Search Results (1,138)

Background/Objectives: Transcranial magneto-acoustic stimulation (TMAS) is an emerging multiphysics neuromodulation modality that introduces magneto-acoustically induced currents into ultrasound-based neural stimulation. However, how this coupled physical input is converted into neuronal recruitment and measurable biological responses remains insufficiently understood. This study aimed to establish a computational–experimental framework for quantifying TMAS-induced neural activation across stimulation dose, spike timing, and calcium-related response domains. Methods: The model combined ultrasound-induced membrane mechanics, charge-based neuronal electrophysiology, pressure-dependent calcium-current modulation, and a Lorentz-force-mediated current source for TMAS. Regular-spiking excitatory neurons and low-threshold-spiking inhibitory interneurons were simulated to estimate excitation thresholds, firing latency, and spike-pattern transitions. Two model-informed stimulation conditions were examined using in vitro calcium imaging, in vivo hippocampal fiber photometry, and c-Fos/microtubule-associated protein 2 (MAP2) immunofluorescence. Results: TMAS reduced the half-maximal effective dose relative to transcranial ultrasound stimulation by 71.4–76.3% in regular spiking neurons and 51.5–58.2% in low-threshold spiking interneurons, while shortening firing latency and expanding burst-response domains. Experimentally, near-threshold TMAS produced larger peak calcium responses and greater c-Fos/MAP2 immunoreactivity than TUS. Conclusions: TMAS enhances neuronal responsiveness more efficiently than TUS by reducing excitation thresholds, improving spike-timing responses, and reshaping firing-pattern transitions. Consistent with the overall computational comparison, experimental measurements showed greater neuronal activation under TMAS than under TUS at matched ultrasound parameters. Full article

As unmanned aerial vehicle (UAV) missions expand from aerial inspection and environmental sensing to physical interaction and autonomous manipulation, rotary-wing UAVs equipped with flexible manipulators offer a promising platform for contact-rich operations in complex environments. Among these tasks, stable near-ground hovering and the grasping of ground targets are particularly challenging because they involve ground-effect aerodynamics, rigid–flexible coupling, and mode transitions caused by contact and load transfer. This review provides a structured, task-oriented critical synthesis of advances in this interdisciplinary field. First, system configurations are classified by aerial-platform architecture, manipulator type, mounting arrangement, and end-effector design, and the suitability of rigid-link, compliant, continuum, and soft manipulation mechanisms for near-ground grasping is assessed. Next, modeling approaches for rotor ground effect, coupled rigid–flexible dynamics, hybrid contact and load-transfer dynamics, model identification, and model reduction are reviewed. Trajectory planning, coordinated stabilization, impedance control, hybrid force/position control, and switching control are then compared across free flight, contact establishment, and payload-carrying hover. Although the reviewed literature provides a substantial theoretical foundation for aerial manipulation, continuum robotics, and multirotor ground effect, direct evidence remains limited for methods that jointly address near-ground aerodynamics, large flexible deformation, and contact-induced load transfer across the complete near-ground grasping sequence with integrated experimental validation. Based on these evidence gaps, this review identifies multiphysics reduced-order modeling and event-driven hybrid control as author-synthesized directions for future investigation. Full article

In this work, a methodology based on the finite element method is proposed for the design and simulation of optical modulators that exploit the magnetostrictive effect, aimed at enhancing the performance of magnetometers. The study focuses on a channel-type optical waveguide whose cross-section is carefully engineered to maximize opto-mechanical interaction while ensuring straightforward integration with magnetostrictive thin films. Fabrication follows the Induced Static Stress (ISS) technique: sputter deposition of a Terfenol-D (Tb_0.3Dy_0.7Fe_1.92) layer onto a bismuth germanium oxide (Bi₁₂GeO₄) substrate creates residual stresses because of mismatched thermal expansion coefficients. During operation, applied magnetic fields induce magnetostrictive deformation, which, together with the pre-existing thermal stresses, modifies the local refractive index via the elasto-optic effect, thereby enabling dynamic guiding and modulation of guided light. Numerical analysis is carried out in COMSOL Multiphysics 5.6, employing coupled structural and optical modules. A fine mesh is generated along the waveguide core, while material parameters such as Young’s modulus, Poisson’s ratio, magnetostriction constant, and refractive indices are specified for each layer. Full article

