Search Results (1,890)

During the drilling of deep ice boreholes in Central Antarctica, one of the key tasks is to collect representative samples of the borehole fluid. The principal challenge is that, during retrieval of the downhole sampler to the surface, the sample is exposed to steep negative temperature gradients, which alter its physical properties and distort the representation of the actual borehole conditions at the sampling depth. For this study, an analytical review of current downhole sampler designs was carried out. For mathematical modeling, the finite difference method was used to solve the two-dimensional axisymmetric heat conduction equation for the "fluid sample–sampler wall" system. The initial temperature distribution was adopted from thermometric data obtained in borehole 5G-5, Vostok Station. The model incorporates actual trip-speed logs recorded during tripping operations. After modeling it was established that the temperature of the near-wall layer of the sample decreases significantly faster than that of the central region and that by the time the sampler reaches the surface, the difference between the sample temperature and the temperature of the surrounding borehole fluid is substantial enough to affect rheological properties of the fluid. The developed model makes it possible to justify the introduction of corrections to the results of direct measurements of fluid properties at the wellhead. Full article

To address the large variability in existing pavement distress in expressway reconstruction and expansion projects in Zhejiang Province, China, a differentiated overlay design and decision-making method based on multi-index evaluation was proposed using the Ningbo section of the Yongtaiwen Expressway as a case study. Based on 3D ground-penetrating radar (GPR), falling weight deflectometer (FWD), and field coring tests, the existing pavement was classified into five conditions: intact pavement, slight and severe surface-layer distress, and slight and severe base-layer distress. For pavements with surface-layer distress, two alternative overlay schemes were designed. Scheme I was defined as a performance-oriented scheme using high-performance SMA/Superpave asphalt layers and an ATB-25 transition layer where necessary to improve fatigue resistance and coordinated structural performance. Scheme II was defined as an economy-oriented scheme using conventional AC layers and crack-resistant or bonding measures to reduce construction cost while maintaining adequate structural capacity. An ABAQUS-based temperature–load coupled finite element model considering the temperature-sensitive viscoelastic characteristics of asphalt layers was established to analyze the mechanical responses and service lives of the overlay schemes, and the entropy weight–TOPSIS method was used for multi-objective comprehensive decision-making. The results showed that temperature–load coupling markedly increased the tensile strain at the bottom of the asphalt overlay and was a key controlling factor in design. All schemes satisfied the 15-year design requirement, while the base-layer fatigue life of the performance-oriented scheme (Scheme I) was generally no lower than that of the cost-oriented scheme (Scheme II), indicating better long-term service reliability. In addition, the relative closeness coefficients of Scheme I under slight and severe surface-layer distress were 0.586 and 0.546, respectively, both higher than those of the cost-oriented scheme. The proposed method can effectively balance technical performance and life-cycle cost and provides a useful reference for differentiated overlay design in similar expressway reconstruction and expansion projects in hot–humid regions. Full article

Background: Mechanical trauma during pregnancy from motor vehicle accidents, falls, and maternal seizures poses significant risks to fetal development. The fetus is protected by multiple hierarchical layers including the uterine wall, amniotic fluid, and cerebrospinal fluid surrounding the brain. Despite the clinical significance of maternal trauma occurring in approximately six to eight percent of pregnancies, previous computational studies have focused primarily on amniotic fluid protection while treating the fetus as a homogeneous structure, without examining the nested protective architecture comprising both amniotic fluid and cerebrospinal fluid as an integrated system. Methods: This investigation implements a nested fluid–structure interaction framework simultaneously capturing three hierarchically organized systems: the uterine wall interacting with amniotic fluid, amniotic fluid interacting with the fetal body, and the cranial system comprising skull, cerebrospinal fluid, and brain tissue. The computational architecture employs smoothed particle hydrodynamics for fluid domains coupled with finite element methods for solid structures. Boundary conditions representing traumatic forces were obtained through experimental protocols using an instrumented medical simulation mannequin performing seizure movements. Results: Computational simulations predicted that amniotic fluid absorbed the majority of impact forces through hydraulic cushioning, while cerebrospinal fluid provided additional stress reduction through pressure redistribution, with model predictions suggesting total stress reduction exceeding ninety percent. Peak fetal brain stress values predicted by the model were below injury thresholds reported in adult neural tissue literature, though direct applicability of these thresholds to fetal tissue remains uncertain. The fetal brain exhibited minimal movement relative to the skull despite complex force cascades. Stress distributions showed elevated values in the frontal lobe and brainstem, though magnitudes remained within ranges that the model suggests may be tolerable. Conclusions: Computational modeling suggests that the nested fluid protection architecture operates as an integrated hierarchical system providing potential mechanical protection through sequential energy dissipation. These findings represent model predictions requiring experimental and clinical validation before translation to clinical practice. Full article

