratio was increased from 0.26 to 0.30. These updates reduced the average eigenfrequency error to 6.12% (isotropic) and 5.83% (transversely isotropic), with the first five modes errors reduced to 3.10% and 2.80%, respectively, substantially improving numerical representation of L5 vertebral mechanics. The updated vertebral FE model and accompanying workflow enhance the reliability of future FE analyses, improve interpretation of Sawbones vertebra biomechanical results, and support vibration-based biomechanical applications such as implant fixation assessment. Full article