Accurate prediction of the dynamic response of wheeled amphibious vehicles during slope water entry is critical for operational safety, as this process involves intense gas–liquid–solid multi-phase coupling with highly transient and nonlinear characteristics. This paper establishes a bidirectional multi-physics co-simulation framework integrating STAR-CCM+ (version 2506) and ABAQUS (version 2024). For the fluid domain, the Reynolds-averaged Navier–Stokes (RANS) equations are solved in conjunction with the Realizable k-ε turbulence model, the volume of fluid (VOF) interface capturing method, and the over-set mesh technique. For the solid domain, rigid body dynamics and contact mechanics formulations are employed, with hull–slope contact constraints explicitly incorporated. Closed-loop coupling between the two domains is achieved via time-step-synchronized data exchange. The proposed method is validated against water entry experiments, with favourable agreement between numerical predictions and experimental measurements. Parametric analysis quantifies the effects of slope angle and initial entry velocity on peak submergence depth, pitch response, and slamming loads. By explicitly incorporating hull–slope contact and friction into the bidirectional coupling process, the proposed framework offers a model- and condition-specific numerical basis for preliminary operating condition screening and safety assessment during cross-medium transition; a generally validated operational envelope remains to be established in future work. Full article

Discrete element method (DEM) has become an essential tool for analyzing particle-scale behavior and complex interactions in agricultural materials and machinery. This review synthesizes recent advances in DEM-based modeling and applications in agricultural engineering. It systematically examines particle modeling strategies, contact model development, and parameter calibration frameworks for representative materials such as soil, seeds, fertilizers, stalks, and roots. The evolution from simple spherical approximations to multi-sphere, bonded, and 3D scan-based reconstructions is highlighted, along with the shift from classical Hertz–Mindlin models to cohesive, elasto-plastic, and fracture-capable formulations. Calibration methods are traced from empirical assignment to systematic frameworks that integrate design of experiments, response surface methodology, and machine-learning-assisted inverse analysis. These advances in fidelity and accuracy have enabled extensive DEM applications in tillage, seeding, fertilization, and harvesting, with emphasis on equipment optimization, mechanism analysis, and performance prediction. Despite the progress, challenges remain in model standardization, parameter transferability, computational cost, and multiphysics coupling. Future work points to unified modeling frameworks, standardized databases, real-time simulation, and integration with artificial intelligence to support digital agriculture and intelligent machinery. This review aims to serve as a reference for high-fidelity DEM modeling and for advancing the digital transformation of agricultural engineering. Full article

A unique V-grooved solar air collector was assessed experimentally and numerically to improve thermohydraulic performance. Experiments were conducted on the collector in Baghdad, Iraq, from 23 March to 3 April 2025, encompassing a mass airflow rate spanning 0.02 to 0.09 kg/s. The proposed design integrates wire mesh layers solely within the upper triangular channels, while the lower triangular channels remain unobstructed. A novel three-dimensional steady-state CFD model was originally developed for evaluating the thermohydraulic performance of a V-grooved solar air heater with and without wire mesh layers. The coupling between fluid flow and heat transfer models was implemented via the finite element method through the COMSOL Multiphysics program. The developed model for the collector incorporating wire mesh layers was validated experimentally, demonstrating strong agreement, with the greatest recorded root mean square error of 1.3597 °C for the outlet air temperature. The findings indicate that the enhanced collector attains a thermal efficiency enhancement of around 1.5 to 2.5 times relative to the baseline design, depending on operating conditions. The thermal efficiency attained levels up to 80%, whereas the thermohydraulic efficiency surpassed 70% at a moderate mass airflow rate. Despite the increase in pressure drops, the performance remained exceptional owing to the substantial enhancement in heat gain supported by the unique architecture. The results illustrate that the proposed design presents a viable solution for efficient solar air heating and can be adequately incorporated into near-zero-energy buildings. Full article