The design of metal nanoparticle-on-a-mirror (NPOM) provides a powerful strategy for optical enhancement in gap plasmonics. Here, we report a systematic numerical study on an NPOM structure composed of gold nanocubes (GNC) and a continuous gold film via the finite element method (FEM). First, we simulated the near-electric field distribution of isolated GNC in a homogeneous medium and compared it with that of the GNC-based NPOM structure, revealing the dominant role of plasmon coupling in the gap region. Second, we systematically investigated the influence of the thickness of the dielectric layer between the GNC and the gold film on the optical enhancement characteristics in the gap region. The results show that the maximum electric field intensity of the resonance peak decays rapidly when the thickness of the dielectric layer is less than 2 nm, decreasing from 5048 (t = 0.5 nm) to 1032 (t =2 nm). Third, we further investigated the influence of the polarization angle of the incident light on the optical enhancement in the gap region. Finally, the dielectric environment n₀ and the refractive index n of the dielectric layer were studied. This work elucidates the unique gap plasmon coupling mechanisms of GNC-based NPOM structures and provides a precise tuning strategy for key structural and optical parameters, endowing the structure with important application prospects in sensing, energy conversion, and photodetection. Full article

The increasing use of layered materials in various modern industries demands effective non-destructive testing methods. Guided wave testing is a promising solution, but accurate dispersion curves are essential for its reliable implementation. These curves are crucial for the appropriate selection of testing parameters and for the reliable interpretation of inspection results. This study, therefore, aims to develop and verify a computationally efficient and versatile tool for calculating dispersion curves in multilayered media. We propose an approach based on the semi-analytical finite element (SAFE) method implemented in COMSOL Multiphysics 6.2. This approach employs commercial finite element software capabilities, including optimized solvers and the ability to handle complex material properties (e.g., layer anisotropy) and geometries, thus avoiding the need for specialized code. We present the theoretical background and implementation details of the proposed approach in COMSOL Multiphysics. The calculated dispersion curves show excellent agreement with those obtained from the established software Dispersion Calculator 3.1, with a relative error of no more than 0.001%. These results confirm the applicability of the developed SAFE implementation for calculating dispersion characteristics of multilayered structures and support its use in developing novel guided wave ultrasonic testing techniques for multilayered composite materials. Full article

This paper presents a novel approach for Cu-Cu bonding processes, incorporating microfluidic technology into chip-level metal bonding to precisely and effectively control the temperature on the bonding layer surface. To achieve effective bonding, fluidic channels with a specific design were created on the backside of the chip, enabling temperature gradient bonding across multiple pairs of bonding surfaces by controlling the fluid velocity at the microchannel inlets. Finite element simulations demonstrate that this method can establish a controlled thermal gradient across the bonding interface, with a maximum temperature difference exceeding 100 °C across a single bonding plane. The results indicate that this technique is not only suitable for copper–copper metal bonding but can also be applied to the bonding of other metal materials, offering a versatile solution for metal bonding in chip fabrication. Full article

While fused silica microlens arrays (MLAs) act as crucial components in the fields of infrared optics and laser systems, direct laser writing has been proposed for the fabrication of MLAs. However, the layer-by-layer slicing strategy generally leads to stepped surface textures formed on the microlens surface, resulting in high surface roughness and limited transmittance. This work proposes a temperature-controlled CO₂ laser polishing method for the fabrication and subsequent smoothing of fused silica microlens arrays. Specifically, an infrared temperature measurement system is integrated into a CO₂ laser direct writing platform. Correspondingly, a proportional-integral-derivative algorithm is used to adjust the laser power in real time based on the temperature deviation at the processing spot, thus maintaining the polishing zone in a molten rather than vaporizing state. Furthermore, a finite element model of laser polishing of fused silica coupled with laser heating and fluid flow is developed, which is used to analyze the spatiotemporal evolution of the temperature field, as well as its correlation with the response of the processed surface. Experimental results show that temperature-controlled laser polishing reduces the surface roughness of the fabricated MLAs by 86.8%, while the transmittance in the visible band remains above 90%. This work provides a feasible closed-loop polishing method and a mechanistic analysis model for the laser polishing of fused silica MLAs. Full article

This study proposes a stress field data-driven prediction method that combines a finite element thermo-mechanical coupling model with a multi-machine learning framework. This method takes the inversion of stress based on the shrinkage behavior of deposition layers as the core logic, extracts the node displacement shrinkage during the cooling to solidification process of the melt pool in the thermal coupling simulation as the key feature input, and constructs extreme gradient boosting (XGBoost), Gaussian process regression (GPR), and deep convolutional neural network (DCNN) models, respectively, to achieve accurate prediction of nodal effect stress and triaxial stress in the laser directed energy deposition (L-DED) node process. The experimental results show that the XGBoost algorithm performs the best in various stress prediction indicators, and its generated stress distribution cloud map is highly consistent with the thermal coupling simulation results, suggesting a strong correlation between deposition layer shrinkage behavior and the stress field under the investigated conditions. In addition, compared to traditional finite element simulations, this method significantly improves computational efficiency while ensuring prediction accuracy, providing a new approach for rapid assessment of residual stresses. Full article