This study addresses the lack of quantitative understanding regarding vent-gas combustion feedback in thermal runaway propagation of manganese-based prismatic battery modules. We develop a coupled multiphysics model integrating solid heat transfer, runaway kinetics, gas ejection, and turbulent combustion, informed by constant-volume reactor tests on a single cell that yield a total gas release of 10.6 mol per cell, with H₂ and CO comprising about 49% of combustibles. Model predictions are validated against three-cell module propagation experiments, capturing sequential failure with simulated inter-cell intervals matching experimental repeats that range from 71 to 105 s. The combustion model assumes auto-ignition-based initiation and therefore does not address the stochastic ignition delays observed in the experiments. It is intended for quantifying propagation acceleration once combustion has commenced. Quantitative heat-flow analysis reveals that gas-phase convective and radiative heating contributes substantially to the total heat flux on adjacent cells during venting, and direct comparison between simulations with and without combustion shows that combustion reduces inter-cell propagation intervals by 8–11%, confirming that combustion actively accelerates propagation. Simulations further resolve the combustion zone extending up to 0.4 m laterally. This framework provides a predictive tool and mechanistic basis for vent-gas management and flame-mitigation strategies in battery energy storage systems. Full article

Traditional fault diagnosis methods for gas-insulated switchgear (GIS) equipment primarily rely on offline detection and periodic maintenance, which suffer from limitations such as poor real-time performance and localization difficulties, thereby compromising the safe and stable operation of ultra-high-voltage power grids. To enhance the accurate identification and localization capabilities of defects within GIS equipment, this study first establishes a multi-physics coupled simulation model integrating temperature field, flow field, and concentration field to analyze gas diffusion characteristics under varying conditions of fault source locations, decomposition product types, and initial concentrations. Subsequently, a GIS busbar gas chamber experimental platform is constructed to validate the simulation model. Finally, a response time matrix, a peak concentration matrix, and a fault coverage index are developed, and a weighted comprehensive evaluation method is employed to optimize sensor placement schemes. The findings reveal that fault source location significantly influences concentration response speed and spatial distribution patterns; SO₂, HF, H₂S, and SOF₂ exhibit distinct diffusion characteristics due to their differing physical properties; and initial concentration primarily affects the non-uniformity during the early diffusion stage. The simulation results demonstrate good agreement with experimental data, with a maximum root-mean-square error of 3.936 × 10⁻⁴. Monitoring point M4 achieves the highest comprehensive score, making it the preferred location for single-sensor deployment. These results provide a theoretical foundation and technical guidance for GIS online monitoring and fault diagnosis. Full article

In molten salt reactors (MSRs), helium bubbling systems can be employed for the continuous removal of gaseous fission products. However, helium injection generates local gas–liquid two-phase flow in the fuel salt and may induce corresponding local perturbations in the calculated neutronic field. To investigate these bubble-scale interactions, this study developed a coupled multi-physics framework integrating the volume of fluid (VOF) method with a multigroup neutron diffusion model. The framework was applied to a localized 20 mm × 40 mm fuel-salt domain containing a single 1 mm helium injection nozzle. Planar 2D and axisymmetric calculations were performed to examine the influence of geometrical representation on bubble growth, detachment, transport, and the corresponding local power response. The axisymmetric formulation was further used to evaluate the sensitivity of the calculated response to the helium mass flow rate. Within this restricted numerical test problem, the coupled framework resolved the evolution of helium bubbles and the associated local changes in the power field under the prescribed boundary conditions. For the centered circular nozzle and symmetry-preserving near-inlet conditions considered, the axisymmetric formulation provided a more geometrically consistent representation of rotational volume weighting and interfacial curvature than the planar 2D formulation. Variations in helium mass flow rate also modified the calculated local bubble behavior and power-response metrics. These results constitute a numerical demonstration of local bubble-resolved multi-physics coupling and should not be interpreted as reactor-scale power predictions, core-wide safety metrics, or design criteria for an MSR helium bubbling system. Full article

Fast full-field forecasting in petroleum reservoir simulation is important for field-development planning, well-control screening, uncertainty assessment, and production optimization, where many two-phase flow scenarios must be evaluated before final high-fidelity simulator verification. This study proposes a Coupled Multi-Physics Operator Learning (COMPOL)-based neural operator proxy with separate local–global Fourier neural operator branches for pressure and oil saturation, coupled through a Perceiver latent aggregation module. The model is trained on 2048 well configurations and evaluated on 512 disjoint held-out validation cases over a 20-step operational horizon generated with tNavigator. In this fixed-geology benchmark, the Coupled neural operator achieves the lowest error on three of the four reported physical metrics: well-level pressure error and both field- and well-level saturation errors. Transolver obtains the lowest full-field pressure error, indicating complementary strength on the smoother global pressure response. Qualitative maps show that the remaining errors are concentrated near wells and moving saturation fronts. A 30-case benchmark shows a $309\times$ speedup over tNavigator. These results suggest that field-specific operator branches with compact pressure–saturation coupling are a promising proxy strategy within the studied setting. Full article

This paper develops a physics-informed neural network (PINN) framework for the lossy telegrapher equations and presents a coupled IEEE 738 thermal balance formulation intended as a structural blueprint for environmentally aware transmission-line digital twins. The baseline electromagnetic PINN maps $(x,t)\mapsto (\widehat{V},\widehat{I})$ and is empirically validated against a finite-difference time-domain (FDTD) reference solver. An augmented parametric framework ${\mathcal{N}}_{\mathit{\theta }}:(x,t,\mathbf{e})\mapsto (\widehat{V},\widehat{I},{\widehat{T}}_{\mathrm{line}})$ is mathematically derived, wherein the ambient vector $\mathbf{e}$ modulates a temperature-dependent resistance $R^{\prime }\left(T_{\mathrm{line}}\right)$ and couples to the telegrapher residuals via a non-linear thermal balance residual $r_T$. Two further constraints, a sag-tension consistency residual $r_S$ and a dynamic line rating (DLR) one-sided penalty $r_{\mathrm{DLR}}$, are formulated for completeness but are explicitly designated as architectural extension hooks running at zero weight (${\omega }_{\mathrm{th}}={\omega }_{\mathrm{sag}}={\omega }_{\mathrm{dlr}}=0$) within the reported microscale numerical benchmarks. Consequently, the empirical validation presented herein strictly concerns the baseline electromagnetic telegrapher PINN. The numerical results demonstrate robust $L^2$ field convergence against FDTD reference data, highly structured error accumulation along physical characteristic curves, and reliable recovery of strongly observable parameters $(L^{\prime },C^{\prime })$ from sparse, noisy terminal measurements. Conversely, the recovery of loss parameters $(R^{\prime },G^{\prime })$ exhibits a severe structural weak identifiability that precisely matches the analytical predictions of a comprehensive Fisher Information Matrix analysis. The core contributions of this work are primarily methodological: (i) a dimensionally consistent, corrected residual formulation for the lossy telegrapher equations; (ii) an explicit positioning of the proposed multi-physics framework within the parametric PINN literature; (iii) a Fisher information identifiability diagnostic illustrating the near-degeneracy of baseline parameter estimation; and (iv) a clean algorithmic separation of forward training, inverse parameter identification, and prospective online updates. Full article

The high penetration of renewable energy and the large-scale development of electric vehicles (EVs) have made voltage violations in distribution networks increasingly prominent. The flexible on-load tap changer (F-OLTC), combining mechanical coarse regulation and power-electronic stepless fine adjustment, offers advantages in both regulation range and speed. However, existing protection strategies fail to account for the impact of EV charging/discharging surges and the multi-physics coupling constraints of the equipment, making it difficult to coordinate voltage quality and equipment lifetime. This paper proposes an adaptive zonal cooperative protection strategy for the F-OLTC that accounts for EV charging/discharging characteristics. An integrated protection architecture is constructed that fuses EV state awareness and equipment health monitoring. A multi-feature fusion fault identification method is proposed to distinguish EV disturbances, external faults, and internal converter faults. A comprehensive margin index is established, and dynamic zoning is achieved based on feature-space clustering. A coordinated stepped and stepless adaptive voltage regulation protection is designed, with dynamic adjustment of thresholds and delays, and fast EV power support is dispatched during mechanical switching transients. Simulations on a modified IEEE 33-node system show that the proposed strategy reduces average voltage deviation by 46.2%, tap operations by 28.6%, voltage sag depth by 81%, recovery time by 70.8%, and network loss by 21.9%, effectively improving voltage quality and reducing mechanical stress on the switching devices, which is expected to contribute to extended maintenance intervals and improved equipment reliability. Full article