Electrotactile feedback is a compact approach for providing tactile cues in robotic teleoperation, but personalized calibration remains time-consuming because tactile perception varies across users. To address this problem, this study develops a subject-informed multi-layer finite element model of fingertip electric-field distribution coupled with a neural-response model and proposes a simulation-derived configuration-ranking method termed the Perceived Correctness Score (PCS). A gradient boosting regression model is then used to recommend among 36 candidate electrode diameter–spacing combinations. Validation was conducted using a custom-developed 3 × 2 multi-channel fingertip electrotactile stimulation system in a shape/area recognition task involving six healthy subjects. The predicted PCS showed a moderate positive correlation with the measured mean recognition accuracy across configurations (Pearson r = 0.48, p < 0.05). The model achieved Top-1 exact matching for three of six subjects and Top-5 coverage for five of six subjects. Compared with conventional exhaustive psychophysical calibration, the proposed method reduced the average configuration time from 122.7 min to 16.0 min, corresponding to an efficiency improvement of 87.0%. These results show that model-guided ranking can substantially reduce the burden of individualized electrotactile configuration. Full article

With the rapid development of new energy vehicles, fast and reliable prediction of power battery collision damage has become increasingly important. Traditional finite-element analysis is computationally expensive and difficult to deploy for rapid prediction under varying conditions. Although learning-based methods are faster, they usually rely on single-fidelity data: high-fidelity data is accurate but scarce and costly, while low-fidelity data is abundant but less reliable. Existing multi-fidelity methods alleviate this issue, yet often suffer from imbalanced sample allocation and weak cross-fidelity modeling. Moreover, current adaptive sampling strategies cannot dynamically determine the appropriate fidelity for different regions of the design space. To address these challenges, we propose HNGP-LCA, a multi-fidelity active learning framework for battery pack collision damage prediction. Our method consists of two components: (1) an Ensemble Nested Gaussian Process module that integrates single-layer and double-layer nested Gaussian process regression to better capture high–low fidelity correlations; and (2) a Location Information Cost-aware Active Learning strategy that leverages positional information to reconstruct expected improvement under different fidelities, enabling dynamic fidelity selection during sampling. Experiments on multiple synthetic benchmarks and a real battery pack engineering case demonstrate that HNGP-LCA achieves a better trade-off among accuracy, efficiency, and cost than strong baselines such as NARCO and MFBO. In the engineering case, it improves prediction accuracy by 0.6% over NARCO and 1.29% over MFBO, while reducing dependence on expensive high-fidelity data. These results show that HNGP-LCA provides an effective and practical solution for battery collision damage prediction. Full article

The rapid and accurate assessment of residual ultimate strength after ship damage is crucial for rescue decision-making and navigation safety, while traditional methods struggle to meet the demands of complex random damage scenarios in terms of efficiency or accuracy. This study proposes a hybrid framework that integrates high-fidelity nonlinear finite element simulation (NFEM) and a Bayesian-regularized backpropagation neural network (BPNN). NFEM is used to accurately simulate a large number of random damage scenarios, generating a physically credible benchmark dataset. BPNN serves as an efficient surrogate prediction model, with its key parameters—the number of hidden layers and the training algorithm—systematically optimized to enhance generalization capability. The results show that: (1) The NFEM simulation results deviate by less than 5% compared to the Smith method, validating the reliability of the dataset. (2) The prediction performance of BPNN is highly dependent on the number of hidden layers and the training algorithm, exhibiting non-monotonic variation, with an optimal parameter combination identified as 8 hidden layers paired with the Bayesian algorithm, achieving a prediction regression value R of 0.91662. (3) Deep networks are prone to overfitting, while shallow networks suffer from insufficient feature capture. (4) The Bayesian algorithm performs best in terms of overfitting resistance and stability. This study not only provides a high-precision and efficient intelligent solution for residual strength assessment of damaged hulls, but its systematic neural network parameter optimization strategy, particularly the approach of identifying optimal depth and selecting anti-overfitting algorithms, also offers an important reference for the design of intelligent damage assessment models for similar engineering structures. Full article