To characterize the three-dimensional evolution of creep damage in deep roadway surrounding rock, this study establishes a COMSOL-based (Multiphysics 6.3) integrated analysis framework coupling creep damage, seismic velocity response and three-dimensional imaging. The framework adopts the Norton–Bailey creep constitutive model, Weibull-distributed material heterogeneity and an elastic damage model to simulate the 210-day creep damage evolution of surrounding rock. The three-dimensional P-wave velocity field is then reconstructed via a regional correlation approach using active-source P-wave first-arrival times. The results show that with progressive damage accumulation, low-velocity zones gradually expand from the intersection of the fault and roadway roof towards both the fault zone and the surrounding roadway. From 90 d to 210 d, these anomalies develop into a large-scale regional low-velocity belt with significantly enhanced spatial connectivity. The fault weak plane exerts a controlling effect on damage localization and the formation of low-velocity zones. Numerical results demonstrate a distinct spatiotemporal correspondence between P-wave low-velocity zones and damage concentration areas. Furthermore, the predicted damage distribution agrees well with the field failure morphology of surrounding rock, verifying that the proposed method can effectively track creep damage evolution in rock masses and provide an analytical basis for dynamic stability evaluation and early warning of deep roadway surrounding rock. Full article

Advanced engineering applications increasingly demand high-performance polymers with exceptional mechanical and thermal properties; however, predicting their processing behavior remains challenging due to complex rheological responses and the lack of integrated experimental–simulation frameworks. This study introduces a novel integrated experimental–computational methodology that combines comprehensive rheological characterization, multi-model fitting, injection molding simulation, and multiphysics finite element analysis (FEA) to investigate the processing capabilities of Polyether Ether Ketone (PEEK) for aircraft bearing applications. Unlike conventional approaches that treat rheological analysis, processing simulation, and structural assessment separately, our framework establishes a coupled material–process–performance relationship through: (i) systematic thermal and mechanical characterization, establishing PEEK’s high melting temperature (343 °C), degradation temperature (575 °C), and tensile strength (95 MPa); (ii) comparative rheological model fitting, demonstrating that the Carreau–Yasuda model accurately predicts non-linear flow behavior with R² = 0.97, outperforming simpler Power Law and Cross models; (iii) CAD-based injection molding simulation, revealing homogeneous flow distribution and optimized pressure profiles; and (iv) thermo-mechanical FEA, coupling thermal expansion with structural stress analysis to evaluate bearing integrity under operational conditions. The key novelty lies in the seamless integration of experimental rheology with multiphysics simulation, validated through rigorous statistical analysis achieving low RMSE (0.6854 MPa for stress, 0.003220 mm for deformation) and high correlation coefficients (R² = 0.97). The results confirm a uniform flow distribution, stable structural performance, and reliable thermo-mechanical response, establishing PEEK’s suitability for high-performance aerospace components. This work contributes a comprehensive, scalable, and transferable framework that bridges experimental analysis and advanced simulation, enabling the predictive optimization of polymer processing parameters and significantly enhancing manufacturing reliability for industrial applications. The findings demonstrate the applicability of the experimental–computational analysis to the investigated PEEK bearing configuration under the specified processing and simulation conditions. Its specific contribution is the application of comparative rheological model fitting and experimentally characterized PEEK properties to the selected bearing geometry and processing conditions. Full article

Porous oil-containing polyimide cages are critical for ensuring long-life lubrication in momentum wheel bearings of spacecraft attitude control systems. However, the underlying microscopic seepage mechanisms governing lubricant release under operational conditions remain inadequately understood, posing challenges for precision lubrication design. This study establishes a three-dimensional stochastic pore structure model of the cage using the Quartet Structure Generation Set (QSGS) method, from which a Representative Elementary Volume (REV) is extracted. A thermo-hydro-mechanical coupled simulation model is developed within the COMSOL 6.3 Multiphysics platform to investigate the seepage behavior. The effects of key operational parameters—rotational speed and thermal gradients—combined with the structural parameter of porosity on the lubricant transport characteristics are systematically elucidated. Based on the apparent outflow rate calculated from the REV model, a simplified formulation for estimating the oil throw-off rate of the porous cage is proposed and experimentally validated. The findings provide a fundamental theoretical framework and a practical design tool for optimizing the lubrication performance of porous cages in high-precision aerospace bearings. Full article