Finite element modeling (FEM) of hybrid bonding stacks for high-density 3D integration suffers from excessive computational load and prohibitive simulation time. To address this critical technical bottleneck, this paper proposes an analytical lumped-distributed equivalent circuit model based on multi-layer structures. The model incorporates both redistribution layer (RDL) parasitics and metal–insulator–semiconductor (MIS) depletion effects for comprehensive signal integrity analysis. Frequency-dependent RLGC electromagnetic parameters were extracted from through-silicon via (TSV) and RDL interconnects. These parameters were numerically calculated using MATLAB R2020a to construct the equivalent circuit model in ADS. The model was subsequently validated against COMSOL finite element simulations. The results demonstrated that the proposed methodology achieved maximum deviations below 5% for all S-parameters in double-layer structures. For 5-layer stacks, errors were controlled within 10% across the 0–40 GHz frequency range. Computation time was reduced from several minutes to seconds. The proposed equivalent circuit method significantly reduces computational time while maintaining accuracy, providing an efficient simulation methodology for signal integrity analysis and verification of hybrid bonding stack structures. Compared to existing single-layer models, this work extends the modeling approach to multi-layer hybrid bonding stacks while comprehensively accounting for both RDL parasitics and MIS depletion effects, addressing a critical gap in the current state of the art. Full article

Bingham fluids, also called Bingham plastics, are used in different industries including the production of food, pharmaceuticals, household products, construction and oil and gas drilling. The behavior of Bingham fluids is viscous above a critical shear stress and rigid-body below the threshold stress value. Knowledge of the size of the entrance region has several applications including hemodynamics and microfluidics. A model for steady Bingham fluid flow in the entrance region between parallel plates is developed using the inlet-filled region concept. A boundary layer model is used to solve the fluid flow dynamics in the inlet region up to the point where the critical shear stress is reached at the edge of the boundary layer. Beyond that point, the boundary layer does not grow, while the velocity profile keeps readjusting in the filled region to asymptotically reach the fully developed flow. The results include boundary layer thickness profiles, dimensionless pressure drop, centerline velocity, friction factor and inlet and entrance region sizes as functions of the Bingham number. The results are validated against the results for the Newtonian fluid case (Bingham fluid yield stress equal to zero) and CFD results, using the finite element method, for nonzero Bingham numbers. In addition, the results are found to asymptotically reach the fully developed flow values for the general Bingham fluid flow case. The effects of the Bingham number are addressed and compared with the literature. The present model is largely analytical, requiring minor numerical tasks. Full article

The Axial Flux Permanent Magnet Synchronous Motor (AFPMSM) has gained significant attention as a core power source for next-generation industrial sectors, including electric vehicles, wind turbines, robot joints, and drone propulsion motors, due to its high power density from a short axial length and large radial dimensions. Despite these structural advantages, cogging torque caused by magnetic interaction between the stator teeth and permanent magnets remains a critical drawback, inducing noise and vibration. While conventional Soft Magnetic Composite (SMC) core methods facilitate 3D flux paths, they suffer from low magnetic permeability, insufficient mechanical strength, and manufacturing complexity. To address these issues, this study proposes a stepped structure model utilizing electrical steel sheets to effectively reduce cogging torque. This structure features radial stacking of identical electrical steel sheets with varying widths, where each layer’s center is incrementally shifted in the rotational direction. This configuration achieves an effect analogous to continuous skewing without specialized 3D machining. To validate the proposed design, 3D Finite Element Analysis (FEA) was conducted. Results demonstrate that the peak-to-peak cogging torque was reduced to approximately 86% of the conventional model’s value, while maintaining the back-EMF reduction rate within 5%. By presenting a novel skewing technique, this research provides a practical alternative for high-precision and high-power AFPMSM. Full article

In laser metal deposition (LMD), complex thermo-mechanical coupling and irregular layer morphology significantly affect residual stress distribution. However, most simulations rely on idealized geometries, limiting prediction accuracy. This study proposes a data-driven framework integrating in situ vision-based morphology reconstruction with thermo-mechanical simulation for high nitrogen steel (HNS). An improved DeepLabv3+ network is developed to extract deposition layer contours under strong illumination and spatter interference, achieving a mean intersection over union (mIoU) of 97.32% and an overall accuracy of 99.42%. The reconstructed morphology is incorporated into a finite element model to enable dynamic heat source tracking and realistic geometric representation. The proposed method demonstrates high morphology reconstruction accuracy, with all measurement errors controlled within 0.91%. The simulated temperature field agrees well with experimental measurements. Furthermore, the predicted residual stress distribution is consistent with X-ray diffraction (XRD) results under different laser power conditions. The results indicate that local surface morphology significantly influences stress concentration, with protrusion regions exhibiting stress peaks up to 989 MPa, markedly higher than those in concave regions. This study improves the accuracy of residual stress prediction in LMD by incorporating real morphology data and provides insight into the relationship between morphological features and stress evolution in additively manufactured HNS components. Full article